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Searching for the Approximation Method used to Perform rationaL inference by INdividuals and Groups

Project description

Identifying how the mind approximates Bayesian models

Bayesian explanations have spread through the field of cognitive science to explain behaviour in various domains – from intuitive physics and causal learning to perception, motor control and language. Nevertheless, people produce stunningly incorrect answers to even the simplest questions about probabilities. A potential resolution to this paradox is that the mind approximates Bayesian solutions by drawing samples, but how it does so remains unknown. The EU-funded SAMPLING project will first identify the algorithms used by the mind to draw samples. Next, it will demonstrate how these sampling algorithms generate classic probabilistic reasoning errors in individuals, upending the longstanding consensus on these effects. Finally, it will use this approach to provide a new perspective on group decision-making.

Objective

Over the past two decades, a wave of Bayesian explanations has swept through cognitive science, explaining behaviour in domains from intuitive physics and causal learning, to perception, motor control and language. Yet people produce stunningly incorrect answers in response to even the simplest questions about probabilities. How can a supposedly Bayesian brain paradoxically reason so poorly with probabilities? Perhaps Bayesian brains do not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead the brain could be approximating Bayesian inference through sampling: drawing samples from its distribution over likely hypotheses over time.

This work aims to put meat on the bones of this hypothesis by identifying the kinds of algorithms used by the brain to draw samples. Previous proposals of simple sampling algorithms both do not match human data, nor scale well to more complex probability distributions and hypothesis spaces. In our first work programme, we will investigate advanced algorithms that have been developed in computer science and statistics, to see which one is employed by the brain to draw samples.

A catalog of reasoning errors has been used to argue against a Bayesian brain, but only with infinite samples does a Bayesian sampler conform to the laws of probability. In our second work programme, we will show how with finite samples the sampling algorithm we identify systematically generates classic probabilistic reasoning errors in individuals, upending the longstanding consensus on these effects. In our third work programme, we will apply the algorithm to group decision making, investigating how the sampling algorithm provides a new perspective on group decision making biases and errors in financial decision making, and harness the algorithm to produce novel and effective ways for human and artificial experts to collaborate.

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Coordinator

THE UNIVERSITY OF WARWICK
Net EU contribution
€ 1 928 168,75
Address
Kirby corner road - university house
CV4 8UW Coventry
United Kingdom

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Region
West Midlands (England) West Midlands Coventry
Activity type
Higher or Secondary Education Establishments
Links
Other funding
€ 0,00

Beneficiaries (1)