A confirmation bias in perceptual decision-making due to hierarchical approximate inference

Richard D Lange, Ankani Chattoraj, Jeffrey M Beck, Jacob L Yates and Ralf M Haefner

PLOS Computational Biology, 2021, vol. 17, issue 11, 1-30

Abstract: Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question. Here, we study this question in classic perceptual decision-making tasks, where, puzzlingly, previous empirical studies differ in the kinds of biases they observe, ranging from primacy to recency, despite seemingly equivalent tasks. We present a new model, based on hierarchical approximate inference and derived from normative principles, that not only explains both primacy and recency effects in existing studies, but also predicts how the type of bias should depend on the statistics of stimuli in a given task. We verify this prediction in a novel visual discrimination task with human observers, finding that each observer’s temporal bias changed as the result of changing the key stimulus statistics identified by our model. The key dynamic that leads to a primacy bias in our model is an overweighting of new sensory information that agrees with the observer’s existing belief—a type of ‘confirmation bias’. By fitting an extended drift-diffusion model to our data we rule out an alternative explanation for primacy effects due to bounded integration. Taken together, our results resolve a major discrepancy among existing perceptual decision-making studies, and suggest that a key source of bias in human decision-making is approximate hierarchical inference.Author summary: When humans and animals accumulate evidence over time, they are often biased. Identifying the mechanisms underlying these biases can lead to new insights into principles of neural computation. The confirmation bias, in which new evidence is given more weight when it agrees with existing beliefs, is a ubiquitous yet poorly understood example of such biases. Here we report that a confirmation bias arises even during perceptual decision-making, and propose an approximate hierarchical inference model as the underlying mechanism. Our model correctly predicts for what stimuli and tasks this bias will be strong, and when it will be weak, a critical prediction that we confirm using old and new data. A quantitative model comparison clearly favors our model over a key alternative: integration to bound. The key dynamic driving the confirmation bias in our model is an interaction between inferences on different timescales, a common scenario in decision-making more generally.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1009517

DOI: 10.1371/journal.pcbi.1009517

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