Choices change the temporal weighting of decision evidence

Reference induces biases in late visual processing

How we perceive a visual stimulus can be influenced by its surrounding context. For example, the presence of a reference skews the perception of a similar feature in a stimulus, a phenomenon called reference repulsion. Ongoing research so far remains inconclusive regarding the stage of visual information processing where such repulsion occurs. We examined the influence of a reference on late visual processing. We measured the repulsion effect caused by an orientation reference presented after an orientation ensemble stimulus. The participants' reported orientations were significantly biased away from the post-stimulus reference, displaying typical characteristics of reference repulsion. Moreover, explicit discrimination choices between the reference and the stimulus influenced the magnitudes of repulsion effects, which can be explained by an encoding-decoding model that differentiates the re-weighting of sensory representations in implicit and explicit processes. These results support the notion that reference repulsion may arise at a late decision-related stage of visual processing, where different sensory decoding strategies are employed depending on the specific task.

Prior information differentially affects discrimination decisions and subjective confidence reports

According to Bayesian models, both decisions and confidence are based on the same precision-weighted integration of prior expectations ("priors") and incoming information ("likelihoods"). This assumes that priors are integrated optimally and equally in decisions and confidence, which has not been tested. In three experiments, we quantify how priors inform decisions and confidence. With a dual-decision task we create pairs of conditions that are matched in posterior information, but differ on whether the prior or likelihood is more informative. We find that priors are underweighted in discrimination decisions, but are less underweighted in confidence about those decisions, and this is not due to differences in processing time. The same patterns remain with exogenous probabilistic cues as priors. With a Bayesian model we quantify the weighting parameters for the prior at both levels, and find converging evidence that priors are more optimally used in explicit confidence, even when underused in decisions.

Dynamic Weighting of Time-Varying Visual and Auditory Evidence During Multisensory Decision Making

Perceptual decision-making in a dynamic environment requires two integration processes: integration of sensory evidence from multiple modalities to form a coherent representation of the environment, and integration of evidence across time to accurately make a decision. Only recently studies started to unravel how evidence from two modalities is accumulated across time to form a perceptual decision. One important question is whether information from individual senses contributes equally to multisensory decisions. We designed a new psychophysical task that measures how visual and auditory evidence is weighted across time. Participants were asked to discriminate between two visual gratings, and/or two sounds presented to the right and left ear based on respectively contrast and loudness. We varied the evidence, i.e., the contrast of the gratings and amplitude of the sound, over time. Results showed a significant increase in performance accuracy on multisensory trials compared to unisensory trials, indicating that discriminating between two sources is improved when multisensory information is available. Furthermore, we found that early evidence contributed most to sensory decisions. Weighting of unisensory information during audiovisual decision-making dynamically changed over time. A first epoch was characterized by both visual and auditory weighting, during the second epoch vision dominated and the third epoch finalized the weighting profile with auditory dominance. Our results suggest that during our task multisensory improvement is generated by a mechanism that requires cross-modal interactions but also dynamically evokes dominance switching.

The computational roots of positivity and confirmation biases in reinforcement learning

Humans do not integrate new information objectively: outcomes carrying a positive affective value and evidence confirming one's own prior belief are overweighed. Until recently, theoretical and empirical accounts of the positivity and confirmation biases assumed them to be specific to 'high-level' belief updates. We present evidence against this account. Learning rates in reinforcement learning (RL) tasks, estimated across different contexts and species, generally present the same characteristic asymmetry, suggesting that belief and value updating processes share key computational principles and distortions. This bias generates over-optimistic expectations about the probability of making the right choices and, consequently, generates over-optimistic reward expectations. We discuss the normative and neurobiological roots of these RL biases and their position within the greater picture of behavioral decision-making theories.

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

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.

Normative Principles for Decision-Making in Natural Environments

The decisions we make are shaped by a lifetime of learning. Past experience guides the way that we encode information in neural systems for perception and valuation, and determines the information we retrieve when making decisions. Distinct literatures have discussed how lifelong learning and local context shape decisions made about sensory signals, propositional information, or economic prospects. Here, we build bridges between these literatures, arguing for common principles of adaptive rationality in perception, cognition, and economic choice. We discuss how a single common framework, based on normative principles of efficient coding and Bayesian inference, can help us understand a myriad of human decision biases, including sensory illusions, adaptive aftereffects, choice history biases, central tendency effects, anchoring effects, contrast effects, framing effects, congruency effects, reference-dependent valuation, nonlinear utility functions, and discretization heuristics. We describe a simple computational framework for explaining these phenomena. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.