Biology subjects

Maloney, L. T.

Publications and source records attributed to Maloney, L. T..

4 recordsLinked to original sources

bioRxiv2026

Allocating resources across time in a cumulative allocation game

We investigate an alternative to decision under risk where players begin with an endowment of wealth, and their goal is to increase this wealth as rapidly as possible by repeatedly investing part of it. In such a Cumulative Allocation Game (CAG), expected wealth can grow exponentially but - if wealth ever goes to zero - the game is over and the participant wins nothing (ruin). CAGs can be used to model a wide range of biological and economic activity from casino gambling to animal foraging to stock market investment (Kelly Criterion). However, there is little previous research concerning human performance in CAGs and how it relates to human performance in similar decision-under-risk tasks. In two experiments we asked participants to complete four CAGs designed to allow us to test a series of hypotheses about human performance and compare it to normative play maximizing their log rate of growth. In the main experimental conditions, participants performance was initially close to normative but their proportion bet decreased by almost a factor of two over the course of the experiment. This reduction in proportion-bet decreased their expected rate of growth by roughly 25% - a substantial deviation from optimal. Why would participants systematically abandon a normative strategy? We conjecture that a possible reason is loss aversion, well documented in decision under risk. We conjecture that while loss aversion in decision under risk is sub-optimal, there is an intrinsic asymmetry between losses and gains in CAGs that could explain the loss aversion we observe.

animal behavior and cognition↗

bioRxiv2026

Dynamic distortion of inferred reward probability shapes choice over time

Many choices are triggered by discrete events whose timing determines which options are rewarded. Without informative sensory evidence between events, behavior must rely on internal estimates of latent variables--most notably elapsed time and reward probability. Existing computational frameworks, including evidence-accumulation models, are not designed for this regime, leaving the principles of time-dependent choice unresolved. Here, we formalize choice as an inference problem governed by uncertainty about both elapsed time and reward over time. Participants learned dynamic reward probabilities to guide choices. Behavior approached optimality but exhibited a systematic distortion of inferred reward probability over time, captured by a linear transformation in log-odds space. Crucially, temporal uncertainty was modulated by reward probability but not by elapsed time itself, contradicting Weber-law scaling. These results identify two interacting computational principles-dynamic mapping of reward probability to choice and reward-based temporal precision- that jointly shape behavior when time and reward must be inferred.

neuroscience↗

bioRxiv2019

The bounded rationality of probability distortion: the bounded log-odds model

AO_SCPLOWBSTRACTC_SCPLOWIn decision-making under risk (DMR) participants choices are based on probability values systematically different from those that are objectively correct. Similar systematic distortions are found in tasks involving relative frequency judgments (JRF). These distortions limit performance in a wide variety of tasks and an evident question is, why do we systematically fail in our use of probability and relative frequency information? We propose a Bounded Log-Odds Model (BLO) of probability and relative frequency distortion based on three assumptions: (1) log-odds: probability and relative frequency are mapped to an internal log-odds scale, (2) boundedness: the range of representations of probability and relative frequency are bounded and the bounds change dynamically with task, and (3) variance compensation: the mapping compensates in part for uncertainty in probability and relative frequency values. We compared human performance in both DMR and JRF tasks to the predictions of the BLO model as well as eleven alternative models each missing one or more of the underlying BLO assumptions (factorial model comparison). The BLO model and its assumptions proved to be superior to any of the alternatives. In a separate analysis, we found that BLO accounts for individual participants data better than any previous model in the DMR literature. We also found that, subject to the boundedness limitation, participants choice of distortion approximately maximized the mutual information between objective task-relevant values and internal values, a form of bounded rationality. SO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWPeople distort probability in decision under risk and many other tasks. These distortions can be large, leading us to make markedly suboptimal decisions. There is no agreement on why we distort probability. Distortion changes systematically with task, hinting that distortions are dynamic compensations for some intrinsic "bound" on working memory. We first develop a model of the bound and the compensation process and then report an experiment showing that the model accounts for individual human performance in decision under risk and relative frequency judgments. Last, we show that the particular compensation in each experimental condition serve to maximize the mutual information between objective decision variables and their internal representations. We distort probability to compensate for our own working memory limitations.

neuroscience↗

bioRxiv2019

The Anticipation of Events in Time

Humans use sensory input to anticipate events. The brains capacity to predict cues in time is commonly assumed to be modulated by two uncertainty parameters, the hazard rate (HR) of event probability and the uncertainty in time estimation, which increases with elapsed time. We investigate both assumptions by manipulating event probability density functions (PDF) in three sensory modalities. First we show, contrary to expectation, that perceptual systems use the reciprocal PDF - and not the HR - to model event probability density. Next we demonstrate that temporal uncertainty does not necessarily grow with elapsed time but also diminishes, depending on the event PDF. Finally we show that reaction time (RT) distributions comprise modality-specific and modality-independent components, the latter likely reflecting similarity in processing of probability density across sensory modalities. The results are consistent across vision, audition, and somatosensation, indicating that probability density is more fundamental than hazard rate in terms of the neural operations determining event anticipation and temporal uncertainty. Previous research identified neuronal activitity related to event probability in multiple levels of the cortical hierarchy such as early and higher sensory (V1, V4), association (LIP), motor and other areas. This work proposed that the elementary neuronal computation in estimation of probability across time is the HR. In contrast, our results suggest that the neurobiological implementation of probability estimation is based on a different, much simpler and more stable computation than HR: the reciprocal PDF of events in time.

neuroscience↗