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.
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]
Kording, K. P., & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nautre, 427, 244-247. [pdf]
Kording, K. P, & Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10 (7), 319-326. [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]
Ernst, M. O., & Banks, M. S. (2002). Humans integrate visual and haptic information a statistically optimal fashion. Nature, 415, 429-433. [link]