bayesian-haptic-perception-dynamics

name	bayesian-haptic-perception-dynamics
description	Bayesian dynamical framework for modeling time-order effects in sequential haptic perception. Captures perceptual biases from prior expectations and temporal structure using drift-diffusion dynamics. Activation: haptic perception, Bayesian dynamics, time-order effects, sequential stimuli, perceptual bias.

Bayesian Dynamical Framework for Haptic Perception

Modeling time-order asymmetries and sequential biases in haptic discrimination using Bayesian drift-diffusion dynamics.

Metadata

• Source: arXiv:2604.19662v1
• Authors: Gastón Avetta, Jose Lobera, Juan José Zárate, et al.
• Published: 2026-04-21
• Category: q-bio.NC (Neurons and Cognition)

Core Methodology

The Time-Order Effect Problem

Perceptual judgments of sequential stimuli show systematic biases:

• Primacy Effect: First stimulus dominates judgment
• Recency Effect: Last stimulus dominates judgment
• Contrast Effects: Perceived differences depend on presentation order

This methodology models these effects using Bayesian drift-diffusion dynamics.

Bayesian Dynamical Framework

1. Prior Expectations

   • Encode prior beliefs about stimulus statistics
   • Model as Gaussian or mixture distributions
   • Update dynamically based on sequential observations

2. Sequential Evidence Accumulation

   • Use drift-diffusion model (DDM) for each stimulus
   • Allow drift rate to depend on prior and previous stimulus
   • Incorporate time-varying decision boundaries

3. Temporal Structure Modeling

   • Inter-stimulus interval (ISI) affects integration
   • Memory decay between stimuli
   • Attentional modulation over time

4. Decision Dynamics

   • Bayesian posterior computation
   • Comparison operation with order-dependent weights
   • Response generation with motor execution noise

Key Innovations

• Dynamical Prior: Prior beliefs evolve based on stimulus sequence
• Order-Dependent Weights: Different weights for first vs second stimulus
• Temporal Integration Window: Optimal time window for evidence accumulation

Implementation Guide

Prerequisites

• Bayesian inference libraries (PyMC, Stan)
• Drift-diffusion model toolboxes (HDDM, PyDDM)
• Signal processing tools
• Statistical analysis packages

Step-by-Step

1. Define Prior Distribution

   • Set prior mean (e.g., 5.0 for neutral reference)
   • Set prior standard deviation (uncertainty)

2. Model Evidence Accumulation

   • Compute drift rate for first stimulus
   • Update posterior after first stimulus
   • Compute drift rate for second stimulus

3. Compute Time-Order Asymmetry

   • Compare forward vs backward sequences
   • Measure decision differences

4. Fit to Behavioral Data

   • Use Bayesian parameter estimation
   • MCMC sampling of model parameters

Code Example

import numpy as np
from scipy.stats import norm

class BayesianHapticPerception:
    """
    Bayesian dynamical model of sequential haptic perception.
    """
    
    def __init__(self, prior_mean=5.0, prior_std=2.0, 
                 drift_rate=0.1, diffusion_noise=1.0,
                 time_step=0.01, max_time=5.0):
        self.prior_mean = prior_mean
        self.prior_std = prior_std
        self.drift_rate = drift_rate
        self.diffusion_noise = diffusion_noise
        self.dt = time_step
        self.max_time = max_time
        
    def compute_posterior(self, stimulus, prior_mean, prior_std,
                         likelihood_std=1.0):
        """
        Compute posterior after observing a stimulus.
        
        Uses Bayesian updating with Gaussian conjugate prior.
        """
        # Bayesian update for Gaussian conjugate prior
        prior_precision = 1.0 / (prior_std ** 2)
        likelihood_precision = 1.0 / (likelihood_std ** 2)
        
        posterior_precision = prior_precision + likelihood_precision
        posterior_mean = (prior_precision * prior_mean + 
                         likelihood_precision * stimulus) / posterior_precision
        posterior_std = np.sqrt(1.0 / posterior_precision)
        
        return posterior_mean, posterior_std
    
    def drift_diffusion_trial(self, drift, boundary=):
        
        evidence = 
        evidence_trace = [evidence]
        time = 
        
         (evidence) < boundary  time < .max_time:
            
            evidence += drift * .dt + np.random.normal(, 
                          np.sqrt(.dt) * .diffusion_noise)
            evidence_trace.append(evidence)
            time += .dt
        
        choice =   evidence >= boundary  -
         time, choice, np.array(evidence_trace)
    
     ():
        
        
        drift_1 = .drift_rate * (stimulus_1 - .prior_mean)
        dt_1, _, _ = .drift_diffusion_trial(drift_1)
        
        
        post_mean, post_std = .compute_posterior(
            stimulus_1, .prior_mean, .prior_std
        )
        
        
        decay_factor = np.exp(-isi / )  
        effective_prior_mean = post_mean * decay_factor + \
                               .prior_mean * ( - decay_factor)
        effective_prior_std = np.sqrt(
            (post_std * decay_factor)** + 
            (.prior_std * ( - decay_factor))**
        )
        
        
        drift_2 = .drift_rate * (stimulus_2 - effective_prior_mean)
        dt_2, decision, _ = .drift_diffusion_trial(drift_2)
        
        total_time = dt_1 + isi + dt_2
        
         decision, total_time
    
     ():
        
        
        forward_choices = []
         _  (n_trials):
            decision, _ = .sequential_discrimination(s_weak, s_strong)
            forward_choices.append(decision)
        
        
        backward_choices = []
         _  (n_trials):
            decision, _ = .sequential_discrimination(s_strong, s_weak)
            
            backward_choices.append(-decision)
        
        
        forward_rate = np.mean(np.array(forward_choices) == )
        backward_rate = np.mean(np.array(backward_choices) == )
        asymmetry = forward_rate - backward_rate
        
         asymmetry, forward_rate, backward_rate


model = BayesianHapticPerception()
asymmetry, fw_rate, bw_rate = model.time_order_asymmetry(
    s_weak=, s_strong=, n_trials=
)
()
()
()

Applications

Cognitive Neuroscience

• Somatosensory Processing: Model S1/S2 cortex dynamics
• Sequential Perception: Study temporal order judgment tasks
• Working Memory: Understand short-term retention of tactile info

Psychophysics

• Haptic Illusions: Explain and predict perceptual illusions
• Cross-Modal Effects: Extend to visual-auditory sequences
• Clinical Assessment: Detect perceptual deficits in patients

Robotics and Haptics

• Tactile Sensing: Improve robot texture discrimination
• Prosthetics: Better encoding of tactile feedback
• VR/AR: Realistic haptic rendering

Decision Making Research

• Sequential Choice: Model multi-alternative decisions
• Context Effects: Study how prior options affect current choice
• Consumer Behavior: Product comparison heuristics

Pitfalls

Model Limitations

• Assumes Gaussian distributions (may not match all stimuli)
• Linear drift rate may be oversimplified
• Single accumulator may miss parallel processing
• No explicit neural implementation

Parameter Challenges

• Many free parameters require extensive fitting
• Parameter trade-offs (drift vs boundary vs non-decision time)
• Individual differences require subject-specific fits
• Limited generalization across stimulus types

Validation Issues

• Difficult to measure internal evidence accumulation
• Response time data alone may not constrain model
• Alternative models may fit equally well
• Neural correlates of Bayesian updates unclear

Related Skills

• drift-diffusion-model
• sequential-decision-making
• perceptual-decision-making
• bayesian-brain
• evidence-accumulation

References

• Avetta, G., et al. (2026). "Modelling time-order effects in haptic perception with a Bayesian dynamical framework." arXiv:2604.19662v1