Biology subjects

Moscatelli, A.

Publications and source records attributed to Moscatelli, A..

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

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↗

bioRxiv2019

The discrimination of tactile speed in absence of texture elements: Role of deformation and vibratory cues

Motion encoding in touch relies on multiple cues, such as displacements of traceable texture elements, friction-induced vibrations, and gross fingertip deformations by shear force. We evaluated the role of deformation and vibration cues in tactile speed discrimination. To this end, we tested the discrimination of speed of a moving smooth glass plate, and compared the precision of the responses when the same task was performed with a plate having a fine texture. Participants performed the task with and without masking vibrations. Speed discrimination was nearly as precise among the two surface types, as assessed by the steep slope of the psychometric function. Consistent with our previous work, high-frequency vibrations impaired the ability of the participants in discriminating surface speed. Results of the current study showed that it is possible to discriminate motion speed even in absence of a raised texture. HighlightsO_LIOn a smooth surface, humans are able to discriminate the speed of a moving surface by frictional motion cues C_LIO_LIThe precision of speed discrimination is nearly the same with smooth and fine-textured surface types C_LIO_LIHigh frequency vibrations impair the ability to discriminate speed of moving surfaces C_LI

neuroscience↗