Inspired by this question: 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?
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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
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For a fruitful discussion of this, which includes some useful links in the comments section, you might want to read this LessWrong post. Whatever human decision making is at this moment in time, it is something that can be quantified and studied. And as a result, we will be able to determine Bayesian methods for making better decisions, given our current architecture and our preferences. I think questions like this are slightly misguided in the following way. Humanity took a tiny step toward Bayesianism and then totally dominated the world-wide ecosystem in an evolutionary millisecond. Are we "very" Bayesian? Well, what do you mean by "very"? We certainly employ Bayesian methods more so than a hermit crab or a dolphin, but we have a long way to go if we want most of our decision making to be enacted through genuine Bayesian reasoning. So I would say that "being Bayesian" can fall on a spectrum. Humans are not near the 0%-Bayesian end of the spectrum, but probably also not near the 100%-Bayesian perspective. A more interesting questions is whether humanity should strive to engineer itself to specifically be more Bayesian. Some see a perfect Bayesian reasoner as a fixed point of Darwinian evolution (because the universal Solomonoff prior is not computable, it's reasonable to think such a fixed point cannot physically exist, but that doesn't mean we shouldn't try to get there anyway.) |
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