Biology subjects

Hare, T. A.

Publications and source records attributed to Hare, T. A..

11 recordsLinked to original sources

bioRxiv2026

Goal-dependent resource-rational compression of attribute differences explains nonlinearities in multi-attribute decision making

Why do multi-attribute choices so often depart from classical weighted-additive decision rules? Rather than attributing such deviations solely to biases or heuristics, we propose a resource-rational account in which value differences are encoded via capacity-limited information channels. Under resource-rational compression, these difference representations are systematically distorted, such that behavior deviates from weighted-additive predictions because value differences are not represented veridically. This theoretical account makes testable predictions about power-law relationships between true and internally represented differences. The amount of power-law-like compression is determined by information-processing capacity, emergent long-tailed prior distributions over attribute differences, and, in choice contexts, goal-dependent subjective weights that govern the allocation of limited capacity across attribute channels. We test and find support for these predictions in an attribute difference-estimation task and by reanalyzing existing food- and social-choice datasets. These results provide converging evidence for a normative, information-theoretic account of systematic nonlinearities in multi-attribute decision making. Together they show how goals can interact with cognitive capacity and priors to shape representational precision in ways that may facilitate or impair decision making.

neuroscience↗

bioRxiv2023

Taste matters: Mapping expectancy-based appetitive placebo effects onto the brain

Expectancies, which are higher order prognostic beliefs, can have powerful effects on experiences, behavior and brain. However, it is unknown where, how, and when, in the brain, prognostic beliefs influence appetitive interoceptive experiences and related economic behavior. This study combined a placebo intervention on hunger with computational modelling and functional magnetic resonance imaging of value-based decision-making. The results show that prognostic beliefs about hunger shape hunger experiences, how much participants value food and food-value encoding in the prefrontal cortex. Computational modelling further revealed that these placebo effects were underpinned by how much and when during the decision process taste and health information are integrated into the accumulation of evidence toward a food choice. The drift weights of both sources of information further moderated ventromedial and dorsolateral prefrontal cortex interactions during choice formation. These findings provide novel insights into the neurocognitive mechanisms that translate higher order prognostic beliefs into non-aversive interoceptive sensitivity and shape decision-making.

neuroscience↗

bioRxiv2022

A new take on model-based and model-free influences on mental effort and striatal prediction errors

A standard assumption in neuroscience is that low-effort model-free learning is automatic and continuously employed, while more complex model-based strategies are only used when the rewards they generate are worth the additional effort. We present evidence refuting this assumption. First, we demonstrate flaws in previous reports of combined model-free and model-based reward prediction errors in the ventral striatum that likely led to spurious results. More appropriate analyses yield no evidence of a model-free prediction errors in this region. Second, we find that task instructions generating more correct model-based behaviour reduce rather than increase mental effort. This is inconsistent with cost-benefit arbitration between model-based and model-free strategies. Together, our data suggest that model-free learning may not be automatic. Instead, humans can reduce mental effort by using a model-based strategy alone rather than arbitrating between multiple strategies. Our results call for re-evaluation of the assumptions in influential theories of learning and decision-making.

neuroscience↗

bioRxiv2021

Goal-Dependent Hippocampal Representations Facilitate Self-Control

Mnemonic influences on decision-making processes are important for linking past experiences and simulations of the future with current goals. The ways in which mnemonic information is represented may be especially critical in situations where one needs to overcome past rewarding experiences and exert self-control. We propose that self-control success or failure may depend on how information is retrieved from memory and how effectively this memory retrieval process can be modified to achieve a specific goal. We test our hypothesis using representational similarity analyses of human neuroimaging data during a dietary self-control task in which individuals must overcome taste temptations to choose healthy foods. We find that self-control is indeed associated with the way individuals represent taste information in the brain and how taste representations adapt to align with different goals. These results provide new insights into the processes leading to self-control success and indicate the need to update the classical view of self-control to continue to advance our understanding of its behavioral and neural underpinnings.

neuroscience↗

bioRxiv2021

Evidence Accumulates for Individual Attributes during Value-Based Decisions

When choosing between different options, we tend to consider specific attribute qualities rather than deliberating over some general sense of the options overall values. The importance of each attribute together with its quality will determine our preference rankings over the available alternatives. Here, we test the hypothesis that the most prominent class of model for simple decisions - sequential sampling or evidence accumulation to bound - can be bolstered by explicitly incorporating variables related to individual attributes in addition to the standard usage of overall value estimates. We examine six datasets in which participants evaluated snack foods both in terms of overall value and individual attributes, then chose between pairs of the same snacks, and show that only models that explicitly incorporate information about the individual attributes are able to reproduce fundamental patterns in the choice data, such as the influence of attribute disparity on decisions, and such models provide quantitatively better fits to the choice outcomes, response times, and confidence ratings compared to models based on overall value alone. Our results provide important evidence that incorporating attribute-level information into computational models helps us to better understand the cognitive processes involved in value-based decision- making.

neuroscience↗

bioRxiv2020

Intra-individual variability in task performance after cognitive training is associated with long-term outcomes in children

The benefits and mechanistic effects of working memory training in children are the subject of much research and debate. The cumulative evidence indicates that training can alter brain structure and function in the short term and have lasting effects on behaviour. We show that five weeks of working memory training led to greater activity in prefrontal and striatal brain regions, better accuracy, and reduced intra-individual variability in response times. The reduction in intra-individual variability can be explained by changes to the evidence accumulation rates and thresholds in a sequential sampling decision model. Critically, intra-individual variability was more closely associated with academic skills and mental health 6-12 months after the end of training than task accuracy. These results indicate that intra-individual variability may be a useful way to quantify the immediate impact of cognitive training interventions and predict the future emergence of academic and socioemotional skills.

neuroscience↗

bioRxiv2019

Genetic Underpinnings of Risky Behavior Relate to Altered Neuroanatomy

Previous research points to the heritability of risk-taking behaviour. However, evidence on how genetic dispositions are translated into risky behaviour is scarce. Here, we report a genetically-informed neuroimaging study of real-world risky behaviour across the domains of drinking, smoking, driving, and sexual behaviour, in a European sample from the UK Biobank (N= 12,675). We find negative associations between risky behaviour and grey matter volume (GMV) in distinct brain regions, including amygdala, ventral striatum, hypothalamus, and dorsolateral prefrontal cortex (dlPFC). These effects replicate in an independent sample recruited from the same population (N=13,004). Polygenic risk scores for risky behaviour, derived from a genome-wide association study in an independent sample (N=297,025), are inversely associated with GMV in dlPFC, putamen, and hypothalamus. This relation mediates ~2.2% of the association between genes and behaviour. Our results highlight distinct heritable neuroanatomical features as manifestations of the genetic propensity for risk taking. One Sentence SummaryRisky behaviour and its genetic associations are linked to less grey matter volume in distinct brain regions.

neuroscience↗

bioRxiv2019

Greater BOLD signal during successful emotional stimulus reappraisal is associated with better dietary self-control

We combined established emotion regulation and dietary choice tasks with fMRI to investigate behavioral and neural associations in self-regulation across the two domains in human participants. We found that increased BOLD activity during the successful reappraisal of positive and negative emotional stimuli was associated with dietary self-control success. This cross-task correlation was present in medial and lateral prefrontal cortex as well as the striatum. In contrast, BOLD activity during the food choice task was not associated with self-reported emotion regulation efficacy. These results suggest that neural processes utilized during the reappraisal of emotional stimuli may also facilitate dietary choices that override palatability in favor of healthfulness. In summary, our findings indicate that the neural systems supporting emotion reappraisal can generalize to other behavioral contexts that require reevaluation of rewarding stimuli and outcomes to promote choices that conform with the current goal.

neuroscience↗

bioRxiv2018

Dissociable mechanisms govern when and how strongly reward attributes affect decisions

Theories and computational models of decision making usually focus on how strongly different attributes are weighted in choice, e.g., as a function of their importance or salience to the decision-maker. However, when different attributes impact on the decision process is a question that has received far less attention. Here, we investigated whether attribute consideration timing has a unique influence on decision making using a time-varying drift diffusion model and data from four separate experiments. Experimental manipulations of attention and neural activity demonstrated that we can dissociate the processes that determine the relative weighting strength and timing of attribute consideration. Thus, the processes determining either the weighting strengths or the timing of attributes in decision making can adapt independently to changes in the environment or goals. Quantifying these separate influences of timing and weighting on choice improves our understanding and predictions of individual differences in decision behaviour.

neuroscience↗

bioRxiv2017

Analysis of two-stage task results: how changes in task structure affect what model-free and model-based strategies predict about the effects of reward and transition on the stay probability

Many studies that aim to detect model-free and model-based influences on behavior employ two-stage behavioral tasks of the type pioneered by Daw and colleagues in 2011. Such studies commonly modify existing two-stage decision paradigms in order to better address a given hypothesis, which is an important means of scientific progress. It is, however, critical to fully appreciate the impact of any modified or novel experimental design features on the expected results. Here, we use two concrete examples to demonstrate that relatively small changes in the two-stage task design can substantially change the pattern of actions taken by model-free and model-based agents as a function of the reward outcomes and transitions on previous trials. In the first, we show that, under specific conditions, purely model-free agents will produce the reward by transition interactions typically thought to characterize model-based behavior on a two-stage task. The second example shows that model-based agents behavior is driven by a main effect of transition-type in addition to the canonical reward by transition interaction whenever the reward probabilities of the final states do not sum to one. Together, these examples emphasize the task-dependence of model-free and model-based behavior and highlight the benefits of using computer simulations to determine what pattern of results to expect from both model-free and model-based agents performing a given two-stage decision task in order to design choice paradigms and analysis strategies best suited to the current question.

neuroscience↗

bioRxiv2017

Model-free reinforcement learning operates over information stored in working-memory to drive human choices

Model-free learning creates stimulus-response associations. But what constitutes a stimulus? Are there limits to types of stimuli a model-free or habitual system can operate over? Most experiments on reward learning in humans and animals have used discrete sensory stimuli, but there is no algorithmic reason that model-free learning should be restricted to external stimuli, and recent theories have suggested that model-free processes may operate over highly abstract concepts and goals. Our study aimed to determine whether model-free learning processes can operate over environmental states defined by information held in working memory. Specifically, we tested whether or not humans can learn explicit temporal patterns of individually uninformative cues in a model-free manner. We compared the data from human participants in a reward learning paradigm using (1) a simultaneous symbol presentation condition or (2) a sequential symbol presentation condition, wherein the same visual stimuli were presented simultaneously or as a temporal sequence that required working memory. We found a significant effect of reward on human behavior in the sequential presentation condition, indicating that model-free learning can operate on information stored in working memory. Further analyses, however, revealed that the behavior of the participants contradicts the basic assumptions of our hypotheses, and it is possible that the observed effect of reward was generated by model-based rather than model-free learning. Thus it is not possible to draw any conclusions from out study regarding model-free learning of temporal sequences held in working memory. We conclude instead that careful thought should be given about how to best explain two-stage tasks to participants.

neuroscience↗