<p>When performing certain tasks, people’s inferences approximate Bayesian inference to a remarkable degree. For example, when people receive both haptic and visual information about the size of an object, they combine this information in a manner that very closely resembles Bayesian inference, taking account of the uncertainties associated with the visual and haptic information (e.g., Ernst &amp; Banks, 2002). This optimality can be observed in many perceptual (Knill &amp; Pouget, 2004) and sensorimotor (Kording &amp; Wolpert, 2004, 2006) tasks and across a range of information sources (e.g., including prior beliefs and multiple sensory inputs). These findings suggest that there must be biological mechanisms that either implement Bayesian inference or implement something that very closely resembles it.</p>
<p>At the same time, there is no consensus regarding <em>how</em> this is done. While there are proposals about how neural populations might perform Bayesian inference (e.g., Ma, Beck, Latham, &amp; Pouget, 2006; Knill &amp; Pouget, 2004; Kover &amp; Bao, 2010), it is difficult to evaluate these proposals at present: the available neuroscientific evidence is quite limited. Moreover, because the most compelling evidence for Bayesian inference is limited to low-level perceptual processes, it is possible that higher-level inferences are implemented by biological mechanisms that do not perform Bayesian inference. Indeed, given the computational difficulty of Bayesian inference in general, it seems all but inevitable that many biological mechanisms will not implement Bayesian inference exactly.</p>
<p>In summary, <em>some</em> biological mechanisms must perform something like Bayesian inference, but researchers have only begun to explore how this happens, and the extent to which high-level perception and cognition rely on Bayesian computations remains unclear.</p>
<p><strong>References</strong></p>
<blockquote>
<p>Knill, D. C., &amp; Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences, 27 (12), 712-719. <a href="http://www.bcs.rochester.edu/people/alex/pub/articles/KnillPougetTINS04.pdf" rel="noreferrer">[pdf</a>]</p>
<p>Kording, K. P., &amp; Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nautre, 427, 244-247. <a href="http://klab.smpp.northwestern.edu/wiki/images/2/28/Kording_Bayes_integrate_sensorimotor_learn_2004.pdf" rel="noreferrer">[pdf</a>]</p>
<p>Kording, K. P, &amp; Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10 (7), 319-326. <a href="http://klab.smpp.northwestern.edu/wiki/images/1/18/Kording_2006_Bayes_decide_theory_sensorimotor.pdf" rel="noreferrer">[pdf</a>]</p>
<p>Ma, W. J., Beck, J. M., Latham, P. E., &amp; Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9 (11), 1432-1438. <a href="http://www.bcs.rochester.edu/people/alex/pub/articles/mabecklathampougetnn06.pdf" rel="noreferrer">[pdf</a>]</p>
<p>Ernst, M. O., &amp; Banks, M. S. (2002). Humans integrate visual and haptic information a statistically optimal fashion. Nature, 415, 429-433. <a href="http://www.nature.com/nature/journal/v415/n6870/abs/415429a.html" rel="noreferrer">[link</a>]</p>
</blockquote>

CC BY-SA 3.0
2012-04-04T00:16:03.617Z
Welcome to cogsci.SE! This is a great answer! Minor comment: could you edit in a reference section at the end to make finding the articles you cite a little bit easier? Thank you!
Artem Kaznatcheev
CC BY-SA 3.0
2012-04-04T00:18:03.727Z
Here is [a relevant discussion on a suggested way of referencing papers](http://meta.cogsci.stackexchange.com/q/169/21).
Steven Jeuris
CC BY-SA 3.0
2012-04-04T00:21:42.690Z
Further, I would love to see your comment about the computational difficulty of bayesian inference expanded into an answer for [this question](http://cogsci.stackexchange.com/q/599/29) if you have the time and energy.
Artem Kaznatcheev
CC BY-SA 3.0
2012-04-03T22:51:24.583Z
2012-04-04T13:16:44.197Z
2020-06-17T09:55:31.633Z
Chris
<p>Inspired by this question: <a href="https://cogsci.stackexchange.com/questions/599/what-are-drawbacks-to-probabilistic-models-of-cognition">What are some of the drawbacks to probabilistic models of cognition?</a></p>

<p>I would like to know more about the biological plausibility of Bayesian models of cognition. Is there any neural evidence that rejects Bayesian models of cognition?</p>

CC BY-SA 3.0
2012-03-27T17:50:12.337Z
what are some papers you looked at already while thinking about this?
Artem Kaznatcheev
CC BY-SA 3.0
2012-03-27T19:16:28.673Z
I think one problem with this question is that bayesian models work on a completely different level of abstraction than neuroscience data. Any data can be interpreted as being optimal for *something*
zergylord
CC BY-SA 3.0
2012-03-30T00:43:59.553Z
IMO this question needs a lot more fleshing out before it can be helpfully answered.
Zelda
CC BY-SA 3.0
2012-04-03T19:22:29.523Z
I edited your question title (and moved the original title to the body), hopefully this captures the spirit of what you were asking, if not feel free to rollback my edit. Also, you might be interested in [this comment/answer](http://cogsci.stackexchange.com/a/687/29)
Artem Kaznatcheev
CC BY-SA 3.0
2012-04-04T01:15:20.640Z
At the [Pillow Lab](http://pillowlab.cps.utexas.edu/) Blog you can find a brief [discussion & summary](http://pillowlab.wordpress.com/2011/06/24/comp-neuro-jc-on-probabilistic-neural-representations/) of two different threads in the conversation about "Probabilistic Representations in the Brain" (with references).
yep
<neuroscience><computational-modeling><bayesian><theoretical-neuroscience>
Biological plausibility of bayesian models of cognition
zachguo
# Biological plausibility of bayesian models of cognition

(Asked by: username_0 on 2012-03-27)

Inspired by this question: [What are some of the drawbacks to probabilistic models of cognition?](https://cogsci.stackexchange.com/questions/599/what-are-drawbacks-to-probabilistic-models-of-cognition)

I would like to know more about the biological plausibility of Bayesian models of cognition. Is there any neural evidence that rejects Bayesian models of cognition?

**Tags:** neuroscience, computational-modeling, bayesian, theoretical-neuroscience

> **Comments:**
> **username_1**: what are some papers you looked at already while thinking about this?  
> **username_2**: I think one problem with this question is that bayesian models work on a completely different level of abstraction than neuroscience data. Any data can be interpreted as being optimal for *something*  
> **username_3**: IMO this question needs a lot more fleshing out before it can be helpfully answered.  
> **username_1**: I edited your question title (and moved the original title to the body), hopefully this captures the spirit of what you were asking, if not feel free to rollback my edit. Also, you might be interested in [this comment/answer](http://cogsci.stackexchange.com/a/687/29)  
> **username_4**: At the [Pillow Lab](http://pillowlab.cps.utexas.edu/) Blog you can find a brief [discussion & summary](http://pillowlab.wordpress.com/2011/06/24/comp-neuro-jc-on-probabilistic-neural-representations/) of two different threads in the conversation about "Probabilistic Representations in the Brain" (with references).  

---

## 1 answers

### Answer by username_5 on 2012-04-03. Score: 17 | Accepted answer

When performing certain tasks, people’s inferences approximate Bayesian inference to a remarkable degree. For example, when people receive both haptic and visual information about the size of an object, they combine this information in a manner that very closely resembles Bayesian inference, taking account of the uncertainties associated with the visual and haptic information (e.g., Ernst & Banks, 2002). This optimality can be observed in many perceptual (Knill & Pouget, 2004) and sensorimotor (Kording & Wolpert, 2004, 2006) tasks and across a range of information sources (e.g., including prior beliefs and multiple sensory inputs). These findings suggest that there must be biological mechanisms that either implement Bayesian inference or implement something that very closely resembles it.

At the same time, there is no consensus regarding *how* this is done. While there are proposals about how neural populations might perform Bayesian inference (e.g., Ma, Beck, Latham, & Pouget, 2006; Knill & Pouget, 2004; Kover & Bao, 2010), it is difficult to evaluate these proposals at present: the available neuroscientific evidence is quite limited. Moreover, because the most compelling evidence for Bayesian inference is limited to low-level perceptual processes, it is possible that higher-level inferences are implemented by biological mechanisms that do not perform Bayesian inference. Indeed, given the computational difficulty of Bayesian inference in general, it seems all but inevitable that many biological mechanisms will not implement Bayesian inference exactly.

In summary, *some* biological mechanisms must perform something like Bayesian inference, but researchers have only begun to explore how this happens, and the extent to which high-level perception and cognition rely on Bayesian computations remains unclear.

**References**

> Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences, 27 (12), 712-719. [[pdf](http://www.bcs.rochester.edu/people/alex/pub/articles/KnillPougetTINS04.pdf)]
>
> Kording, K. P., & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nautre, 427, 244-247. [[pdf](http://klab.smpp.northwestern.edu/wiki/images/2/28/Kording_Bayes_integrate_sensorimotor_learn_2004.pdf)]
>
> Kording, K. P, & Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10 (7), 319-326. [[pdf](http://klab.smpp.northwestern.edu/wiki/images/1/18/Kording_2006_Bayes_decide_theory_sensorimotor.pdf)]
>
> Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9 (11), 1432-1438. [[pdf](http://www.bcs.rochester.edu/people/alex/pub/articles/mabecklathampougetnn06.pdf)]
>
> Ernst, M. O., & Banks, M. S. (2002). Humans integrate visual and haptic information a statistically optimal fashion. Nature, 415, 429-433. [[link](http://www.nature.com/nature/journal/v415/n6870/abs/415429a.html)]

> **Comments:**
> **username_1**: Welcome to cogsci.SE! This is a great answer! Minor comment: could you edit in a reference section at the end to make finding the articles you cite a little bit easier? Thank you!  
> **username_6**: Here is [a relevant discussion on a suggested way of referencing papers](http://meta.cogsci.stackexchange.com/q/169/21).  
> **username_1**: Further, I would love to see your comment about the computational difficulty of bayesian inference expanded into an answer for [this question](http://cogsci.stackexchange.com/q/599/29) if you have the time and energy.  

---

