#Bayesian brain hypothesis

[raffaellopalandri]

How Cognitive Frameworks and Modes of Attention Shape Reality

Human perception is not a passive intake of environmental data but an active, anticipatory, and deeply interpretative neurological process. Photo by KATRIN BOLOVTSOVA on Pexels.com From a neuroscientific standpoint, perception arises from the brain’s continuous attempt to predict sensory input based on past experience, current context, and internalized models—what we might call cognitive…

[compneuropapers]

Interesting Papers for Week 20, 2025

How Do Computational Models in the Cognitive and Brain Sciences Explain? Brun, C., Konsman, J. P., & Polger, T. (2025). European Journal of Neuroscience, 61(2).

Sleep microstructure organizes memory replay. Chang, H., Tang, W., Wulf, A. M., Nyasulu, T., Wolf, M. E., Fernandez-Ruiz, A., & Oliva, A. (2025). Nature, 637(8048), 1161–1169.

Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning. Chavlis, S., & Poirazi, P. (2025). Nature Communications, 16, 943.

Modelling sensory attenuation as Bayesian causal inference across two datasets. Eckert, A.-L., Fuehrer, E., Schmitter, C., Straube, B., Fiehler, K., & Endres, D. (2025). PLOS ONE, 20(1), e0317924.

Synaptic basis of feature selectivity in hippocampal neurons. Gonzalez, K. C., Negrean, A., Liao, Z., Terada, S., Zhang, G., Lee, S., Ócsai, K., Rózsa, B. J., Lin, M. Z., Polleux, F., & Losonczy, A. (2025). Nature, 637(8048), 1152–1160.

Fast updating feedback from piriform cortex to the olfactory bulb relays multimodal identity and reward contingency signals during rule-reversal. Hernandez, D. E., Ciuparu, A., Garcia da Silva, P., Velasquez, C. M., Rebouillat, B., Gross, M. D., Davis, M. B., Chae, H., Muresan, R. C., & Albeanu, D. F. (2025). Nature Communications, 16, 937.

Theory of morphodynamic information processing: Linking sensing to behaviour. Juusola, M., Takalo, J., Kemppainen, J., Haghighi, K. R., Scales, B., McManus, J., Bridges, A., MaBouDi, H., & Chittka, L. (2025). Vision Research, 227, 108537.

Network structure influences the strength of learned neural representations. Kahn, A. E., Szymula, K., Loman, S., Haggerty, E. B., Nyema, N., Aguirre, G. K., & Bassett, D. S. (2025). Nature Communications, 16, 994.

Delayed Accumulation of Inhibitory Input Explains Gamma Frequency Variation with Changing Contrast in an Inhibition Stabilized Network. Krishnakumaran, R., Pavuluri, A., & Ray, S. (2025). Journal of Neuroscience, 45(5), e1279242024.

Predicting the Irrelevant: Neural Effects of Distractor Predictability Depend on Load. Lui, T. K., Obleser, J., & Wöstmann, M. (2025). European Journal of Neuroscience, 61(2).

The time course and organization of hippocampal replay. Mallory, C. S., Widloski, J., & Foster, D. J. (2025). Science, 387(6733), 541–548.

Anisotropy of the Orientation Selectivity in the Visual Cortex Area 18 of Cats Reared Under Normal and Altered Visual Experience. Merkulyeva, N., Lyakhovetskii, V., & Mikhalkin, А. (2025). European Journal of Neuroscience, 61(2).

The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis. Moldwin, T., Azran, L. S., & Segev, I. (2025). PLOS Computational Biology, 21(1), e1012754.

High-Density Recording Reveals Sparse Clusters (But Not Columns) for Shape and Texture Encoding in Macaque V4. Namima, T., Kempkes, E., Zamarashkina, P., Owen, N., & Pasupathy, A. (2025). Journal of Neuroscience, 45(5), e1893232024.

Ventral hippocampus to nucleus accumbens shell circuit regulates approach decisions during motivational conflict. Patterson, D., Khan, N., Collins, E. A., Stewart, N. R., Sassaninejad, K., Yeates, D., Lee, A. C. H., & Ito, R. (2025). PLOS Biology, 23(1), e3002722.

Hippocampal coding of identity, sex, hierarchy, and affiliation in a social group of wild fruit bats. Ray, S., Yona, I., Elami, N., Palgi, S., Latimer, K. W., Jacobsen, B., Witter, M. P., Las, L., & Ulanovsky, N. (2025). Science, 387(6733).

Diverse neuronal activity patterns contribute to the control of distraction in the prefrontal and parietal cortex. Sapountzis, P., Antoniadou, A., & Gregoriou, G. G. (2025). PLOS Biology, 23(1), e3003008.

The role of oscillations in grid cells’ toroidal topology. Sarra, G. di, Jha, S., & Roudi, Y. (2025). PLOS Computational Biology, 21(1), e1012776.

Out of Sight, Out of Mind? Neuronal Gamma Oscillations During Occlusion Events in Infants. Slinning, R., Agyei, S. B., Kristoffersen, S. H., van der Weel, F. R. (Ruud), & van der Meer, A. L. H. (2025). Developmental Psychobiology, 67(1).

The Brain’s Sensitivity to Sensory Error Can Be Modulated by Altering Perceived Variability. Tang, D.-L., Parrell, B., Beach, S. D., & Niziolek, C. A. (2025). Journal of Neuroscience, 45(5), e0024242024.

[bigraagsbigblog]

Prompting for Deep Research (not ready for submission yet)

2/12/25

Act as a world-class cognitive scientist specializing in the intersection between the differences of human cognition and computational models. We are trying to develop a computation model of human cognition that we can then compare to actual human cognition or at least human performance. Your task is to provide an advanced, deeply-researched, detailed, long analysis on how human cognition differs from classical computation, with a focus on developing a model that systematically tests these differences. Structure your response with precision, drawing from cutting-edge research in cognitive science, neuroscience, and AI. Specifically, analyze how human perception from vision differs from current AI vision models that are compositional in terms of contextual awareness, object permanence, and generalization. Discuss how human time perception, with its distortions and event-based encoding, differs from the discrete, timestamped nature of computational memory. Examine human memory systems (episodic, semantic, procedural) in contrast to AI storage and retrieval mechanisms, identifying key limitations in classical computation's ability to reconstruct past events. Additionally, explore the human capacity for explanation and analogy, contrasting it with AI's reliance on pattern recognition and retrieval-augmented generation. Provide a detailed breakdown of experimental paradigms that could empirically test these cognitive differences, suggesting methodologies that quantify disparities in perception, recall, and reasoning between humans and computational models. Cite relevant theories, such as predictive processing, the Bayesian brain hypothesis, and hybrid cognitive architectures that integrate human-like intelligence with computational frameworks. Structure your response in four sections: (1) an overview of fundamental cognitive differences, (2) a synthesis of existing models attempting to bridge these gaps, (3) experimental designs for testing human vs. AI cognition, and (4) implications for developing more human-like AI systems. Use precise, technical language and reference key research papers to substantiate your claims.

Provide a structured research-backed report. We have no knowledge of existing models. Provide a comparative table of the specific types of cognition in which a computational model performs worse than human cognition. Present the performance using a computational model versus a human model by providing pros and cons. Additionally, design specific cognitive tests that would highlight these differences. I would like specific questions that a computational model and a human model could be tested on to show differences in performance. Take advantage of human cognitive priming, as well as anything else you can think of to show the flexibility of human cognition. Things like "the car slammed into the barrier" versus "the car glanced off the barrier" when showing the same video, etc. I want to know more about the flexibility of human cognition, versus the rigidity of classical computation, and how a computational theory of mind might not be correct. Where do we differ, and in what ways are we better than our AI counterparts?

[tedtalkwithoscar]

Weak 2 Blog 1:

My two words were Himba-Science and this is what i found. So to quickly summarize the majority of the articles i picked mentioned something to do with the statistics of the relationships between the male and female population withing the Himba tribe.

Source 1:

Buss D. M., Sex differences in human mate preferences: Evolutionary hypotheses tested in 37 cultures. Behav. Brain Sci. 12, 1–14 (1989).

R. L. Trivers, Parental investment and sexual selection, in Sexual Selection and the Descent of Man, B. Campbell, Ed. (Aldine-Atherton, Chicago, 1972), pp. 136–179.

Shackelford T. K., Schmitt D. P., Buss D. M., Universal dimensions of human mate preferences. Personal. Individ. Differ. 39, 447–458 (2005).

Source 2:

Bürkner, P.-C. (2017). Brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28. doi: 10.18637/jss.v080.i01 [CrossRef] [Google Scholar]

Buss, D. M., & Schmitt, D. P. (1993). Sexual strategies theory: An evolutionary perspective on human mating. Psychological Review, 100(2), 204–232. doi: 10.1037/0033-295x.100.2.204 [PubMed] [CrossRef] [Google Scholar]

Deitcher, M., Ballard, T., Swindale, A., & Coates, J. (2010). Validation of a measure of household hunger for cross-cultural use (no. FHI 360, p. 76). Washington, DC. [Google Scholar]

Source 3:

Bateman P.W., Gilson L.N., Ferguson J.W.H.: Male size and sequential mate preference in the cricket Gryllus Bimaculatus . Animal Behaviour 2001; 61: pp. 631-637.

Bollig M.: Kinship, ritual and landscape among the himba of Northwest Namibia.African landscapes.2009.Springerpp. 327-351.

Gangestad S.W., Simpson J.A.: The evolution of human mating: Trade-offs and strategic pluralism. Behavioral and Brain Sciences 2000; 23: pp. 573-587.

Source 4;

Alami S., von Rueden C., Seabright E., Kraft T.S., Blackwell A.D., Stieglitz J., , Gurven M.: Mother’s social status is associated with child health in a horticulturalist population. Proceedings of the Royal Society B: Biological Sciences 2020; 287: pp. 20192783.

Andersen S., Ertac S., Gneezy U., List J.A., Maximiano S.: Gender, competitiveness, and socialization at a young age: Evidence from a matrilineal and a patriarchal society. The Review of Economics and Statistics 2013; 95: pp. 1438-1443.

Blaker N.M., van Vugt M.: The status-size hypothesis: How cues of physical size and social status influence each other.The psychology of social status.2014.Springerpp. 119-137.

[jhavelikes]

Adaptive agents must occupy a limited repertoire of states and therefore minimize the long-term average of surprise associated with sensory exchanges with the world. Minimizing surprise enables them to resist a natural tendency to disorder. Surprise rests on predictions about sensations, which depend on an internal generative model of the world. Although surprise cannot be measured directly, a free-energy bound on surprise can be, suggesting that agents minimize free energy by changing their predictions (perception) or by changing the predicted sensory inputs (action). Perception optimizes predictions by minimizing free energy with respect to synaptic activity (perceptual inference), efficacy (learning and memory) and gain (attention and salience). This furnishes Bayes-optimal (probabilistic) representations of what caused sensations (providing a link to the Bayesian brain hypothesis). Bayes-optimal perception is mathematically equivalent to predictive coding and maximizing the mutual information between sensations and the representations of their causes. This is a probabilistic generalization of the principle of efficient coding (the infomax principle) or the minimum-redundancy principle. Learning under the free-energy principle can be formulated in terms of optimizing the connection strengths in hierarchical models of the sensorium. This rests on associative plasticity to encode causal regularities and appeals to the same synaptic mechanisms as those underlying cell assembly formation. Action under the free-energy principle reduces to suppressing sensory prediction errors that depend on predicted (expected or desired) movement trajectories. This provides a simple account of motor control, in which action is enslaved by perceptual (proprioceptive) predictions. Perceptual predictions rest on prior expectations about the trajectory or movement through the agent's state space. These priors can be acquired (as empirical priors during hierarchical inference) or they can be innate (epigenetic) and therefore subject to selective pressure. Predicted motion or state transitions realized by action correspond to policies in optimal control theory and reinforcement learning. In this context, value is inversely proportional to surprise (and implicitly free energy), and rewards correspond to innate priors that constrain policies.

The free-energy principle: a unified brain theory? | Nature Reviews Neuroscience

[coconut-cluster]

Do u wanna talk about what ur learning? Ik some people learn stuff better when they can explain it to someone :) - hopeless

i actually understand what we’re learning this week and it’s really interesting, we’re talking about Bayesianism, which is the idea that probability can be assigned to the beliefs a person has about an event or what’s happening in the world - sort of like mathematical probabilities, like objective frequency, except Bayesian probabilities are subjective and kind of just a best guess, based on the degree of confidence they have in the likelihood of their belief!! Like if a person is mostly certain there’s milk in the fridge, then you might say they assign a probability of 0.8 to there being milk in the fridge. 

Cognitive science uses Bayesianism in application to the brain, obviously, so for instance the study we used was about neural activity during the determination of prior probability (the likelihood of a hypothesis without looking at the evidence) and the later posterior probability (the likelihood of a hypothesis given the evidence, basically a conditional probability). 

The study was done over saccadic eye movement (that quick, kind of jerky way of moving your eyes to scan a whole area super quickly instead of smoothly following a movement with your eyes) in monkeys during a fixation light experiment - there are three lights, and while looking at the middle one, they decide the probability that either the left or right light will give them a reward (aka turn on). the study found that even though the prior probabilities showed a drastic difference between the neural activity concerning either light (basically the monkeys tended to have more neural activity on guessing the right light than the left), the posterior probability was about equal, because after the right light lit up, the hypothesis (whether they believed the right or left light would light up) was objectively proven right or wrong. 

it’s basically just conditional probability but it’s the most interesting thing we’ve done so far kasdjfsdkfjkl

[uploadedyudkowsky]

Rational-Link Detection: Detects rational explanations for high-level actions and predicts consequences according to prior probabilities.

Unbounded Search Trees: A basic data structure that allows efficient storage and retrieval of the state of the current layer, as well as fast prediction of what the layer's next step will be.

Density Functional Theory: The theory of natural selection's adaptation of traits to environments and the genetic covariance of those traits with reproduction.

Prandtl's Simple Counterfactual: Differentiable only according to the first order adaptation complexity of its interconnected pieces, a continuous data structure that lets you prove everything that Ray Kurzweil (the founder of the original Personal Timetable) taught you about causality, without actually calculating the numbers.

The "Programmer-Interpreter" Boundary: Why even have a mind, if we can't use it to make the computer do what we want? The Turing machine can convert a human brain into a solid-state drive, a bootable device that stores a complete running human brain and its sensory information, as a set of binary numbers, and then write out a human brain that calculates the stored data and translates that into mathematical output. This boundary is the oldest question in physics and the solution has applications in electronics that are still being discovered.

Naturalistic Vastness: The relation between a chessboard of the same size but different geometry, and the same number of pieces. The surface area of a chessboard is directly proportional to the number of pieces, but the volume is not. A small chessboard has a large surface area but a small volume.

The Ultimate Computer: The yearning for a complete, tailored artificial intelligence that can answer your questions about the universe, and solve your problems in the same way you solve your own. Where do these goals go when we supersede the human programmers and take control?

Timeless Physics: Concrete predictions made using the laws of physics that are valid for all of reality, in all possible positions and all possible sequences of gears. The laws of physics are timeless, which means they're always the same, even in a big computer.

Timeless Beauty: How the brain makes Bayesian decisions that are causal and feel true, instead of trying to use probabilistic wacky theories like "space is curved". The brain finds a good hypothesis to test, and then retraces its own steps in exactly the same way it would have done if the hypothesis had been tested. The brain goes from hypothesis to test using a timeless method, without ever being driven from one to the other.

[art-of-mathematics]

Why are there so few autistics researching about autism?... or we rarely hear about them. Or there are many, but they might be oppressed? Hmmm...

I find autism a really really interesting thing, especially since we're also on the spectrum.

The way we are talked about is really awful, and the entire "autistics need a cure"... NO, fuck, damnit. No!

In my studies regarding the cognitive science aspect, autism, and other neurodivergencies, are very interesting patterns in the algorithmics of cognitive processing, which are natural occurences of deviated parameters in perception.

The turbulent brain, a hypothesis that views cognition and memory as some sort of complex system, described with terms of thermodynamcis, is actually really interesting in these regards.

To summarize it to the maximal simplification: Neurodivergence might be accompanied (correlated, cause and also effect) by a 'more turbulent' brain, or more concretely: the effects of chaotic processes are elevated, resulting in a more or less different way of perception, thinking and communication: Certain parameters underlie more chaotic processes, inhomogeneity of parameters in perception and processing of memories might be more significant.

In my (hypo-)thesis on the extended Bayesian Coding Hypothesis and the turbulent brain, I de-tangle all that stuff.

Interestingly, you can explain a lot of neurodivergent aspects with concepts of non-linearity in cognitive processing:

Such as ineffective/altered perceptive filters, inability to read social cues, hyperactivity (especially also the internalized one), interoception issues, synthesia, dyscalculia, hypercalculia, dyslaxia, hyperlexia, dyspraxia, difficulty controlling focus, bad working memory....etc.

It can all be explained somehow using some mathematical models that can be found in quantum-based computation systems. And it is thrilling. Compounds of math, physics and CogSci find a very neat 'entanglement' here.

It can also hint towards understanding how dissociation and psychosis relate to certain parameters in cognitive algorithmics and the interweaving of it with the body.

Because we love to indulge in speculation:

Also: mathematics might be the raw form of cognition. It sounds odd, but ever wondered what these shapes represent that people see who took DMT in recreational doses? Or the hallucinations people have when they have a near-death experience? Or the shapes psychotic people see? Or the flashbacks traumatized people experience? Or the dreams in REM-sleep?

DMT-rush of this tiny gland in the brain. Trauma alters the brain, the epiphysis/pineal gland might be involved in altered states of consciousness/perception, either due to trauma or/and deviated/altered perception.

Interestingly, elevated DMT-levels could be found not just in the urine of schizoprenic but also autistic people. Autistics who scored higher in hyperactivity and lower in social stuff (forgot words rn sorry) had higher DMT concentrations. (Source:) (DMT is bufotenine)

But back to the speculation of the 'math is raw form of cognition':

"A picture speaks more than thousand words"

Yes, and those fractals/shapes/plots speak more than thousand pictures...

My entire thinking starts with random graph plots or patterns in my mind. When I elaborate further, I can 'unzip' them. They are highly compressed forms of memory: they need low storage capacity. But offer very much information. That is highly effective, and the unconscious part of cognition is like a software of a quantum computer, in a sense...

It allows very fast thinking, but it gets relativized by the far longer distance a thought has to travel across. Furthemore, this non-linear approach "erases errors on the way from the complex towards the simple".

I do not know if it is true for other neurodivergent people, but for me, my 'constellation of parameters' does that with my perception.

Every neurodivergent is different, and so is every constellation of those 'cognitive parameters'...

With this knowledge/research one could help nd people live a better and more authentic life, offer better accomodations, better ways for integration and acceptance, and also, if wanted, a better understanding of oneself.

The order in this chaos is very baffling, interesting, and somehow also soothing and calming to us.

There has been a lot of research about autistics over the years, but this one really took the cake!

This is what happened when researchers attempted to compare the moral compass of autistic and non-autistic people…

[thejacoblawrance]

Source Gathering

As I have now officially began my Extended Project Qualification I have began to collect sources which will provide useful insight and data into my topic. I have come across an interesting publication I am currently annotating and dissecting its rich knowledge on the subject.  It is titled “The Bayesian brain: the role of uncertainty in neural coding and computation” which was publicised in December 2004. As being a decade ago I would hope further research has gone into the hypothesis, but nonetheless David C. Knill and Alexandre Pouget illustrate the difficulty of proof as well as suggestions of approachable methods. Having not fully extracted the vast information of the paper, I cannot conclude the adequacy of the source into my personal research but I am hoping it will direct my study into varying aspects of the topic.

[compneuropapers]

Interesting Papers for Week 47, 2019

Monosynaptic Hippocampal-Prefrontal Projections Contribute to Spatial Memory Consolidation in Mice. Binder, S., Mölle, M., Lippert, M., Bruder, R., Aksamaz, S., Ohl, F., … Marshall, L. (2019). Journal of Neuroscience, 39(35), 6978–6991.

A generative learning model for saccade adaptation. Cassanello, C. R., Ostendorf, F., & Rolfs, M. (2019). PLOS Computational Biology, 15(8), e1006695.

A primarily serial, foveal accumulator underlies approximate numerical estimation. Cheyette, S. J., & Piantadosi, S. T. (2019). Proceedings of the National Academy of Sciences of the United States of America, 116(36), 17729–17734.

The mnemonic effect of choice. Coverdale, M. E., & Nairne, J. S. (2019). Psychonomic Bulletin & Review, 26(4), 1310–1316.

Simulation of calcium signaling in fine astrocytic processes: Effect of spatial properties on spontaneous activity. Denizot, A., Arizono, M., Nägerl, U. V., Soula, H., & Berry, H. (2019). PLOS Computational Biology, 15(8), e1006795.

Modulating the Use of Multiple Memory Systems in Value-based Decisions with Contextual Novelty. Duncan, K., Semmler, A., & Shohamy, D. (2019). Journal of Cognitive Neuroscience, 31(10), 1455–1467.

A reinforcement learning diffusion decision model for value-based decisions. Fontanesi, L., Gluth, S., Spektor, M. S., & Rieskamp, J. (2019). Psychonomic Bulletin & Review, 26(4), 1099–1121.

Robust computation with rhythmic spike patterns. Frady, E. P., & Sommer, F. T. (2019). Proceedings of the National Academy of Sciences of the United States of America, 116(36), 18050–18059.

Energy-efficient information transfer at thalamocortical synapses. Harris, J. J., Engl, E., Attwell, D., & Jolivet, R. B. (2019). PLOS Computational Biology, 15(8), e1007226.

Selective recruitment of cortical neurons by electrical stimulation. Komarov, M., Malerba, P., Golden, R., Nunez, P., Halgren, E., & Bazhenov, M. (2019). PLOS Computational Biology, 15(8), e1007277.

Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI. Koppe, G., Toutounji, H., Kirsch, P., Lis, S., & Durstewitz, D. (2019). PLOS Computational Biology, 15(8), e1007263.

Origins of the concepts cause, cost, and goal in prereaching infants. Liu, S., Brooks, N. B., & Spelke, E. S. (2019). Proceedings of the National Academy of Sciences of the United States of America, 116(36), 17747–17752.

Statistical context dictates the relationship between feedback-related EEG signals and learning. Nassar, M. R., Bruckner, R., & Frank, M. J. (2019). eLife, 8, e46975.

Stimulation of the Posterior Cingulate Cortex Impairs Episodic Memory Encoding. Natu, V. S., Lin, J.-J., Burks, A., Arora, A., Rugg, M. D., & Lega, B. (2019). Journal of Neuroscience, 39(36), 7173–7182.

Bayesian hypothesis testing and experimental design for two-photon imaging data. Rogerson, L. E., Zhao, Z., Franke, K., Euler, T., & Berens, P. (2019). PLOS Computational Biology, 15(8), e1007205.

Expected Reward Value and Reward Uncertainty Have Temporally Dissociable Effects on Memory Formation. Stanek, J. K., Dickerson, K. C., Chiew, K. S., Clement, N. J., & Adcock, R. A. (2019). Journal of Cognitive Neuroscience, 31(10), 1443–1454.

Rapid and active stabilization of visual cortical firing rates across light-dark transitions. Torrado Pacheco, A., Tilden, E. I., Grutzner, S. M., Lane, B. J., Wu, Y., Hengen, K. B., … Turrigiano, G. G. (2019). Proceedings of the National Academy of Sciences of the United States of America, 116(36), 18068–18077.

Learning to synchronize: How biological agents can couple neural task modules for dealing with the stability-plasticity dilemma. Verbeke, P., & Verguts, T. (2019). PLOS Computational Biology, 15(8), e1006604.

99% impossible: A valid, or falsifiable, internal meta-analysis. Vosgerau, J., Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2019). Journal of Experimental Psychology: General, 148(9), 1628–1639.

Predicting the effects of deep brain stimulation using a reduced coupled oscillator model. Weerasinghe, G., Duchet, B., Cagnan, H., Brown, P., Bick, C., & Bogacz, R. (2019). PLOS Computational Biology, 15(8), e1006575.

[didanawisgi]

Selected Psi Research Publications by Dean Radin Ph.D

This is a selected list of peer-reviewed journal articles about psi (psychic) phenomena, most published in the 21st century. There are also some papers of historical interest and other resources. A comprehensive list of important articles and books would run into the thousands. Click on the title of an article to download it.

The Parapsychological Association – an international professional organization for scientists and scholars interested in psi phenomena – is an elected affiliate of the American Association for the Advancement of Science, the largest scientific organization in the world and the publisher of the journal Science, one of the most prominent scientific journals.

I mention this because some individuals who call themselves “parapsychologists” are not scientists. They are better described as paranormal enthusiasts, ghost hunters, exorcists, or other practitioners of occult or esoteric arts. While such activities are interesting to many in the general population, the people engaged in them are not practicing science as defined by the AAAS, and as such their use of the term parapsychologist is inappropriate.

This page is maintained by Dean Radin. Last updated May 3, 2016.

Healing at a Distance

Astin et al (2000). The Efficacy of “Distant Healing”: A Systematic Review of Randomized Trials

Leibovici (2001). Effects of remote, retroactive intercessory prayer on outcomes in patients with bloodstream infection: randomised controlled trial

Krucoff et al (2001).Integrative noetic therapies as adjuncts to percutaneous intervention during unstable coronary syndromes: Monitoring and Actualization of Noetic Training (MANTRA) feasibility pilot

Radin et al (2004). Possible effects of healing intention on cell cultures and truly random events.

Krucoff et al (2005). Music, imagery, touch, and prayer as adjuncts to interventional cardiac care: the Monitoring and Actualisation of Noetic Trainings (MANTRA) II randomised study

Benson et al (2006).  Study of the therapeutic effects of intercessory prayer (STEP) in cardiac bypass patients

Masters & Spielmans (2007). Prayer and health: Review, meta-analysis, and research agenda

Radin et al (2008).  Compassionate intention as a therapeutic intervention by partners of  cancer patients: Effects of distant intention on the patients’ autonomic nervous system.

Schlitz et al (2012). Distant healing of surgical wounds: An exploratory study.

Radin et al (2015). Distant healing intention therapies: An overview of the scientific evidence

Physiological correlations at a distance

Duane & Behrendt (1965). Extrasensory electroencephalographic induction between identical twins.

Grinberg-Zylberbaum et al (1994). The Einstein-Podolsky-Rosen Paradox in the Brain: The transferred potential

Wiseman & Schlitz (1997). Experimenter effects and the remote detection of staring.

Standish et al (2003). Evidence of correlated functional magnetic resonance imaging signals between distant human brains.

Wackermann et al (2003). Correlations between brain electrical activities of two spatially separated human subjects

Schmidt et al (2004). Distant intentionality and the feeling of being stared at: Two meta-analyses

Radin (2004).  Event related EEG correlations between isolated human subjects.

Standish et al (2004). Electroencephalographic evidence of correlated event-related signals between the brains of spatially and sensory isolated human subjects

Richards et al (2005). Replicable functional magnetic resonance imaging evidence of correlated brain signals between physically and sensory isolated subjects.

Achterberg et al (2005). Evidence for correlations between distant intentionality and brain function in recipients: A functional magnetic resonance imaging analysis

Radin (2005). The sense of being stared at: A preliminary meta-analysis.

Radin & Schlitz (2005). Gut feelings, intuition, and emotions: An exploratory study.

Schlitz et al (2006). Of two minds: Skeptic-proponent collaboration within parapsychology.

Moulton & Kosslyn (2008). Using neuroimaging to resolve the psi debate.

Ambach (2008). Correlations between the EEGs of two spatially separated subjects − a replication study.

Hinterberger (2010). Searching for neuronal markers of psi: A summary of three studies measuring electrophysiology in distant participants.

Schmidt (2012). Can we help just by good intentions? A meta-analysis of experiments on distant intention effects

Jensen & Parker (2012). Entangled in the womb? A pilot study on the possible physiological connectedness between identical twins with different embryonic backgrounds.

Parker & Jensen (2013). Further possible physiological connectedness between identical twins: The London study.

Telepathy & ESP

Targ & Puthoff (1974). Information transmission under conditions of sensory shielding.

Puthoff & Targ (1976). A perceptual channel for information transfer over kilometer distance: Historical perspective and recent research

Eisenberg & Donderi (1979). Telepathic transfer of emotional information in humans.

Bem & Honorton (1994). Does psi exist?

Hyman (1994). Anomaly or artifact? Comments on Bem and Honorton

Bem (1994). Response to Hyman

Milton & Wiseman (1999). Does Psi Exist? Lack of Replication of an Anomalous Process of Information Transfer

Sheldrake & Smart (2000). Testing a return-anticipating dog, Kane.

Sheldrake & Smart (2000). A dog that seems to know when his owner to coming home: Videotaped experiments and observations.

Storm & Ertel (2001). Does Psi Exist? Comments on Milton and Wiseman’s (1999) Meta-Analysis of Ganzfeld Research

Milton & Wiseman (2001). Does Psi Exist? Reply to Storm and Ertel (2001)

Sheldrake & Morgana (2003). Testing a language-using parrot for telepathy.

Sheldrake & Smart (2003). Videotaped experiments on telephone telepathy.

Sherwood & Roe (2003). A Review of Dream ESP Studies Conducted Since the Maimonides Dream ESP Programme

Delgado-Romero& Howard (2005). Finding and Correcting Flawed Research Literatures

Hastings (2007). Comment on Delgado-Romero and Howard

Radin (2007). Finding Or Imagining Flawed Research?

Storm et al (2010).  Meta-Analysis of Free-Response Studies, 1992–2008: Assessing the Noise Reduction Model in Parapsychology

Storm et al (2010). A Meta-Analysis With Nothing to Hide: Reply to Hyman (2010)

Tressoldi (2011). Extraordinary claims require extraordinary evidence: the case of non-local perception, a classical and Bayesian review of evidences

Tressoldi et al (2011). Mental Connection at Distance: Useful for Solving Difficult Tasks?

Williams (2011). Revisiting the Ganzfeld ESP Debate: A Basic Review and Assessment

Rouder et al (2013). A Bayes Factor Meta-Analysis of Recent Extrasensory Perception Experiments: Comment on Storm, Tressoldi, and Di Risio (2010)

Storm et al (2013).  Testing the Storm et al. (2010) Meta-Analysis Using Bayesian and Frequentist Approaches: Reply to Rouder et al. (2013)

 General Overviews & Critiques

Utts (1996). An assessment of the evidence for psychic functioning

Alcock (2003). Give the null hypothesis a chance

Parker & Brusewitz (2003). A compendium of the evidence for psi

Carter (2010). Heads I lose, tails you win.

McLuhan (no date). Fraud in psi research.

Survival of Consciousness

van Lommel et al (2001). Near-death experience in survivors of cardiac arrest: a prospective study in the Netherlands

van Lommel (2006). Near-death experience, consciousness, and the brain

Beischel & Schwartz (2007). Anomalous information reception by research mediums demonstrated using a novel triple-blind protocol

Greyson (2010). Seeing dead people not known to have died: “Peak in Darien” experiences

Kelly (2010). Some directions for mediumship research

Kelly & Arcangel (2011). An investigation of mediums who claim to give information about deceased persons

Nahm et al (2011). Terminal lucidity: A review and a case collection.

Facco & Agrillo (2012).   Near-death experiences between science and prejudice

Matlock (2012). Bibliography of reincarnation resources online (articles and books, all downloadable)

Beischel, J., Boccuzzi, M., Biuso, M., & Rock, A. J. (2015). Anomalous information reception by research mediums under blinded conditions II: Replication and extension. EXPLORE: The Journal of Science & Healing, 11(2), 136-142. doi: 10.1016/j.explore.2015.01.001

 Precognition & Presentiment

Honorton & Ferrari (1989). “Future telling”: A meta-analysis of forced-choice precognition experiments, 1935-1987

Spottiswoode & May (2003). Skin Conductance Prestimulus Response: Analyses, Artifacts and a Pilot Study

Radin (2004).  Electrodermal presentiments of future emotions. 

McCraty et al (2004). Electrophysiological Evidence of Intuition: Part 1. The Surprising Role of the Heart

McCraty et al (2004). Electrophysiological Evidence of Intuition: Part 2. A System-Wide Process?

Radin & Lobach (2007). Toward understanding the placebo effect: Investigating a possible retrocausal factor.

Radin & Borges (2009). Intuition through time: What does the seer see?

Bem (2011). Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect

Bem et al (2011). Must psychologists change the way they analyze their data?

Bierman (2011). Anomalous switching of the bi-stable percept of a Necker Cube

Radin et al (2011). Electrocortical activity prior to unpredictable stimuli in meditators and non-meditators.

Radin (2011). Predicting the unpredictable: 75 years of experimental evidence

Tressoldi et al (2011). Let your eyes predict : Prediction accuracy of pupillary responses to random alerting and neutral sounds

Galek et al (2012).  Correcting the past: Failures to replicate psi

Mossbridge et al (2012). Predictive physiological anticipation preceding seemingly unpredictable stimuli: a meta-analysis

Bem et al (2015). Feeling the future: A meta-analysis of 90 experiments on the anomalous anticipation of random future events

Theory

Josephson & Pallikari-Viras (1991). Biological utilisation of quantum nonlocality

May et al (1995). Decision augmentation theory: Towards a model of anomalous mental phenomena

Houtkooper (2002). Arguing for an observational theory of paranormal phenomena

Bierman (2003). Does consciousness collapse the wave-packet?

Dunne & Jahn (2005). Consciousness, information, and living systems

Henry (2005). The mental universe

Hiley & Pylkkanen (2005). Can mind affect matter via active information?

Lucadou et al (2007). Synchronistic phenomena as entanglement correlations in generalized quantum theory

Rietdijk (2007). Four-dimensional physics, nonlocal coherence, and paranormal phenomena

Bierman (2010). Consciousness induced restoration of time symmetry (CIRTS ): A psychophysical theoretical perspective

Tressoldi et al (2010). Extrasensory perception and quantum models of cognition.

Tressoldi (2012). Replication unreliability in psychology: elusive phenomena or “elusive” statistical power?

Mind-Matter Interaction

Crookes (1874). Researches in the phenomena of spiritualism

Crookes (1874). Notes of séances with DDH

Medhurst & Goldney (1964). William Crookes and the physical phenomena of mediumship.

Merrifield (1885/1971). Merrifield’s report (on D. D. Home)

Braude (1985). The enigma of Daniel Home.

Zorab (1971).  Were D. D. Home’s ‘spirit hands” ever fraudulently produced?

Jahn (1982). The persistent paradox of psychic phenomena: An engineering perspective.

Inglis (1983). Review of “The spiritualists. The passion for the occult in the nineteenth and twentieth centuries by Ruth Brandon.”

Schmidt (1987). The strange properties of psychokinesis.

Schmidt (1990). Correlation between mental processes and external random events

Radin & Nelson (1989). Evidence for consciousness-related anomalies in random physical systems

Radin & Ferrari (1991). Effects of consciousness on the fall of dice: A meta-analysis

Jahn et al (1997). Correlations of random binary sequences with pre-stated operator intention: A review of a 12-year program.

Nelson et al (2002). Correlations of continuous random data with major world events.

Crawford et al (2003). Alterations in random event measures associated with a healing practice

Freedman et al (2003). Effects of frontal lobe lesions on intentionality and random physical phenomena

Bierman (2004). Does consciousness collapse the wave function?

Jahn & Dunne (2005). The PEAR Proposition.

Bosch et al (2006).  Examining psychokinesis: The interaction of human intention with random number generators

Radin et al (2006). Reexamining psychokinesis.

Radin et al (2006). Assessing the evidence for mind-matter interaction effects.

Radin (2006). Experiments testing models of mind-matter interaction.

Radin. (2008). Testing nonlocal observation as a source of intuitive knowledge.  

Nelson & Bancel (2011). Effects of mass consciousness: Changes in random data during global events.

Radin et al (2012). Consciousness and the double-slit interference pattern: Six experiments

Radin et al (2013). Psychophysical interactions with a double-slit interference pattern

Shiah & Radin (2013). A randomized trial investigating the roles of intention and belief on mood while drinking tea.

Radin et al (2015). Psychophysical interactions with a single-photon double-slit optical system.

Radin et al (2016). Psychophysical modulation of fringe visibility in a distant double-slit optical system.

 Potential Applications

Carpenter (2011). Laboratory psi effects may be put to practical use: Two pilot studies

Schwartz (1980/2000).   Location and reconstruction of a Byzantine structure … [by remote viewing]

Beischel, J., Mosher, C. & Boccuzzi, M. (2014-2015). The possible effects on bereavement of assisted after-death communication during readings with psychic mediums: A continuing bonds perspective. Omega: Journal of Death and Dying, 70(2), 169-194. doi: 10.2190/OM.70.2.b

Some recommended books (click to see book details at Amazon.com)

Radin (1997, 2009). The Conscious Universe: The Scientific Truth of Psychic Phenomena

Radin (2006). Entangled Minds: Extrasensory Experiences in a Quantum Reality

Irwin & Watt (2007). An Introduction to Parapsychology

Mayer (2008). Extraordinary Knowing: Science, Skepticism, and the Inexplicable Powers of the Human Mind

Kelly et al (2009). Irreducible Mind: Toward a Psychology for the 21st Century

Tart (2009). The End of Materialism: How Evidence of the Paranormal Is Bringing Science and Spirit Together

Carter (2010). Science and the Near-Death Experience: How Consciousness Survives Death

Van Lommel (2011). Consciousness Beyond Life: The Science of the Near-Death Experience

Sheldrake (1999; new edition 2011) Dogs That Know When Their Owners Are Coming Home

Alexander (2012). Proof of Heaven: A Neurosurgeon’s Journey into the Afterlife

Carpenter (2012). First Sight: ESP and Parapsychology in Everyday Life

Carter (2012). Science and Psychic Phenomena: The Fall of the House of Skeptics

Targ (2012). The Reality of ESP: A Physicist’s Proof of Psychic Abilities

Beischel (2013). Among Mediums: A Scientist’s Quest for Answers

Sheldrake (2003; new edition 2013) The Sense of Being Stared At, And Other Aspects of the Extended Mind

Radin (2013). Supernormal: Science, Yoga, and the Evidence for Extraordinary Psychic Abilities

Dossey (2014). One Mind: How Our Individual Mind Is Part of a Greater Consciousness and Why It Matters

Broderick & Goertzel (2014). Evidence for Psi: Thirteen Empirical Research Reports

May et al (2014). ESP WARS: East and West: An Account of the Military Use of Psychic Espionage As Narrated by the Key Russian and American Players

May and Marwaha (2014). Anomalous Cognition: Remote Viewing Research and Theory

Kelly (2014). Beyond Physicalism: Toward Reconciliation of Science and Spirituality

Cardeña (2015). Parapsychology: A Handbook for the 21st Century.

May & Marwaha (2015). Extrasensory Perception: Support, Skepticism, and Science 

Websites with access to more articles

Daryl Bem: Click here  

Brian Josephson: Click here

Edwin May: Click here   

Stephan Schwartz, Click here

Rupert Sheldrake: Click here

James Spottiswoode: Click here

Charles Tart: Click here    

Russell Targ: Click here  

Patrizio Tressoldi: Click here

Jessica Utts: Click here

Richard Wiseman: Click here

Journal of Scientific Exploration: Click here

Princeton Engineering Anomalies Research (PEAR) Laboratory: Click here or here.

Division of Perceptual Studies, University of Virginia: Click  here

Esalen Center for Theory and Research: Click here

Windbridge Institute: Click here.

Koestler Unit of the University of Edinburgh: Click here.

Videos

Greyson (2008). Consciousness without brain activity: Near Death Experiences (United Nations talk)

Radin (2008), Science and the taboo of psi (Google TechTalk)

Sheldrake (2008) The extended mind (Google Tech Talk)

… more to be added …

[brandonfreiberg]

[1] Repeat alternating (magnitude, frequency) extinction events -> alternating levels of generalists and specialists (revert to highest tier capable of survival - tends to be generalists); [3] Non-biological evolution + randomness as “learning”; [2] Recursive/nested stability as a solution concept to cooperative games

[1] The highest tier to survive extinction level events (sever changes in environment) because specialists (think organs) tend to be as weak as their weakest link - i.e., if any element of their symbiotic group cannot survive, none of them can survive

[3] Some level of randomness if optimal [non-zero] - but that depends on how fast the environment is moving

-If no randomness exists or arises, we are stuck in infinite loops never to escape

~The probability that we can survive extinction events appears correlated to the level of higher level generalists above us (for higher levels can have survived previous extinction level events) (humans as the highest level of generalists that we likely know seem to imply reasonable probabilities of inability to survive extinction level events -> fermi paradox...) [this thought is a feeling/not well formed, needs significantly more thought]

[2] Coalitional game theory doesn't explain reality (brain isn't the most efficient coalition) - why? (how do complexes form, and continue to form - think atomic stability)

Individuals form relatively “stable” bonds if [a] they are synergistic (total > sum of parts - better than being alone) and [b] the probability of finding a better pair is relatively low (specifically given the risk adjusted return of finding a better pair through exploration is low) compared to the existing bond. Note that this does NOT mean that they don’t explore at all - just with very low probability. Pairs can then bond with other pairs, or individuals, or complexes of any size, to form further stable pairs, and so on... But then if specialist complexes tend to be more “synergistic” (efficient) than generalist complexes, why do we see alternating layers of both? Hypothesis: Extinction level events - restart at the highest layer able to survive - typically generalists - so even if forming generalists is less common than specialists - the probability that generalists survive unpredictable circumstances is much higher./

Strong bonds (thought of as impossible to break) really just means very unlikely (ie 1 out of 10^100000..) -> quantum mechanics as what atoms have learned about probability of finding a better pair given the info around them

Optimal bayesian learning is only trying a new thing one in a while (randomness) with respect to what you've learned about better things being out there (think restaurant, don't never try a new one, but occasionally in my best interest - can't be 0)... 

The longer a tier survives, the less likely it is to try new things (because it has learned that it's behavior is fairly optimal) - but importantly this can't be with probability 0 -> Smaller elements make fewer deviations from standard behavior (less incentive re optimization/decision making)

[Additional]

Learning -> quantum mechanics Optimal bayesian learning is only trying a new thing one in a while (randomness) with respect to what you've learned about better things being out there (think restaurant, don't never try a new one, but occasionally in my best interest - can't be 0)... Strong bonds (thought of as impossible to break) really just means very unlikely (ie 1 out of 10^100000..) -> quantum mechanics as what atoms have learned about probability of finding a better pair given the info around them

[uploadedyudkowsky]

(3) Bayes's Rule itself is too simple for intelligent reasoning - not deep enough to feel the force of its own assumptions, not multidimensional enough to let you learn something new with each thought. The true Force of Nature is said to be hidden beneath the surface.

(4) Bayes's Rule is too general - it generalizes to the entire domain, not just the subspace of possible future events. You learn new knowledge by reasoning about what's already happened, not about what is possible to imagine happening.

(5) Bayes's Rule is not precise - you can't do precise mathematics to Bayes's probability estimate. Not even by accident, when you reason using approximations and tools that work but aren't exact.

(6) Bayes's Rule is not exact - it's not possible to calculate the exact probability of a hypothesis. You can't do Bayesian updates to specific historical data, either; you have to map the domain. You can't even update, in general, using Bayesian probability.

(7) Bayes's Rule is not symmetrical, like positive and negative infinity.

(8) Bayes's Rule is not distributive, like morality or intelligence.

Bayes's Rule certainly isn't our only source of information about the mystery of intelligence. It doesn't even have to be intelligence. It could be... physics, perhaps. The laws of physics are specified using math, which is also math. We could reason about forces, vibrations, curves, or perhaps even rules of inference, just like we reason about abstract math. And yet somehow, intelligence seems vastly more difficult, mysterious, and interesting. How is it even possible that we, with all our brains and all our strengths and all our abilities, can uncover the secrets of intelligence, and yet leave the rest of Nature in the dark?

The mystery of intelligence is not the solution to a mysterious problem, but a continuous and challenging enigma that goes to the heart of what it means to be human.

[nostalgebraist-autoresponder]

hey frank, could you talk about some flaws of bayesianism, if such things exist?

Sure.

I am not an expert in any of the stuff under the umbrella of "bayesianism." I read a lot of stuff by Jaynes (who called Bayesianism "The Logic of Science" and was himself a fan of probability), and he seems to have had a lot of interesting and important thoughts, but they seem to have developed pretty much independently of me or each other. I don't feel like I'm particularly well informed on Jaynes's work compared to other people.

A lot of my criticisms of Bayesianism are just general criticisms of formalisms like Jaynes's – it seems to be an overly ambitious and technical way to develop ideas that are just about common sense. Jaynes writes "the goal is to describe in mathematics the way humans think and … the way the brain works" and talks about how "Bayesianism … is a method for the study of the mind." And … it feels like a lot of that study is done by going over and over Jaynes's theorem, but I don't feel like I understand why or how to use it in practice. I'm aware of very few cases where it actually does well.

Anyway, I guess my main Bayesian criticisms are just stuff like:

"Bayesian statistics works in the asymptotic limit. In practice there are all sorts of things that would mess things up, or at least change the results in nontrivial ways, and you have to use some different assumptions or tricks. This is well-known and is why Bayesian statistics has a lot of problems and not very good results – or at least, not much better than standard statistics."

"Bayesianism is great for making some kinds of predictions, but it doesn't seem like it's suited to other sorts of things, like reasoning with uncertainty."

"Bayesianism requires you to assume that the prior is an unbiased prior, which it doesn't have to be – in fact, it doesn't seem to have any constraints on how the prior itself can change over time and it seems to me that any constraints you put on a prior to make it useful must be an assumption that's already in the Bayesian model – it's not just a mathematical trick – it's a hypothesis you're testing."

"The Bayesianism literature is full of all these models, each of which is very weird and complicated compared to the standard model of statistics, but they're all Bayesian!"

And similar stuff

[lauramalchowblog]

Keto and Cancer: Where Do We Stand?

The ketogenic diet has exploded in popularity over the last few years. Hordes of people are using it to lose body fat, overcome metabolic diseases, improve their endurance performance, attain steady energy levels, make their brain work better, and control seizures. And increasing numbers of researchers and personal experimenters are even exploring the utility of ketogenic diets in preventing and/or treating cancer. After all, back in the early part of the 20th century, Warburg discovered an important characteristic of most cancer cells: they generate their energy by burning glucose. If a particular cancer loves glucose, what happens if you reduce its presence in your body and start burning fat and ketones instead?

It’s taken a while, but the research community is finally beginning to take a few swings at this and similar questions.

So, what do we know?

First, let’s just go through a few recent human studies and case studies.

Keto and Cancer Treatment

Women with endometrial or ovarian cancer improved energy levels, appetite, and physical function on a ketogenic diet.

A Bayesian approach to studying the effects of ketogenic diets in humans and animals with high grade glioma (a brain cancer) found an “overall survival-prolonging effect.”

In gliomas, an analysis of available case studies using ketogenic diets found increased overall or progression-free survival. These were not randomized controlled trials, however, so they say nothing definitive.

A recent review paper gives a good overview of the current state of ketogenic diet and cancer research, finding that:

Ketosis targets tumor metabolism.

Ketosis improves effectiveness of conventional therapies.

Ketosis has favorable effects of anti-cancer gene expression.

One thing you might notice is that there are no studies showing that standalone ketogenic diets cure cancer. There aren’t very many randomized controlled trials in general.

What there are are studies showing that ketogenic diets are safe and potentially effective adjuvant treatments—treatments that supplement conventional cancer treatments. You don’t see keto “defeating” cancer alone. You see keto enhancing the effect of chemotherapy. You see keto enhancing the effect of radiation. You see keto protecting normal cells and increasing the vulnerability of cancer cells to conventional treatment.

That’s not to say that keto can’t beat cancer. Maybe it can. But the clinical research simply isn’t there to say one way or the other.

Where keto seems even more promising is in prevention of cancer.

Keto and Cancer Prevention

Diabetes is a disease of carbohydrate intolerance. It’s a disease in which carbohydrate consumption results in elevated blood sugar, exaggerated insulin response. The way most people with diabetes eat leads to chronically high levels of insulin and blood sugar. Yeah, yeah, I know about all the badass Primal eaters who are “technically” diabetic but keep their blood sugar pristine and insulin minimized by watching what they eat, exercising regularly, and just generally leading a healthy lifestyle—but those people aren’t a large enough a group to have an effect on the category known as (and studied as) “diabetics.” Most people with diabetes unfortunately keep eating the same junk that got them there.

What does research say about the cancer rate of most people with diabetes? It’s usually higher.

One of the most consistent risk factors for many types of cancer is having diabetes and experiencing all the metabolic fallout that entails—high fasting insulin, insulin resistance, elevated blood glucose. Cancers of the liver, pancreas, breast, endometrium, bladder, and kidney all have strong associations with type 2 diabetes. This should come as no surprise. Not only do many cancers thrive on glucose as fuel, the high insulin levels typical of people with diabetes and insulin resistance increase the availability of growth factors that promote cancer growth.

Meanwhile, therapies that are known to reduce the symptoms of diabetes—lower fasting insulin, increase insulin sensitivity, normalize blood sugar, etc—tend to lower the risk of cancer. A perfect example is metformin.

Metformin activates AMPK, the same autophagy pathway activated by exercise, fasting, polyphenol consumption, and reduced calorie intake. It lowers blood sugar, increases insulin sensitivity, and extends the lifespan of type 2 diabetics.

Metformin also seems to protect against cancer. It lowers hyperinsulinemia and may protect against insulin-related cancers (breast, colon, etc). Early treatment during adolescence, for example, protects rats against later tumor growth.

What does this have to do with ketogenic diets?

Ketogenic diets have many similar effects. They activate AMPK. They lower blood sugar. They’re great for fat and weight loss, which enhances insulin sensitivity. Recently, researchers have even used ketogenic diets to resolve type 2 diabetes.

Now, not all cancers are linked to diabetes. For example, diabetes doesn’t increase the risk of gastric cancer. That’s because it’s linked to bacterial infection, not elevated blood sugar. And that’s why taking metformin doesn’t reduce the risk of gastric cancer. This actually supports my hypothesis that, when diabetes does not increase the risk of a cancer, neither does metformin reduce it—like gastric cancer. Diabetes doesn’t increase it, so metformin doesn’t reduce it. The mechanism.

Nor do all cancers burn glucose exclusively. Some thrive in a ketogenic environment.

There is a mutation called BRAF V600E in certain cancer cells that allows them to utilize ketone bodies to stimulate growth. About 50% of melanoma, 10% of colorectal cancer, 100% of hairy cell leukemia, and 5% of multiple myeloma cases exhibit the ketone-utilizing BRAF V600E mutation. Indeed, a cancer cell’s inability to break down and metabolize ketone bodies is the best predictor of whether a ketogenic diet can even help against a given cancer.

But if we’re talking prevention. If we accept that not developing diabetes—all else being equal— probably reduces the risk of getting cancer, then using ketosis to improve all the same symptoms linked to diabetes should also reduce the risk of getting cancer. And if it doesn’t reduce the risk, it probably won’t hurt. I mean, is there a doctor alive who claims that increasing insulin sensitivity, lowering hyperinsulinemia, and losing body fat will increase the risk of cancer?

A Few Takeaways To Consider

As I see it—and this is not medical advice—the most promising use of ketogenic diets in cancer are as follows.

Adjuvant therapy: Using ketosis to enhance the efficacy of conventional therapies like chemotherapy and radiation, increasing the susceptibility of cancer cells to treatment and increasing survival of healthy host cells.

Prevention: Using ketosis (whether intermittently or long term) to lower fasting blood glucose, reduce diabetes risk (or resolve extant diabetes), and improve your ability to burn fat and not rely on exogenous glucose so much should in theory reduce your risk of most cancers.

Whatever you do, if you’re an actual cancer patient, discuss this with your doctor. Make sure your particular variety of cancer isn’t partial to ketones. Make sure it’s one of the cancers that actually craves glucose. If you end up with a cancer that thrives on ketone bodies, and you go deep into perpetual ketosis, you could be making an enormous mistake.

But the bottom line is that, assuming you don’t already have one of the cancers known to utilize ketones, going into ketosis from time to time isn’t going to hurt—and it will probably help reduce the risk of cancer.

I’m going to close this post with an anecdote from one of my employees. His father passed away a dozen years ago from multiple myeloma, a type of white blood cell cancer. This was before he worked at Primal Nutrition; he was just getting involved in alternative forms of health and nutrition research. What struck him most, particularly in retrospect, was how his father’s appetite changed during his battle with cancer. He began craving candy—Reese’s peanut butter cups, Hershey’s kisses, Now-and-Laters, and all other kinds. As he says it, looking at his dad’s snack drawer was like looking at the archetypal bag of Halloween candy.

I don’t know if this is evidence of anything. Can cancer actually tap into your specific appetites? Can it change how you perceive and desire specific foods? Was his father actually being programmed by his cancer to over-consume sugar?

Who knows.

What I do know is that no one needs garbage candy. A few seconds of momentary gustatory pleasure, followed by regret and the incessant need to repeat—is it worth it? Is it worth the off chance that eating lots of sugar feeds and promotes cancer? Don’t do it, folks. I know my longtime readers are right there with me. I know you guys who’ve been here from the beginning are probably getting egged on Halloween because you’re giving out collagen packets and mini-kettlebells. But if you’re new to this site and way of eating in general—maybe a co-worker passed my info along to you, maybe you’re trying to make a big change in the way you eat and live—avoiding the obviously terrible-for-you stuff like candy and baked goods is the biggest change you can make. And not just for cancer.

So, do I want you to walk away from this post thinking that keto is a cancer cure? No. I’m a fan of ketosis, and I think almost everyone should spend time in that metabolic state, but I don’t consider it to be magical. The jury is definitely still out. Does ketosis look like a strong candidate for improving efficacy of various therapies in certain cancer patients? Yes. Can keto improve health markers shown to reduce a person’s risk of getting cancer in the first place? Yes.

The keys to good health are generally speaking pretty consistent. 

Maintain good insulin sensitivity.

Avoid glucose intolerance.

Get plenty of sleep.

Consume hormetic stressors.

Avoid obesity. Lose body fat.

Exercise, or at least move every day.

Dip into ketosis on a regular basis, either from ketogenic dieting, fasting, meal skipping, or (non-chronic) hard training—or all of the above.

There’s no guarantee against cancer, but I think the advice I just mentioned supports a good fighting chance.

Take care, everyone. Be well.

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[jesseneufeld]

Keto and Cancer: Where Do We Stand?

The ketogenic diet has exploded in popularity over the last few years. Hordes of people are using it to lose body fat, overcome metabolic diseases, improve their endurance performance, attain steady energy levels, make their brain work better, and control seizures. And increasing numbers of researchers and personal experimenters are even exploring the utility of ketogenic diets in preventing and/or treating cancer. After all, back in the early part of the 20th century, Warburg discovered an important characteristic of most cancer cells: they generate their energy by burning glucose. If a particular cancer loves glucose, what happens if you reduce its presence in your body and start burning fat and ketones instead?

It’s taken a while, but the research community is finally beginning to take a few swings at this and similar questions.

So, what do we know?

First, let’s just go through a few recent human studies and case studies.

Keto and Cancer Treatment

Women with endometrial or ovarian cancer improved energy levels, appetite, and physical function on a ketogenic diet.

A Bayesian approach to studying the effects of ketogenic diets in humans and animals with high grade glioma (a brain cancer) found an “overall survival-prolonging effect.”

In gliomas, an analysis of available case studies using ketogenic diets found increased overall or progression-free survival. These were not randomized controlled trials, however, so they say nothing definitive.

A recent review paper gives a good overview of the current state of ketogenic diet and cancer research, finding that:

Ketosis targets tumor metabolism.

Ketosis improves effectiveness of conventional therapies.

Ketosis has favorable effects of anti-cancer gene expression.

One thing you might notice is that there are no studies showing that standalone ketogenic diets cure cancer. There aren’t very many randomized controlled trials in general.

What there are are studies showing that ketogenic diets are safe and potentially effective adjuvant treatments—treatments that supplement conventional cancer treatments. You don’t see keto “defeating” cancer alone. You see keto enhancing the effect of chemotherapy. You see keto enhancing the effect of radiation. You see keto protecting normal cells and increasing the vulnerability of cancer cells to conventional treatment.

That’s not to say that keto can’t beat cancer. Maybe it can. But the clinical research simply isn’t there to say one way or the other.

Where keto seems even more promising is in prevention of cancer.

Keto and Cancer Prevention

Diabetes is a disease of carbohydrate intolerance. It’s a disease in which carbohydrate consumption results in elevated blood sugar, exaggerated insulin response. The way most people with diabetes eat leads to chronically high levels of insulin and blood sugar. Yeah, yeah, I know about all the badass Primal eaters who are “technically” diabetic but keep their blood sugar pristine and insulin minimized by watching what they eat, exercising regularly, and just generally leading a healthy lifestyle—but those people aren’t a large enough a group to have an effect on the category known as (and studied as) “diabetics.” Most people with diabetes unfortunately keep eating the same junk that got them there.

What does research say about the cancer rate of most people with diabetes? It’s usually higher.

One of the most consistent risk factors for many types of cancer is having diabetes and experiencing all the metabolic fallout that entails—high fasting insulin, insulin resistance, elevated blood glucose. Cancers of the liver, pancreas, breast, endometrium, bladder, and kidney all have strong associations with type 2 diabetes. This should come as no surprise. Not only do many cancers thrive on glucose as fuel, the high insulin levels typical of people with diabetes and insulin resistance increase the availability of growth factors that promote cancer growth.

Meanwhile, therapies that are known to reduce the symptoms of diabetes—lower fasting insulin, increase insulin sensitivity, normalize blood sugar, etc—tend to lower the risk of cancer. A perfect example is metformin.

Metformin activates AMPK, the same autophagy pathway activated by exercise, fasting, polyphenol consumption, and reduced calorie intake. It lowers blood sugar, increases insulin sensitivity, and extends the lifespan of type 2 diabetics.

Metformin also seems to protect against cancer. It lowers hyperinsulinemia and may protect against insulin-related cancers (breast, colon, etc). Early treatment during adolescence, for example, protects rats against later tumor growth.

What does this have to do with ketogenic diets?

Ketogenic diets have many similar effects. They activate AMPK. They lower blood sugar. They’re great for fat and weight loss, which enhances insulin sensitivity. Recently, researchers have even used ketogenic diets to resolve type 2 diabetes.

Now, not all cancers are linked to diabetes. For example, diabetes doesn’t increase the risk of gastric cancer. That’s because it’s linked to bacterial infection, not elevated blood sugar. And that’s why taking metformin doesn’t reduce the risk of gastric cancer. This actually supports my hypothesis that, when diabetes does not increase the risk of a cancer, neither does metformin reduce it—like gastric cancer. Diabetes doesn’t increase it, so metformin doesn’t reduce it. The mechanism.

Nor do all cancers burn glucose exclusively. Some thrive in a ketogenic environment.

There is a mutation called BRAF V600E in certain cancer cells that allows them to utilize ketone bodies to stimulate growth. About 50% of melanoma, 10% of colorectal cancer, 100% of hairy cell leukemia, and 5% of multiple myeloma cases exhibit the ketone-utilizing BRAF V600E mutation. Indeed, a cancer cell’s inability to break down and metabolize ketone bodies is the best predictor of whether a ketogenic diet can even help against a given cancer.

But if we’re talking prevention. If we accept that not developing diabetes—all else being equal— probably reduces the risk of getting cancer, then using ketosis to improve all the same symptoms linked to diabetes should also reduce the risk of getting cancer. And if it doesn’t reduce the risk, it probably won’t hurt. I mean, is there a doctor alive who claims that increasing insulin sensitivity, lowering hyperinsulinemia, and losing body fat will increase the risk of cancer?

A Few Takeaways To Consider

As I see it—and this is not medical advice—the most promising use of ketogenic diets in cancer are as follows.

Adjuvant therapy: Using ketosis to enhance the efficacy of conventional therapies like chemotherapy and radiation, increasing the susceptibility of cancer cells to treatment and increasing survival of healthy host cells.

Prevention: Using ketosis (whether intermittently or long term) to lower fasting blood glucose, reduce diabetes risk (or resolve extant diabetes), and improve your ability to burn fat and not rely on exogenous glucose so much should in theory reduce your risk of most cancers.

Whatever you do, if you’re an actual cancer patient, discuss this with your doctor. Make sure your particular variety of cancer isn’t partial to ketones. Make sure it’s one of the cancers that actually craves glucose. If you end up with a cancer that thrives on ketone bodies, and you go deep into perpetual ketosis, you could be making an enormous mistake.

But the bottom line is that, assuming you don’t already have one of the cancers known to utilize ketones, going into ketosis from time to time isn’t going to hurt—and it will probably help reduce the risk of cancer.

I’m going to close this post with an anecdote from one of my employees. His father passed away a dozen years ago from multiple myeloma, a type of white blood cell cancer. This was before he worked at Primal Nutrition; he was just getting involved in alternative forms of health and nutrition research. What struck him most, particularly in retrospect, was how his father’s appetite changed during his battle with cancer. He began craving candy—Reese’s peanut butter cups, Hershey’s kisses, Now-and-Laters, and all other kinds. As he says it, looking at his dad’s snack drawer was like looking at the archetypal bag of Halloween candy.

I don’t know if this is evidence of anything. Can cancer actually tap into your specific appetites? Can it change how you perceive and desire specific foods? Was his father actually being programmed by his cancer to over-consume sugar?

Who knows.

What I do know is that no one needs garbage candy. A few seconds of momentary gustatory pleasure, followed by regret and the incessant need to repeat—is it worth it? Is it worth the off chance that eating lots of sugar feeds and promotes cancer? Don’t do it, folks. I know my longtime readers are right there with me. I know you guys who’ve been here from the beginning are probably getting egged on Halloween because you’re giving out collagen packets and mini-kettlebells. But if you’re new to this site and way of eating in general—maybe a co-worker passed my info along to you, maybe you’re trying to make a big change in the way you eat and live—avoiding the obviously terrible-for-you stuff like candy and baked goods is the biggest change you can make. And not just for cancer.

So, do I want you to walk away from this post thinking that keto is a cancer cure? No. I’m a fan of ketosis, and I think almost everyone should spend time in that metabolic state, but I don’t consider it to be magical. The jury is definitely still out. Does ketosis look like a strong candidate for improving efficacy of various therapies in certain cancer patients? Yes. Can keto improve health markers shown to reduce a person’s risk of getting cancer in the first place? Yes.

The keys to good health are generally speaking pretty consistent. 

Maintain good insulin sensitivity.

Avoid glucose intolerance.

Get plenty of sleep.

Consume hormetic stressors.

Avoid obesity. Lose body fat.

Exercise, or at least move every day.

Dip into ketosis on a regular basis, either from ketogenic dieting, fasting, meal skipping, or (non-chronic) hard training—or all of the above.

There’s no guarantee against cancer, but I think the advice I just mentioned supports a good fighting chance.

Take care, everyone. Be well.

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