Biology subjects

Reinagel, P.

Publications and source records attributed to Reinagel, P..

7 recordsLinked to original sources

bioRxiv2026

Quantifying uncertainty in drift diffusion models of decision making under temporal dependence and parameter variability

Decision-making behavior changes over time, exhibiting temporal correlation and nonstationarity. Existing drift diffusion model (DDM) fitting methods either do not provide uncertainty quantification for parameter estimates, or rely on restrictive assumptions that decisions are independent and that parameters remain constant over time, potentially underestimating uncertainty. To address these limitations, we propose a computationally efficient method for estimating analytic uncertainties in DDM parameters that are robust to temporal dependence and unmodeled parameter variability, while explicitly modeling nonstationary variability through covariates. We apply this method to rat decision-making in a two-alternative forced-choice (2AFC) visual task, revealing dynamic decision-making states across multiple timescales. A Python implementation of the method is provided.

animal behavior and cognition↗

bioRxiv2021

Some rats take more time to make easier perceptual decisions

When subjects control the duration of sampling a sensory stimulus before making a decision, they generally take more time to make more difficult sensory discriminations. This has been found to be true of many rats performing visual tasks. But two rats performing visual motion discrimination were found to have inverted chronometric response functions: their average response time paradoxically increased with stimulus strength. We hypothesize that corrective decision reversals may underlie this unexpected observation.

neuroscience↗

bioRxiv2021

When is it Worth Working for Water? A Utility Maximization Theory

In the laboratory, animals motivation to work tends to be positively correlated with reward magnitude. But in nature, rewards earned by work are essential to survival (e.g., working to find water), and the payoff of that work can vary on long timescales (e.g., seasonally). Under these constraints, the strategy of working less when rewards are small could be fatal. We found that instead, rats in a closed economy did more work for water rewards when the rewards were stably smaller, a phenomenon also observed in human labor supply curves. Like human consumers, rats also showed elasticity of demand, consuming far more water per day when its price in effort was lower. The neural mechanisms underlying such "rational" market behaviors remain largely unexplored. We propose a dynamic utility maximization model that can account for the dependence of rat labor supply (trials/day) on the wage rate (ml/trial), and also predict the temporal dynamics of when rats work. Based on data from mice, we hypothesize that SFOGLUT neurons in lamina terminalis continuously compute the instantaneous marginal utility of voluntary work for water reward, and causally determine the amount and timing of work.

animal behavior and cognition↗

bioRxiv2020

A qualitative difference in decision-making of rats vs. humans explained by quantitative differences in behavioral variability

When observers make rapid, difficult perceptual decisions, their response time is highly variable from trial to trial. In a visual motion discrimination task, it has been reported that human accuracy declines with increasing response time, whereas rat accuracy increases with response time. This is of interest because different mathematical theories of decision-making differ in their predictions regarding the correlation of accuracy with response time. On the premise that perceptual decision-making mechanisms are likely to be conserved among mammals, we seek to unify the rodent and primate results in a common theoretical framework. We show that a bounded drift diffusion model (DDM) can explain both effects with variable parameters: trial-to-trial variability in the starting point of the diffusion process produces the pattern typically observed in rats, whereas variability in the drift rate produces the pattern typically observed in humans. We further show that the same effects can be produced by deterministic biases, even in the absence of parameter stochasticity or parameter change within a trial.

neuroscience↗

bioRxiv2019

Is N-Hacking Ever OK? A Simulation-based study

After an experiment has been completed, a trend may be observed that is "not quite significant". Sometimes in this situation, researchers collect more data in an effort to achieve statistical significance. Such "N-hacking" is condemned because it can lead to an excess of false positive results. I use simulations to demonstrate how N-hacking causes false positives. However, in a parameter regime relevant for many experiments, the increase in false positives is quite modest. Moreover, results obtained this way have higher Positive Predictive Value than non-incremented experiments of the same sample size and statistical power. In other words, adding a few more observations to shore up a nearly-significant result can increase the reproducibility of results, counter to some current rhetoric. Many experiments are non-confirmatory, and unplanned sample augmentation with reasonable decision rules would not cause rampant irreproducibility in that context.

scientific communication and education↗

bioRxiv2019

The interaction between elapsed time and decision accuracy differs between humans and rats performing the same visual motion discrimination task

A stochastic visual motion discrimination task is widely used to study rapid decision-making in humans and animals. Among trials of the same sensory difficulty within a block of fixed decision strategy, humans and monkeys are widely reported to make more errors in the individual trials with longer reaction times. This finding has posed a challenge for the drift-diffusion model of sensory decision-making, which in its basic form predicts that errors and correct responses should have the same reaction time distributions. We previously reported that rats also violate this model prediction, but in the opposite direction: for rats, motion discrimination accuracy was highest in the trials with the longest reaction times. To rule out task differences as the cause of our divergent finding in rats, the present study tested humans and rats using the same task and analyzed their data identically. We confirmed that rats accuracy increased with reaction time, whereas humans accuracy decreased with reaction time in the same task. These results were further verified using a new temporally-local analysis method, ruling out that the observed trend was an artifact of non-stationarity in the data of either species. The main effect was found whether the signal strength (motion coherence) was varied in randomly interleaved trials or held constant within a block. The magnitude of the effects increased with motion coherence. These results provide new constraints useful for refining and discriminating among the many alternative mathematical theories of decision-making.

animal behavior and cognition↗

bioRxiv2018

A Sparse Unreliable Distributed Code Underlies the Limits of Behavioral Discrimination

The cortical code that underlies perception must enable subjects to perceive the world at timescales relevant for behavior. We find that mice can integrate visual stimuli very quickly (<100 ms) to reach plateau performance in an orientation discrimination task. To define features of cortical activity that underlie performance at these timescales, we measured single unit responses in the mouse visual cortex at timescales relevant to this task. In contrast to high contrast stimuli of longer duration, which elicit reliable activity in individual neurons, stimuli at the threshold of perception elicit extremely sparse and unreliable responses in V1 such that the activity of individual neurons do not reliably report orientation. Integrating information across neurons, however, quickly improves performance. Using a linear decoding model, we estimate that integrating information over 50-100 neurons is sufficient to account for behavioral performance. Thus, at the limits of perception the visual system is able to integrate information across a relatively small number of highly unreliable single units to generate reliable behavior.

neuroscience↗