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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 <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).
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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 <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).
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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 <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).
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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 <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).
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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 <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).
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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 <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).
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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 <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).
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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: