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pspm toolbox version 6.0.0  (MathWorks Inc)


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    MathWorks Inc pspm toolbox version 6.0.0
    Model-based physiological responses to different fear levels. Each response was computed using a GLM-based approach as implemented in the <t>PSPM</t> toolbox. For each subject, we specified one regressor for each of the four levels, plus one regressor for catch trials. For pupil size ( A ), skin conductance ( B ) and heart period ( C ), each data point corresponds to the maximum of the response for each fear regressor, with one data point per subject. For respiration amplitude ( D ), since the response is biphasic, each data point corresponds to the peak of the early response. For all physiological measures, we found a significant difference between low-fear (first quartile) and high-fear trials (last quartile).
    Pspm Toolbox Version 6.0.0, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/pspm+toolbox+version+6%2E0%2E0/pmc11985500-110-28-35?v=MathWorks+Inc
    Average 90 stars, based on 1 article reviews
    pspm toolbox version 6.0.0 - by Bioz Stars, 2026-06
    90/100 stars

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    1) Product Images from "SpiderPhy dataset: A multimodal dataset of Physiological, Psychometric and Behavioral Responses to fear stimuli"

    Article Title: SpiderPhy dataset: A multimodal dataset of Physiological, Psychometric and Behavioral Responses to fear stimuli

    Journal: Scientific Data

    doi: 10.1038/s41597-025-04908-x

    Model-based physiological responses to different fear levels. Each response was computed using a GLM-based approach as implemented in the PSPM toolbox. For each subject, we specified one regressor for each of the four levels, plus one regressor for catch trials. For pupil size ( A ), skin conductance ( B ) and heart period ( C ), each data point corresponds to the maximum of the response for each fear regressor, with one data point per subject. For respiration amplitude ( D ), since the response is biphasic, each data point corresponds to the peak of the early response. For all physiological measures, we found a significant difference between low-fear (first quartile) and high-fear trials (last quartile).
    Figure Legend Snippet: Model-based physiological responses to different fear levels. Each response was computed using a GLM-based approach as implemented in the PSPM toolbox. For each subject, we specified one regressor for each of the four levels, plus one regressor for catch trials. For pupil size ( A ), skin conductance ( B ) and heart period ( C ), each data point corresponds to the maximum of the response for each fear regressor, with one data point per subject. For respiration amplitude ( D ), since the response is biphasic, each data point corresponds to the peak of the early response. For all physiological measures, we found a significant difference between low-fear (first quartile) and high-fear trials (last quartile).

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    pspm toolbox version 6.0.0  (MathWorks Inc)
    90
    MathWorks Inc pspm toolbox version 6.0.0
    Model-based physiological responses to different fear levels. Each response was computed using a GLM-based approach as implemented in the <t>PSPM</t> toolbox. For each subject, we specified one regressor for each of the four levels, plus one regressor for catch trials. For pupil size ( A ), skin conductance ( B ) and heart period ( C ), each data point corresponds to the maximum of the response for each fear regressor, with one data point per subject. For respiration amplitude ( D ), since the response is biphasic, each data point corresponds to the peak of the early response. For all physiological measures, we found a significant difference between low-fear (first quartile) and high-fear trials (last quartile).
    Pspm Toolbox Version 6.0.0, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/pspm+toolbox+version+6%2E0%2E0/pmc11985500-110-28-35?v=MathWorks+Inc
    Average 90 stars, based on 1 article reviews
    pspm toolbox version 6.0.0 - by Bioz Stars, 2026-06
    90/100 stars
    		  Buy from Supplier

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    Model-based physiological responses to different fear levels. Each response was computed using a GLM-based approach as implemented in the PSPM toolbox. For each subject, we specified one regressor for each of the four levels, plus one regressor for catch trials. For pupil size ( A ), skin conductance ( B ) and heart period ( C ), each data point corresponds to the maximum of the response for each fear regressor, with one data point per subject. For respiration amplitude ( D ), since the response is biphasic, each data point corresponds to the peak of the early response. For all physiological measures, we found a significant difference between low-fear (first quartile) and high-fear trials (last quartile).

    Journal: Scientific Data

    Article Title: SpiderPhy dataset: A multimodal dataset of Physiological, Psychometric and Behavioral Responses to fear stimuli

    doi: 10.1038/s41597-025-04908-x

    Figure Lengend Snippet: Model-based physiological responses to different fear levels. Each response was computed using a GLM-based approach as implemented in the PSPM toolbox. For each subject, we specified one regressor for each of the four levels, plus one regressor for catch trials. For pupil size ( A ), skin conductance ( B ) and heart period ( C ), each data point corresponds to the maximum of the response for each fear regressor, with one data point per subject. For respiration amplitude ( D ), since the response is biphasic, each data point corresponds to the peak of the early response. For all physiological measures, we found a significant difference between low-fear (first quartile) and high-fear trials (last quartile).

    Article Snippet: Some examples of what we considered to be “good” vs. “bad” signals for the present analysis are provided in Fig. . Our main analyses were performed using the PsPM toolbox Version 6.0.0 which ran on Matlab R2022a.

    Techniques: