Modelling time-order effects in haptic perception with a Bayesian dynamical framework

2026-04-21

AI Summary

This study addresses the temporal order bias in tactile perception arising from the sequence of stimulus presentation by proposing the first computational model grounded in dynamic Bayesian inference. The model conceptualizes perception as a Bayesian process that integrates noisy sensory inputs with an internal representation evolving over time. Through a combination of psychophysical experiments, parametric modeling, and geometric analysis of perceptual space, the model accurately reproduces the direction, magnitude, and inter-subject variability of observed effects using only a few parameters. A key innovation lies in revealing that perceptual space exhibits approximate symmetry under subject-specific geometric transformations, thereby transcending conventional analytical frameworks anchored to physical stimulus coordinates and offering a novel perspective on the mechanisms underlying sequential tactile perception.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Perceptual judgments of sequential stimuli are systematically biased by prior expectations and by the temporal structure of sensory input. In haptic discrimination tasks, these effects often manifest as time-order asymmetries, whereby the perceived difference between two stimuli depends on their presentation order. Here, we introduce a dynamical Bayesian model that accounts for these biases by combining noisy sensory measurements with an evolving internal representation of stimulus intensity. The model formalizes perception as an inference process in which prior expectations are updated by incoming stimuli and propagate in time between observations. We test the model on psychophysical data from vibrotactile discrimination experiments, in which participants compare pairs of sequential stimuli with varying intensities. With a small number of parameters, the model quantitatively reproduces both the direction and magnitude of time-order effects across subjects, as well as the observed inter-individual variability. The inferred parameters provide a compact description of perceptual biases in terms of prior expectations and noise characteristics. Beyond fitting the data, the model induces a transformation of stimulus space, leading to a subject-dependent geometry of perceived stimuli. In this transformed space, perceptual judgments exhibit approximate symmetries that are absent in the physical stimulus coordinates. These results suggest that temporal biases in perception can be understood as a consequence of dynamical inference, and that they impose non-trivial geometric constraints on perceptual representations.

Problem

• time-order effects
• haptic perception
• perceptual bias
• sequential stimuli
• temporal asymmetry

Innovation

• Bayesian dynamical model
• time-order effects
• haptic perception
• perceptual inference
• stimulus space geometry