Biology subjects

Schrater, P.

Publications and source records attributed to Schrater, P..

5 recordsLinked to original sources

bioRxiv2026

Rationalizing sensorimotor deficits in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by socio-communicative, sensory, and motor anomalies. Recently, a host of probabilistic models have been proposed to account for the varied phenotypes observed in ASD. These computational psychiatry efforts, however, have (1) largely focused on simple tasks that artificially segregate action from perception, and (2) unequivocally assumed that both neurotypical and divergent populations attempt to optimize the same cost function. Here, we ask individuals with ASD and neurotypical (NT) controls to perform a task in virtual reality (VR): they should navigate to, and stop at, the position of briefly presented targets in a closed action-perception loop. First, we demonstrate that while summary task performance is insufficient to distinguish between ASD and NT individuals, data-rich virtual trajectories do differentiate these groups. This suggests that ASD and control participants use distinct navigational strategies and/or cost functions. Next, we leverage the theoretical framework of Inverse Rational Control (IRC) to interpret the sensorimotor anomalies present in ASD. IRC does not assume that agents are optimal, but rational: that is, agents attempt to maximize reward according to an internal model of the task, which may not match the true task and environment. Applying IRC to this VR navigation task revealed both sensory and motor differences between ASD and neurotypical participants. The sensory differences are consistent with Bayesian inference under altered weighting of sensory evidence. The motor differences manifest as more frequent and abrupt actions in ASD, which we explain by a reduced subjective action cost in ASD. Together, these results demonstrate the utility of model-based characterization of psychopathology and suggest that the computational study of ASD should consider not only perceptual anomalies, but also subjective costs.

neuroscience↗

bioRxiv2025

An integrative computational approach for obstacle avoidance during action selection

Action selection in cluttered environments, where individuals must simultaneously pursue goals and avoid obstacles, presents a significant challenge for the brain. To understand the underlying mechanisms of action selection in such contexts, we propose a computational model that extends stochastic optimal control theory through a novel framework for obstacle avoidance. The model decomposes action selection as a weighted combination of individual control policies, each generated for either target approach or obstacle avoidance. By integrating value information from goals, obstacles, and actions into a unified measure of "relative desirability", the model dynamically determines the contribution of each policy to the overall action selection process. We evaluated the framework using simulated target-reaching tasks in cluttered environments based on previous human studies. The results showed that the model captures key features of human motor behavior, including the influence of obstacle properties on movement trajectories and the transient tendency to initiate movements toward obstacles before avoidance. This work offers new insights into the dynamic interaction between approach and avoidance behaviors, providing a comprehensive framework for understanding action selection in complex and naturalistic settings. Author SummaryEvery day, we make countless movements that require us to reach for something while avoiding obstacles--for example, grabbing a cup without knocking over nearby objects. How the brain selects such actions in cluttered environments remains poorly understood. In this study, we developed a computational model that explains how people plan and control movements when both goals and obstacles are present. Our model assumes that the brain prepares multiple potential actions at once--some for reaching the goal and others for avoiding obstacles--and then combines them based on how desirable each option is at any moment. Using computer simulations of reaching movements, we found that the model reproduced key patterns observed in human behavior, including the brief tendency to move toward an obstacle before steering away from it. By showing how the brain might continuously balance approach and avoidance drives, our work offers a new way to understand how people make rapid and flexible movement decisions in complex, everyday settings.

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bioRxiv2021

A mixture of generative models strategy helps humans generalize across tasks

What role do generative models play in generalization of learning in humans? Our novel multi-task prediction paradigm--where participants complete four sequence learning tasks, each being a different instance of a common generative family--allows the separate study of within-task learning (i.e., finding the solution to each of the tasks), and across-task learning (i.e., learning a task differently because of past experiences). The very first responses participants make in each task are not yet affected by within-task learning and thus reflect their priors. Our results show that these priors change across successive tasks, increasingly resembling the underlying generative family. We conceptualize multi-task learning as arising from a mixture-of-generative-models learning strategy, whereby participants simultaneously entertain multiple candidate models which compete against each other to explain the experienced sequences. This framework predicts specific error patterns, as well as a gating mechanism for learning, both of which are observed in the data.

neuroscience↗

bioRxiv2019

Population coding of strategic variables during foraging in freely-moving macaques

Until now it has been difficult to examine the neural bases of foraging in naturalistic environments because previous approaches have relied on restrained animals performing trial-based foraging tasks. Here, we allowed unrestrained monkeys to freely interact with concurrent reward options while we wirelessly recorded population activity in dorsolateral prefrontal cortex (dlPFC). The animals decided when and where to forage, based on whether their prediction of reward was fulfilled or violated. This prediction was not solely based on a history of reward delivery, but also on the understanding that waiting longer improves the chance of reward. The decoded reward dynamics were continuously represented in a subspace of the high-dimensional population activity, and predicted animals subsequent choice better than the true experimental variables and as well as the raw neural activity. Our results indicate that monkeys foraging strategy is based on a cortical model of reward dynamics as animals freely explore their environment.

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bioRxiv2017

What if you are not certain? A common computation underlying action selection, reaction time and confidence judgment

From what to wear to a friends party, to whether to stay in academia or pursue a career in industry, nearly all of our decisions are accompanied by a degree of confidence that provides an assessment of the expected outcome. Although significant progress has been made in understanding the computations underlying confidence judgment, the preponderance of studies focuses on perceptual decisions, in which individuals sequentially sample noisy information and accumulate it as evidence until a threshold is exceeded. Once a decision is made, they initiate an action to implement the choice. However, we often have to make decisions during ongoing actions in dynamic environments where the value and the availability of the alternative options can change with time and previous actions. The current study aims to decipher the computations underlying confidence judgment in action decisions that are made in a dynamic environment. Using a reaching task in which movements are initiated to multiple potential targets, we show that action selection, reaction time and choice confidence all emerge from a common computation in which parallel prepared actions compete based on the overall desirability of targets and action plans.

neuroscience↗