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margdarsanme · 4 years

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NCERT Class 12 Psychology Chapter 6 Attitude And Social Cognition

NCERT Class 12 Psychology Chapter 6 Attitude And Social Cognition Solutions

NCERT TEXTBOOK QUESTIONS SOLVED :

Q. 1.Define attitude. Discuss the components of an attitude. Ans.: Attitudes are state of the mind, set of views or thoughts or ideas regarding some topic which have an evaluative feature (positive, negative or neutral). These are relatively stable predispositions. . Various components of an attitude are as follows:

(i) The thought component is referred to as the cognitive aspect of attitude. (ii) The emotional component is known as the affective aspect. (iii) The tendency to act is called the behavioural (conative) aspect.

These three aspects have been referred to as the A-B-C (affective-behavioural-cognitive)components of attitude. Attitudes are themselves not a tendency to behave or act in certain ways. They are part of cognition, along with an emotional component which cannot be observed from outside.

Q. 2. Are attitudes learnt? Explain how? Ans.: By enlarge attitudes are learnt. They are formed on the basis of learning.

Formation of Attitudes:

The term ‘attitude formation’ refers to the movement we make from having no attitude toward an object to having a positive or negative attitude. There are some factors which contribute to the formation of attitudes: 1. Learning:

The attitudes are acquired through learning. Many of our views are acquired in situations in which we interact with others or merely observe their behaviour.

-Classical Conditioning / Leaming by Association:

A basic form of learning in which one stimulus, initially neutral, acquires the capacity to evoke reactions through repeated pairing with another stimulus.

Player often develop a strong liking for the bat by which they made good runs. Many students start liking a subject if they like the teacher.

-Instrumental Conditioning /Leaming Altitude by Reward and Punishment:

A basic form of learning in which responses that lead to positive outcomes or that permit avoidance of negative outcomes are strengthened.

It applies to the situations when people learn attitudes which are systematically rewarded by significant others, such as parents, teachers or friends.

-Observational Learning:

A basic form of learning in which individuals acquire new forms of behaviour or through observing others.

Children are keen observers and learn a whole lot of things from their parents and other family members.

They learn many attitudes about other ethnic groups, neighbours and idealize simply by observing the behaviours of adults.

2. Learning through Exposure to Information:

Many attitudes are learnt in a social context without physical presence of others. Print media, electronic media, biographies of self-actualized people facilitate attitude formations.

3. Learning Attitudes through Group or Cultural Norms:

Norms are unwritten rules of behaviour. Gradually these norms may become part of our social cognition, in the form of attitudes, e.g., offering money, sweets, fruits and flowers in a place of worship is a normative behaviour in various religions. People imitate such behaviour shown by others as socially approved and develop positive attitude towards it.

Q. 3.What are the factors that influence the formation of an attitude? Ans.: The following factors provide the context for the learning of attitude through various processes:

(i) Family and School Environment: parents and other family-members play a significant role in attitude formation. Learning of attitudes within the family and school usually takes place by association, through rewards and punishment and through modelling.

(ii)Reference Groups: Attitudes towards political, religious and social groups, occupations, national and other issues are often developed through reference groups. Reference groups indicate to an individual the norms regarding acceptable behaviour and ways of thinking. Various institutions, religion, culture and communities are form of reference groups.

(iii) Personal Experiences: Many attitudes are formed, not in the family environment or through reference groups, but through direct personal experiences which bring about a drastic change in our attitude towards people and our own life.

(iv) Media Related Influences: Technological advances have made audio-visual media and internet as very powerful sources for attitude formation. School textbooks also influence attitude formation. The media can be used to create consumerist attitude. The media can exert both good and bad influences on attitudes. Q. 4. Is behaviour always a reflection of one’s attitude? Explain with a relevant example. Ans.: An individual’s attitude may not always be exhibited through behaviour. Likewise one’s actual behaviour may be contrary to one’s attitude towards a particular topic. Psychologists have found that there would be consistency between attitude and behaviour when:

(i) The attitude is strong and occupies a central place in the attitude system. (ii) The person is aware of his/her attitudes. (iii) Person’s behaviour is not being watched or evaluated by others. (iv) Person thinks that the behaviour would have a positive consequences.

Richard La Piere, an American social psychologist, conducted the following study. He asked a Chinese couple to travel across the United States, and stay in different hotels. Only once during these occasions they were refused service by one of the hotels. La Piere sent out questionnaires to managers of hotels and tourist homes in the same areas where the Chinese couple had travelled asking them if they would give accommodation to Chinese guest. A very large percentage said that they would not do so. This response showed a negative attitude towards the Chinese, which was inconsistent with the positive behaviour that was actually shown towards the travelling Chinese couple. Attitudes may not always predict actual pattern of one’s behaviour. Q. 5. Highlight the importance of schemas in social cognition. Ans.:

Social schemas (schemata) are mental structure.

They function as a framework to process social information. These schemas lead to emergence of prototypes.

Prototypes are concepts which have most of the defining features of a concept, class or family.

These are best representatives of the population.

Various stereotypes emerge from these schemas. In other words stereotypes are category-based schemas.

Q. 6. Differentiate between prejudice and stereotype. Ans.: Prejudices are negative attitude.

These are bias about others.

Prejudices are baseless and false.

It refers to biased attitude formed about an individual or a group of people.

These are usually negative.

It has three components, i.e., A-B-C-

(a) Affective (Emotional i.e dislike or hatred). (b) Behavioural (i.e., discrimination). (c) Cognitive (Believes, i.e., stereotypes).

Stereotypes are cognitive component of prejudice. It is strongly influenced by the processing of incoming social information.

These are over-generalized beliefs, e.g., girls are talkative.

Stereotypes are category based schemas.

These may be positive or negative or neutral.

Stereotype has no emotional blending.

From stereotypes, prejudices may emerge very easily.

Stereotypes are usually formed for the groups.

Stereotypes are pre conceived notions.

Q. 7. Prejudice can exist without discrimination and vice-versa. Comment. Ans.: Prejudices can exist without being shown in the form of discrimination. Similarly, discrimination can be shown without prejudice.

Wherever prejudice and discrimination exist, conflicts are very likely to arise between groups within the same society.

We all witnessed many instances of discrimination, with and without prejudice, based on gender, religion, community, caste, physical handicap, and illnesses such as AIDS.

Moreover, in many cases, discriminatory behaviour can be curbed by law. But, the cognitive and emotional components of prejudice are more difficult to change.

Q. 8. Describe the important factors that influence impression formation. Ans.: Impression formation is a process by which impression about others is converting into more or less induring cognitions or thoughts about them.In short, impression formation is a process through which we draw quick conclusion/ inferences regarding others.

Factors facilitating Impression Formation:

Nature of the phenomena (familiar or unfamiliar).

Personality traits of the perceiver.

Social schemas stored in the mind of perceiver.

Situational factors.

The process of impression formation consists of the following three sub-processes:

Selection: We take into account only some bits of information about the target person.

Organization: the selected information is combined in a systematic way.

Inference: We draw a conclusion about what kind of person the target is.

Some specific qualities, that influence impression formation, are:

The information presented first has a stronger effect than the information presented at the end. This is called the primacy effect.

We have a tendency to think that a target person who has one set of positive qualities must also be having other specific positives that are associated with first set. This is known as halo effect, e.g., if we think that a person is ‘tidy’ then we are likely to think that this person must also be hard/working.

Whatever information comes at the end may have a stronger influence on impression formation. This is known as the recency effect.

Q. 9.Explain how the attribution made by an ‘actor’ would be different from that of an ‘observer’. Ans.: Actor observer phenomena refers to the tendency to attribute our own behaviour mainly to situational causes but the behaviour of others mainly to internal (dispositional) cause.

A distinction is found between the attribution that a person makes for actorrole and observer-role. Person makes attribution for his/her own positive and negative experiences, it is actor role and the attribution made for another person’s positive and negative experience is observer-role.

For example, if we get good marks, we will attribute it to our own ability and hard work (actor-role, internal attribution for a positive experience). If we get bad marks, we will say we were unlucky or test was difficult (actor-role, external attribution for negative experience).

On the other hand, if our classmate gets good marks, we will attribute his/her success to good luck or easy test (observer-role, external attribution for positive experience). If same classmate gets bad marks, we are likely to feel that his/her failure was because of low ability or due to lack of effort (observer-role, internal attribution for a negative experience).

The reason for the difference between the actor and observer roles is that people want to have a nice image of themselves, as compared to others. Q. 10. How does social facilitation take place? Ans.:

It is form of group influence.

Social facilitation refers to a concept that performance on specific task is influenced by the mere presence of others.

Norman Triplett observed that individuals show better performance in presence of others, than when they are performing the same task alone.

Better performance in presence of others is because the person experiences arousal, which makes the person react in a more intense manner.

The arousal is because the person feels he or she is being evaluated. Cottrell called this idea evaluation apprehension. The person will be praised if performance is good (reward); is criticised if performance is bad (punishment). We wish to get praise and avoid criticism, therefore we try to perform well and avoid criticism.

As in case of complex task, the person may be afraid of making mistakes. And the fear of criticism or punishment is stronger.

If the others present are also performing same task, this is called a situation of co-action. When task is simple or a familiar one, performance is better under co-action than when the person is alone.

Task performing can be facilitated and improved or inhibited and worsened by the presence of others. If we are working together in a larger group, the less effort each member puts in. This phenomena is called social loafing, based on diffusion of responsibility. Diffusion of responsibility can also be frequently seen in situations where people are expected to help. Q. 11.Explain the concept of pro-social behaviour. Or Describe the factors influencing Pro-social behaviour. Ans.: Pro-social behaviour is any positively valued behaviour that does good to another person, is done without any pressure from outside and without any expectation of a reward or a return. Humans are social beings. Most of their activities are organized with the help of others. We cannot live and grow unless there is support from others. We often engage in helping others. Such efforts are considered as pro-social behaviour. For any behaviour to be pro¬social, it should fulfil the following conditions:

There has to be an intention to benefit the other person. Any pro-social act, which one accomplishes by compulsion or as a requirement of a job, does not merit to be called ‘pro-social’.

The behaviour should be considered socially desirable by the other members of the society. Obviously, helping a thief in stealing is not a pro-social behaviour.

If an act intended to benefit others is also expect to benefit the helper, it cannot be termed as ‘pro-social’.

The intentions and the consequent positive behaviour are more important considerations of pro-social behaviour than the actual benefits. The other term which are used interchangeably with pro-social behaviour is altruism. The literal meaning of altruism is “doing things or acting for the interest of others without any ulterior motive.” It is a behaviour that reflects an unselfish concern for the welfare of others. All charitable, humanitarian, philanthropic activities, which people do without any self-interest, come under the category of altruism behaviour.

Determinants of Pro-social Behaviour: The pro-social behaviour depends on many factors. (i) Pro-social behaviour is based on an inborn, natural tendency in human beings to help other members of their own species. (ii) Pro-social behaviour is influenced by learning through modelling and positive reinforcement in the family. (iii) Cultural factors influence pro-social behaviour. Some cultures actively encourage people to help the needy and distressed. Individuals in cultures suffering from a shortage of resources may not show a high level of pro-social behaviour. (iv) Pro-social behaviour is expressed when the situation activates certain ‘social norms’ that require

helping others.

Three norms have been mentioned in context of pro-social behaviour: (a) The norms of social responsibility. We should help anyone who needs help without considering any other factors. (b) The norms of reciprocity. We should help persons who have helped us in the past. (c) The norms of equity. We should help others whenever we find that it is fair to do so. (v) Pro-social behaviour is affected by the expected reactions of the person who is being helped. For example, people might be unwilling to give money to a needy person because they feel that the person might feel insulted, or may become dependent. (vi) Pro-social behaviour is more likely to be shown individuals who have a high level of empathy, that is, the capacity to feel the distress of the person who is to be helped, e.g., Mother Teresa.

Factors inhibiting Pro-social Behaviour: (a) Diffusion of Responsibility: Pro-social behaviour may be reduced when the number of bystanders is more than one. On the other hand, if there is only one bystander, this person is more likely to take responsibility and actually help the victim. It happens because each person thinks that others will take the responsibility. (b) Feeling State of the Individual: Person in a bad mood, being busy with one’s own problems or feeling that the person to be helped is responsible for his/her problem, may not help others. Q. 12.Your friend eats too much junk food, how would you be able to bring about a change in his/her attitude towards food? Ans.: Since my friend eats junk food, it will have some reinforcing value to him/her. In order to bring about a change in his/her attitude towards food, following techniques could be used: First of all what needs to be targeted is ‘the way he/she thinks about junk food, i.e., the cognitive aspect of his/her positive attitude towards junk food. The modification in his/ her attitude can be achieved through using rational as well as emotional appeal and, if required, help of a dietician whose credibility in this area is very high will be of great use. Attitude changes may be brought about by: 1. Encouraging him/her to read about what is required for a healthy development, e.g., minerals etc. which are not found in junk food. 2. Showing him/her the negative consequences of eating junk food like obesity, other health-related problems by exposing him/her to real life examples. 3. Request people around him/her like family-members, elders and teachers whom he/ she likes. If they provide a role model, i.e., not to consume junk food then he/she will imitate their behaviour. 4. Giving his/her choice and space to decide what is right for him/her considering both the aspects of the food and asking him/her to focus and think about the future problems associated with his/her liking. All these things can bring about cognitive dissonance which might ultimately bring about the attitude change.

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sociamediaz2 · 4 years

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Extracting Covid-19 insights from Bing search data (Sociamediaz Digital Marketing)

Looking for info about Extracting Covid-19 insights from Bing search data (Sociamediaz Digital Marketing)

The Covid-19 pandemic has created a global health crisis that is unprecedented in our lifetimes. It is hard to point to any other health crisis in recent history that has captivated the collective mindset of the entire human population all at once. Covid-19 and coronavirus are the top concerns for governments, public health authorities, news media, and everyday citizens across the world. Staying informed about the virus and the disease is one of the best ways to slow the spread of the virus and therefore we see a large demand for Coronavirus information from the general public.

However, since Coronavirus is impacting every facet of our lives – from school closures and social distancing to quarantines and lockdowns, not to mention job losses and stimulus checks – it is easy to see how the information need is growing in fast and unexpected ways.

As is true for many other topics, search engine query logs may be able to give insight into the information gaps associated with Covid-19. The global reach of the Bing search engine and the amount of time people spend on their devices (especially in quarantine) make the Bing search query logs a potentially valuable source of information about the pandemic which may have insights that can be useful for the public and public health authorities. It is also a very direct way of looking at all the other topics that don’t explicitly reference the crisis, but reflect people’s experience of living through it, such as topics related to the economy, working from home, online education, and home fitness.

Announcing the Bing search dataset for Coronavirus Intent

We are pleased to announce that we have already made Covid-19 query data freely available on GitHub as the Bing search dataset for Coronavirus intent, with scheduled updates every month over the course of the pandemic. This dataset includes explicit Covid-19 search queries containing terms such as corona, coronavirus, and covid, as well as implicit Covid-19 queries that are used to access the same set of web page search results (using the technique of random walks on the click graph). Top-ranked examples in the US show that such implicit queries are crucial for understanding specific concerns – e.g., “hand sanitizer”, “n95 mask”, and “stimulus check for 2020” – in addition to the more general concerns such as “coronavirus update”. To protect user privacy, infrequent queries and queries containing personal data are filtered from the dataset, while raw query counts are replaced by popularity scores between 1 and 100 reflecting normalized query counts for a given day and country.

We have already seen how Covid-19 has elevated the level of data-oriented discussion, with daily updates in Covid-19 statistics (e.g., tests conducted, confirmed cases), as well as projections estimated by epidemiological and machine learning models, all communicated using data visualizations such as bar charts, line charts, and choropleth maps. The dataset itself also shows a growing public demand for interactive visualizations of trusted data sources; for example, “coronavirus map” queries peaked on March 11, “johns hopkins coronavirus map” queries peaked on April 2, and “johns hopkins coronavirus dashboard” queries peaked on April 6. Not only can our dataset provide inputs to machine learning models aiming to draw on population search behaviour as a signal, it can also provide a new data source for the trusted Covid-19 dashboards already in use, as well as powering entirely new interactive experiences based on use of search data to map the what, when, and where of population-level Covid-19 concerns.

In each of these cases, the data science community plays the crucial role of translating raw data tables into practical data tools for Covid-19 response efforts. We are already seeing examples of the Bing search dataset for Coronavirus intent being discovered and used by members of the community. In particular, it has been featured on the deeplearning.ai blog of the company run by Professor Andrew Ng, used for exploratory visual analysis in Python and R, and included as a data science resource by the Academic Data Science Alliance and Coronavirus Tech Handbook. In the rest of this blog post, we share how our data scientists are using Power BI to create interactive dashboards to this dataset and transform our understanding of the unfolding crisis.

Discovering insights in the Bing Covid-19 dataset

One of the fastest ways to begin exploring the dataset is through the use of interactive visualizations, such as in dashboards created using the freely-available Power BI Desktop. Here is an example page from a Power BI report we created to visualize and explore the Bing search dataset for Coronavirus intent, showing the count of unique US-based queries each week from January to April:

Our analysis shows that the number of such unique queries is highly correlated with total query volume (0.99), indicating rapid growth in query volume beginning in mid-February and peaking in mid-March. In this view, we can also use the search box to filter queries based on their content, for example all queries, in this dataset, containing the word “cruise”:

Here we see three spikes in interest: early February, early March, and early April. To learn more about the content of the queries driving these peaks, we can switch to a different view showing the top queries by day:

Now we can see that the February peak relates to the top query “japan cruise ship quarantine”, the March peak relates to “carnival cruise coronavirus”, and the April peak relates to renewed interest in “cruise ship coronavirus”. This view also captures daily spikes in specific queries, such as “honolulu cruise ships coronavirus” appearing as a large blue column on March 19. Note also how top queries like “cruise ship quarantine” and “carnival cruise cancellations 2020” reflect implicit coronavirus intents, in that they are intimately connected with coronavirus without referencing it explicitly. We can filter this view to examine the evolving concerns of US Bing users through March and April, as shown below:

We see that in our dataset, “hand sanitizer” is the top ranked concern in early March and retains a stable level of popularity overall, but is overtaken by other queries over time. These include “stimulus checks for 2020” on March 18, “sba disaster loans” on March 23, “easter 2020” on March 24, “how to make a face mask” on April 3, “crocs for healthcare workers” on April 20, and “remdesivir” on April 29. This clearly shows how search queries track what is in the news and on top of people’s minds about the pandemic.

We can analyze queries by geography as well as time. For the week of April 4, we can see that there is nationwide interest in “stimulus”, but queries containing “stimulus” only reach maximum overall popularity in Oklahoma and Mississippi:

In contrast, for the following week of April 12, queries containing “stimulus” reach maximum popularity in all states except Nevada, Idaho, Wyoming, Kentucky, South Carolina, and Maine:

We can also use the dataset to make comparisons between states, countries, and topics. For example, here is a comparison of Washington state and New York state from March, this time for all queries containing the term “mask”:

We can see that the two states share a broadly similar time course and have many common queries in Coronavirus intent dataset, including the top shared query “face masks”. However, we can also observe subtle differences, such as Washington state having more unique queries and a greater share of common queries relating to mask patterns and the creation of homemade face masks, and New York state showing the opposing bias towards n95 masks and face masks for sale.

Here is another comparison, this time of symptom-related queries across the US for the month of March:

We see that queries containing “cough” and “fever” both made an initial jump in popularity on March 12, maintained high query levels for the following two weeks, then dissipated in early April. The greatest interest in either symptom came on March 26 in relation to the news story “woman coughs on food”. The most popular fever-related query by far was also “can you have coronavirus without fever?”

Finally, if we look at worldwide queries, we can notice that the implicit intent detection also captures relevant queries in other languages. For example, here we can see the Japanese transliteration of “corona” (“コロナ”) featuring as a top implicit query not just from Japan, but from Japanese speakers around the world:

Comparing trends in Bing Covid-19 queries to other data streams

In addition to comparing the relative popularity and types of queries within the dataset, we can also use Power BI to compare the number of daily unique queries against other time series potentially related to aspects of the Covid-19 crisis.

When we compare daily unique Covid-19 queries to the closing price of the S&P 500 index, we observed a strong inverse correlation (-0.89), with “fear” as a potential common cause. While the initial drop in the index preceded the rise in unique queries by several days, the recovery in April lagged the falling query count by almost two weeks. It will be interesting to see the relationship between these two datasets as we move further into summer and if we see another increase in confirmed cases.

Similarly, when we compare unique query counts from the dataset to the number of confirmed cases using the New York Times dataset, we observe that the shape of the confirmed cases distribution roughly follows that of the daily query count, only shifted. This relationship becomes clearer when we smooth both curves using a 7-day rolling sum and translate the query curve to maximize the correlation. Across all states, we see an average such correlation of 0.71 and lag of 21 days from daily unique queries to daily new cases:

This correlation is especially striking for some states, including New York:

While correlation does not imply causation, the degree of correlation and the broad similarly of lag observed across states (potentially related to the length of the incubation period) is the kind of visual insight that may be investigated further over the course of the pandemic.

Conclusion

By publishing the Bing Coronavirus query set, we have tried to bridge at least part of the information gap that exists between our need to understand the public experience of the Coronavirus pandemic and the public availability of datasets that yield insights into this experience. As with any dataset, however, users of the data need to consider potential sources of bias, for example the relative popularity of the Bing search engine in different geographic regions, as well as the relative populations of those regions. It is always advisable to account for such factors before generating insights.

Since the pandemic does not appear to be going away in the near future, the evolving data will continue to reveal insights into what is ‘top of mind’ for different populations over time. It is our sincere hope that this dataset can provide an important signal as we deal with the impact of Coronavirus and the need for reliable information that it creates. After all, we are all in this together. The Bing search dataset for Coronavirus intent is already available on GitHub, meaning anyone with data science or business intelligence skills can begin exploring the data today. We are actively seeking feedback on the dataset at [email protected], and would especially love to hear from anyone who has used the dataset to support Covid-19 response efforts.

from Bing Blogs https://blogs.bing.com/search/2020_07/Extracting-Covid-19-insights-from-Bing-search-data

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