In the field of AI, Bayesian inference has been found to be effective at helping machines approximate some human abilities, such as image recognition. But are there grounds for believing that this is how human thought processes work more generally? Do our beliefs, judgments, and decisions follow the rules of Bayesian inference? Imagine taking a shot at a basketball hoop. Wolpert, who has conducted a number of studies on how people control their movements, believes that as we go through life our brains gather statistics for different movement tasks, and combine these in a Bayesian fashion with sensory data, together with estimates of the reliability of that data.
Professor Wolpert believes that as we go through life our brains gather statistics for different movement tasks, and combine these in a Bayesian fashion with other data. Until now. The problem with trying to tickle yourself is that your body is so good at predicting the results of your movement and reacting that it cancels out the effect.
But by delaying people's movements, Wolpert was able to mess with the brain's predictions just enough to bring back the element of surprise.
This revealed the brain's highly attuned estimates of what will happen when you move your finger in a certain way, which are very similar to what a Bayesian calculation would produce from the same data. Consider the following formula:. This tells us the probability of event B occurring given that event E has happened. This is known as a conditional probability, and it is derived by multiplying the conditional probability of E given B , by the probability of event B , divided by the probability of event E. The same basic idea can be applied to beliefs. In this context, P B E is interpreted as the strength of belief B given evidence E , and P B is our prior level of belief before we came across evidence E.
When new evidence arises, we can repeat the calculation, with our last posterior belief becoming our next prior. The more evidence we assess, the sharper our judgements should get. A study by Tom Griffiths of the University of California, Berkeley, and Josh Tenenbaum of MIT asked people to make predictions of how long people would live, how much money films would make, and how long politicians would last in office.
The only data they were given to work with was the running total so far: current age, money made so far, and years served in office to date. People's predictions, the researchers found, were very close to those derived from Bayesian calculations. This is one of a number of studies that provide evidence of probabilistic models underlying the way we learn and think. However, the route the brain takes to reach such apparently Bayesian conclusions is not always obvious.
For starters, it is fairly easy to come up with probability puzzles that should yield to Bayesian methods, but that regularly leave many people flummoxed. For instance, many people will tell you that if you toss a series of coins, getting all heads or all tails is less likely than getting, for instance, tails—tails—heads—tails—heads. Participants are asked to pick one of three doors — A, B, or C — behind one of which lies a prize. Long story short: most people think switching will make no difference, when in fact it improves your chances of winning.
The details are online bit. Surely a Bayesian brain would be perfectly placed to cope with calculations such as these?
Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao
Even Sir David Spiegelhalter, professor of the public understanding of risk at the University of Cambridge, admits to mistrusting his intuition when it comes to probability. Psychologists have uncovered plenty of examples where our brains fail to weigh up probabilities correctly. The work of Nobel prize winner and bestselling author Daniel Kahneman, among others, has thrown light on the strange quirks of how we think and act — yielding countless examples of biases and mental shortcuts that produce questionable decisions.
For instance, we are more likely to notice and believe information if it confirms our existing beliefs. We consistently assign too much weight to the first piece of evidence we encounter on a subject. We overestimate the likelihood of events that are easy to remember — which means the more unusual something is, the more likely our brains think it is to happen.
These weird mental habits are a world away from what you would expect of a Bayesian brain. Life is full of really hard problems, which our brains must try and solve in a state of uncertainty and constant change.
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We expect in 30 soon, maybe by the end of Would you like to write a page article for us? The first half of this piece modify it, so that it is new would be good. Very interesting and excitant the Bayesian model theory since shed new light the way human brain knows the world. My question is regarding we knows the world as extrrnal or the world conception is internas.
Your email address will not be published. The greatest theory of all time? What is predictive processing? Your internal model also called generative model because it generates predictions is structured as a bidirectional hierarchical cascade : The model is a cascade because it involves multiple levels of processing, multiple cortical areas in the brain. The model is hierarchical because it comprises higher and lower processing layers: lower levels process simple data e.
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What makes the brain Bayesian? Imagine, for instance, you want to cross a street: 7 Based on a vast set of hypotheses B about the current situation, your brain computes a hierarchical cascade of predictions P B , including priors about how shapes, colors, and noises will change for low-level perceptual inference , priors about you moving your eyes, head, and legs for low-level active inference , priors about vehicles in motion and changing traffic lights for high-level perceptual inference , 8 and a prior about you standing on the other side of the street for high-level active inference.
Your model contextualizes that data to get P E and estimates the likelihoods P E B of the data, given your hypotheses. The prediction errors, weighed by expected precisions, propagate up the hierarchical cascade, level by level, and alter the corresponding set of hypotheses, thus updating the model to reduce future prediction errors; or they are actively reduced through motor commands to the muscles that move your eyes, head, and legs more about that second option in a second.
What does this explain? This is perceptual inference : reducing prediction error by updating your model so that sensory inputs match with prior expectations. Through perception, you make your model more similar to the world. Action is proprioceptive 10 prediction and the inferential process of minimizing prediction error by changing sensory inputs. Through motor action, you make the world conform to your model. As the predicted proprioceptive states are not yet actual, actions change the world to make them so which means that all your actions are essentially self-fulfilling prophecies.
Unlike with perceptual inference, the model parameters are not updated, but kept stable.
Computational Resource Demands of a Predictive Bayesian Brain
Once a concrete opportunity to act arises, it entrains multilevel cascades of lower-level predictions to change the world in a way that makes the high-level prediction come true i. The achievement of conscious goals is a good example of predictive processing operating at large spatiotemporal scales, for it may take minutes, months, or years to achieve a goal, and it may require one, say, to travel overseas.
Emotion is interoceptive 11 prediction and the active inferential process of minimizing prediction error by triggering physiological changes see Seth Attention is expected precision optimization; it modulates the weight of prediction errors. The better your predictions about the causal, probabilistic structure of the world, the more effectively you can engage with it.
Memory consists of the learned parameters of your internal model, whereas its non-acquired parameters would be the innate knowledge evolution has genetically built into your nervous system. Self-awareness is the inferential process of minimizing prediction error by changing your internal self-model, i. Belief is a hyperprior; a systemic prior with a high degree of abstraction; a high-level prediction that entails general knowledge about the world.
Some examples: Physical beliefs. Physiological beliefs. You expect to see something when you open your eyes and you expect fire to hurt and burn your skin. Psychological beliefs. You expect a great performance to make you feel proud and you expect to feel regret when you shy away from a challenge.
Social beliefs. You expect happy people to smile, offensive words to trigger a reaction, and power to corrupt. Cultural beliefs.
Are Brains Bayesian?
You expect cars to slow down as they approach stop signs, special offers to be highlighted in stores, and handshakes to not last an hour. Notice how the Bayesian brain also manifests in some of our deepest values : We value truth because accurate beliefs about the world allow us to make good predictions. We value simplicity because simple beliefs enable us to generate high-level predictions quickly. We value wisdom because reflected life experience equips us with relatively reliable hyperpriors that minimize long-term prediction error.
Five objections Sensory deprivation Problem. Human rationality Problem. Offline cognition Problem. Phenomenology Problem. Falsifiability Problem. Conclusion Predictive processing provides a framework for understanding all areas of neuroscience and cognitive science at a computational level. Like I found this helpful or interesting. Leave a Reply Cancel reply Your email address will not be published.
This book brings together contributions from both experimental and theoretical neuroscientists that examine the brain mechanisms of perception, decision making, and motor control according to the concepts of Bayesian estimation. After an overvi After an overview of the mathematical concepts, including Bayes theorem, that are basic to understanding the approaches discussed, contributors discuss how Bayesian concepts can be used for interpretation of such neurobiological data as neural spikes and functional brain imaging.
Next, they examine the modeling of sensory processing, including the neural coding of information about the outside world, and finally, they explore dynamic processes for proper behaviors, including the mathematics of the speed and accuracy of perceptual decisions and neural models of belief propagation. Keywords: normative predictions , ideal sensory system , prior knowledge , observation , mechanistic interpretation , dynamic functioning , brain circuit , deciphering experimental data , theoretical neuroscientists , brain mechanisms.
Computational Resource Demands of a Predictive Bayesian Brain | SpringerLink
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