#Semantic vs. Keyword based targeting

[mtbcleadgenbuzz]

Semantic Seo: How It Can Boost Your Website's Visibility

Semantic Seo: How It Can Boost Your Website’s Visibility In today’s digital age, having a website is crucial for businesses looking to succeed in the online marketplace. However, simply having a website is not enough to guarantee success. For your website to be visible and attract visitors, it needs to be optimized for search engines. This is where Search Engine Optimization (SEO) comes in – the…

[youthmonk1]

How an SEO Agency in Mumbai Can Boost Your Google Rankings

Search engine optimization (SEO) is no longer a luxury; it’s the lifeblood of digital success. Partnering with a seasoned SEO agency in Mumbai can transform your website from an online brochure into a powerful, ranking‑machine that pulls in highly targeted traffic 24/7. Below, we’ll explore how a Mumbai‑based agency elevates your Google rankings—while weaving in essential facets such as web design, content strategy, and technical refinement.

Strategic Synergy of SEO and Web Design Services Dubai

Modern algorithms reward seamless user experiences. A skilled team will audit your site’s architecture and UX in much the same way high‑end web design services Dubai approach their builds—prioritizing speed, mobile responsiveness, and intuitive navigation. By eliminating code bloat, compressing assets, and adopting a mobile‑first layout, the Mumbai SEO specialists ensure lower bounce rates and higher dwell time. Google interprets these positive engagement signals as proof of relevance, subsequently pushing your pages up the results page.

Localized Precision With SEO Service in Mumbai

Search intent often has a hyper‑local dimension: people want restaurants “near me” or lawyers “in Mumbai.” A dedicated SEO service in Mumbai optimizes your Google Business Profile, embeds localized keywords naturally into service pages, and secures authoritative citations from region‑specific directories. Add strategic schema markup—such as organization, FAQ, and local business schemas—and your site gains rich‑snippet eligibility, making your listing more eye‑catching and click‑worthy on SERPs.

Authority Building Through Mumbai SEO Services

Backlinks remain a top‑three ranking factor. Reliable Mumbai SEO services follow an ethical, “white‑hat” outreach methodology—guest posting on industry blogs, earning coverage in niche publications, and acquiring contextual links from influencers. Each relevant, high‑domain‑authority backlink signals trust to Google’s algorithm. Combined with a consistent on‑page content cadence—think in‑depth how‑tos, city‑centric guides, and data‑driven resources—your brand establishes topical relevance, climbing steadily for competitive keywords.

Technical Excellence via Website Development in Mumbai

Your website’s codebase influences crawlability, indexation, and ultimately, rankings. Specialists in website development in Mumbai work hand‑in‑hand with their SEO counterparts to conduct thorough technical audits. They repair broken links, implement canonical tags to prevent duplicate‑content penalties, and configure XML sitemaps for efficient crawl paths. Just as important, Core Web Vitals—Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS)—are optimized to pass Google’s stringent performance thresholds. The result? Faster load times and higher positions in organic search.

Content Alignment From Leading SEO Providers in Mumbai

Keyword research is an art backed by robust data tools. Top‑tier seo providers in mumbai map keyword clusters to each stage of the buyer journey. Informational blog posts answer “how,” “what,” and “why” questions; comparison pieces tackle “best” and “vs” queries; and conversion pages focus on transactional intent. By aligning content length, structure, and semantic entities with user expectations, they reduce pogo‑sticking and amplify topical authority. Google’s RankBrain recognizes this alignment, rewarding the site with elevated rankings.

Measuring, Iterating, and Scaling

What gets measured gets improved. An adept Mumbai SEO agency sets up granular tracking—GA4 events, Search Console insights, and heat‑map analytics—to capture performance data. They iterate on-page elements (titles, snippets, internal links) and A/B‑test new call‑to‑action placements. Continuous optimization based on empirical evidence prevents ranking plateaus and keeps your growth curve trending upward.

Future‑Proofing Your Rankings

Google’s algorithm updates roll out several times a year. A forward‑thinking agency monitors SERP volatility, E‑E‑A‑T guidelines, and emerging SERP features (e.g., AI‑powered overviews). By maintaining technical hygiene, refreshing evergreen content, and diversifying traffic sources (news, video, image search), they insulate your rankings from algorithmic shocks.

Final Thoughts

An SEO agency in Mumbai brings a holistic skill set—spanning cutting‑edge UX reminiscent of web design services Dubai, hyper‑local optimization via seo service in mumbai, authority growth through mumbai seo services, rock‑solid foundations powered by website development in mumbai, and data‑driven content crafted by leading seo providers in mumbai. When these elements converge, your website doesn’t just climb Google’s ladder—it secures a durable, revenue‑generating foothold at the top.

Ready to transform your organic presence? Align with experts who treat rankings not as vanity metrics but as catalysts for sustainable business growth.

[codingnectars]

The Essential Guide to Organic Research for Sustainable Growth

Defining Organic Research in Digital Marketing

Organic research represents the systematic approach to gathering market intelligence without paid amplification. This methodology focuses on understanding and improving a brand's natural online presence through:

Analyzing genuine user search patterns and behaviors

Identifying content opportunities that align with audience needs

Developing strategies to enhance visibility without paid promotion

Building lasting authority in search engine rankings

Unlike paid advertising which delivers temporary results, organic research creates sustainable competitive advantages by:

✔ Establishing long-term search visibility ✔ Developing deeper audience understanding ✔ Creating content that continues generating value over time ✔ Building authentic brand authority

This approach forms the foundation for successful SEO, content marketing, and digital growth strategies that withstand algorithm changes and market fluctuations.

The Strategic Value of Organic Research

1. Search Engine Optimization Advantages

Search algorithms increasingly prioritize content that demonstrates:

Strong relevance to user queries

Comprehensive coverage of topics

Natural engagement signals

Authoritative backlink profiles

Organic research identifies the precise factors that influence rankings, enabling businesses to: • Target high-potential keywords • Structure content for maximum visibility • Build sustainable link equity • Avoid algorithm penalties

2. Content Precision and Effectiveness

Thorough organic research ensures content:

Directly addresses audience pain points

Matches current search trends and behaviors

Fills gaps in existing market content

Delivers measurable business impact

This precision leads to: → Higher conversion rates → Improved engagement metrics → Stronger brand positioning → Increased content ROI

3. Competitive Market Positioning

Comprehensive organic research enables brands to:

Identify underserved content niches

Discover competitor weaknesses

Capitalize on emerging trends

Differentiate value propositions

These insights create opportunities to establish market leadership positions that competitors struggle to replicate.

Core Methodologies for Effective Organic Research

1. Comprehensive Keyword Analysis

Advanced keyword research involves:

Identifying primary and secondary keyword targets

Analyzing search volume and difficulty trends

Mapping keyword relationships and hierarchies

Tracking seasonal fluctuations

Monitoring competitor keyword strategies

Best practices include: • Using semantic keyword clustering • Prioritizing question-based queries • Balancing head and long-tail terms • Incorporating local search variations

2. Competitor Benchmarking

Effective competitor analysis examines:

Content depth and quality

Technical SEO implementations

Backlink profile strengths

Engagement rate patterns

Content refresh frequency

This process reveals: → Untapped content opportunities → Technical optimization gaps → Link building prospects → Content upgrade possibilities

3. Search Intent Mapping

Precise intent analysis requires:

Classifying query types (informational, navigational, etc.)

Analyzing SERP features for each intent type

Understanding user journey stages

Identifying content format preferences

Recognizing commercial vs. non-commercial signals

This ensures content: • Matches user expectations • Appears in relevant SERP features • Guides users through conversion funnels • Maximizes engagement potential

4. Content Opportunity Identification

Strategic gap analysis involves:

Comparing competitor content inventories

Identifying unanswered user questions

Recognizing emerging topic trends

Discovering underserved content formats

Spotting outdated industry resources

These insights enable creation of: → Definitive content resources → Evergreen reference materials → Trend-responsive pieces → Comprehensive content hubs

5. Performance Optimization Tracking

Continuous improvement requires monitoring:

Engagement depth metrics

Conversion path analysis

Content decay patterns

SERP feature performance

Ranking fluctuation causes

This data informs: • Content refresh schedules • Structural improvements • Internal linking strategies • Topic expansion opportunities

Implementing an Organic Research Framework

Phase 1: Foundation Building

Conduct comprehensive keyword research

Analyze top competitor strategies

Map search intent landscapes

Identify core content opportunities

Phase 2: Content Development

Create pillar content resources

Develop supporting cluster content

Optimize for featured snippets

Implement structured data markup

Phase 3: Performance Enhancement

Monitor ranking progress

Analyze user behavior signals

Refresh underperforming content

Expand successful content themes

Phase 4: Continuous Optimization

Track industry trends

Update research methodologies

Refine content strategies

Scale successful approaches

The Future of Organic Research

Emerging trends include:

AI-assisted research analysis

Voice search optimization

Predictive search trend modeling

Hyper-personalized content strategies

Visual search optimization

Brands that master organic research will gain: ✔ Sustainable competitive advantages ✔ Higher quality traffic ✔ Stronger customer relationships ✔ Increased marketing efficiency ✔ Long-term business growth

By implementing a disciplined organic research framework, businesses can build digital assets that continue delivering value far into the future, creating marketing channels that compound in effectiveness over time with coding nectar.

[theflowdev]

Ranking Higher on Google: The Role of Keywords in SEO

Introduction

In today’s digital world, ranking higher on Google is the ultimate goal for businesses, bloggers, and content creators. But what’s the secret to getting there? Keywords.

Keywords are the foundation of SEO (Search Engine Optimization), helping search engines understand what your content is about and matching it to relevant user queries. However, using keywords effectively goes beyond just inserting them into your content—it requires strategic research, placement, and optimization.

This guide will break down the role of keywords in SEO, their impact on Google rankings, and how to optimize them to boost your online visibility.

Why Keywords Matter in SEO

Keywords bridge the gap between user intent and your content. They:

Help search engines categorize and rank web pages based on relevance.

Drive organic traffic by matching your content with what users are searching for.

Improve user experience (UX) by ensuring content meets user expectations.

Influence click-through rates (CTR) when optimized in meta titles and descriptions.

Enhance conversion rates by targeting keywords that match different stages of the buyer’s journey.

But Google’s algorithm has evolved, and simply stuffing keywords into content no longer works. Instead, an intent-based, data-driven approach is required for keyword optimization.

Types of Keywords and Their Role in SEO

1. Short-Tail Keywords (Head Keywords)

Usually one or two words (e.g., "SEO tips," "best restaurants").

High search volume but high competition.

Harder to rank for but valuable for brand awareness.

2. Long-Tail Keywords

Three or more words (e.g., "best SEO tips for beginners," "top restaurants in Munich for dinner").

Lower search volume but higher conversion rates.

More specific, helping businesses target niche audiences.

3. LSI (Latent Semantic Indexing) Keywords

Contextually related keywords that support the main keyword (e.g., for “SEO strategy,” related terms could be “organic search,” “Google rankings,” or “on-page SEO”).

Google uses LSI keywords to understand content relevance beyond exact matches.

4. Branded Keywords

Keywords that include a brand name (e.g., "Nike running shoes," "Ahrefs SEO tool").

Helps businesses dominate search results for their brand.

5. Geo-Targeted Keywords

Keywords that include location-based terms (e.g., "best SEO agency in Berlin," "restaurants near me").

Essential for local SEO and appearing in Google’s Local Pack.

6. Transactional & Commercial Keywords

Transactional: Used by people ready to buy (e.g., “buy SEO tools online,” “hire an SEO consultant”).

Commercial Investigation: Used for research before purchasing (e.g., "best SEO tools 2024," "Ahrefs vs. SEMrush comparison").

Using a mix of these keywords ensures higher visibility and engagement across different user intents.

How to Find the Right Keywords for SEO

Step 1: Use Google’s Free Tools

Google Search Console – Find keywords your site already ranks for.

Google Trends – Discover trending keywords and seasonal patterns.

Google Autocomplete & “People Also Ask” – Find related search terms and common user queries.

Step 2: Leverage SEO Keyword Research Tools

Ahrefs & SEMrush – Find keyword difficulty, search volume, and competitor rankings.

Ubersuggest – Generate long-tail keyword ideas.

AnswerThePublic – Find commonly asked questions related to your industry.

Step 3: Analyze Competitor Keywords

Use Ahrefs’ Site Explorer or SEMrush’s Keyword Gap Tool to identify the keywords your competitors are ranking for.

Study their top-performing pages and content structure.

Step 4: Identify Keyword Intent

Informational: “How to optimize keywords for SEO” → Blog posts, guides.

Navigational: “Google Keyword Planner tool” → Product pages, branded content.

Commercial: “Best keyword research tools” → Comparison articles, reviews.

Transactional: “Buy SEO software” → Sales pages, landing pages.

Matching keyword intent with content type improves ranking potential.

Optimizing Keywords for Higher Google Rankings

Once you’ve found the right keywords, proper placement and optimization are key.

1. On-Page Keyword Optimization

Title Tag & Meta Description – Place primary keywords at the beginning for better CTR.

Headings (H1, H2, H3) – Use variations of your keywords to structure content naturally.

URL Structure – Keep URLs short and keyword-rich (e.g., example.com/seo-keyword-strategy).

First 100 Words – Introduce the main keyword early for relevance.

Alt Text & Image Names – Optimize images using descriptive keywords.

Internal Linking – Use anchor text with keywords to improve website structure.

2. Content Optimization for User Experience

Write naturally – Avoid keyword stuffing.

Use LSI & Semantic Keywords to improve topic depth.

Optimize for Featured Snippets by answering common user queries in a concise, structured format.

3. Off-Page Optimization for Keyword Rankings

Backlinks – Build high-quality links from authoritative websites using keyword-rich anchor text.

Social Signals – Share content across social media platforms to improve visibility.

Guest Blogging – Publish content on relevant, high-traffic websites using targeted keywords.

Measuring Keyword Performance & Refining Your Strategy

SEO is an ongoing process. Track and refine your keyword strategy using these key metrics:

Organic Traffic – Check Google Analytics for keyword-driven traffic.

Keyword Rankings – Monitor changes in keyword positions using Ahrefs or SEMrush.

CTR (Click-Through Rate) – Improve underperforming meta titles & descriptions.

Bounce Rate & Dwell Time – Ensure content matches search intent.

Conversion Rate – Track how well keywords lead to conversions.

A/B Testing: Experiment with different title tags, content formats, and CTAs to see what works best.

Future of Keywords in SEO: AI & Voice Search

With Google’s AI-driven search (SGE) and voice search adoption, keyword optimization is evolving.

1. AI & Predictive SEO

AI tools like Surfer SEO & Clearscope analyze ranking patterns and suggest keyword improvements.

Google’s BERT & NLP updates focus on search intent and contextual meaning over exact keywords.

2. Voice Search Optimization

Optimize for conversational queries (e.g., “What’s the best SEO strategy?”).

Use FAQ-style content and structured data markup.

By staying ahead of these trends, businesses can future-proof their SEO strategy.

Conclusion

Keywords are the backbone of SEO, but ranking higher on Google requires more than just inserting keywords into content. A successful keyword strategy involves:

✔ Data-driven research using Google & SEO tools. ✔ Understanding search intent to align content with user needs. ✔ Optimizing keyword placement across on-page elements. ✔ Building backlinks & off-page authority for higher rankings. ✔ Tracking performance & refining strategy based on analytics.

By implementing a strategic, intent-driven keyword approach, you can boost your rankings, increase organic traffic, and drive more conversions in a competitive SEO landscape.

[marketing-technology]

Contextual Targeting: Definition, Types, and Advantages

Digital advertisers have traditionally relied heavily on behavioral targeting, using browser cookies to display ads. However, this method often neglects real-time content relevance and user resonance, leading to irrelevant ad displays that can hinder performance marketing campaign results.

According to a SurveyMonkey report, about 74% of users believe there are too many ads on digital platforms, with 44% finding these ads irrelevant to their needs. Marketing leaders are now exploring contextual targeting as a more effective advertising strategy, especially as the era of cookie-based targeting wanes. Contextual marketing is seen as a key method to ensure privacy and enhance ad relevance.

In this article, we will explore the definition of contextual targeting, its benefits, and how it compares to behavioral targeting.

What is Contextual Targeting?

Contextual targeting is an advertising strategy where marketers place ads based on the content of a specific web page. For example, if a company sells AI products, its ads would appear on pages featuring content about AI and its applications. This strategy ensures that ads are relevant to the content being consumed, thereby increasing engagement.

Types of Contextual Targeting

There are several types of contextual targeting that marketers can utilize:

Category Contextual Targeting: This type involves displaying ads based on broad categories such as automotive, finance, or beauty. It targets a wide audience but may lack precision.

Keyword Contextual Targeting: Marketers select specific keywords to target their audience more accurately. This method provides greater flexibility and precision in ad placement.

Semantic Contextual Targeting: Using advanced machine learning, semantic targeting analyzes the context of a web page to determine ad relevance. This method offers a more sophisticated and accurate targeting approach.

Contextual Targeting vs. Behavioral Targeting

While contextual targeting focuses on displaying ads based on the content of a web page, behavioral targeting relies on the user's online activities. Behavioral targeting tracks user behavior such as clicks, time spent on pages, and search history to serve ads relevant to past actions.

For instance, behavioral targeting might show ads for AI chatbots to a user who has previously searched for them. In contrast, contextual targeting would display similar ads on a web page discussing AI-driven chatbots.

Advantages of Contextual Targeting

Contextual targeting offers several benefits that can help marketers reach their audience more effectively:

Reaching the Right Audience: This method allows marketers to quickly engage with a receptive audience by displaying ads on relevant web pages, prompting users to take action.

Cost-effective Strategy: Contextual targeting is generally more affordable than behavioral marketing campaigns, making it ideal for businesses with limited budgets.

Easy Implementation: This strategy is straightforward to implement, requiring minimal time and data. Tools like Google Display Network facilitate quick campaign setups.

Customized Ad Experiences: Delivering personalized experiences is crucial in digital marketing. Contextual targeting enables tailored interactions, fostering trust and engagement with the target audience.

Increased Sales: By displaying relevant ads to interested users, contextual targeting can drive higher website traffic and conversion rates, ultimately boosting sales and business growth.

Compliance with Privacy Laws: Unlike cookie-based targeting, contextual targeting does not rely on user data collection, ensuring compliance with regulations like the GDPR.

Conclusion

Businesses are increasingly adopting contextual advertising strategies to connect with the right audience at the right time and place. This approach allows marketers to deliver relevant ads to users interested in similar topics, maximizing engagement and ROI. Effective contextual targeting campaigns can significantly enhance digital marketing efforts, making it a valuable tool in the advertiser's arsenal.

[zidigi]

The Basics of Programmatic SEO

Defining Programmatic SEO

Before we plunge into the depths of its transformative power, let’s establish a clear understanding of what Programmatic SEO entails. Programmatic SEO outperforms manual SEO by using automated processes and data-driven insights to increase a website’s exposure on search engine result pages (SERPs).

The Role of Automation in SEO

Automation, a cornerstone of programmatic SEO, brings efficiency and precision to the optimization process. Automation simplifies various chores, like link building, content generation, and keyword research, freeing marketers to concentrate on strategy and originality.

Critical Components of Programmatic SEO

Advanced Keyword Targeting

In the world of SEO, keywords reign supreme. Programmatic SEO furthers this by employing sophisticated algorithms to identify primary, long-tail, and latent semantic keywords. This ensures a comprehensive approach to targeting user queries.

Dynamic Content Optimization

Intelligent Link Building Strategies

For SEO to be successful, a strong backlink profile must be developed. Programmatic SEO employs intelligent algorithms to identify high-quality, relevant link-building websites. This strategic approach improves a site’s authority and establishes credibility within its niche.

Programmatic SEO vs Traditional SEO

Speed and Efficiency

Traditional SEO methods often involve time-consuming, manual processes. Programmatic SEO, on the other hand, operates at the speed of algorithms, reducing the time required for optimization tasks. This speed improves efficiency and allows for real-time adaptation to market trends.

Data-Driven Decision Making

In the traditional SEO landscape, decisions are often based on experience and intuition. Programmatic SEO relies on data-driven insights, ensuring every strategy is backed by analytics. This results in more informed and impactful decision-making processes.

Adaptability to Algorithm Changes

Search engine algorithms are continually evolving. Programmatic SEO’s data-driven nature enables it to adapt swiftly to algorithm changes, ensuring that a website remains optimized despite dynamic search engine updates.

The Impact of Programmatic SEO on Online Visibility

Enhanced SERP Rankings

Securing a top spot on the SERPs is the ultimate goal of every SEO plan. Programmatic SEO’s data-driven precision and dynamic optimization contribute to consistently higher rankings, ensuring increased visibility for the target audience.

Improved User Experience

Search engine rankings heavily consider user experience. Programmatic SEO’s focus on dynamic content optimization aligns the website with user preferences, leading to a more satisfying and engaging experience. This, in turn, contributes to improved rankings and prolonged user interactions.

Expanded Reach and Targeting

Programmatic SEO doesn’t just optimize for the current audience; it anticipates and adapts to the evolving needs of potential customers. Programmatic SEO broadens a brand’s online footprint by expanding reach and targeting new demographics, unlocking untapped markets.

Challenges and Considerations

Balancing Automation and Human Touch

Automation increases efficiency, but finding the right balance between automation and human contact is essential. Programmatic SEO should complement, not replace, human marketers’ creativity and strategic thinking.

Data Security and Privacy

Advanced Insights through Programmatic SEO Analytics

In the dynamic landscape of online visibility, insights are the currency that drives strategic decisions. Programmatic SEO takes analytics to a new level, providing advanced insights beyond traditional metrics.

Understanding User Behavior in Real-Time

Programmatic SEO analytics delve into user behavior in real time, offering a granular view of how visitors interact with a website. This includes the pages they navigate, the time spent on each page, and their actions. By understanding user behavior at this level of detail, marketers can tailor content and optimize user journeys for maximum engagement.

Predictive Analytics for Future Trends

The capacity of programmatic SEO to forecast future trends based on historical data is one of its most notable characteristics. By leveraging predictive analytics, businesses can stay ahead of the curve, anticipating shifts in user preferences and search engine algorithms. With proactive optimization made possible by this foresight, a website can remain competitive and relevant in the ever-evolving digital landscape.

Keyword Performance and Market Trends

While traditional SEO analytics provide insights into keyword performance, programmatic SEO takes it further. Advanced algorithms analyze keyword trends, identifying emerging terms and predicting their potential impact. This helps stay ahead of competitors and allows businesses to capitalize on trending topics, giving organic website traffic.

Competitor Analysis and Benchmarking

Programmatic SEO analytics extend beyond individual website performance to include comprehensive competitor analysis. By benchmarking against competitors, businesses gain valuable insights into market dynamics, identifying areas for improvement and innovation. This competitive intelligence is instrumental in refining strategies to outperform peers in the online arena.

Personalization Strategies in Programmatic SEO

Tailoring Content for Diverse Audiences

The capacity of programmatic SEO to customize content according to user preferences is one of its most important advantages. Through advanced data analysis, the system identifies different audience segments and tailors content to resonate with each group. This level of personalization goes beyond basic demographics, considering user behavior, preferences, and engagement patterns.

Dynamic Landing Pages for Targeted Campaigns

Programmatic SEO enables the creation of dynamic landing pages that adapt in real time based on user interactions. This means delivering a personalized experience for targeted campaigns from when a user clicks on a search result. Dynamic landing pages enhance relevance and increase conversion rates, whether promotional offers or specific product information.

A/B Testing for Continuous Optimization

Personalization is an iterative process, and programmatic SEO facilitates continuous optimization through A/B testing. Marketers can fine-tune their strategies based on user responses by experimenting with different content variations, calls-to-action, and user journeys. This iterative approach ensures that personalization efforts are not static but evolve with changing user preferences.

Behavioral Retargeting for Seamless Engagement

The Role of Artificial Intelligence in Programmatic SEO

Machine Learning for Adaptive Optimization

Machine learning, a kind of artificial intelligence, forms the basis of programmatic SEO. Machine learning algorithms analyze large-scale data to find patterns and trends, enabling the system to adjust and improve tactics instantly. With the help of adaptive optimization, SEO initiatives are ensured to keep up with the constantly evolving search algorithm and user landscape.

Natural Language Processing for Content Optimization

Natural Language Processing (NLP) is a game-changer in content optimization within programmatic SEO. Natural Language Processing (NLP) improves material relevancy by deciphering user searches and their purpose. Providing material that meets user expectations raises search ranks and enhances the entire user experience.

Predictive Modeling for Strategic Planning

Predictive modeling, another facet of artificial intelligence, is crucial in strategic planning within programmatic SEO. Predictive models estimate the possible effects of various methods by examining past data and market patterns. Because of this insight, marketers can allocate resources where they can produce the most results.

Voice Search Optimization for the Future

Programmatic SEO leverages artificial intelligence to optimize for voice search, which is becoming increasingly common. Voice search optimization involves understanding natural language queries and tailoring content to match conversational patterns. By staying at the forefront of voice search trends, businesses can future-proof their online visibility strategies.

In embracing programmatic SEO, businesses unlock a treasure trove of advanced insights, personalized strategies, and the transformative power of artificial intelligence. As we navigate the intricacies of analytics, personalization, and AI, it becomes evident that programmatic SEO is not just a tool; it’s a strategic imperative for those aiming to dominate the digital landscape.

Programmatic SEO stands as a game-changer in online visibility. Its automation-driven approach and data-driven decision-making propel businesses toward higher SERP rankings, improved user experiences, and expanded market reach. While challenges exist, the transformative potential of Programmatic SEO cannot be overstated. Accepting this change is not an option for companies looking to prosper in the digital age; it is a strategic necessity. Ready to unleash the power of Programmatic SE for your business? Explore the possibilities with elatre.com and elevate your online visibility today.

[jennifermurphseo]

Beyond Keywords: The Multifaceted Approach Needed to Climb Google SERP Rankings

While keywords have long been the focal point of search engine optimization (SEO), the reality is that Google's ranking algorithms have evolved to consider a myriad of factors beyond simple keyword usage. To truly succeed in climbing Google SERP rankings, businesses must adopt a multifaceted approach that encompasses various elements of SEO strategy.

Beyond Keywords: Understanding Google's Complex Ranking Factors

In today's SEO landscape, success goes beyond keyword optimization. Google considers a range of factors when determining rankings, including user experience signals, content quality, backlinks, and mobile friendliness.

User Experience Signals

Google prioritizes websites that offer a positive user experience, including factors such as page load speed, mobile responsiveness, and ease of navigation.

Content Quality and Relevance

High-quality, relevant content remains a cornerstone of SEO success. Google rewards websites that provide valuable information to users and penalizes those with thin or low-quality content.

Backlinks and Authority

Backlinks from authoritative websites signal to Google that a site is reputable and trustworthy. Building a strong backlink profile is essential for climbing SERP rankings.

Mobile Friendliness

With the majority of searches now conducted on mobile devices, Google prioritizes mobile-friendly websites in its rankings. Ensuring your site is optimized for mobile is crucial for SEO success.

The Importance of User Experience (UX) in SEO

A positive user experience is not only important for visitors but also for search engine rankings. Factors such as website speed, mobile responsiveness, and navigational structure all contribute to a website's UX and its performance in search results.

Website Speed and Performance

Pages that load quickly provide a better user experience and are more likely to rank highly on Google SERPs. Optimizing images, minimizing server response times, and utilizing caching can help improve website speed.

Mobile Responsiveness

With the increasing prevalence of mobile search, websites must be optimized for various screen sizes and devices to ensure a seamless user experience across all platforms.

Navigational Structure

A clear and intuitive navigation structure not only helps users find what they're looking for but also makes it easier for search engine crawlers to index your site's content.

Crafting High-Quality Content for Better Rankings

Content remains king in the world of SEO, but not all content is created equal. To rank highly on Google SERPs, businesses must focus on creating high-quality, relevant content that addresses the needs and interests of their target audience.

Long-Form Content vs. Thin Content

Google tends to favor long-form content that provides comprehensive information on a topic. Thin, superficial content is unlikely to rank well in search results.

Keyword Optimization in Content

While keyword stuffing is no longer effective, strategically incorporating relevant keywords into your content can still help improve rankings. Natural language and semantic variations are key.

Semantic SEO and Entity-Based Content

Semantic SEO focuses on understanding the context and intent behind search queries, allowing businesses to create content that aligns with user intent and ranks well for related topics.

Building Authority Through Backlinks

Backlinks are a crucial ranking factor in Google's algorithms, serving as endorsements from other websites. However, not all backlinks are created equal, and quality trumps quantity when it comes to link building.

Natural Link Building Strategies

Earning backlinks naturally through the creation of valuable content, outreach, and relationship building is the most effective long-term strategy for building authority.

Guest Blogging and Outreach

Guest blogging on reputable websites and reaching out to influencers and industry leaders can help attract high-quality backlinks and increase brand visibility.

Monitoring and Disavowing Toxic Links

Regularly monitoring your backlink profile and disavowing toxic or spammy links can help protect your site from penalties and maintain its credibility in the eyes of Google.

Technical SEO Considerations for Improved Rankings

Technical SEO encompasses the backend aspects of website optimization that directly impact search engine visibility and crawling.

Website Crawling and Indexing

Ensuring that search engine crawlers can easily access and index your website's content is essential for appearing in search results.

Schema Markup and Structured Data

Schema markup helps search engines understand the content and context of your website, leading to enhanced visibility and rich snippets in search results.

HTTPS and Security

Google prioritizes secure websites, so migrating to HTTPS and ensuring your site's security protocols are up to date can positively impact your SERP rankings.

Leveraging Social Signals and Engagement

Social signals, such as likes, shares, and comments, can indirectly impact search rankings by increasing brand visibility and driving traffic to your site.

Social Media Presence and Brand Signals

Maintaining an active presence on social media platforms can help establish brand authority and generate social signals that contribute to SEO.

Social Sharing and Virality

Creating shareable content and encouraging social sharing can amplify your reach and attract valuable backlinks and referral traffic.

Local SEO Strategies for Businesses

For businesses with physical locations or local clientele, optimizing for local search is essential for attracting nearby customers.

Google My Business Optimization

Claiming and optimizing your Google My Business listing can help improve your visibility in local search results and attract customers in your area.

Local Citations and Reviews

Building citations on local directories and encouraging positive reviews from satisfied customers can further boost your local SEO efforts.

Monitoring and Analyzing Performance

Regularly monitoring your website's performance in search results and analyzing key metrics can help identify areas for improvement and fine-tune your SEO strategy.

Google Analytics and Search Console

Google Analytics and Search Console provide valuable insights into your website's traffic, performance, and search visibility, allowing you to make data-driven decisions.

Key Performance Indicators (KPIs) for SEO

Tracking KPIs such as organic traffic, keyword rankings, and conversion rates can help gauge the effectiveness of your SEO efforts and measure return on investment.

Conclusion

In conclusion, to succeed in Google SERP rankings, businesses need a holistic approach to SEO. This involves focusing on user experience, quality content, building authority with backlinks, technical optimization, leveraging social signals, and implementing local SEO strategies. Hire SEO expert can also greatly enhance these efforts. Prioritizing these elements and staying updated on trends and algorithms can boost visibility and organic traffic on Google.

[hydralisk98]

hydralisk98′s web projects tracker:

Core principles=

Fail faster

‘Learn, Tweak, Make’ loop

This is meant to be a quick reference for tracking progress made over my various projects, organized by their “ultimate target” goal:

(START)

(Website)=

Install Firefox

Install Chrome

Install Microsoft newest browser

Install Lynx

Learn about contemporary web browsers

Install a very basic text editor

Install Notepad++

Install Nano

Install Powershell

Install Bash

Install Git

Learn HTML

Elements and attributes

Commenting (single line comment, multi-line comment)

Head (title, meta, charset, language, link, style, description, keywords, author, viewport, script, base, url-encode, )

Hyperlinks (local, external, link titles, relative filepaths, absolute filepaths)

Headings (h1-h6, horizontal rules)

Paragraphs (pre, line breaks)

Text formatting (bold, italic, deleted, inserted, subscript, superscript, marked)

Quotations (quote, blockquote, abbreviations, address, cite, bidirectional override)

Entities & symbols (&entity_name, &entity_number, &nbsp, useful HTML character entities, diacritical marks, mathematical symbols, greek letters, currency symbols, )

Id (bookmarks)

Classes (select elements, multiple classes, different tags can share same class, )

Blocks & Inlines (div, span)

Computercode (kbd, samp, code, var)

Lists (ordered, unordered, description lists, control list counting, nesting)

Tables (colspan, rowspan, caption, colgroup, thead, tbody, tfoot, th)

Images (src, alt, width, height, animated, link, map, area, usenmap, , picture, picture for format support)

old fashioned audio

old fashioned video

Iframes (URL src, name, target)

Forms (input types, action, method, GET, POST, name, fieldset, accept-charset, autocomplete, enctype, novalidate, target, form elements, input attributes)

URL encode (scheme, prefix, domain, port, path, filename, ascii-encodings)

Learn about oldest web browsers onwards

Learn early HTML versions (doctypes & permitted elements for each version)

Make a 90s-like web page compatible with as much early web formats as possible, earliest web browsers’ compatibility is best here

Learn how to teach HTML5 features to most if not all older browsers

Install Adobe XD

Register a account at Figma

Learn Adobe XD basics

Learn Figma basics

Install Microsoft’s VS Code

Install my Microsoft’s VS Code favorite extensions

Learn HTML5

Semantic elements

Layouts

Graphics (SVG, canvas)

Track

Audio

Video

Embed

APIs (geolocation, drag and drop, local storage, application cache, web workers, server-sent events, )

HTMLShiv for teaching older browsers HTML5

HTML5 style guide and coding conventions (doctype, clean tidy well-formed code, lower case element names, close all html elements, close empty html elements, quote attribute values, image attributes, space and equal signs, avoid long code lines, blank lines, indentation, keep html, keep head, keep body, meta data, viewport, comments, stylesheets, loading JS into html, accessing HTML elements with JS, use lowercase file names, file extensions, index/default)

Learn CSS

Selections

Colors

Fonts

Positioning

Box model

Grid

Flexbox

Custom properties

Transitions

Animate

Make a simple modern static site

Learn responsive design

Viewport

Media queries

Fluid widths

rem units over px

Mobile first

Learn SASS

Variables

Nesting

Conditionals

Functions

Learn about CSS frameworks

Learn Bootstrap

Learn Tailwind CSS

Learn JS

Fundamentals

Document Object Model / DOM

JavaScript Object Notation / JSON

Fetch API

Modern JS (ES6+)

Learn Git

Learn Browser Dev Tools

Learn your VS Code extensions

Learn Emmet

Learn NPM

Learn Yarn

Learn Axios

Learn Webpack

Learn Parcel

Learn basic deployment

Domain registration (Namecheap)

Managed hosting (InMotion, Hostgator, Bluehost)

Static hosting (Nertlify, Github Pages)

SSL certificate

FTP

SFTP

SSH

CLI

Make a fancy front end website about 

Make a few Tumblr themes

===You are now a basic front end developer!

Learn about XML dialects

Learn XML

Learn about JS frameworks

Learn jQuery

Learn React

Contex API with Hooks

NEXT

Learn Vue.js

Vuex

NUXT

Learn Svelte

NUXT (Vue)

Learn Gatsby

Learn Gridsome

Learn Typescript

Make a epic front end website about 

===You are now a front-end wizard!

Learn Node.js

Express

Nest.js

Koa

Learn Python

Django

Flask

Learn GoLang

Revel

Learn PHP

Laravel

Slim

Symfony

Learn Ruby

Ruby on Rails

Sinatra

Learn SQL

PostgreSQL

MySQL

Learn ORM

Learn ODM

Learn NoSQL

MongoDB

RethinkDB

CouchDB

Learn a cloud database

Firebase, Azure Cloud DB, AWS

Learn a lightweight & cache variant

Redis

SQLlite

NeDB

Learn GraphQL

Learn about CMSes

Learn Wordpress

Learn Drupal

Learn Keystone

Learn Enduro

Learn Contentful

Learn Sanity

Learn Jekyll

Learn about DevOps

Learn NGINX

Learn Apache

Learn Linode

Learn Heroku

Learn Azure

Learn Docker

Learn testing

Learn load balancing

===You are now a good full stack developer

Learn about mobile development

Learn Dart

Learn Flutter

Learn React Native

Learn Nativescript

Learn Ionic

Learn progressive web apps

Learn Electron

Learn JAMstack

Learn serverless architecture

Learn API-first design

Learn data science

Learn machine learning

Learn deep learning

Learn speech recognition

Learn web assembly

===You are now a epic full stack developer

Make a web browser

Make a web server

===You are now a legendary full stack developer

[...]

(Computer system)=

Learn to execute and test your code in a command line interface

Learn to use breakpoints and debuggers

Learn Bash

Learn fish

Learn Zsh

Learn Vim

Learn nano

Learn Notepad++

Learn VS Code

Learn Brackets

Learn Atom

Learn Geany

Learn Neovim

Learn Python

Learn Java?

Learn R

Learn Swift?

Learn Go-lang?

Learn Common Lisp

Learn Clojure (& ClojureScript)

Learn Scheme

Learn C++

Learn C

Learn B

Learn Mesa

Learn Brainfuck

Learn Assembly

Learn Machine Code

Learn how to manage I/O

Make a keypad

Make a keyboard

Make a mouse

Make a light pen

Make a small LCD display

Make a small LED display

Make a teleprinter terminal

Make a medium raster CRT display

Make a small vector CRT display

Make larger LED displays

Make a few CRT displays

Learn how to manage computer memory

Make datasettes

Make a datasette deck

Make floppy disks

Make a floppy drive

Learn how to control data

Learn binary base

Learn hexadecimal base

Learn octal base

Learn registers

Learn timing information

Learn assembly common mnemonics

Learn arithmetic operations

Learn logic operations (AND, OR, XOR, NOT, NAND, NOR, NXOR, IMPLY)

Learn masking

Learn assembly language basics

Learn stack construct’s operations

Learn calling conventions

Learn to use Application Binary Interface or ABI

Learn to make your own ABIs

Learn to use memory maps

Learn to make memory maps

Make a clock

Make a front panel

Make a calculator

Learn about existing instruction sets (Intel, ARM, RISC-V, PIC, AVR, SPARC, MIPS, Intersil 6120, Z80...)

Design a instruction set

Compose a assembler

Compose a disassembler

Compose a emulator

Write a B-derivative programming language (somewhat similar to C)

Write a IPL-derivative programming language (somewhat similar to Lisp and Scheme)

Write a general markup language (like GML, SGML, HTML, XML...)

Write a Turing tarpit (like Brainfuck)

Write a scripting language (like Bash)

Write a database system (like VisiCalc or SQL)

Write a CLI shell (basic operating system like Unix or CP/M)

Write a single-user GUI operating system (like Xerox Star’s Pilot)

Write a multi-user GUI operating system (like Linux)

Write various software utilities for my various OSes

Write various games for my various OSes

Write various niche applications for my various OSes

Implement a awesome model in very large scale integration, like the Commodore CBM-II

Implement a epic model in integrated circuits, like the DEC PDP-15

Implement a modest model in transistor-transistor logic, similar to the DEC PDP-12

Implement a simple model in diode-transistor logic, like the original DEC PDP-8

Implement a simpler model in later vacuum tubes, like the IBM 700 series

Implement simplest model in early vacuum tubes, like the EDSAC

[...]

(Conlang)=

Choose sounds

Choose phonotactics

[...]

(Animation ‘movie’)=

[...]

(Exploration top-down ’racing game’)=

[...]

(Video dictionary)=

[...]

(Grand strategy game)=

[...]

(Telex system)=

[...]

(Pen&paper tabletop game)=

[...]

(Search engine)=

[...]

(Microlearning system)=

[...]

(Alternate planet)=

[...]

(END)

[thehyperfuel]

B2B SEO: HOW IT IS DIFFERENT FROM B2C SEO

The practice of increasing a B2B website’s visibility in search engine results across all keyword and search opportunities that have the potential to create targeted traffic and engagement is known as business-to-business (B2B) search engine optimization (SEO). The methods and approaches required to get visibility in search results distinguish B2B SEO from B2C SEO. The following are some of the most significant differences that have an impact on SEO:

The length and complexity of the sales cycle

Content that is most likely to rank

Poor volume keywords have a low keyword value.

Lead generation vs. eCommerce

Marketers who use account-based marketing

You may build a more successful SEO plan that produces optimal business outcomes by understanding the concept of B2B SEO and the intricacies of these differentiators.

1. SALES CYCLE LENGTH

Long sales cycles are standard in B2B business models, necessitating a substantially longer user journey. There are considerably more touchpoints in this trip than in traditional B2C user experiences, and a lot more information has to be given to a potential buyer. As a result, there are many types of content that correspond to various stages of the buying cycle. Gartner has divided the buying cycle into six stages:

Identifying the issue 

Exploration of potential solutions

Creating requirements

Choosing a supplier

Validation of the solution

Creating a consensus

Each of these stages contains a set of keywords that correspond to the buyer’s requirements. In many circumstances, the buyer is an entity made up of several people, each playing a particular function in the buying process. As a result, the volume of information and the various voices and user focus required for that content are frequently far more complicated than a B2C business model/website. As a result, keyword research and content strategy are more complex to acquire visibility for specific terms in search results.

Aligning SEO best practices and specialized SEO content initiatives with a bigger marketing strategy is particularly difficult. Integrating major B2B firms’ SEO objectives into their content creation plans necessitates a lot of planning, coordination, and, in most cases, a lot of education for various stakeholders within the marketing team. This is especially true for instructional content at the top of the funnel, which is less directly linked to potential purchasers.

2. TYPE OF CONTENT MOST LIKELY TO RANK 

Regardless of SEO considerations, B2B enterprises want informational material to raise visibility. However, given the present search engine landscape, it has never been more critical, particularly in the case of Google. Google used to be incapable of comprehending purpose, entities, or semantic relationships. Those were the days when content with the most powerful link connectivity dominated search engine ranks for relevant keywords, regardless of the type of content. As search engine algorithms have improved over the last decade, search engines have become considerably better at providing consumers with information that is more relevant to their query. This includes a focus on knowledge and resource-oriented content with a higher top-of-the-funnel or awareness orientation for many non-brand, broad keywords.

That means that, rather than pages with specific solutions, software, or services, B2B search results are now dominated by definition pages, relevant blog posts, and other resources that enable knowledge gathering and study, rather than pages with specific solutions, software, or services.

One of the significant differences between B2B and B2C search engine optimization is the problems and solutions necessary to integrate that SEO strategy into a larger content marketing strategy. Content strategy is critical for B2B SEO, and a vital component of your SEO content strategy is evaluating the search results for the keywords your B2B company is targeting and determining the style of content that is most likely to rank for those keywords. B2C websites may target terms with this kind of informative bias in some circumstances, but they do not in many others.

3. LOW-VOLUME KEYWORDS’ KEYWORD VALUE

Another significant distinction between most B2B and B2C websites is the possible value of a single visit and the potential worth of long-tail, low-volume keyword traffic. Long-tail keyword traffic is critical for both B2B and B2C organizations. However, the proportional value of each visit has the potential to be tenfold larger due to the pricing point of many B2B solutions versus B2C offerings. Obviously, this is a broad generalization, but it appears to be correlated to overall traffic numbers being less precise as a metric of success for B2B websites than B2C websites.

This places a higher emphasis on highly targeted, low-volume keyword ranks that generate highly qualified leads, as compared to a B2C site, where somewhat less targeted keyword rankings that generate significant amounts of traffic may be more profitable. The distinction between these two mindsets has an impact on where you put your focus and many aspects of your SEO strategy, such as content strategy and link acquisition tactics. Furthermore, it can potentially alter the KPIs you use to assess performance radically.

4. LEAD GENERATION VS. ECOMMERCE

One of the most significant differences between B2B and B2C websites is that most B2B websites lack eCommerce platforms that require optimization. B2B websites tend to be more concerned with lead generation and engagement metrics, whereas B2C websites are more concerned with sales and revenue. In terms of SEO, this implies that B2B sites don’t have to spend as much time optimizing data feeds, image optimization across an array of products, or eCommerce platforms, which are notorious for having a slew of technical faults. B2B sites, on the other hand, tend to have a lot more informational content that requires more advanced strategies for crosslinking that content and integrating new content into the user journey in a way that maximizes visibility, improves page rank flow, and keeps the overall site architecture as flat as possible. This is particularly difficult in an enterprise setting with various teams, platforms, site sections or subdomains, and hundreds, if not thousands, of old web pages.

SEO COMPARISON: B2B VS. B2C

The distinctions between a B2B and a B2C SEO campaign aren’t always clear. LikeB2C websites, some B2B sites also have an eCommerce component, and similarly, some B2C websites, like most B2B sites, have a significant amount of knowledge-sharing content. In general, the differences between B2B and B2C SEO are crucial to understand to establish unique tactics that will help you be more successful in the B2B arena.

No one ever said that growing your business would be easy, and no one ever achieved their goals alone. With the right business-to-business (B2B) digital marketing partner and effective strategy, you can attain the momentum you need to reach new heights with your business.

The Hyper Fuel is a top-rated B2B digital marketing agency counted amongst the top B2B marketing service providers worldwide. Because when it comes to business growth, there is no better way to fuel your sales than The Hyper Fuel! Call us today to get started on the path to faster growth.

[juniperpublishers-crdoj]

Grocery Store Tours are an Effective Way to Provide Nutrition Education to Low Income Minority Populations

Authored by Christina Ralston

Abstract

Objectives: The objective of this study was to provide nutrition education via grocery store tours to improve the nutrition knowledge of the participants and to foster positive behavioral changes to decrease the incidence of obesity and its' related diseases such as diabetes and hypertension in the Memphis community. According to the Health Belief Model, providing "cue to action” in the form of nutrition education will bring about positive behavior change.

Methods: Participants attended a 90 -minute grocery store tour and discussion and completed a 10- question survey at the end of the tour. The surveys were analyzed for participants learning and anticipated behavioral changes. Numeric answers on the survey were totaled as a percent of the total answers and open- ended questions were analyzed using semantic content analysis.

Results: Prior to the tours, participants (n=125) reported eating fewer than the recommended servings of fruit and vegetables per day (78%). However, 88% reported that they would increase their intake based on

Conclusions and implications: Grocery store tours proved to be an effective method of providing nutrition education for adults. This supports the Health Belief Model that procing "cues to action” does bring about behavior change. By understanding proper nutrition, participants may be able to avoid obesity and its related co-morbidities.

Keywords: Education; Nutrition; Grocery store tours

    Introduction

The state of Tennessee has a high rate of adult obesity (33.8%) and adults who are overweight (34.9%) in comparison to other states [1]. Tennessee's high school students have the second highest rate of obesity (18.6%), in the nation [1]. Obesity is linked to high rates of hypertension (38.5%) and diabetes (12.7%) [1]. Furthermore, an increased rate of heart disease, arthritis, and obesity-related cancers are more prevalent in Tennessee. Poor diet contributes to increased medical costs as overweight individuals spend more on healthcare each year than their non-overweight counterparts. Memphis has the highest obesity rate of any city in the US. Memphis has a high (63%) African American (AA) population and AAs have a higher than average rate of obesity and diseases associated with it. In the US, non-Hispanic blacks make up 48% of obese individuals [2]. Additionally, Memphis has a high poverty rate (26.2%) and residents may lack an understanding of how to eat healthy on a budget. Lack of education about proper nutrition, the ability to incorporate healthy options into their diet and the knowledge of healthy food preparation, may contribute to the high rate of obesity among AAs. Proper nutrition education is essential for those who want to eat healthy, lose weight, reduce blood pressure and glucose to avoid heart disease and premature death. There are several models that explain why an individual would or would not act to improve their health.

Accordingtothe Health BeliefModel, there are three components to behavior change; an individuals' perceptions of the likelihood of getting a disease, the modifying factors and the likelihood of action [3]. In the first component, an individual’s perceived susceptibility to the disease and the perceived seriousness of the disease are considered. In the second component, modifying factors, demographic and sociopsychological variables and cues to action (outside influences such as education) are considered. These modifying factors along with individual perceptions result in the third component, the likelihood of action, which means the likelihood that a person will act to prevent the "disease.” Therefore, nutrition education should address the individual perceptions of the disease (percent of AAs who are obese, have diabetes, etc.) and the modifying factors (education on prevention and treatment via education). According to the model, when these two components are addressed, behavior change will be the outcome.

Other evidence supports the hypothesis that proper nutrition education produces a positive behavioral change. Rustad and Smith offered three nutrition education sessions with presentations, demonstrations and activities to 118 low- income women (ages 23-45) at various community settings [4]. The education sessions contained pre- and post-tests pre- and post-test to evaluate comprehension and behavioral changes. The researchers detected improvements in nutrition knowledge and favorable nutrition behavioral changes based on pre- and posttest answers (P <.05). These authors concluded that short term nutrition education provided to women on their level does brings about positive behavioral change.

In 2009, a telephone survey of WIC participants in California determined that improved nutrition awareness and positive behavioral changes resulted from nutrition education [5]. After receiving nutrition education about fruits and vegetables, whole grains, and lower-fat milk, the survey respondents reported improved awareness of the value of whole grains, low fat dairy, and increased color variety in fruits and vegetables, as well sometimes” foods such as desserts and fried foods. Improvements in family consumption of fruits, vegetables, whole grains, and low-fat dairy were positive behavioral benefits of the study.

Nutrition education may assist people to live healthier, but many people don't have access to proper nutrition education or can't afford to consult a registered dietitian. Due to the demand for an intervention to end the cycle of obesity in the Memphis community, the University of Memphis (UM) nutrition department has taken great strides to educate the city. The initiative UM has taken includes education on healthy eating habits and ways to revamp the way the public shops for groceries. Additionally, education is provided on the causes of obesity related diseases and exactly how diet effects these diseases and the incidence of obesity and its’ related diseases in the AA population of Memphis. One to two times a month, grocery store tours are held at local supermarkets to educate the public on smart and healthy ways to shop for food. These group tours are hands-on strolls through the stores, which allows everyone to participate and learn from the dietetic intern in an informal setting. The purpose of this study was to determine the efficacy of providing nutrition education, in the form of grocery store tours, to improve the nutrition and disease knowledge of the participants and to foster positive behavioral changes, to decrease the incidence of obesity and its' related diseases, such as diabetes and hypertension, in the Memphis community.

    Materials and Methods

Starting in January 2015, dietetic interns began to provide grocery store tours for people in low income areas of Memphis.The tours focused mainly on increasing fruit and vegetable intake, although healthier options from each area of the grocery store were discussed. The tour guide led the participants through the grocery store and described the healthier items in each area, such as hydrogenated vegetable oil vs olive oil and processed cheese vs natural cheese. Participants in the tour were provided with specific food items food items to taste and they were asked to complete a 10-question survey at the end ofthe tour Figure 1. No demographic data were collected. At the end of the tour, participants entered a drawing to win a prize. All prizes were related to food and nutrition, such as kitchen towels, pot holders, and measuring cups and spoons. Interns were trained in compliance with the Grocery Store Tour Training Kit [6]. A training session was held in the fall of 2014 and every August thereafter, for to account for new interns. Interns conducted the tours on Saturday mornings, in pairs and provided tours to 5 stores in lower income areas of Memphis and 1in North Mississippi.

Grocery store tours were advertised at the public libraries and surrounding businesses in 5 areas of Memphis and 1 in North Mississippi. The program director and her graduate assistant for the project also spread the word by appearing on two Memphis television stations and one radio station every fall and spring. In February 2017, the local newspaper, Memphis Commercial Appeal, published an article about the tours. Although the tours were targeted to AAs, they were open to everyone and the tours were free of charge. The study was exempt from Institutional Review Board approval at the University of Memphis. Tour surveys were analyzed to determine the outcome of the program. The numeric answers were totaled and expressed as a percentage of the total. Tour participants were asked open ended questions about what they learned from the grocery store tour and the responses were analyzed using semantic content analysis [6]. Procedural steps for content analysis of open ended questions were as follows: Participants' responses were read by the program director and by a research assistant separately to look for overall themes and patterns. Using inductive analysis, common themes, patterns, and categories were generated and identified. These common themes, patterns and categories were assigned code words, and these code words were approved for use in the analysis [7]. The researcher and research assistant independently read and identified significant statements in line-by-line analysis, using the approved codes. Coded segments were matched for rater reliability. Frequencies of themes, patterns, and categories were calculated and by various identifiers.

    Results

A total of 125 participants from January 2015 to March 2017 took part in a grocery store tour. Some participants didn’t complete the entire survey or provided multiple answers for a single question. Thus, the sample sizes of tables differ. Survey results revealed that only 33% believed they were healthy eaters and only 22% reported eating the recommended 4-5 fruits and vegetables a day. Because of the tour, 88% planned to increase their fruit and vegetable consumption.

The following themes arose from the coded segments of the question, what did you learn about nutrition today: food labels, be healthy, reduce salt intake, buying fruits and vegetables, balanced diet, fats. When asked specifically what they learned about fruits and vegetables, more specific themes arose. Table 1 & 2 show the results of the three open-ended questions about participant learning with the themes and percentages for each theme. Table 1 shows the overall nutrition learning outcome from the grocery tours. Thirty seven percent of the participants stated they learned the importance of food labels and how to read them. Table 2 displays increased awareness among the participants concerning fruits and vegetables. Twenty nine percent learned how to choose and eat produce, as well as the seasons for certain fruits. Thirty two percent learned that the order of preference for vegetables is fresh, frozen, and then canned.

When asked if they enjoyed taste testing the foods on the tour, 92% said yes. New foods were tasted by 68% of the participants. Most participants had not tasted edamame, dried fruit, sun-dried tomatoes, chick peas, artichoke, kefir/yogurt, or a yogurt smoothie. When asked to name the best part of the store tour participants provided more than one answer (n=154); 32% said learning from the interns, 20% said taste testing/tasting something new and 14% said learning how to cook/prepare in a healthier manner for nutrition benefits. Seventeen percent of participants reported learning how to read and understand a food label was the best part of the tour, while other responses were: "seeing the store in a new light”, "sharing ideas with other participants” and "all of it was great”.

    Discussion

The participants in the store tours enjoyed learning from the interns and sharing information with each other, perhaps because the grocery store is a non-threatening learning environment. Participants were encouraged to ask questions on the spot about concepts which they didn't understand, such as which fats are healthiest. Store tour participants reported increased awareness of a variety of fruits and vegetables, how to select ripe ones, their importance in a healthy diet, and how to prepare them for consumption. These findings support the findings of the WIC study in which survey respondents indicated that they learned the importance of eating a variety of fruits and vegetables and that they and their families would increase their fruit and vegetable consumption, as 88% of our participants planned to do [5]. Similarly, the FOOD cents (low socio-economic) adult nutrition education program in Western Australia aimed to provide knowledge, skills and motivation to purchase nutrient dense foods on a budget for take-out over the past 20 years [8]. Smith and Jones performed a randomized study to examine the efficiency of the Expanded Food and Nutrition Education Program for low-income families [9]. Individuals that were part of this study were either assigned to immediate education or delayed education. During the first eight weeks, participants with immediate education received intervention, but the delayed education participants did not receive any intervention. Results showed that both the immediate and delayed education control groups improved behavior during their pre- and post-education. The current study supports the results of the WIC study and the FOOD cents study and the notion that "cues to action” (outside influences such as education) does bring about behavior change in low income populations.

    Conclusion

The store tours given by interns, in this study, proved to be an effective way to teach the public how to shop and cook in a manner which should lead to better overall health. Providing nutrition education in a familiar, relaxed environment may be a better way of teaching lower income, minority populations, as opposed to teaching in a more formal setting, such as a clinic or physicians' office. Research supports that proper nutrition education does lead to behavior changes and thus may decrease the incidence of obesity, diabetes and hypertension in Memphis.

    Funding

This project was funded by: The Better Produce for Health Foundation, Office of Minority Health and Disparities Elimination, Division of Health Disparities Tennessee Department of Health, Kroger stores of Memphis and the AARP Fresh Savings Program.

    Authors Contribution

Williams Hooker developed the idea for the program. In addition, Dr Williams Hooker provided the oversight, conducted the analysis, and wrote the manuscript. Ms Ralston and Abounassif implemented the program, organized the tours and tour leaders, and contributed to the manuscript.

To Know More About Current Research in Diabetes & Obesity Journal  Please click on: https://juniperpublishers.com/crdoj/index.php

To Know More About Open Access Journals Please click on: https://juniperpublishers.com/index.php

[manuscriptedpod]

When scenes speak louder than words: Verbal encoding does not mediate the relationship between scene meaning and visual attention

Manuscript authors: Gwendolyn Rehrig, Taylor R. Hayes, John M. Henderson, and Fernanda Ferreira 

Read aloud by the first author. Please refer to the manuscript documents linked below for in-text citations, references, correspondence information, author affiliations, and figures.

Published manuscript: https://link.springer.com/article/10.3758/s13421-020-01050-4

     DOI: doi:10.3758/s13421-020-01050-4

Preprint: https://psyarxiv.com/3h7au

Supplemental material: https://osf.io/8mbyv/

Citation: Rehrig, G., Hayes, T. R., Henderson, J. M., & Ferreira, F. (2020). When scenes speak louder than words: Verbal encoding does not mediate the relationship between scene meaning and visual attention. Memory & Cognition, 48(7), 1181-1195.

Transcript

Gwendolyn Rehrig: When scenes speak louder than words: Verbal encoding does not mediate the relationship between scene meaning and visual attention By Gwendolyn Rehrig, Taylor R. Hayes, John M. Henderson, and Fernanda Ferreira. 

Abstract: The complexity of the visual world requires that we constrain visual attention and prioritize some regions of the scene for attention over others. The current study investigated whether verbal encoding processes influence how attention is allocated in scenes. Specifically, we asked whether the advantage of scene meaning over image salience in attentional guidance is modulated by verbal encoding, given that we often use language to process information. In two experiments, 60 subjects studied scenes (30 in experiment 1 and 60 in experiment 2) for 12 seconds each in preparation for a scene recognition task. Half of the time, subjects engaged in a secondary articulatory suppression task concurrent with scene viewing. Meaning and saliency maps were quantified for each of the experimental scenes. In both experiments, we found that meaning explained more of the variance in visual attention than image salience did, particularly when we controlled for the overlap between meaning and salience, with and without the suppression task. Based on these results, verbal encoding processes do not appear to modulate the relationship between scene meaning and visual attention. Our findings suggest that semantic information in the scene steers the attentional ship, consistent with cognitive guidance theory. 

Keywords: scene processing, visual attention, meaning, salience, language 

Introduction 

Because the visual world is information-rich, observers prioritize certain scene regions for attention over others to process scenes efficiently. While bottom-up information from the stimulus is clearly relevant, visual attention does not operate in a vacuum, but rather functions in concert with other cognitive processes to solve the problem at hand. What influence, if any, do extra-visual cognitive processes exert on visual attention?  

Two opposing theoretical accounts of visual attention are relevant to the current study: saliency-based theories and cognitive guidance theory. According to saliency-based theories, salient scene regions—those that contrast with their surroundings based on low-level image features (for example, luminance, color, orientation)—pull visual attention across a scene, from the most salient location to the least salient location in descending order. Saliency-based explanations cannot explain that physical salience does not determine which scene regions are fixated and that top-down task demands influence attention more than physical salience does. Cognitive guidance theory can account for these findings: the cognitive system pushes visual attention to scene regions, incorporating stored knowledge about scenes to prioritize regions that are most relevant to the viewer’s goals. Under this framework, cognitive systems—for example, long- and short-term memory, executive planning, etc.—operate together to guide visual attention. Coordination of cognitive systems helps to explain behavioral findings where saliency-based attentional theories fall short. For example, viewers look preferentially at meaningful regions of a scene (for example, those containing task-relevant objects), even when they are not visually salient (for example, under shadow), despite the presence of a salient distractor. 

Recent work has investigated attentional guidance by representing the spatial distribution of image salience and scene meaning comparably. Henderson and Hayes introduced meaning maps to quantify the distribution of meaning over a scene. Raters on mTurk saw small scene patches presented at two different scales and judged how meaningful or recognizable each patch was. Meaning maps were constructed by averaging the ratings across patch scales and smoothing the values. Image salience was quantified using Graph-Based Visual Salience. The feature maps were correlated with attention maps that were empirically derived from viewer fixations in scene memorization and aesthetic judgement tasks. Meaning explained greater variance in attention maps than salience did, both for linear and semipartial correlations, suggesting that meaning plays a greater role in guiding visual attention than image salience does. This replicated when attention maps constructed from the same dataset were weighted on fixation duration, when viewers described scenes aloud, during free-viewing of scenes, when meaning was not task-relevant, and even when image salience was task-relevant. In sum, scene meaning explained variation in attention maps better than image salience did across experiments and tasks, supporting the cognitive guidance theory of attentional guidance. 

One question that remains unexplored is whether other cognitive processes indirectly influence cognitive guidance of attention. For example, it is possible that verbal encoding may modulate the relationship between scene meaning and visual attention: Perhaps the use of language, whether vocalized or not, pushes attention to more meaningful regions. While only two of the past experiments were explicitly linguistic in nature (scene description), the remaining tasks did not control for verbal encoding processes. 

There is evidence that observers incidentally name objects silently during object viewing. Meyer et al. asked subjects to report whether a target object was present or not in an array of objects, which sometimes included competitors that were semantically related to the target or were semantically unrelated, but had a homophonous name (for example, bat the tool vs. bat the animal). The presence of competitors interfered with search, which suggests information about the objects (name, semantic information) became active during viewing, even though that information was not task-relevant. In a picture-picture interference study, Meyer and Damian presented target objects that were paired with distractor objects with phonologically similar names, and instructed subjects to name the target objects. Naming latency was shorter when distractor names were phonologically similar to the name of the target object, suggesting that activation of the distractor object’s name occurred and facilitated retrieval of the target object’s name. Together, the two studies demonstrate a tendency for viewers to incidentally name objects they have seen.  

Cross-linguistic studies on the topic of linguistic relativity employ verbal interference paradigms to demonstrate that performance on perceptual tasks can be mediated by language processes. For example, linguistic color categories vary across languages even though the visual spectrum of colors is the same across language communities. A 2007 study showed that observers discriminated between colors faster when the colors belonged to different linguistic color categories, but the advantage disappeared with verbal interference. These findings indicate that language processes can mediate performance on perceptual tasks that are ostensibly not linguistic in nature, and a secondary verbal task that prevents task-incidental language use can disrupt the mediating influence of language. Similar influences of language on ostensibly non-linguistic processes, and the disruption thereof by verbal interference tasks, have been found for spatial memory, event perception, categorization, and numerical representations, to name a few. 

The above literature suggests we use internal language during visual processing, and in some cases those language processes may mediate perceptual processes. Could the relationship between meaning and visual attention observed previously have been modulated by verbal encoding processes? To examine this possibility, we used an articulatory suppression manipulation to determine whether verbal encoding mediates attentional guidance in scenes.

In the current study, observers studied 30 scenes for 12 seconds each for a later recognition memory test. The scenes used in the study phase were mapped for meaning and salience. We conducted two experiments in which subjects performed a secondary articulatory suppression task half of the time in addition to memorizing scenes. In Experiment 1, the suppression manipulation was between-subjects, and the articulatory suppression task was to repeat a three digit sequence aloud during the scene viewing period. We chose this suppression task because we suspected subjects might adapt to and subvert simpler verbal interference such as a syllable repetition, and because digit sequence repetition imposes less cognitive load than n-back tasks. In Experiment 2, we implemented a within-subject design using two experimental blocks: one with the sole task of memorizing scenes, the other with an additional articulatory suppression task. Because numerical stimuli may be processed differently than other verbal stimuli, we instead asked subjects to repeat the names of a sequence of three shapes aloud during the suppression condition. In the recognition phase of both experiments, subjects viewed 60 scenes—30 that were present in the study phase, 30 foils—and indicated whether or not they recognized the scene from the study phase.  

We tested two competing hypotheses about the relationship between verbal encoding and attentional guidance in scenes. If verbal encoding indeed mediated the relationship between meaning and attentional guidance in our previous work, we would expect observers to direct attention to meaningful scene regions only when internal verbalization strategies are available to them. Specifically, meaning should explain greater variance in attention maps than saliency in the control condition, and meaning should explain less or equal variance in attention as salience when subjects suppressed internal language use. Conversely, if verbal encoding did not mediate attentional guidance in scenes, the availability of verbalization strategies should not affect attention, and so we would expect to find an advantage of meaning over salience whether or not subjects engaged in a suppression task.        

Experiment 1: Methods  

Sixty-eight undergraduates enrolled at the University of California, Davis participated for course credit. All subjects were native speakers of English, at least 18 years old, and had normal or corrected-to-normal vision. They were naive to the purpose of the experiment and provided informed consent as approved by the University of California, Davis Institutional Review Board. Six subjects were excluded from analysis because their eyes could not be accurately tracked, 1 due to an equipment failure, and 1 due to experimenter error; data from the remaining 60 subjects were analyzed (30 subjects in each condition). 

Scenes were 30 digitized and luminance-matched photographs of real-world scenes used in a previous experiment. Of these, 10 depicted outdoor environments, and 20 depicted indoor environments. People were not present in any scenes. Another set of 30 digitized images of comparable scenes (similar scene categories and time period, no people depicted) were selected from a Google image search and served as memory foils. Because we did not evaluate attentional guidance for the foils, meaning and salience were not quantified for these scenes, and the images were not luminance-matched. 

Digit sequences were selected randomly without replacement from all three digit numbers ranging from 100 to 999 (900 numbers total), then segmented into 30 groups of 30 sequences each such that each digit sequence in the articulatory suppression condition was unique. 

Eye movements were recorded with an SR Research EyeLink 1000+ tower mount eyetracker at a 1000 Hz sampling rate. Subjects sat 83 cm away from a monitor such that scenes subtended approximately 26° x 19° visual angle at a resolution of 1024 x 768 pixels, presented in 4:3 aspect ratio. Head movements were minimized using a chin and forehead rest integrated with the eyetracker’s tower mount. Subjects were instructed to lean against the forehead rest to reduce head movement while allowing them to speak during the suppression task. Although viewing was binocular, eye movements were recorded from the right eye. The experiment was controlled using SR Research Experiment Builder software. Data were collected on two systems that were identical except that one subject computer operated using Windows 10, and the other used Windows 7.

Subjects were told they would see a series of scenes to study for a later memory test. Subjects in the articulatory suppression condition were told each trial would begin with a sequence of 3 digits, and were instructed to repeat the sequence of digits aloud during the scene viewing period. After the instructions, a calibration procedure was conducted to map eye position to screen coordinates. Successful calibration required an average error of less than 0.49° and a maximum error below 0.99°. 

Following successful calibration, there were 3 practice trials to familiarize subjects with the task prior to the experimental trials. In the suppression condition, during these practice trials participants studied three-digit sequences prior to viewing the scene. Practice digit sequences were 3 randomly sampled sequences from the range 1 to 99, in 3-digit format (for example, “0 3 6” for 36). Subjects pressed any button on a button box to advance throughout the task. 

Each subject received a unique pseudo-random trial order that prevented two scenes of the same type (for example, a kitchen) from occurring consecutively. A trial proceeded as follows. First, a five-point fixation array was displayed to check calibration. The subject fixated the center cross and the experimenter pressed a key to begin the trial if the fixation was stable, or reran the calibration procedure if not. Before the scene, subjects in the articulatory suppression condition saw the instruction “Study the sequence of digits shown below. Your task is to repeat these digits over and over out loud for 12 seconds while viewing an image of the scene” along with a sequence of 3 digits separated by spaces (for example, “8 0 9”), and pressed a button to proceed. The scene was shown for 12 seconds, during which time eye-movements were recorded. After 12 seconds elapsed, subjects pressed a button to proceed to the next trial. The trial procedure repeated until all 30 trials were complete. 

Figure 1 shows a schematic of the trial procedure. The first phase shows a fixation array against a gray background. Four peripheral fixations are black, and the central fixation is red. The experimenter presses a button to advance from this screen. In the articulatory suppression condition only, a digit sequence display is then shown, which displays a digit sequence to be rehearsed in white text against a gray background. Subjects press a button on a button box to advance. The button box, represented in the figure as 5 circles corresponding to each of the 5 buttons, has a yellow circle at the top, with a white circle directly below it. On either side of the white circle are a green circle on the left and a red circle on the right. Below the white circle is a blue circle. The third shows an example of a real-world scene. The example scene shows an indoor scene of an area near an entryway. There is a wooden dresser with electronics on it, and cleaning supplies adjacent to the wooden dresser. The walls are sea green, and the floor is tiled in black and white. There is an umbrella in the background in front of a radiator. The scene is shown for 12 seconds, after which an end of trial screen appears to let inform subjects they can press a button on the button box to proceed. 

A recognition memory test followed the experimental trials, in which subjects were shown the 30 experimental scenes and 30 foil scenes they had not seen previously. Presentation order was randomized without replacement. Subjects were informed that they would see one scene at a time and instructed to use the button box to indicate as quickly and accurately as possible whether they had seen the scene earlier in the experiment. After the instruction screen, subjects pressed any button to begin the memory test. In a recognition trial, subjects saw a scene that was either a scene from the study phase or a foil image. The scene persisted until a “Yes” or “No” button press occurred, after which the next trial began. Response time and accuracy were recorded. This procedure repeated 60 times, after which the experiment terminated. 

Fixations and saccades were parsed with EyeLink’s standard algorithm using velocity and acceleration thresholds. Eye movement data were imported offline into Matlab using the Visual EDF2ASC tool packaged with SR Research DataViewer software. The first fixation was excluded from analysis, as were saccade amplitude and fixation duration outliers. 

Attention maps were generated by constructing a matrix of fixation counts with the same x,y dimensions as the scene, and counting the total fixations corresponding to each coordinate in the image. The fixation count matrix was smoothed with a Gaussian low pass filter with circular boundary conditions and a frequency cutoff of -6dB. For the scene-level analysis, all fixations recorded during the viewing period were counted. For the fixation analysis, separate attention maps were constructed for each ordinal fixation. 

We generated meaning maps using the context-free rating method introduced in Henderson & Hayes (2017). Each 1024 x 768 pixel scene was decomposed into a series of partially overlapping circular patches at fine and coarse spatial scales. The decomposition resulted in 12,000 unique fine-scale patches and 4,320 unique coarse-scale patches, totaling 16,320 patches. 

Raters were 165 subjects recruited from Amazon Mechanical Turk. All subjects were located in the United States, had a HIT approval rating of 99% or more, and participated once. Subjects provided informed consent and were paid $0.50. 

All but one subject rated 300 random patches extracted from the 30 scenes. Subjects were instructed to rate how informative or recognizable each patch was using a 6-point Likert scale (‘very low’, ‘low’, ‘somewhat low’, ‘somewhat high’, ‘high’, ‘very high’). Prior to rating patches, subjects were given two examples each of low-meaning and high-meaning patches in the instructions to ensure they understood the task. Patches were presented in random order. Each patch was rated 3 times by 3 independent raters totaling 48,960 ratings per scene. Because there was high overlap across patches, each fine patch contained data from 27 independent raters and each coarse patch from 63 independent raters. 

Meaning maps were generated from the ratings for each scene by averaging, smoothing, and combining the fine and coarse scale maps from the corresponding patch ratings. The ratings for each pixel at each scale in each scene were averaged, producing an average fine and coarse rating map for each scene. The fine and coarse maps were then averaged. Because subjects in the eyetracking task showed a consistent center bias in their fixations, we applied center bias to the maps using a multiplicative down-weighting of scores in the map periphery. “Center bias” is the tendency for fixations to cluster around the center of the scene and to be relatively absent in the periphery of the image. The final map was blurred using a Gaussian filter via the Matlab function ‘imgaussfilt’ with a sigma of 10. 

Image-based saliency maps were constructed using the Graph-Based Visual Saliency toolbox in Matlab with default parameters. We used GBVS because it is a state-of-the-art model that uses only image-computable salience. While there are newer saliency models that predict attention better, these models incorporate high-level image features through training on viewer fixations and object features, which may index semantic information. We used GBVS to avoid incorporating semantic information in image-based saliency maps, which could confound the comparison with meaning.   

Prior to analysis, feature maps were normalized to a common scale using image histogram matching via the Matlab function ‘imhistmatch’ in the Image Processing Toolbox. The corresponding attention map for each scene served as the reference image. Map normalization was carried out within task conditions: for the map-based analysis of the control condition, feature maps were normalized to the attention map derived from fixations in the control condition only, and likewise for the suppression condition. Results did not differ between the current analysis and a second analysis using feature maps normalized to the same attention map generated from fixations in the control condition. 

Figure 2 shows a schematic of the meaning mapping procedure on the top row and representation saliency, meaning, and attention maps on the bottom row. The first panel of row 1 shows the same example real-world scene (the entryway). The second panel shows the fine-scale spatial grid, which consists of small overlapping circles on the scene. An example small-scale patch is shown on the grid. The patch shows a small group of objects on the dresser. The third panel shows the coarse-scale spatial grid, which consists of larger overlapping circles, and an example coarse-scale scene patch that overlaps with the example small-scale scene patch. It shows the same small group of objects, but additionally shows the top drawer of the dresser and adjacent objects (a phone and a modem) that were not visible in the small scale patch. The fourth panel shows six examples of patches that received high meaning ratings or low meaning ratings. The three high meaning patches show a phone on the dresser, a handle of a drawer on the dresser, and a candle on the dresser. The there low meaning patches all show only surfaces: the green wall in a corner of the room, the door, and several tiles from the floor. The second row shows the saliency, meaning, and attention maps, all of which are heatmaps of the same dimensions as the scene image. Dark colors (black and dark red) indicate low map values, and bright colors (white and yellow) indicate high map values. The saliency map shows high values that correspond to contrasts between objects and the tile floor, a dark gray vacuum cleaner against a white door, white crown molding contrasting with the green walls, and the outlines around each drawer or the dresser. The meaning map shows high values corresponding to the top of the dresser where many small objects are located and dresser drawers, with more diffuse middle values (orange-red) corresponding to objects around the dresser. The attention map for the control condition has several bright spots corresponding to higher fixation density. The hot spots overlap with the objects on the top of the dresser, the vacuum cleaner, other cleaning supplies next to the dresser, and the objects in the background. The attention map for the suppression condition is more or less identical.  

We computed correlations (R2) across the maps of 30 scenes to determine the degree to which saliency and meaning overlap with one another. We excluded the peripheral 33% of the feature maps when determining overlap between the maps to control for the peripheral downweighting applied to both, which otherwise would inflate the correlation between them. On average, meaning and saliency were correlated, and this relationship differed from zero.   

Experiment 1: Results  

To determine what role verbal encoding might play in extracting meaning from scenes, we asked whether the advantage of meaning over salience in explaining variance in attention would hold in each condition. To answer this question, we conducted two-tailed paired t-tests within task conditions. 

To determine whether we obtained adequate effect sizes for the primary comparison of interest, we conducted a sensitivity analysis using G*Power 3.1. We computed the effect size index dz—a standardized difference score—and the critical t statistic for a two-tailed paired t-test with 95% power and a sample size of 30 scenes. The analysis revealed a critical t value of 2.05 and a minimum dz of 0.68.

We correlated meaning and saliency maps with attention maps to determine the degree to which meaning or salience guided visual attention. Squared linear and semipartial correlations (R2) were computed within each condition for each of the 30 scenes. The relationship between meaning and salience, respectively, and visual attention was analyzed using t-tests. Cohen’s d was computed to estimate effect size, interpreted as small, medium, or large following Cohen (1988).           

In the control condition, when subjects were only instructed to memorize scenes, meaning accounted for 34% of the average variance in attention and salience accounted for 21%. The advantage of meaning over salience was significant. In the articulatory suppression condition, when subjects additionally had to repeat a sequence of digits aloud, meaning accounted for 37% of the average variance in attention whereas salience accounted for 23%. The advantage of meaning over salience was also significant when the task prevented verbal encoding.

Because meaning and salience are correlated, we partialed out the shared variance explained by both meaning and salience. In the control condition, when the shared variance explained by salience was accounted for, meaning explained 15% of the average variance in attention, while salience explained only 2% of the average variance once the variance explained by meaning was accounted for. The advantage of meaning over salience was significant. In the articulatory suppression condition, meaning explained 16% of the average unique variance after shared variance was partialed out, while salience explained only 2% of the average variance after shared variance with meaning was accounted for, and the advantage was significant.  

Figure 3a shows scatter box plots for linear correlations on the left panel and semipartial correlations that explain the unique variance explained by meaning and salience, respectively, on the right panel. The y-axis for both panels ranges from 0.00 to 1.00. In the scatter box plots, the mean is shown on the center line and 95% confidence intervals as boxes around the mean. Whiskers correspond to plus or minus one standard deviation. Dots correspond to individual data points. Between the control condition and the suppression condition, image salience—indicated in blue—explains essentially the same amount of variance, and the boxes are almost identical. The box (and central line) for meaning is slightly higher and larger in the suppression condition than in the control. On the right panel, which shows semipartial correlations, the picture is much the same except the box plots for the variance explained by salience are barely visible—thick, dark lines hovering just above 0 on the y-axis. The boxes for meaning look even more similar across conditions than they did for the linear correlations, but unlike the boxes for salience they are clearly visible and hover around 0.15 on the y-axis.

To summarize, we found a large advantage of meaning over salience in explaining variance in attention in both conditions, for both linear and semipartial correlations. For all comparisons, the value of the t statistic and dz exceeded the thresholds obtained in the sensitivity analysis. 

Following our previous work, we examined early fixations to determine whether salience influences early scene viewing. We correlated each feature map (meaning, salience) with attention maps at each fixation. Squared linear and semipartial correlations (R2) were computed for each fixation, and the relationship between meaning and salience with attention, respectively, was assessed for the first three fixations using paired t-tests.  

In the control condition, meaning accounted for 37% of the average variance in attention during the first fixation, and 14% and 13% during the second and third fixations, respectively. Salience accounted for 9%, 8%, and 7% of the average variance during the first, second, and third fixations, respectively. The advantage of meaning was significant for all three fixations. For subjects in the suppression condition, meaning accounted for 42% of the average variance during the first fixation, 21% during the second, and 17% during the third fixation. Salience accounted for 10% of the average variance during the first fixation and 9% during the second and third fixations. The advantage of meaning over salience was significant for all three fixations.            

To account for the correlation between meaning and salience, we partialed out shared variance explained by both meaning and salience, then repeated the fixation analysis on the semipartial correlations. In the control condition, after the shared variance explained by both meaning and salience was partialed out, meaning accounted for 30% of the average variance at the first fixation, 10% of the variance during the second fixation, and 8% during the third fixation. After shared variance with meaning was partialed out, salience accounted for only 2% of the average unique variance at the first and third fixations and 3% at the second fixation. The advantage of meaning was significant for all three fixations. In the articulatory suppression condition, after the shared variance with salience was partialled out, meaning accounted for 34% of the average variance during the first fixation, 14% at the second fixation, and 10% during the third fixation. After the shared variance with meaning was partialled out, on average salience accounted for 2% of the variance at all three fixations. The advantage of meaning was significant for all three fixations.  

Figure 3b shows line graphs for linear correlations on the top row and semipartial correlations that explain the unique variance explained by meaning and salience on the bottom row. The y-axis for both panels ranges from 0.00 to 1.00. Lines in the graph corresponding to the suppression condition are dashed. Error bars around each point indicate 95% confidence intervals. For linear correlations, blue lines corresponding to image salience hover at or below 0.1 for the entire period shown (fixations 1-38), and are almost completely overlapping between conditions. Red lines for meaning start out quite high—around 0.4—and decrease after the first fixation, but both red lines are higher than the blue lines for image salience throughout. The same trend is visible on the graph showing semipartial correlations, except the blue lines for salience are barely above 0 on the y-axis, and the red lines for meaning are more clearly distinguishable from those for salience, but not terribly distinguishable from one another (across conditions). In both graphs, the dashed red lines corresponding to meaning in the suppression condition are higher than the solid red lines for the control condition.       

In sum, early fixations revealed a consistent advantage of meaning over salience, counter to the claim that salience influences attention during early scene viewing. The advantage was present for the first three fixations in both conditions, when we analyzed both linear and semipartial correlations, and all effect sizes were medium or large.            

To confirm that subjects took the memorization task seriously, we totaled the number of hits, correct rejections, misses, and false alarms on the recognition task for each subject, each of which ranged from 0 to 30. Recognition performance was high in both conditions. On average, subjects in the control condition correctly recognized scenes shown in the memorization task 95% of the time, while subjects who engaged in the suppression task during memorization correctly recognized scenes 90% of the time. Subjects in the control conditions falsely reported that a foil scene had been present in the memorization scene set 3% of the time on average, and those in the suppression condition false alarmed an average of 4% of the time. Overall, subjects in the control condition had higher recognition accuracy, though the difference in performance was small. 

Figure 4a shows recognition task performance for each subject using violin plots with data points superimposed. Red violin plots indicate hits, green violin plots indicate correct rejections, blue violins indicate misses, and purple violins show false alarms. Recognition performance for the control condition is shown on the left, and for the suppression condition on the right. In both conditions, there are more hits and correct rejections than misses or false alarms, reflecting high accuracy. However, in the suppression condition (on the right), the violins are thinner and taller for all conditions, indicating more variation in the data for the suppression condition than the control.  

We then computed d’ with log-linear correction to handle extreme values (ceiling or floor performance) using the dprime function from the psycho package in R, resulting in 30 data points per condition (1 data point per subject). On average, d’ scores were higher in the control condition than the articulatory suppression condition. The difference in performance was not significant, and the effect size was small.

Figure 4b shows d’ scores for the control condition and the suppression condition as violin plots with data points superimposed. The gray violin corresponds to d’ for the control condition, and is much higher on the y-axis and wider than that of the suppression condition, showing less variation in the data and slightly better performance in that condition. The yellow violin corresponds to the suppression condition, and it is more narrow and tall, though most of the d’ scores (all but 2) fall within the same range for both conditions.  In sum, recognition was numerically better for subjects who were only instructed to study the scenes as opposed to those who additionally completed an articulatory suppression task, but the difference was not significant.  

Experiment 1: Discussion 

The results of Experiment 1 suggest that incidental verbalization does not modulate the relationship between scene meaning and visual attention during scene viewing. However, the experiment had several limitations. First, we implemented the suppression manipulation between-subjects rather than within-subjects out of concern that subjects might infer the hypothesis in a within-subject paradigm and skew the results. Second, because numerical cognition is unique, it is possible that another type of verbal interference would affect the relationship between meaning and attention. Third, we tested relatively few scenes (only 30). 

We conducted a second experiment to address these limitations and replicate the advantage of meaning over salience despite verbal interference. In Experiment 2, the verbal interference consisted of sequences of common shape names (for example, square, heart, circle) rather than digits, and the interference paradigm was implemented within-subject using a blocked design. We added 30 scenes to the Experiment 1 stimulus set, yielding 60 experimental items total. 

We tested the same two competing hypotheses in Experiments 1 and 2: If verbal encoding mediates the relationship between meaning and attentional guidance, and the use of numerical interference in Experiment 1 was insufficient to disrupt that mediation, then the relationship between meaning and attention should be weaker when incidental verbalization is not available, in which case meaning and salience may explain comparable variance in attention. If verbal encoding does not mediate attentional guidance in scenes and our Experiment 1 results cannot be explained by numerical interference specifically, then we expect meaning to explain greater variance in attention both when shape names are used as interference and when there is no verbal interference. 

The method for Experiment 2 was the same as Experiment 1, with the following exceptions. 

Sixty-five undergraduates enrolled at the University of California, Davis participated for course credit. All were native speakers of English, at least 18 years old, and had normal or corrected-to-normal vision. They were naive to the purpose of the experiment and provided informed consent as approved by the University of California, Davis Institutional Review Board. Four subjects were excluded from analysis because their eyes could not be accurately tracked, and an additional subject was excluded due to excessive movement; data from the remaining 60 subjects were analyzed. 

We selected the following common shapes for the suppression task: circle, cloud, club, cross, arrow, heart, moon, spade, square, and star. Names for the shapes were monosyllabic for eight shape names and disyllabic for two shape names. Shape sequences consisted of 3 shapes randomly sampled without replacement from the set of 10.

Scenes were 60 digitized and luminance-matched photographs of real-world scenes. Thirty were used in Experiment 1, and an additional 30 were drawn from another study. Of the additional scenes, 16 depicted outdoor environments, and 14 depicted indoor environments, and each of the 30 scenes belonged to a unique scene category. People and text were not present in any of the scenes. 

Another set of 60 digitized images of comparable scenes (similar scene categories from the same time period, no people depicted) served as foils in the memory test. Thirty of these were used in Experiment 1, and an additional 30 were distractor images drawn from a previous study. The Experiment 1 scenes and the additional 30 scenes were equally distributed across blocks. 

The apparatus was identical to that used in Experiment 1. 

Subjects were informed that they would complete two separate experimental blocks, and that in one block each trial would begin with a sequence of 3 shapes that they would repeat aloud during the scene viewing period. 

Following successful calibration, there were 4 practice trials to familiarize subjects with the task prior to the experimental trials. The first 2 practice trials were control trials, and the rest were articulatory suppression trials. These consisted of shape sequences (for example, cloud arrow cloud) that were not repeated in the experimental trials. Before the practice trials, subjects were shown all of the shapes used in the suppression task, alongside the names of each shape.  

Figure 5a shows the shape familiarization screen, which depicts all 10 shapes in black against a gray background, accompanied by white text labels to provide the name we wanted subjects to use for each shape. From left to right and top to bottom, the shapes shown are circle, cloud, club, cross, arrow, heart, moon, spade, square, and star. 

The trial procedure was identical to Experiment 1, except that the pre-scene articulatory suppression condition displayed the instruction “Study the sequence of shapes shown below. Your task is to repeat these shapes over and over out loud for 12 seconds while viewing an image of the scene”, followed by a sequence of 3 shapes (for example, square, heart, cross) until the subject pressed a button. 

Figure 5b shows the shape sequence display in the suppression condition, which includes instructions in white text against a gray background, with an example shape sequence shown in black. The shape sequence shown is square, heart, cross. 

Following the experimental trials in each block, subjects performed a recognition memory in which 30 experimental scenes they saw earlier in the block and 30 foil scenes that they had not seen previously were shown. The remainder of the recognition memory task procedure was identical to that of Experiment 1. The procedure repeated 60 times, after which the block terminated. Following completion of the first block, subjects started the second with another calibration procedure. In the second block, subjects saw the other 30 scenes (and 30 memory foils) that were not displayed during the first block, and participated in the other condition (suppression if the first block was the control, and vice versa). Each subject completed 60 experimental trials and 120 recognition memory trials total. The scenes shown in each block and the order of conditions were counterbalanced across subjects.  

Attention maps were generated in the same manner as Experiment 1. 

Meaning maps for 30 scenes added in Experiment 2 were generated using the same procedure as the scenes tested in Experiment 1, with the following exceptions.  Raters were 148 UC Davis undergraduate students recruited through the UC Davis online subject pool. All were 18 years or older, had normal or corrected-to-normal vision, and reported no color blindness. Subjects received course credit for participation.  

In each survey, catch patches showing solid surfaces (for example, a wall) served as an attention check. Data from 25 subjects who did not attend to the task (responded incorrectly on fewer than 85% of catch trials), or did not respond to more than 10% of the questions, were excluded. Data from the remaining 123 raters were used to construct meaning maps. 

Saliency maps were generated in the same manner as in Experiment 1. Maps were normalized in the same manner as in Experiment 1. 

We determined the degree to which saliency and meaning overlap for the 30 new scenes by computing feature map correlations across the maps of 30 scenes, excluding the periphery to control for the peripheral downweighting associated with center biasing operations. On average, meaning and saliency were correlated, and this relationship differed from zero. 

We again conducted a sensitivity analysis, which revealed a critical t value of 2.00 and a minimum dz of 0.47.

We correlated meaning and saliency maps with attention maps in the same manner as in Experiment 1. Squared linear and semipartial correlations (R2) were computed within each condition for each of the scenes. The relationship between meaning and salience with visual attention was analyzed using t-tests. Cohen’s d was computed, and effect sizes were interpreted in the same manner as the Experiment 1 results.   

We examined early fixations to replicate the early advantage of meaning over image salience observed in Experiment 1 and previous work. We correlated each feature map (meaning, salience) with attention maps at each fixation. Map-level correlations and t-tests were conducted in the same manner as Experiment 1. 

Experiment 2: Results 

We sought to replicate the results of Experiment 1 using a more robust experimental design. If verbal encoding is not required to extract meaning from scenes, we expected an advantage of meaning over salience in explaining variance in attention for both conditions. We again conducted paired t-tests within task conditions. 

Meaning accounted for 36% of the average variance in attention in the control condition and salience accounted for 25%. The advantage of meaning over salience was significant and the effect size was large. Meaning accounted for 45% of the variance in attention in the suppression condition and salience accounted for 27%. Consistent with Experiment 1, the advantage of meaning over salience was significant even with verbal interference, and the effect size was large. 

To account for the relationship between meaning and salience, we partialed out the shared variance explained by both. When the shared variance explained by salience was accounted for in the control condition, meaning explained 15% of the average variance in attention, while salience explained 3% of the average variance after accounting for the variance explained by meaning. The advantage of meaning over salience was significant, and the effect size was large. Meaning explained 20% of the unique variance on average after shared variance was partialed out in the articulatory suppression condition, and salience explained 2% of the average variance after shared variance with meaning was accounted for, and the advantage was significant with a large effect size. 

Figure 6a shows scatter box plots for linear correlations on the left panel and semipartial correlations that explain the unique variance explained by meaning and salience, respectively, on the right panel. The y-axis for both panels ranges from 0.00 to 1.00. In the scatter box plots, the mean is shown on the center line and 95% confidence intervals as boxes around the mean. Whiskers correspond to plus or minus one standard deviation. Dots correspond to individual data points. Between the control condition and the suppression condition, image salience—indicated in blue—explains essentially the same amount of variance, hovering around 0.25 on the y-axis, and the boxes are almost identical. The box (and central line) for meaning is higher and larger in the suppression condition than in the control, both of which are higher than image salience. On the right panel, which shows semipartial correlations, the picture is much the same except the box plots for the variance explained by salience are barely visible—thick, dark lines hovering just above 0 on the y-axis. The box for meaning in the control condition hovers around 0.15, but in the suppression condition it is higher and hovers around 0.20. Both boxes for meaning are clearly visible and higher than the blue boxes for image salience.

Consistent with Experiment 1, we found a large advantage of meaning over salience in accounting for variance in attention in both conditions, for both linear and semipartial correlations, and the value of the t statistic and dz exceeded the thresholds obtained in the sensitivity analysis. 

In the control condition, meaning accounted for 30% of the average variance in attention during the first fixation, 17% during the second, and 16% during the third. Salience accounted for 11% of the variance at the first fixation and 10% of the variance during the second and third fixations. The advantage of meaning was significant for all three fixations, and effect sizes were medium or large. In the suppression condition, meaning accounted for 45% of the average variance during the first fixation, 32% during the second, and 25% during the third. Salience accounted for 13% of the average variance during the first fixation,15% during the second, and 11% during the third. The advantage of meaning over salience was significant for all three fixations. 

Because meaning and salience were correlated, we partialed out shared variance explained by both and analyzed semipartial correlations computed for each of the initial three fixations. In the control condition, after the shared variance explained by both meaning and salience was partialed out, meaning accounted for 23% of the average variance at the first fixation, 11% of the variance during the second, and 9% during the third. After shared variance with meaning was partialed out, salience accounted for 3% of the average unique variance at the first fixation and 4% at the second and third. The advantage of meaning was significant for all three fixations. In the suppression condition, after the shared variance with salience was partialled out, meaning accounted for 35% of the variance on average during the first fixation, 20% of the variance at the second, and 16% during the third. After the shared variance with meaning was partialled out, on average salience accounted for 2% of the variance at the first and third fixations and 3% of the variance at the second. The advantage of meaning was significant for all three fixations, with large effect sizes.

Figure 6b shows line graphs for linear correlations on the top row and semipartial correlations that explain the unique variance explained by meaning and salience on the bottom row. The y-axis for both panels ranges from 0.00 to 1.00. Lines in the graph corresponding to the suppression condition are dashed. Error bars around each point indicate 95% confidence intervals. For linear correlations, blue lines corresponding to image salience again hover at or below 0.1 for the entire period shown (fixations 1-38), and are almost completely overlapping between conditions. Red lines for meaning start out quite high—around 0.3 and 0.45 for the control and suppression conditions, respecitvely—and decrease after the first fixation, but both red lines are higher than the blue lines for image salience until time points 33-38. The same trend is visible on the graph showing semipartial correlations, except the blue lines for salience are barely above 0 on the y-axis. The red lines for meaning are very clearly distinguishable from those for salience. In both graphs, the dashed red lines corresponding to meaning in the suppression condition are higher than the solid red lines for the control condition, moreso than they were for the Experiment 1 data shown in Figure 3b.  

The results of Experiment 2 replicated those of Experiment 1: meaning held a significant advantage over salience when the entire viewing period was considered and when we limited our analysis to early viewing, both for linear and semipartial correlations. 

As an attention check, we totaled the number of hits, correct rejections, misses, and false alarms on the recognition task for each subject. The totals for each response category ranged from 0 to 30. Recognition performance was high in both conditions. In the control condition, subjects correctly recognized scenes shown in the memorization task 97% of the time on average, while subjects correctly recognized scenes 91% of the time after they had engaged in the suppression task during memorization. In the control condition, subjects falsely reported that a foil had been present in the memorization scene set 1% of the time on average, and in the suppression condition, the average false alarm rate was 2%. Overall, recognition accuracy was higher in the control condition than the suppression condition, though the difference was small.  

Figure 7a shows recognition task performance for each subject using violin plots with data points superimposed. Red violin plots indicate hits, green violin plots indicate correct rejections, blue violins indicate misses, and purple violins show false alarms. Recognition performance for the control condition is shown on the left, and for the suppression condition on the right. In both conditions, there are more hits and correct rejections than misses or false alarms overall, reflecting high accuracy. However, in the suppression condition (on the right), the violins are thinner and taller for all conditions, indicating more variation in the data for the suppression condition than the control, and the difference is more apparent in Figure 7a than it was in Figure 4a for Experiment 1.  

We then computed d’ in the same manner as Experiment 1. In the control condition, d’ scores were higher on average than in the suppression condition. To determine whether the difference in means was significant, we conducted a paired t-test, which revealed a significant difference with a large effect size. 

Figure 7b shows d’ scores for the control condition and the suppression condition as violin plots with data points superimposed. The gray violin corresponds to d’ for the control condition. It is shaped like a funnel in that it is very wide at the top, indicating most data points corresponded to high recognition accuracy. It is much higher on the y-axis and wider than that of the suppression condition, is represented by a yellow violin plot which is shaped like an upside-down wine bottle indicating more data points corresponding to poorer accuracy in the suppression condition, and greater variation in the suppression condition.  

For Experiment 2, while recognition accuracy was high overall, recognition was significantly better in the control condition, when subjects memorized scenes and did not engage in the suppression task. 

Experiment 2: Discussion 

The attention results of Experiment 2 replicated those of Experiment 1, providing further evidence that incidental verbalization does not modulate the relationship between scene meaning and visual attention during scene viewing. Recognition performance was significantly worse in the suppression condition than in the control condition, which we cannot attribute to individual differences given that the interference manipulation was implemented within-subject. One possibility is that the shape name interference imposed greater cognitive load than the digit sequence interference; however, we cannot determine whether that was the case based on the current experiment. 

General Discussion 

The current study tested two competing hypotheses concerning the relationship (or lack thereof) between incidental verbal encoding during scene viewing and attentional guidance in scenes. First, the relationship between scene meaning and visual attention could be mediated by verbal encoding, even when it occurs incidentally. Second, scene meaning guides attention regardless of whether incidental verbalization is available, and verbal encoding does not mediate use of scene meaning. We tested these hypotheses in two experiments using an articulatory suppression paradigm in which subjects studied scenes for a later memorization task and either engaged in a secondary task (digit or shape sequence repetition) to suppress incidental verbalization, or had no secondary task. In both experiments, we found an advantage of meaning over salience in explaining the variance in attention maps whether or not incidental verbalization was suppressed. Our results did not support the hypothesis that verbal encoding mediates attentional guidance by meaning in scenes. To the extent that observers use incidental verbalization during scene viewing, it does not appear to mediate the influence of meaning on visual attention, suggesting that meaning in scenes is not necessarily interpreted through the lens of language.            

Our attentional findings do not support saliency-based theories of attentional guidance in scenes. Instead, they are consistent with prior work showing that regions with higher image salience are not fixated more and that top-down information, including task demands, plays a greater role than image salience in guiding attention from as early as the first fixation. Consistent with cognitive guidance theory, scene meaning—-which captures the distribution of information across the scene—-predicted visual attention better in both conditions than image salience did. Because our chosen suppression manipulation interfered with verbalization strategies without imposing undue executive load, our findings demonstrate that the advantage of meaning over salience was not modulated by the use of verbal encoding during scene viewing. Instead, we suggest that domain-general cognitive mechanisms (for example, a central executive) may push attention to meaningful scene regions, although additional work is required to test this idea. 

Many of the previous studies that showed an effect of internal verbalization strategies (via interference paradigms) tested simpler displays, such as arrays of objects, color patches, or cartoon images, while our stimuli were real-world photographs. Unlike real-world scenes, observers cannot extract scene gist from simple arrays, and may process cartoons less efficiently than natural scenes. It is possible that verbal encoding exerts a greater influence on visual processing for simpler stimuli: the impoverished images may put visual cognition at a disadvantage because gist and other visual information that we use to efficiently process scenes are not available.       

We cannot know with certainty whether observers in our suppression task were unable to use internal verbal encoding. However, we would expect the secondary verbal task to have at least impeded verbalization strategies, and that should have impacted the relationship between meaning and attention if verbal encoding is involved in processing scene meaning. Furthermore, the suppression tasks we used (3-digit or 3-shape sequences) were comparable to tasks that eliminated verbalization effects in related work, and so should have suppressed inner speech. We suspect that a more demanding verbal task would have imposed greater cognitive load, which could confound our results because we would not be able to separate effects of verbal interference from those of cognitive load.  

Subjects in the control condition did not perform a secondary non-verbal task (for example, a visual working memory task). Given that our findings did not differ across conditions, we suspect controlling for the secondary task’s cognitive load would not have affected the outcome. Recall that prior work has shown digit repetition tasks do not pose excessive cognitive load, and we would have expected lower recognition accuracy in the suppression condition if the demands of the suppression task were too great. However, we cannot be certain the verbal task did not impose burdensome cognitive load in our paradigm, and therefore this remains an issue for further investigation. 

Our results are limited to attentional guidance when memorizing scenes. It is possible that verbal encoding exerts a greater influence on other aspects of visual processing, or that the extent to which verbal encoding plays a role depends on the task. Verbal interference may be more disruptive in a scene categorization task, for example, than in scene memorization, given that categorization often involves verbal labels. 

The current study investigated whether internal verbal encoding processes (for example, thought in the form of language) modulate the influence of scene meaning on visual attention. We employed a verbal interference paradigm to control for incidental verbalization during a scene memorization task, which did not diminish the relationship between scene meaning and attention. Our findings suggest that verbal encoding does not mediate scene processing, and contribute to a large body of empirical support for cognitive guidance theory.   

Supplemental material available on the Open Science Framework under a project with the title as the current manuscript. 

This work has been published in the journal Memory & Cognition, Volume 48, issue 7, inclusive pages 1181-1195. A preprint of the accepted version of the manuscript is available on PsyArxiv under the same name. Please refer to either document for correspondence information, author affiliations, references, statistics, and figures.

[thanhtuandoan89]

LSI Keywords: What Are They and Why Do They Matter in SEO?

Posted by JessicaFoster

The written content on your website serves to not only inform and entertain readers, but also to grab the attention of search engines to improve your organic rankings.

And while using SEO keywords in your content can help you get found by users, focusing solely on keyword density doesn’t cut it when it comes to creating SEO-friendly, reader-focused content.

This is where LSI keywords come in.

LSI keywords serve to add context to your content, making it easier to understand by search engines and readers alike. Want to write content that ranks and wows your readers? Learn how to use LSI keywords the right way.

What are LSI keywords?

Latent Semantic Indexing (LSI) keywords are terms that are conceptually related to the main keyword you’re targeting in your content. They help provide context to your content, making it easier for readers and search engines to understand what your content is about.

Latent semantic analysis

LSI keywords are based on the concept of latent semantic analysis, which is a technique for understanding natural language processing. In other words, it analyzes the relationship between one word and another in order to make sense of the overall content.

Search engine algorithms use latent semantic analysis to understand web content and ultimately determine what content best fits what the user is actually searching for when they use a certain keyword in their search.

Why are LSI keywords important for SEO?

The use of LSI keywords in your content helps search engines understand your content and therefore makes it easier for search engines to match your content to what users are searching for.

Exact keyword usage is less important than whether your overall content fits the user’s search query and the intention behind their search. After all, the goal of search engines is to showcase content that best matches what users are searching for and actually want to read.

LSI keywords are not synonyms

Using synonyms in your content can help add context to your content, but these are not the same as LSI keywords. For example, a synonym for the word “sofa” could be “couch”, but some LSI keywords for “couch” would be terms like “leather”, “comfortable”, “sleeper”, and “sectional”.

When users search for products, services, or information online, they are likely to add modifiers to their main search term in order to refine their search. A user might type something like “red leather sofa” or “large sleeper sofa”. These phrases still contain the primary keyword “sofa”, but with the addition of semantically-related terms.

How to find LSI keywords to use in your content

One of the best ways to find LSI keywords is to put yourself in the mind of someone who is searching for your primary keyword. What other details might they be searching for? What terms might they use to modify their search?

Doing a bit of brainstorming can help set your LSI keyword research off on the right track. Then, you can use a few of the methods below to identify additional LSI keywords, phrases, and modifiers to use in your content.

Google autocomplete

Use Google to search for your target keyword. In most cases, Google’s autocomplete feature will fill the search box with semantically-related terms and/or related keywords.

For the keyword “sofa”, we can see some related keywords (like “sofa vs couch”) as well as LSI keywords like “sofa [bed]”, “[corner] sofa”, and ‘[leather] sofa”.

Competitor analysis

Search for your target keyword and click on the first few competing pages or articles that rank highest in the search results. You can then use the find function to search the content for your primary keyword and identify LSI keywords that bookend that key term.

For example, a search for “digital marketing services” may yield several competitor service pages. You can then visit these pages, find the phrase “digital marketing services”, and see what semantically-related keywords are tied in with your target keyword.

Some examples might include:

“Customizable”

“Full-service”

“Results-driven”

“Comprehensive”

“Custom”

“Campaigns”

“Agency”

“Targeted”

“Effective”

You can later use these LSI keywords in your own content to add context and help search engines understand the types of services (or products) you offer.

LSI keyword tools

If conducting manual LSI keyword research isn’t your forte, you can also use designated LSI keyword tools. Tools like LSIGraph and UberSuggest are both options that enable you to find semantic keywords and related keywords to use in your content.

LSIGraph is a free LSI keyword tool that helps you “Generate LSI keywords Google loves”. Simply search for your target keyword and LSIGraph will come up with a list of terms you can consider using in your content.

In the image above, you can see how LSIGraph searched its database to come up with a slew of LSI keywords. Some examples include: “[reclining] sofa”, “sofa [designs]”, and “[discount] sofas”.

Content optimization tools

Some on-page optimization tools include LSI keyword analysis and suggestions directly within the content editor.

Surfer SEO is one tool that provides immediate LSI keyword recommendations for you to use in your content and analyzes the keyword density of your content in real-time.

Here we see that Surfer SEO makes additional keyword suggestions related to the primary term “rainboots”. These LSI keywords include: “little”, “pair”, “waterproof”, “hunter”, “rubber”, “men’s”, and so on.

Using LSI keywords to improve SEO

You can use any or all of the LSI keywords you identified during your research as long as they are applicable to the topic you are writing about and add value to your content. Using LSI keywords can help beef up your content, but not all of the terms you identify will relate to what you are writing about.

For example, if you sell women’s rain boots, including LSI terms like “men’s” or “masculine” may not tie in to what you’re offering. Use your best judgment in determining which terms should be included in your content.

In terms of using LSI keywords throughout your content, here are a few places you can add in these keywords to improve your SEO:

Title tags

Image alt text

Body content

H2 or H3 subheadings

H1 heading

Meta description

LSI keywords made simple

Identifying and using LSI keywords is made simple when you take a moment to consider what your target audience is searching for. They aren’t just searching for your primary keyword, but are likely using semantically-related terms to refine their search and find the exact service, product, or information they are searching for.

You can also use data-driven keyword research and content optimization tools to identify LSI keywords that are showing up in other high-ranking articles and web pages. Use these terms in your own content to improve your on-page SEO and attract more users to your website.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[drummcarpentry]

LSI Keywords: What Are They and Why Do They Matter in SEO?

Posted by JessicaFoster

The written content on your website serves to not only inform and entertain readers, but also to grab the attention of search engines to improve your organic rankings.

And while using SEO keywords in your content can help you get found by users, focusing solely on keyword density doesn’t cut it when it comes to creating SEO-friendly, reader-focused content.

This is where LSI keywords come in.

LSI keywords serve to add context to your content, making it easier to understand by search engines and readers alike. Want to write content that ranks and wows your readers? Learn how to use LSI keywords the right way.

What are LSI keywords?

Latent Semantic Indexing (LSI) keywords are terms that are conceptually related to the main keyword you’re targeting in your content. They help provide context to your content, making it easier for readers and search engines to understand what your content is about.

Latent semantic analysis

LSI keywords are based on the concept of latent semantic analysis, which is a technique for understanding natural language processing. In other words, it analyzes the relationship between one word and another in order to make sense of the overall content.

Search engine algorithms use latent semantic analysis to understand web content and ultimately determine what content best fits what the user is actually searching for when they use a certain keyword in their search.

Why are LSI keywords important for SEO?

The use of LSI keywords in your content helps search engines understand your content and therefore makes it easier for search engines to match your content to what users are searching for.

Exact keyword usage is less important than whether your overall content fits the user’s search query and the intention behind their search. After all, the goal of search engines is to showcase content that best matches what users are searching for and actually want to read.

LSI keywords are not synonyms

Using synonyms in your content can help add context to your content, but these are not the same as LSI keywords. For example, a synonym for the word “sofa” could be “couch”, but some LSI keywords for “couch” would be terms like “leather”, “comfortable”, “sleeper”, and “sectional”.

When users search for products, services, or information online, they are likely to add modifiers to their main search term in order to refine their search. A user might type something like “red leather sofa” or “large sleeper sofa”. These phrases still contain the primary keyword “sofa”, but with the addition of semantically-related terms.

How to find LSI keywords to use in your content

One of the best ways to find LSI keywords is to put yourself in the mind of someone who is searching for your primary keyword. What other details might they be searching for? What terms might they use to modify their search?

Doing a bit of brainstorming can help set your LSI keyword research off on the right track. Then, you can use a few of the methods below to identify additional LSI keywords, phrases, and modifiers to use in your content.

Google autocomplete

Use Google to search for your target keyword. In most cases, Google’s autocomplete feature will fill the search box with semantically-related terms and/or related keywords.

For the keyword “sofa”, we can see some related keywords (like “sofa vs couch”) as well as LSI keywords like “sofa [bed]”, “[corner] sofa”, and ‘[leather] sofa”.

Competitor analysis

Search for your target keyword and click on the first few competing pages or articles that rank highest in the search results. You can then use the find function to search the content for your primary keyword and identify LSI keywords that bookend that key term.

For example, a search for “digital marketing services” may yield several competitor service pages. You can then visit these pages, find the phrase “digital marketing services”, and see what semantically-related keywords are tied in with your target keyword.

Some examples might include:

“Customizable”

“Full-service”

“Results-driven”

“Comprehensive”

“Custom”

“Campaigns”

“Agency”

“Targeted”

“Effective”

You can later use these LSI keywords in your own content to add context and help search engines understand the types of services (or products) you offer.

LSI keyword tools

If conducting manual LSI keyword research isn’t your forte, you can also use designated LSI keyword tools. Tools like LSIGraph and UberSuggest are both options that enable you to find semantic keywords and related keywords to use in your content.

LSIGraph is a free LSI keyword tool that helps you “Generate LSI keywords Google loves”. Simply search for your target keyword and LSIGraph will come up with a list of terms you can consider using in your content.

In the image above, you can see how LSIGraph searched its database to come up with a slew of LSI keywords. Some examples include: “[reclining] sofa”, “sofa [designs]”, and “[discount] sofas”.

Content optimization tools

Some on-page optimization tools include LSI keyword analysis and suggestions directly within the content editor.

Surfer SEO is one tool that provides immediate LSI keyword recommendations for you to use in your content and analyzes the keyword density of your content in real-time.

Here we see that Surfer SEO makes additional keyword suggestions related to the primary term “rainboots”. These LSI keywords include: “little”, “pair”, “waterproof”, “hunter”, “rubber”, “men’s”, and so on.

Using LSI keywords to improve SEO

You can use any or all of the LSI keywords you identified during your research as long as they are applicable to the topic you are writing about and add value to your content. Using LSI keywords can help beef up your content, but not all of the terms you identify will relate to what you are writing about.

For example, if you sell women’s rain boots, including LSI terms like “men’s” or “masculine” may not tie in to what you’re offering. Use your best judgment in determining which terms should be included in your content.

In terms of using LSI keywords throughout your content, here are a few places you can add in these keywords to improve your SEO:

Title tags

Image alt text

Body content

H2 or H3 subheadings

H1 heading

Meta description

LSI keywords made simple

Identifying and using LSI keywords is made simple when you take a moment to consider what your target audience is searching for. They aren’t just searching for your primary keyword, but are likely using semantically-related terms to refine their search and find the exact service, product, or information they are searching for.

You can also use data-driven keyword research and content optimization tools to identify LSI keywords that are showing up in other high-ranking articles and web pages. Use these terms in your own content to improve your on-page SEO and attract more users to your website.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[lakelandseo]

LSI Keywords: What Are They and Why Do They Matter in SEO?

Posted by JessicaFoster

The written content on your website serves to not only inform and entertain readers, but also to grab the attention of search engines to improve your organic rankings.

And while using SEO keywords in your content can help you get found by users, focusing solely on keyword density doesn’t cut it when it comes to creating SEO-friendly, reader-focused content.

This is where LSI keywords come in.

LSI keywords serve to add context to your content, making it easier to understand by search engines and readers alike. Want to write content that ranks and wows your readers? Learn how to use LSI keywords the right way.

What are LSI keywords?

Latent Semantic Indexing (LSI) keywords are terms that are conceptually related to the main keyword you’re targeting in your content. They help provide context to your content, making it easier for readers and search engines to understand what your content is about.

Latent semantic analysis

LSI keywords are based on the concept of latent semantic analysis, which is a technique for understanding natural language processing. In other words, it analyzes the relationship between one word and another in order to make sense of the overall content.

Search engine algorithms use latent semantic analysis to understand web content and ultimately determine what content best fits what the user is actually searching for when they use a certain keyword in their search.

Why are LSI keywords important for SEO?

The use of LSI keywords in your content helps search engines understand your content and therefore makes it easier for search engines to match your content to what users are searching for.

Exact keyword usage is less important than whether your overall content fits the user’s search query and the intention behind their search. After all, the goal of search engines is to showcase content that best matches what users are searching for and actually want to read.

LSI keywords are not synonyms

Using synonyms in your content can help add context to your content, but these are not the same as LSI keywords. For example, a synonym for the word “sofa” could be “couch”, but some LSI keywords for “couch” would be terms like “leather”, “comfortable”, “sleeper”, and “sectional”.

When users search for products, services, or information online, they are likely to add modifiers to their main search term in order to refine their search. A user might type something like “red leather sofa” or “large sleeper sofa”. These phrases still contain the primary keyword “sofa”, but with the addition of semantically-related terms.

How to find LSI keywords to use in your content

One of the best ways to find LSI keywords is to put yourself in the mind of someone who is searching for your primary keyword. What other details might they be searching for? What terms might they use to modify their search?

Doing a bit of brainstorming can help set your LSI keyword research off on the right track. Then, you can use a few of the methods below to identify additional LSI keywords, phrases, and modifiers to use in your content.

Google autocomplete

Use Google to search for your target keyword. In most cases, Google’s autocomplete feature will fill the search box with semantically-related terms and/or related keywords.

For the keyword “sofa”, we can see some related keywords (like “sofa vs couch”) as well as LSI keywords like “sofa [bed]”, “[corner] sofa”, and ‘[leather] sofa”.

Competitor analysis

Search for your target keyword and click on the first few competing pages or articles that rank highest in the search results. You can then use the find function to search the content for your primary keyword and identify LSI keywords that bookend that key term.

For example, a search for “digital marketing services” may yield several competitor service pages. You can then visit these pages, find the phrase “digital marketing services”, and see what semantically-related keywords are tied in with your target keyword.

Some examples might include:

“Customizable”

“Full-service”

“Results-driven”

“Comprehensive”

“Custom”

“Campaigns”

“Agency”

“Targeted”

“Effective”

You can later use these LSI keywords in your own content to add context and help search engines understand the types of services (or products) you offer.

LSI keyword tools

If conducting manual LSI keyword research isn’t your forte, you can also use designated LSI keyword tools. Tools like LSIGraph and UberSuggest are both options that enable you to find semantic keywords and related keywords to use in your content.

LSIGraph is a free LSI keyword tool that helps you “Generate LSI keywords Google loves”. Simply search for your target keyword and LSIGraph will come up with a list of terms you can consider using in your content.

In the image above, you can see how LSIGraph searched its database to come up with a slew of LSI keywords. Some examples include: “[reclining] sofa”, “sofa [designs]”, and “[discount] sofas”.

Content optimization tools

Some on-page optimization tools include LSI keyword analysis and suggestions directly within the content editor.

Surfer SEO is one tool that provides immediate LSI keyword recommendations for you to use in your content and analyzes the keyword density of your content in real-time.

Here we see that Surfer SEO makes additional keyword suggestions related to the primary term “rainboots”. These LSI keywords include: “little”, “pair”, “waterproof”, “hunter”, “rubber”, “men’s”, and so on.

Using LSI keywords to improve SEO

You can use any or all of the LSI keywords you identified during your research as long as they are applicable to the topic you are writing about and add value to your content. Using LSI keywords can help beef up your content, but not all of the terms you identify will relate to what you are writing about.

For example, if you sell women’s rain boots, including LSI terms like “men’s” or “masculine” may not tie in to what you’re offering. Use your best judgment in determining which terms should be included in your content.

In terms of using LSI keywords throughout your content, here are a few places you can add in these keywords to improve your SEO:

Title tags

Image alt text

Body content

H2 or H3 subheadings

H1 heading

Meta description

LSI keywords made simple

Identifying and using LSI keywords is made simple when you take a moment to consider what your target audience is searching for. They aren’t just searching for your primary keyword, but are likely using semantically-related terms to refine their search and find the exact service, product, or information they are searching for.

You can also use data-driven keyword research and content optimization tools to identify LSI keywords that are showing up in other high-ranking articles and web pages. Use these terms in your own content to improve your on-page SEO and attract more users to your website.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[epackingvietnam]

LSI Keywords: What Are They and Why Do They Matter in SEO?

Posted by JessicaFoster

The written content on your website serves to not only inform and entertain readers, but also to grab the attention of search engines to improve your organic rankings.

And while using SEO keywords in your content can help you get found by users, focusing solely on keyword density doesn’t cut it when it comes to creating SEO-friendly, reader-focused content.

This is where LSI keywords come in.

LSI keywords serve to add context to your content, making it easier to understand by search engines and readers alike. Want to write content that ranks and wows your readers? Learn how to use LSI keywords the right way.

What are LSI keywords?

Latent Semantic Indexing (LSI) keywords are terms that are conceptually related to the main keyword you’re targeting in your content. They help provide context to your content, making it easier for readers and search engines to understand what your content is about.

Latent semantic analysis

LSI keywords are based on the concept of latent semantic analysis, which is a technique for understanding natural language processing. In other words, it analyzes the relationship between one word and another in order to make sense of the overall content.

Search engine algorithms use latent semantic analysis to understand web content and ultimately determine what content best fits what the user is actually searching for when they use a certain keyword in their search.

Why are LSI keywords important for SEO?

The use of LSI keywords in your content helps search engines understand your content and therefore makes it easier for search engines to match your content to what users are searching for.

Exact keyword usage is less important than whether your overall content fits the user’s search query and the intention behind their search. After all, the goal of search engines is to showcase content that best matches what users are searching for and actually want to read.

LSI keywords are not synonyms

Using synonyms in your content can help add context to your content, but these are not the same as LSI keywords. For example, a synonym for the word “sofa” could be “couch”, but some LSI keywords for “couch” would be terms like “leather”, “comfortable”, “sleeper”, and “sectional”.

When users search for products, services, or information online, they are likely to add modifiers to their main search term in order to refine their search. A user might type something like “red leather sofa” or “large sleeper sofa”. These phrases still contain the primary keyword “sofa”, but with the addition of semantically-related terms.

How to find LSI keywords to use in your content

One of the best ways to find LSI keywords is to put yourself in the mind of someone who is searching for your primary keyword. What other details might they be searching for? What terms might they use to modify their search?

Doing a bit of brainstorming can help set your LSI keyword research off on the right track. Then, you can use a few of the methods below to identify additional LSI keywords, phrases, and modifiers to use in your content.

Google autocomplete

Use Google to search for your target keyword. In most cases, Google’s autocomplete feature will fill the search box with semantically-related terms and/or related keywords.

For the keyword “sofa”, we can see some related keywords (like “sofa vs couch”) as well as LSI keywords like “sofa [bed]”, “[corner] sofa”, and ‘[leather] sofa”.

Competitor analysis

Search for your target keyword and click on the first few competing pages or articles that rank highest in the search results. You can then use the find function to search the content for your primary keyword and identify LSI keywords that bookend that key term.

For example, a search for “digital marketing services” may yield several competitor service pages. You can then visit these pages, find the phrase “digital marketing services”, and see what semantically-related keywords are tied in with your target keyword.

Some examples might include:

“Customizable”

“Full-service”

“Results-driven”

“Comprehensive”

“Custom”

“Campaigns”

“Agency”

“Targeted”

“Effective”

You can later use these LSI keywords in your own content to add context and help search engines understand the types of services (or products) you offer.

LSI keyword tools

If conducting manual LSI keyword research isn’t your forte, you can also use designated LSI keyword tools. Tools like LSIGraph and UberSuggest are both options that enable you to find semantic keywords and related keywords to use in your content.

LSIGraph is a free LSI keyword tool that helps you “Generate LSI keywords Google loves”. Simply search for your target keyword and LSIGraph will come up with a list of terms you can consider using in your content.

In the image above, you can see how LSIGraph searched its database to come up with a slew of LSI keywords. Some examples include: “[reclining] sofa”, “sofa [designs]”, and “[discount] sofas”.

Content optimization tools

Some on-page optimization tools include LSI keyword analysis and suggestions directly within the content editor.

Surfer SEO is one tool that provides immediate LSI keyword recommendations for you to use in your content and analyzes the keyword density of your content in real-time.

Here we see that Surfer SEO makes additional keyword suggestions related to the primary term “rainboots”. These LSI keywords include: “little”, “pair”, “waterproof”, “hunter”, “rubber”, “men’s”, and so on.

Using LSI keywords to improve SEO

You can use any or all of the LSI keywords you identified during your research as long as they are applicable to the topic you are writing about and add value to your content. Using LSI keywords can help beef up your content, but not all of the terms you identify will relate to what you are writing about.

For example, if you sell women’s rain boots, including LSI terms like “men’s” or “masculine” may not tie in to what you’re offering. Use your best judgment in determining which terms should be included in your content.

In terms of using LSI keywords throughout your content, here are a few places you can add in these keywords to improve your SEO:

Title tags

Image alt text

Body content

H2 or H3 subheadings

H1 heading

Meta description

LSI keywords made simple

Identifying and using LSI keywords is made simple when you take a moment to consider what your target audience is searching for. They aren’t just searching for your primary keyword, but are likely using semantically-related terms to refine their search and find the exact service, product, or information they are searching for.

You can also use data-driven keyword research and content optimization tools to identify LSI keywords that are showing up in other high-ranking articles and web pages. Use these terms in your own content to improve your on-page SEO and attract more users to your website.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

#túi_giấy_epacking_việt_nam #túi_giấy_epacking #in_túi_giấy_giá_rẻ #in_túi_giấy #epackingvietnam #tuigiayepacking

[bfxenon]

LSI Keywords: What Are They and Why Do They Matter in SEO?

Posted by JessicaFoster

The written content on your website serves to not only inform and entertain readers, but also to grab the attention of search engines to improve your organic rankings.

And while using SEO keywords in your content can help you get found by users, focusing solely on keyword density doesn’t cut it when it comes to creating SEO-friendly, reader-focused content.

This is where LSI keywords come in.

LSI keywords serve to add context to your content, making it easier to understand by search engines and readers alike. Want to write content that ranks and wows your readers? Learn how to use LSI keywords the right way.

What are LSI keywords?

Latent Semantic Indexing (LSI) keywords are terms that are conceptually related to the main keyword you’re targeting in your content. They help provide context to your content, making it easier for readers and search engines to understand what your content is about.

Latent semantic analysis

LSI keywords are based on the concept of latent semantic analysis, which is a technique for understanding natural language processing. In other words, it analyzes the relationship between one word and another in order to make sense of the overall content.

Search engine algorithms use latent semantic analysis to understand web content and ultimately determine what content best fits what the user is actually searching for when they use a certain keyword in their search.

Why are LSI keywords important for SEO?

The use of LSI keywords in your content helps search engines understand your content and therefore makes it easier for search engines to match your content to what users are searching for.

Exact keyword usage is less important than whether your overall content fits the user’s search query and the intention behind their search. After all, the goal of search engines is to showcase content that best matches what users are searching for and actually want to read.

LSI keywords are not synonyms

Using synonyms in your content can help add context to your content, but these are not the same as LSI keywords. For example, a synonym for the word “sofa” could be “couch”, but some LSI keywords for “couch” would be terms like “leather”, “comfortable”, “sleeper”, and “sectional”.

When users search for products, services, or information online, they are likely to add modifiers to their main search term in order to refine their search. A user might type something like “red leather sofa” or “large sleeper sofa”. These phrases still contain the primary keyword “sofa”, but with the addition of semantically-related terms.

How to find LSI keywords to use in your content

One of the best ways to find LSI keywords is to put yourself in the mind of someone who is searching for your primary keyword. What other details might they be searching for? What terms might they use to modify their search?

Doing a bit of brainstorming can help set your LSI keyword research off on the right track. Then, you can use a few of the methods below to identify additional LSI keywords, phrases, and modifiers to use in your content.

Google autocomplete

Use Google to search for your target keyword. In most cases, Google’s autocomplete feature will fill the search box with semantically-related terms and/or related keywords.

For the keyword “sofa”, we can see some related keywords (like “sofa vs couch”) as well as LSI keywords like “sofa [bed]”, “[corner] sofa”, and ‘[leather] sofa”.

Competitor analysis

Search for your target keyword and click on the first few competing pages or articles that rank highest in the search results. You can then use the find function to search the content for your primary keyword and identify LSI keywords that bookend that key term.

For example, a search for “digital marketing services” may yield several competitor service pages. You can then visit these pages, find the phrase “digital marketing services”, and see what semantically-related keywords are tied in with your target keyword.

Some examples might include:

“Customizable”

“Full-service”

“Results-driven”

“Comprehensive”

“Custom”

“Campaigns”

“Agency”

“Targeted”

“Effective”

You can later use these LSI keywords in your own content to add context and help search engines understand the types of services (or products) you offer.

LSI keyword tools

If conducting manual LSI keyword research isn’t your forte, you can also use designated LSI keyword tools. Tools like LSIGraph and UberSuggest are both options that enable you to find semantic keywords and related keywords to use in your content.

LSIGraph is a free LSI keyword tool that helps you “Generate LSI keywords Google loves”. Simply search for your target keyword and LSIGraph will come up with a list of terms you can consider using in your content.

In the image above, you can see how LSIGraph searched its database to come up with a slew of LSI keywords. Some examples include: “[reclining] sofa”, “sofa [designs]”, and “[discount] sofas”.

Content optimization tools

Some on-page optimization tools include LSI keyword analysis and suggestions directly within the content editor.

Surfer SEO is one tool that provides immediate LSI keyword recommendations for you to use in your content and analyzes the keyword density of your content in real-time.

Here we see that Surfer SEO makes additional keyword suggestions related to the primary term “rainboots”. These LSI keywords include: “little”, “pair”, “waterproof”, “hunter”, “rubber”, “men’s”, and so on.

Using LSI keywords to improve SEO

You can use any or all of the LSI keywords you identified during your research as long as they are applicable to the topic you are writing about and add value to your content. Using LSI keywords can help beef up your content, but not all of the terms you identify will relate to what you are writing about.

For example, if you sell women’s rain boots, including LSI terms like “men’s” or “masculine” may not tie in to what you’re offering. Use your best judgment in determining which terms should be included in your content.

In terms of using LSI keywords throughout your content, here are a few places you can add in these keywords to improve your SEO:

Title tags

Image alt text

Body content

H2 or H3 subheadings

H1 heading

Meta description

LSI keywords made simple

Identifying and using LSI keywords is made simple when you take a moment to consider what your target audience is searching for. They aren’t just searching for your primary keyword, but are likely using semantically-related terms to refine their search and find the exact service, product, or information they are searching for.

You can also use data-driven keyword research and content optimization tools to identify LSI keywords that are showing up in other high-ranking articles and web pages. Use these terms in your own content to improve your on-page SEO and attract more users to your website.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!