Biology subjects

Rothkopf, C. A.

Publications and source records attributed to Rothkopf, C. A..

12 recordsLinked to original sources

bioRxiv2026

How to pour a cup of coffee

Pouring a drink feels deceptively trivial, yet it requires guiding a boundary-free fluid into a vessel without spilling, overflowing, or toppling it -- a task at which robots remain notoriously brittle. How humans achieve this so effortlessly is unknown, as motor control has predominantly been studied in brief, highly constrained laboratory tasks, leaving the control principles underlying ecological tasks largely unknown. Here we measured continuous sensorimotor control during liquid pouring across various containers, vessels, and speed demands. Despite substantial variation in movement trajectories and durations, individuals maintained a strikingly invariant preferred fill level. Counterintuitively, fill level variability decreased at higher fill levels, and precision was maintained even under time pressure. A stochastic optimal control model combining a data-driven nonlinear approximation of flow dynamics with a cost that balanced individualised fill level, energy expenditure and flow-rate reproduced the behaviour. Humans thus pour optimally, given their sensorimotor limits and idiosyncratic notion of "full".

neuroscience↗

bioRxiv2026

Adaptive experiments in high-dimensional feature spaces: A particle filtering approach

Behavioral experiments are often infeasible when stimulus spaces have many dimensions or when testing time is limited. One way to address this challenge is adaptive stimulus selection, where informative stimuli are chosen dynamically based on participants responses. However, in high-dimensional spaces, identifying such stimuli is computationally demanding. Here, we describe High-dimensional Online Particle Estimation (HOPE), which selects informative stimuli in less than a second for up to 50 dimensions, enabling efficient estimation of high-dimensional psychometric functions. We validate HOPE through simulations and a face-categorization experiment in an 18-dimensional parameter space with human participants. Compared to uniform stimulus presentation, HOPE reduces uncertainty over model parameters two-to three-times faster, reaching the same certainty in half the trials or fewer. This efficiency enables psychophysical studies that were previously impractical due to the exponential scaling of trial requirements.

animal behavior and cognition↗

bioRxiv2026

Tactile suppression during movement as optimal integration of somatosensory feedback across time

Goal-directed movements generate tactile signals from the moving body, yet many are perceptually suppressed. Prevailing accounts propose that the nervous system reduces tactile sensitivity by prioritizing internal predictions over somatosensory feedback, contrasting with the view that suppression reflects peripheral masking. However, both accounts lack quantitative explanations of temporal dynamics and dependence on the motor task. Here, we show that tactile suppression is a consequence of optimal state estimation during movement. Using optimal feedback control theory, we derived how the nervous system should dynamically weight uncertain internal predictions against noisy somatosensory feedback. Human participants performed goal-directed reaching movements while vibrotactile stimuli were delivered at different time points. Suppression weakened as internal uncertainty about hand distance to target increased, consistent with greater reliance on sensory feedback. Model comparison identifies tactile suppression as dynamic, uncertainty-dependent state estimation rather than peripheral masking, establishing a normative principle for tactile sensitivity during action.

neuroscience↗

bioRxiv2024

Adaptation optimizes sensory encoding of future stimuli

Sensory neurons continually adapt their response characteristics according to recent sensory input. However, it is unclear how such a reactive process shaped by sensory history can benefit the organism going forward. Here, we test the hypothesis that adaptation indeed acts proactively in the sense that it optimally adjusts sensory encoding for the future, i.e. for the next expected sensory input. We first quantified adaptation induced changes in sensory encoding by psychophysically measuring discrimination thresholds for visual orientation under different adaptation conditions. Using an information theoretic analysis, we found that adaptation consistently reallocates coding resources such that encoding accuracy peaks at the adaptor orientation while total coding capacity remains constant. We then asked whether this characteristic change in encoding accuracy is predicted by the temporal statistics of natural visual input. By analyzing the retinal input of freely behaving human subjects in natural environments, we found that the distribution of local visual orientations in the retinal input stream at any moment in time is also peaked at the mean orientation computed over a short input history leading up to that moment. We further tested our hypothesis with a recurrent neural network trained to predict the next frame of natural scene videos (PredNet). We simulated our human adaptation experiment with PredNet while analyzing its internal sensory representation. We found that the network exhibited the same change in encoding accuracy as observed in human subjects, and as predicted by the natural input statistics. Taken together, our results suggest that adaptation induced changes in encoding accuracy are an attempt of the visual systems to be best possibly prepared for future sensory input.

neuroscience↗

bioRxiv2022

A Dynamic Bayesian Actor Model explains Endpoint Variability in Homing Tasks

Goal-directed navigation requires integrating information from a variety of internal and external spatial cues, representing them internally, planning, and executing motor actions sequentially. However, a comprehensive computational account of how these processes interact in an ambiguous, uncertain, and noisy environment giving rise to biases and variability observed in navigation behavior is currently unavailable. In this paper, we introduce an optimal control under uncertainty model, which provides a computational-level explanation of how landmarks and path integration interact and are combined to reduce variability in navigation. We apply our model to trajectory and end-point data from three previously published studies that employ a variant of the triangle completion task with landmarks. Contrary to observer models, which attribute human end-point variability to perceptual cue combination processes only, this dynamic Bayesian actor model provides a unifying account of a wide range of phenomena found in this task by considering variability in perception, action, and internal representations jointly. Taken together, these findings have wide-ranging implications for the analysis and interpretation of human navigation behavior, including resolution of seemingly contradictory results on cue integration in navigation.

neuroscience↗

bioRxiv2022

A method to estimate contact regions between hands and objects during human multi-digit grasping

In order to grasp an object successfully, we must select appropriate contact regions for our hands on the surface of the object. However, identifying such regions is challenging. Here, we describe a workflow to estimate contact regions from marker-based tracking data. Participants grasp real objects, while we track the 3D position of both the objects and the hand including the fingers joints. We first determine joint Euler angles from a selection of tracked markers positioned on the back of the hand. Then, we use state-of-the-art hand mesh reconstruction algorithms to generate a mesh model of the participants hand in the current pose and 3D position. Using objects that were either 3D printed, or 3D scanned--and are thus available as both real objects and mesh data--allows us to co-register the hand and object meshes. In turn, this allows us to estimate approximate contact regions by calculating intersections between the hand mesh and the co-registered 3D object mesh. The method may be used to estimate where and how humans grasp objects under a variety of conditions. Therefore, the method could be of interest to researchers studying visual and haptic perception, motor control, human-computer interaction in virtual and augmented reality, and robotics. SUMMARYWhen we grasp an object, multiple regions of the fingers and hand typically make contact with the objects surface. Reconstructing such contact regions is challenging. Here, we present a method for approximately estimating contact regions, by combining marker-based motion capture with existing deep learning-based hand mesh reconstruction.

neuroscience↗

bioRxiv2022

The neuroeconomics of individual differences in saccadic decisions

Active exploration of the visual environment requires choosing the next gaze target, which has been characterized as depending on attentional biases and on oculomotor biases. However, disentangling these two factors has provided contradictory results. Here, we conceptualize active gaze selection as a decision-making process in the context of neuroeconomics, allowing us to employ a new saccadic preference elicitation experiment and to quantify behavioral biases with a random utility model. Subjects showed similar preferences for shorter amplitude saccades in line with previous research and shared a preference for saccades back towards the previous gaze location, contrary to numerous reports on spatial "inhibition of return". Surprisingly, the experiment revealed marked individual differences in the preferences for global gaze directions, which were stable across most participants after one week. Thus, this study provides the first measurement of intrinsic saccadic utilities independently of spatial image properties and quantitatively dissociates stable individual differences in global gaze selection.

neuroscience↗

bioRxiv2021

Putting perception into action: Inverse optimal control for continuous psychophysics

Psychophysical methods are a cornerstone of psychology, cognitive science, and neuroscience where they have been used to quantify behavior and its neural correlates for a vast range of mental phenomena. Their power derives from the combination of controlled experiments and rigorous analysis through signal detection theory. Unfortunately, they require many tedious trials and preferably highly trained participants. A recently developed approach, continuous psychophysics, promises to transform the field by abandoning the rigid trial structure involving binary responses and replacing it with continuous behavioral adjustments to dynamic stimuli. However, what has precluded wide adoption of this approach is that current analysis methods recover perceptual thresholds, which are one order of magnitude larger compared to equivalent traditional psychophysical experiments. Here we introduce a computational analysis framework for continuous psychophysics based on Bayesian inverse optimal control. We show via simulations and on previously published data that this not only recovers the perceptual thresholds but additionally estimates subjects action variability, internal behavioral costs, and subjective beliefs about the experimental stimulus dynamics. Taken together, we provide further evidence for the importance of including acting uncertainties, subjective beliefs, and, crucially, the intrinsic costs of behavior, even in experiments seemingly only investigating perception.

animal behavior and cognition↗

bioRxiv2020

Learning in the eyes: specific changes in gaze patterns track explicit and implicit visual learning

What is the link between eye movements and sensory learning? Although some theories have argued for a permanent and automatic interaction between what we know and where we look, which continuously modulates human information- gathering behavior during both implicit and explicit learning, there exist surprisingly little evidence supporting such an ongoing interaction. We used a pure form of implicit learning called visual statistical learning and manipulated the explicitness of the task to explore how learning and eye movements interact. During both implicit exploration and explicit visual learning of unknown composite visual scenes, eye movement patterns systematically changed in accordance with the underlying statistical structure of the scenes. Moreover, the degree of change was directly correlated with the amount of knowledge the observers acquired. Our results provide the first evidence for an ongoing and specific interaction between hitherto accumulated knowledge and eye movements during both implicit and explicit learning.

animal behavior and cognition↗

bioRxiv2020

Intuitive physical reasoning about objects' masses transfers to a visuomotor decision task consistent with Newtonian physics

While interacting with objects during every-day activities, e.g. when sliding a glass on a counter top, people obtain constant feedback whether they are acting in accordance with physical laws. However, classical research on intuitive physics has revealed that peoples judgements systematically deviate from predictions of Newtonian physics. Recent research has explained these deviations not as consequence of misconceptions about physics but instead as the consequence of the probabilistic interaction between inevitable perceptual uncertainties and prior beliefs. How intuitive physical reasoning relates to visuomotor actions is much less known. Here, we present an experiment in which participants had to slide pucks under the influence of naturalistic friction in a simulated virtual environment. The puck was controlled by the duration of a button press, which needed to be scaled linearly with the pucks mass and with the square-root of initial distance to reach a target. Over four phases of the experiment, uncertainties were manipulated by altering the availability of sensory feedback and providing different degrees of knowledge about the physical properties of pucks. A hierarchical Bayesian model of the visuomotor sliding task incorporating perceptual uncertainty and press-time variability found substantial evidence that subjects adjusted their button-presses so that the sliding was in accordance with Newtonian physics. After observing collisions between pucks, subjects transferred the relative masses inferred perceptually to adjust subsequent sliding actions. Crucial in the modeling was the inclusion of a cost function, which quantitatively captures participants implicit sensitivity to errors due to their motor variability. Taken together, in the present experiment we find evidence that our participants transferred their intuitive physical reasoning to a subsequent visuomotor control task in accordance with Newtonian physics and weigh potential outcomes with cost functions based on their knowledge about their own variability. Author summaryDuring our daily lives we interact with objects around us governed by Newtonian physics. While people are known to show multiple systematic errors when reasoning about Newtonian physics, recent research has provided evidence that this is the consequence of perceptual uncertainties and partial knowledge about object properties. Here, we carried out an experiment to investigate whether people transfer their intuitive physical reasoning to how they interact with objects. Using a simulated virtual environment in which participants had to slide different pucks into a target region by the length of a button press, we found evidence that they could do so in accordance with the underlying physical laws. Moreover, our participants watched movies of colliding pucks and subsequently transferred their beliefs about the relative masses of the observed pucks to the sliding task. Remarkably, this transfer was consistent with Newtonian physics and could well be explained by a computational model that takes participants perceptual uncertainty, action variability, and preferences into account.

animal behavior and cognition↗

bioRxiv2018

Modeling Sensory-Motor Decisions in Natural Behavior

Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses these factors. Based on this model, a modular inverse reinforcement learning algorithm is developed to estimate both the rewards and discount factors from human behavioral data, which allows predictions of human navigation behaviors in virtual reality with high accuracy across different subjects and with different tasks. Complex human navigation trajectories in novel environments can be reproduced by an artificial agent that is based on the modular model. This model provides a strategy for estimating the subjective value of actions and how they influence sensory-motor decisions in natural behavior.\n\nAuthor summaryIt is generally agreed that human actions can be formalized within the framework of statistical decision theory, which specifies a cost function for actions choices, and that the intrinsic value of actions is controlled by the brains dopaminergic reward machinery. Given behavioral data, the underlying subjective reward value for an action can be estimated through a machine learning technique called inverse reinforcement learning. Hence it is an attractive method for studying human reward-seeking behaviors. Standard reinforcement learning methods were developed for artificial intelligence agents, and incur too much computation to be a viable model for real-time human decision making. We propose an approach called modular reinforcement learning that decomposes a complex task into independent decision modules. This model includes a frequently overlooked variable called the discount factor, which controls the degree of impulsiveness in seeking future reward. We develop an algorithm called modular inverse reinforcement learning that estimates both the reward and the discount factor. We show that modular reinforcement learning may be a useful model for natural navigation behaviors. The estimated rewards and discount factors explain human walking direction decisions in a virtual-reality environment, and can be used to train an artificial agent that can accurately reproduce human navigation trajectories.

neuroscience↗

bioRxiv2017

Optimal planning of eye movements

The capability of directing gaze to relevant parts in the environment is crucial for our survival. Computational models based on ideal-observer theory have provided quantitative accounts of human gaze selection in a range of visual search tasks. According to these models, gaze is directed to the position in a visual scene, at which uncertainty about task relevant properties will be reduced maximally with the next look. However, in tasks going beyond a single action, delayed rewards can play a crucial role thereby necessitating planning. Here we investigate whether humans are capable of planning more than the next single eye movement. We found evidence that our subjects behavior was better explained by an ideal planner compared to the ideal observer. In particular, the location of the first fixation differed depending on the stimulus and the time available for the search. Overall, our results are the first evidence that our visual system is capable of planning.

animal behavior and cognition↗