Probabilistic Biases Meet the Bayesian Brain (original) (raw)
Related papers
Why Bayesian brains perform poorly on explicit probabilistic reasoning problems
2022
There is a growing body of evidence suggesting that the neural processes underlying perception, learning, and decision-making approximate Bayesian inference. Yet, humans perform poorly when asked to solve explicit probabilistic reasoning problems. In response, some have argued that certain brain processes are Bayesian while others are not; others have argued that reasoning errors can be explained by either inaccurate generative models or limitations of approximation algorithms. In this paper, we offer a complementary perspective by considering how a Bayesian brain would implement conscious reasoning processes more generally. These considerations require making two distinctions, each of which highlights a fundamental reason why Bayesian brains should not be expected to perform well at explicit inference. The first distinction is between inferring probability distributions over hidden states and representing probabilities as hidden states. The former assumes that the brain’s dynamics ...
Neural substrates of cognitive biases during probabilistic inference
Nature Communications, 2016
Decision making often requires simultaneously learning about and combining evidence from various sources of information. However, when making inferences from these sources, humans show systematic biases that are often attributed to heuristics or limitations in cognitive processes. Here we use a combination of experimental and modelling approaches to reveal neural substrates of probabilistic inference and corresponding biases. We find systematic deviations from normative accounts of inference when alternative options are not equally rewarding; subjects’ choice behaviour is biased towards the more rewarding option, whereas their inferences about individual cues show the opposite bias. Moreover, inference bias about combinations of cues depends on the number of cues. Using a biophysically plausible model, we link these biases to synaptic plasticity mechanisms modulated by reward expectation and attention. We demonstrate that inference relies on direct estimation of posteriors, not on c...
Cognition, 1996
Professional probabilists have long argued over what probability means, with, for example, Bayesians arguing that probabilities refer to subjective degrees of confidence and frequentists arguing that probabilities refer to the frequencies of events in the world. Recently, Gigerenzer and his colleagues have argued that these same distinctions are made by untutored subjects, and that, for many domains, the human mind represents probabilistic information as frequencies. We analyze several reasons why, from an ecological and evolutionary perspective, certain classes of problemsolving mechanisms in the human mind should be expected to represent probabilistic information as frequencies. Then, using a problem famous in the "heuristics and biases" literature for eliciting base rate neglect, we show that correct Bayesian reasoning can be elicited in 76% of subjects-indeed, 92% in the most ecologically valid condition-simply by expressing the problem in frequentist terms. This result adds to the growing body of literature showing that frequentist representations cause various cognitive biases to disappear, including overconfidence, the conjunction fallacy, and base-rate neglect. Taken together, these new findings indicate that the conclusion most common in the literature on judgment under uncertainty-that our inductive reasoning mechanisms do not embody a calculus of probability-will have to be reexamined. From an ecological and evolutionary perspective, humans may turn out to be good intuitive statisticians after all.
Epistemic Irrationality in the Bayesian Brain
British Journal for the Philosophy of Science , 2019
A large body of research in cognitive psychology and neuroscience draws on Bayesian statistics to model information processing within the brain. Many theorists have noted that this research seems to be in tension with a large body of experimental results purportedly documenting systematic deviations from Bayesian updating in human belief formation. In response, proponents of the Bayesian brain hypothesis contend that Bayesian models can accommodate such results by making suitable assumptions about model parameters (for example, priors, likelihoods, and utility functions). To make progress in this debate, I argue that it is fruitful to focus not on specific experimental results but rather on what I call the 'sources of epistemic irrationality' in human cognition. I identify four such sources and I explore whether and, if so, how Bayesian models can be reconciled with them: (1) processing costs; (2) evolutionary suboptimality; (3) motivated cognition; and (4) error management.
Probability biases as Bayesian inference
Judgment and Decision Making, 2006
In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the laboratory conditions in which they are demonstrated, they can be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions.
Psychological review, 2017
Recent debates in the psychological literature have raised questions about the assumptions that underpin Bayesian models of cognition and what inferences they license about human cognition. In this paper we revisit this topic, arguing that there are 2 qualitatively different ways in which a Bayesian model could be constructed. The most common approach uses a Bayesian model as a normative standard upon which to license a claim about optimality. In the alternative approach, a descriptive Bayesian model need not correspond to any claim that the underlying cognition is optimal or rational, and is used solely as a tool for instantiating a substantive psychological theory. We present 3 case studies in which these 2 perspectives lead to different computational models and license different conclusions about human cognition. We demonstrate how the descriptive Bayesian approach can be used to answer different sorts of questions than the optimal approach, especially when combined with principl...
Uncertainty, reward, and attention in the Bayesian brain
2009
preparation for submission to Psychological Review. During my PhD I also contributed to two fMRI studies investigating the subcortical basis of salience computation in the human brain, both in preparation. I co-wrote a commentary on a target article by Ned Block in Behavioural and Brain Sciences (Hulme and Whiteley, 2007), and a paper based on work done prior to my PhD Acta Psychologica (Whiteley et al., 2008).
Cognitive factors affecting subjective probability assessment
1994
Prior probabilities are central to Bayesian statistics. The Bayesian paradigm assumes that people can express uncertainty in terms of subjective probability distributions. This article will consider Hogarth's 1975 assessment that \man is a selective, sequential information processing system with limited capacity,. .. ill-suited for assessing probability distributions." Particular attention will be paid to when people make normatively \good" or \poor" probability assessments, what techniques are e ective in eliciting \good," coherent probability assessments, and on how these ideas are relevant to the practicing Bayesian statistician. While there are situations where experts can make well-calibrated judgments, it will be argued that more research needs to be done into the e ects of expertise, training, and feedback.