Biology subjects

Balestrucci, P.

Publications and source records attributed to Balestrucci, P..

4 recordsLinked to original sources

bioRxiv2025

Modeling Psychophysical Data in R: A Comparative Study of Four Model Frameworks

Inferential models in psychophysics are essential for quantifying the relation between physical properties of the stimulus and their perceptual representations. The psychometric function is typically used to model the responses of individual participants in forced-choice experiments. The accuracy and the noise of the response can be estimated from the Point of Subjective Equality (PSE) and the Just Noticeable Difference (JND) of the function, respectively. Traditionally, a two-level approach is used to model the behavior of a group of participants, where psychometric functions are first fitted to individual participant data, followed by hypothesis testing across participants on parameters of interest. Recent studies have introduced alternative approaches based on hierarchical models, such as Generalized Linear Mixed Models (GLMM) and Models within the Bayesian Hierarchical Framework (BHF), to analyze in a single framework the responses from multiple participants. These two approaches can be effectively implemented in R, thanks to its flexibility and robust statistical capabilities. Here, we provide a tutorial on how to model and analyze data from psychophysical experiments in R, using both two-level and hierarchical frameworks. Our goal is to provide researchers with a practical guide for building a complete and reproducible analysis pipeline, using core R functionalities together with custom packages, and facilitate rigorous and efficient data analysis in psychophysics.

neuroscience↗

bioRxiv2024

Incomplete adaptation to surface movement during hand reaching

Humans can effectively adapt to changes in the environment to maintain adequate motor performance in a vast range of situations. However, residual errors tend to persist when strong a priori assumptions about the statistical regularities of the environment are violated. In our study, we challenged the expectation that inanimate objects are usually at rest. To this end, we used a robotic interface to move a plate over which participants slid their finger while reaching towards a target. We found limited evidence of adaptation after prolonged exposure to this perturbation, and only when visual feedback about hand position was provided. Although participants were aware of the motion of the contact surface, explicit knowledge about its direction was limited. Our results provide important insights on the limits of adaptation to motion perturbation in the somatosensory system, which can inform the design of technology applications such as haptic interfaces and collaborative robots.

neuroscience↗

bioRxiv2022

System identification reveals multiple interacting states in visuomotor adaptation

Many characteristics of sensorimotor adaptation are well predicted by the Kalman filter, a relatively simple learning algorithm for the optimal estimation of dynamic variables given noisy measurements. Yet not all Kalman filter predictions are confirmed empirically, suggesting that the model might not be sufficient to describe the observed behavior. In our study, we propose that sensorimotor adaptation can be modeled with multiple interacting states, each one described as a Kalman filter, to better reflect the architecture plant of the physical system implementing the behavior (i.e., the different motor and sensory components involved in adaptation). To test our hypothesis, we measured motor error in a series of rapid reaching tasks in which we introduced different conditions of feedback uncertainty and systematic perturbations. We then applied system identification procedures to the resulting adaptation response to test which system architecture would best fit the data. We considered three possible architectures: one with a single Kalman filter, with two filters in series, or two filters in parallel. We found that this latter structure consistently provided a significantly better fit than the others. When evaluating the identified system parameters, we found that the learning rates of both states decreased under higher uncertainty, while the weight assigned to the slower state increased. Moreover, the dual-state parallel system accounted for the presence of a constant residual bias in the adaptation to a step offset, which has been repeatedly reported but is in disaccord with the predictions of the single Kalman filter. We propose that the identified system architecture reflects how the error is assigned to different components of the physical plant responsible for adaptation: namely, as uncertainty increases, the controller assigns a larger contribution in error reduction to the slower state, which is more likely to have been responsible for the measured error.

neuroscience↗

bioRxiv2022

Psychophysics with R: The R Package MixedPsy

Psychophysical methods are widely used in neuroscience to investigate the quantitative relation between a physical property of the world and its perceptual representation provided by the senses. Recent studies introduced the Generalized Linear Mixed Model (GLMM) to fit the responses of multiple participants in psychophysical experiments. Another approach (two-level approach) requires fitting psychometric functions to each individual participant data using a Generalized Linear Model (GLM), and then testing the hypotheses on the multiple participants by means of a second level analysis. For either options, the implementation of the statistical analysis in R is possible and beneficial. Here, we introduce the package MixedPsy to model and fit psychometric data in R, either with two-level and GLMM approaches. The package, freely available in the CRAN repository, uses different methods for the estimation of Point of Subjective Equivalence (PSE) and Just Noticeable Difference (JND), and provides utilities for immediate visualization and plotting of the fitted results. This manuscript aims to provide researchers with a practical tutorial for implementing a complete analysis pipeline for psychophysical data using MixedPsy and other packages and basic functionalities of the R programming environment.

neuroscience↗