Psychophysics with R: The R Package MixedPsy

Balestrucci, Priscilla; Ernst, Marc O.; Moscatelli, Alessandro

doi:10.1101/2022.06.20.496855

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…As such, all reported models use linear fits of RDK coherence. We additionally used the "MixedPsy" package in R (Balestrucci et al, 2022) to calculate the point of subjective equality (PSE) from the mixed-effects models. The PSE in the present context refers to the coherence level at which participants are equally likely to judge an RDK as ascending or descending.…”

Section: Discussionmentioning

confidence: 99%

The Role of Attention in Eliciting a Musically Induced Visual Motion Aftereffect

Cormier,

Tsang,

Van Hedger

2023

Preprint

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Previous studies have reported visual motion aftereffects (MAEs) following prolonged exposure auditory stimuli depicting motion, such as ascending or descending musical scales. The role of attention in modulating these cross-modal MAEs, however, remains unclear. The present study manipulated the level of attention directed to musical scales depicting motion and assessed subsequent changes in MAE strength. In Experiment 1, participants either responded to an occasional secondary auditory stimulus presented concurrently with the musical scales (diverted-attention condition) or focused on the scales (control condition). In Experiment 2, all participants engaged in a continuous secondary auditory task presented concurrently and dichotically with the musical scales. Visual motion perception in both experiments was assessed via random dot kinematograms (RDKs) varying in motion coherence. Results from Experiment 1 replicated prior work, in that extended listening to ascending scales resulted in a greater likelihood of judging RDK motion as descending, in line with the MAE. In contrast, the MAE was eliminated in Experiment 2. Results from Experiment 2 could not be explained by a lack of explicit awareness of the musical scales. These results suggest that attention is necessary in eliciting an auditory-induced visual MAE.

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Section: Discussionmentioning

confidence: 99%

The Role of Attention in Eliciting a Musically Induced Visual Motion Aftereffect

Cormier,

Tsang,

Van Hedger

2023

Preprint

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“…Then, we extracted the average Point of Subjective Equality (PSE) and the Just Noticeable Difference (JND) using the MixedPsy R package [25] . Besides these data, we were also interested in the absolute thresholds, i.e.…”

Section: Methodsmentioning

confidence: 99%

An investigation on humans’ sensitivity to environmental temperature

Laura,

Andrea,

Massimiliano

et al. 2023

Preprint

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Thermal perception research has focused on detecting temperature changes on specific body parts, while investigating thermal comfort aims to understand comfortable temperatures in an environment. However, understanding overall body thermal perception remains challenging. To address this, we used four climate chambers with temperatures between 24°C ± 1°C, where 26 participants transitioned between chambers and indicated if the temperature felt warmer or colder. We collected 3120 temperature comparisons and analysed the data using generalized linear mixed-effects models (GLMM). Results showed a Point of Subjective Equality (PSE) of − 0.13°C (± 0.1°C), a Just Noticeable Difference (JND) of 0.38°C (± 0.1°C), revealing the minimum detectable temperature change, a negative absolute threshold of − 0.91°C (± 0.28°C) and a positive absolute threshold of 0.8°C (± 0.34°C). These findings demonstrate participants’ accurate temperature discrimination abilities (JND) and limited variation between individuals, suggesting an intrinsic mechanism governing temperature sensitivity. The GLMM analysis demonstrated remarkable accuracy in explaining the observed data, with an R-squared value of 0.72. In conclusion, our experiment provides compelling evidence of human sensitivity to environmental temperatures, addressing a long-standing question in the field of thermal perception.

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“…We used the packages MixedPsy (Balestrucci et al, 2022) and lme4 (Bates et al, 2015) for model fitting. Supplementary Tables S5, S6 report results for the frequentist approach (GLMM).…”

Section: First Data-set: the Role Of Vibration In Tactile Speed Perce...mentioning

confidence: 99%

“…In our previous study, we showed the advantages of using generalized linear mixed models (GLMMs) to estimate the responses of multiple participants at the population level (Moscatelli et al, 2012). A fairly comprehensive literature on fitting GLM and GLMM exists in different programming languages, including R, Python, and Matlab (Linares and López-Moliner, 2016;Schütt et al, 2016;Moscatelli and Balestrucci, 2017;Prins and Kingdom, 2018;Balestrucci et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Bayesian hierarchical models and prior elicitation for fitting psychometric functions

Mezzetti

Ryan²,

Balestrucci³

et al. 2023

Front. Comput. Neurosci.

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Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods. The aim of this article is to revisit hierarchical models in a Bayesian framework. Bayesian models have been previously discussed for the analysis of psychometric functions although this approach is still seldom applied. The main advantage of using Bayesian models is that if the prior is informative, the uncertainty of the parameters is reduced through the combination of prior knowledge and the experimental data. Here, we evaluate uncertainties between and within participants through posterior distributions. To demonstrate the Bayesian approach, we re-analyzed data from two of our previous studies on the tactile discrimination of speed. We considered different methods to include a priori knowledge in the prior distribution, not only from the literature but also from previous experiments. A special type of Bayesian model, the power prior distribution, allowed us to modulate the weight of the prior, constructed from a first set of data, and use it to fit a second one. Bayesian models estimated the probability distributions of the parameters of interest that convey information about the effects of the experimental variables, their uncertainty, and the reliability of individual participants. We implemented these models using the software Just Another Gibbs Sampler (JAGS) that we interfaced with R with the package rjags. The Bayesian hierarchical model will provide a promising and powerful method for the analysis of psychometric functions in psychophysical experiments.

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Psychophysics with R: The R Package MixedPsy

Cited by 4 publications

References 31 publications

The Role of Attention in Eliciting a Musically Induced Visual Motion Aftereffect

The Role of Attention in Eliciting a Musically Induced Visual Motion Aftereffect

An investigation on humans’ sensitivity to environmental temperature

Bayesian hierarchical models and prior elicitation for fitting psychometric functions

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