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Increased temporal sensitivity for threat: A Bayesian generalized linear mixed modeling approach.

Jason Tipples1

  • 1Psychology Group, School of Social, Psychological & Communication Sciences, Leeds Beckett University, Leeds, UK. W.Tipples@leedsbeckett.ac.uk.

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Summary
This summary is machine-generated.

Fearful individuals overestimate the duration of threatening animal images, similar to threat-related facial expressions. This suggests threat perception impacts time estimation, with higher temporal sensitivity for threat stimuli.

Keywords:
Bayesian modelingTemporal processing

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Area of Science:

  • Cognitive Psychology
  • Time Perception
  • Emotion Research

Background:

  • Threat-related facial expressions are overestimated in duration, increasing with fearfulness.
  • Emotion is hypothesized to accelerate temporal information accumulation.
  • Previous research on temporal sensitivity may have methodological limitations.

Purpose of the Study:

  • To test if duration overestimation for threat extends to animal images.
  • To investigate the relationship between fearfulness and time overestimation for threat stimuli.
  • To demonstrate the utility of Bayesian generalized linear mixed modeling (GLMM) for temporal tasks.

Main Methods:

  • Fifty-three participants completed a temporal bisection task.
  • Stimuli included pictures of threatening and nonthreatening animals.
  • Bayesian generalized linear mixed modeling (GLMM) was employed for analysis.

Main Results:

  • Participants overestimated the duration of threatening animal images.
  • This overestimation increased with self-reported fearfulness.
  • Temporal sensitivity was higher for threat images than nonthreat images.

Conclusions:

  • Threat perception influences time estimation, generalizing beyond facial expressions to animal imagery.
  • Bayesian GLMM provides more accurate temporal performance estimates, including temporal sensitivity.
  • Researchers are encouraged to adopt Bayesian GLMM for temporal bisection data analysis.