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Perceptual Decision Parameters and Their Relation to Self-Reported Pain: A Drift Diffusion Account
Jonas Zaman1, Katja Wiech2, Johan W S Vlaeyen3
1Health Psychology, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium; Center for the Psychology of Learning and Experimental Psychopathology, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium.
The Journal of Pain
|July 2, 2019
Summary
Cognitive processes significantly influence pain perception. This study reveals that pain intensity ratings are predicted by decision-making bias and sensory processing speed, offering insights into psychological pain treatments.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Pain Research
Background:
- Pain intensity perception is modulated by cognitive factors, but underlying mechanisms remain unclear.
- Expectations can alter how individuals experience pain, yet the precise neural and cognitive underpinnings are not fully understood.
Purpose of the Study:
- To investigate the cognitive mechanisms, specifically decision-making bias and sensory processing speed, that influence pain intensity ratings.
- To explore how induced pain-related expectations affect subjective pain perception in healthy volunteers.
Main Methods:
- A conditioning protocol was used to induce pain-related expectations by pairing movements with stimuli.
- Participants (N=37) categorized stimuli and rated perceived pain intensity.
- A Hierarchical Drift Diffusion model was applied to categorization data.
Main Results:
- An a priori decision-making bias towards expected sensations was observed (P < .001).
- Both the degree of decision-making bias (P = .004) and the speed of sensory processing (P < .001) were found to predict pain intensity ratings.
- Findings suggest pain report variations stem from either biased inferences or altered sensory processing.
Conclusions:
- Changes in reported pain intensity can be attributed to distinct cognitive mechanisms: decision-making bias or sensory processing speed.
- Identifying these latent mechanisms using computational models may enable the development of personalized psychological pain treatments.
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