Biology subjects

Prat-Carrabin, A.

Publications and source records attributed to Prat-Carrabin, A..

9 recordsLinked to original sources

bioRxiv2026

Efficient coding makes and breaks Webers law

Webers law is a rare quantitative regularity in psychology, yet its origins remain debated. Here we provide causal evidence that it arises from the more fundamental principle of efficient coding. This principle posits that representational resources are allocated according to stimulus frequencies: distributions skewed toward smaller stimuli thus result in discriminability decreasing with magnitude, as in Webers law. Skewing frequencies in the other direction--making large magnitudes more frequent than small ones--enabled us to invert this pattern, and to break Webers law. In discrimination tasks with three different sensory modalities, human subjects discriminability across stimuli was sensitive to the stimulus distribution, and this adaptation improved task performance. These findings establish efficient coding as a dynamic, organizing principle, explaining when and why Webers law holds.

neuroscience↗

bioRxiv2025

Distributed range adaptation in human parietal encoding of numbers

The brain's representations are encoded in the collective activity of neural populations, whose non-monotonic tuning properties define the population code. Adaptive behavior calls for flexible retuning of this code as stimuli statistics change across contexts. Yet how such adaptation occurs is unknown. Using fMRI during a numerosity-estimation task with variable number ranges, we show that number representations in human parietal cortex dynamically recalibrate to context, enabling context-sensitive behavioral accuracy. The tuning properties of neural populations collectively shift and rescale with the range. This distributed range adaptation achieves efficient coding in real time: neural precision scales with the range and predicts corresponding changes in behavioral precision. Individuals with stronger neural adaptation show larger behavioral adjustments. These findings extend static sensory efficient coding to adaptive representations of abstract magnitudes. Such flexible population tuning may constitute a canonical mechanism of encoding networks that enables the brain to sustain precise, adaptive behavior.

neuroscience↗

bioRxiv2025

Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks

As the statistics of sensory environments often change, neural sensory systems must adapt to maintain useful representations. Efficient coding prescribes that neuronal tuning curves should be optimized to the prior, but whether they can adapt rapidly is unclear. Empirically, tuning curves after repeated stimulus presentations exhibit "adapter repulsion", whose underlying mechanism remains uncertain, and which contrasts with the "prior attraction" expected under many efficient-coding models. We propose a gain-adaptive, recurrent sensory network model in which gains optimize a novel efficient-coding objective balancing accuracy and spiking cost. From the propagation of modulated gains throughout the network emerge quickly adaptive tuning curves. The model accounts for subtle adapter-repulsion effects under peaked priors and predicts fast prior attraction under broader distributions, for which we provide supporting behavioral evidence. Our framework reconciles seemingly contradictory adaptive phenomena, under a unified theoretical and mechanistic model of efficient coding mediated by gain modulation in recurrent circuits.

neuroscience↗

bioRxiv2024

Imprecise counting of observations in averaging tasks predicts primacy and recency effects

Primacy and recency effects -- wherein early and recent observations exert disproportionate influence on judgments -- have long been noted in cognitive tasks involving the sequential presentation of information. In studies where human subjects make decisions based on the average of a sequence of numbers, recency effects are typically modeled phenomenologically through exponential discounting, while primacy effects are neglected altogether. Here, we exhibit the prevalence of both effects in such tasks, and propose that they result from the observers imprecision in their running tally of how many pieces of information they have received. If their approximate counting follows a central tendency -- a typical Bayesian pattern -- then past information is overweighted near the beginning of the sequence, while new numbers are overweighted towards the end of the sequence. Thus both primacy and recency effects are simultaneously predicted by this single mechanism. The model moreover nests exponential discounting as a special case in which the observer has no information about the count. The behavioral data suggests that subjects indeed misestimate the count of observations, with biases similar to those observed in numerosity-estimation tasks. Finally, we present evidence that the central tendency of subjects shifts towards lower counts in tasks with shorter sequence lengths, consistent with a Bayesian estimation of the counts. These findings provide new insights into the cognitive processes underlying serial-position effects in averaging tasks, with broader implications for other cognitive domains.

animal behavior and cognition↗

bioRxiv2024

Bayes vs. Weber: how to break a law of psychophysics

A classic result of psychophysics is that human perceptual estimates are more variable for larger magnitudes. This Weber behavior, however, has typically not been the focus of the prominent Bayesian paradigm. Here we examine the variability of a Bayesian observer, in comparison with human subjects. In two preregistered experiments, we manipulate the prior distribution and the reward function in a numerosity-estimation task. When large numerosities are more frequent or more rewarding, the Bayesian observer exhibits an anti-Weber behavior, in which larger magnitudes yield less variable responses. Human subjects exhibit a similar pattern, thus breaking a long-standing result of psychophysics. Nevertheless, subjects responses are best reproduced by a logarithmic encoding of magnitudes, a proposal of Fechner often regarded as accounting for Weber behavior. We thus obtain an anti-Weber behavior together with a Fechner encoding. Our results suggest that the increasing variability may be primarily due to the skewness of natural priors.

neuroscience↗

bioRxiv2024

Endogenous Precision of the Number Sense

The behavioral variability in psychophysical experiments and the stochasticity of sensory neurons have revealed the inherent imprecision in the brains representations of environmental variables1-6. Numerosity studies yield similar results, pointing to an imprecise number sense in the brain7-13. If the imprecision in representations reflects an optimal allocation of limited cognitive resources, as suggested by efficient-coding models14-26, then it should depend on the context in which representations are elicited25,27. Through an estimation task and a discrimination task, both involving numerosities, we show that the scale of subjects imprecision increases, but sublinearly, with the width of the prior distribution from which numbers are sampled. This sublinear relation is notably different in the two tasks. The double dependence of the imprecision -- both on the prior and on the task -- is consistent with the optimization of a tradeoff between the expected reward, different for each task, and a resource cost of the encoding neurons activity. Comparing the two tasks allows us to clarify the form of the resource constraint. Our results suggest that perceptual noise is endogenously determined, and that the precision of percepts varies both with the context in which they are elicited, and with the observers objective.

animal behavior and cognition↗

bioRxiv2022

Resource-Rational Account of Sequential Effects in Human Prediction

An abundant literature reports on sequential effects observed when humans make predictions on the basis of stochastic sequences of stimuli. Such sequential effects represent departures from an optimal, Bayesian process. A prominent explanation posits that humans are adapted to changing environments, and erroneously assume nonstationarity of the environment, even if the latter is static. As a result, their predictions fluctuate over time. We propose a different explanation in which sub-optimal and fluctuating predictions result from cognitive constraints (or costs), under which humans however behave rationally. We devise a framework of costly inference, in which we develop two classes of models that differ by the nature of the constraints at play: in one case the precision of beliefs comes at a cost, resulting in an exponential forgetting of past observations, while in the other beliefs with high predictive power are favored. To compare model predictions to human behavior, we carry out a prediction task that uses binary random stimuli, with probabilities ranging from .05 to .95. Although in this task the environment is static and the Bayesian belief converges, subjects predictions fluctuate and are biased toward the recent stimulus history. Both classes of models capture this attractive effect, but they depart in their characterization of higher-order effects. Only the precision-cost model reproduces a repulsive effect, observed in the data, in which predictions are biased away from stimuli presented in more distant trials. Our experimental results reveal systematic modulations in sequential effects, which our theoretical approach accounts for in terms of rationality under cognitive constraints.

animal behavior and cognition↗

bioRxiv2020

Efficient coding of numbers explains decision bias and noise

Human subjects differentially weight different stimuli in averaging tasks, which has been interpreted as reflecting encoding bias. We examine the alternative hypothesis that stimuli are encoded with noise, then optimally decoded. Under a model of efficient coding, the amount of noise should vary across stimuli, and depend on statistics of the stimuli. We investigate these predictions through a task in which participants are asked to compare the averages of two series of numbers, each sampled from a prior distribution that varies across blocks of trials. Subjects encode numbers with a bias and a noise that both depend on the number. Infrequently occurring numbers are encoded with more noise. We show how an efficient-coding, Bayesian-decoding model accounts for these patterns, and best captures subjects behaviour. Finally, our results suggest that Wei and Stockers "law of human perception", which relates the bias and variability of sensory estimates, also applies to number cognition.

animal behavior and cognition↗

bioRxiv2019

Human inference in changing environments

To make informed decisions in natural environments that change over time, humans must update their beliefs as new observations are gathered. Studies exploring human inference as a dynamical process that unfolds in time have focused on situations in which the statistics of observations are history-independent. Yet temporal structure is everywhere in nature, and yields history-dependent observations. Do humans modify their inference processes depending on the latent temporal statistics of their observations? We investigate this question experimentally and theoretically using a change-point inference task. We show that humans adapt their inference process to fine aspects of the temporal structure in the statistics of stimuli. As such, humans behave qualitatively in a Bayesian fashion, but, quantitatively, deviate away from optimality. Perhaps more importantly, humans behave suboptimally in that their responses are not deterministic, but variable. We show that this variability itself is modulated by the temporal statistics of stimuli. To elucidate the cognitive algorithm that yields this behavior, we investigate a broad array of existing and new models that characterize different sources of suboptimal deviations away from Bayesian inference. While models with output noise that corrupts the response-selection process are natural candidates, human behavior is best described by sampling-based inference models, in which the main ingredient is a compressed approximation of the posterior, represented through a modest set of random samples and updated over time. This result comes to complement a growing literature on sample-based representation and learning in humans.

neuroscience↗