Constantin A. Rothkopf

Disentangling visual and motor contributions to continuous psychophysics

This study disentangles visual and motor contributions to tracking performance in continuous psychophysics by analyzing cross-correlogram metrics under visual impairment and hand dominance conditions.

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

This study shows tactile suppression during movement results from optimal state estimation, where the nervous system dynamically weights internal predictions against sensory feedback based on uncertainty.

How to pour a cup of coffee

This study investigates how humans pour liquids into containers, finding that fill level remains consistent despite varying movement patterns and conditions.

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

This paper introduces HOPE, a method for efficiently estimating high-dimensional psychometric functions by adaptively selecting stimuli, reducing uncertainty faster than traditional methods.

Adaptation optimizes sensory encoding of future stimuli

The paper shows that sensory adaptation optimally reallocates coding resources to maximize encoding accuracy for future expected stimuli based on natural input statistics.

Intuitive sensorimotor decisions under risk take Newtonian physics into account

The paper shows humans integrate sensorimotor variability, Newtonian physics, and risk decision-making to optimize rewards in a virtual puck-sliding task.

A Dynamic Bayesian Actor Model explains Endpoint Variability in Homing Tasks

This paper introduces a dynamic Bayesian actor model to explain endpoint variability in homing tasks by integrating perception, action, and internal representations.

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

This paper presents a method to estimate contact regions between hands and objects during grasping using motion capture and hand mesh reconstruction.

The neuroeconomics of individual differences in saccadic decisions

This study measures intrinsic saccadic utilities and dissociates stable individual differences in global gaze selection from spatial image properties using a neuroeconomic framework.

Modeling Sensory-Motor Decisions in Natural Behavior

The paper proposes a modular reinforcement learning model to estimate rewards and discount factors from human behavior data, enabling accurate prediction of navigation decisions in virtual reality.