The success of the Bayesian approach to perception suggests probabilistic perceptual representations. But if perceptual representation is probabilistic, why doesn't normal conscious perception reflect the full probability distributions that the probabilistic point of view endorses? For example, neurons in MT/V5 that respond to the direction of motion are broadly tuned: a patch of cortex that is tuned to vertical motion also responds to horizontal motion, but when we see vertical motion, foveally, in good conditions, it does not look at all (...) horizontal. This article argues that the best Bayesian approach to this problem does not require probabilistic representation. (shrink)

It is widely held that it is unhelpful to model our epistemic access to modal facts on the basis of perception, and postulate the existence of a bodily mechanism attuned to modal features of the world. In this paper I defend modalizing mechanisms. I present and discuss a decision-theoretic model in which agents with severely limited cognitive abilities, at the end of an evolutionary process, have states which encode substantial information about the probabilities with which the outcomes of a certain (...) Bernoulli process occur. Thus, in the model, a process driven by very simple, thoroughly naturalistic mechanisms eventuates in modal sensitivity. (shrink)

We present three experiments using a novel problem in which participants update their estimates of propensities when faced with an uncertain new instance. We examine this using two different causal structures (common cause/common effect) and two different scenarios (agent‐based/mechanical). In the first, participants must update their estimate of the propensity for two warring nations to successfully explode missiles after being told of a new explosion on the border between both nations. In the second, participants must update their estimate of the (...) accuracy of two early warning tests for cancer when they produce conflicting reports about a patient. Across both experiments, we find two modal responses, representing around one‐third of participants each. In the first, “Categorical” response, participants update propensity estimates as if they were certain about the single event, for example, certain that one of the nations was responsible for the latest explosion, or certain about which of the two tests is correct. In the second, “No change” response, participants make no update to their propensity estimates at all. Across the three experiments, the theory is developed and tested that these two responses in fact have a single representation of the problem: because the actual outcome is binary (only one of the nations could have launched the missile; the patient either has cancer or not), these participants believe it is incorrect to update propensities in a graded manner. They therefore operate on a “certainty threshold” basis, whereby, if they are certain enough about the single event, they will make the “Categorical” response, and if they are below this threshold, they will make the “No change” response. Ramifications are considered for the “categorical” response in particular, as this approach produces a positive‐feedback dynamic similar to that seen in the belief polarization/confirmation bias literature. (shrink)

We describe a computational model of two central aspects of people's probabilistic reasoning: descriptive probability estimation and inferential probability judgment. This model assumes that people's reasoning follows standard frequentist probability theory, but it is subject to random noise. This random noise has a regressive effect in descriptive probability estimation, moving probability estimates away from normative probabilities and toward the center of the probability scale. This random noise has an anti-regressive effect in inferential judgement, however. These regressive and anti-regressive effects explain (...) various reliable and systematic biases seen in people's descriptive probability estimation and inferential probability judgment. This model predicts that these contrary effects will tend to cancel out in tasks that involve both descriptive estimation and inferential judgement, leading to unbiased responses in those tasks. We test this model by applying it to one such task, described by Gallistel et al. ). Participants' median responses in this task were unbiased, agreeing with normative probability theory over the full range of responses. Our model captures the pattern of unbiased responses in this task, while simultaneously explaining systematic biases away from normatively correct probabilities seen in other tasks. (shrink)