Perceptual Decision Parameters and Their Relation to Self-Reported Pain: A Drift Diffusion Account

Abstract

Pain intensity ratings are subject to various cognitive modulations-yet the mechanisms underlying this influence are still not understood. In a conditioning protocol, pain-related expectations were induced through pairing predefined movements with a noxious or innocuous stimulus in either a predictable or unpredictable fashion. Healthy volunteers (N = 37) categorized the stimuli as either painful or non-painful and rated their perceived intensity. Using a Hierarchical Drift Diffusion model based on the categorization data, we found that an a priori decision-making bias evolved towards the expected sensations (p < .001). In particular, our findings suggest that differences in both the amount of decision-making bias (p = .004) and the speed sensory processing predict pain intensity ratings (p < .001). As such, changes in pain ratings could be based in either of these processes, which may require a different approach when targeted as part of psychological pain treatment. Perspective Changes in reported pain levels were linked to two distinct mechanisms, suggesting that increased pain reports could be attributed to either enhanced sensory processing or biased inferences. Our results might contribute to development of person-tailored treatments based on the identification of latent mechanisms using computational models.

Uncertainty in a context of pain: disliked but also more painful?

“…Higher ratings were found for the noxious and lower ratings for the innocuous stimulus when their intensity was predictable. 42 Given the precise prior expectations, these results are in line with the proposed framework. However, other studies found overall higher pain ratings when stimuli were presented in blocks of variable compared with fixed intensities.…”

Confidence in subjective pain is predicted by reaction time during decision making

“…Although participants did not make decisions until after the heat stimulus subsided, it is possible that decision-making processes are most relevant during stimulation as participants gather evidence about the heat. Indeed, previous work has indicated that drift diffusion models can predict pain reports 53,54 , and that pain is associated with variations in both starting point and evidence accumulation. Future studies should evaluate whether on-line behavioral measures collected concurrent with noxious stimulation might be better predictors of confidence than those associated with post-stimulus ratings.…”

Perceptual variability: Implications for learning and generalization

“…Furthermore, this perceptual variability is not mere noise originating from our senses, as assumed by most theories concerning generalization, but can also be explained by various perceptual models (e.g., Davis & Love, 2010;Feldman, Griffiths, & Morgan, 2009;Friston & Kiebel, 2009;Hoskin et al, 2019;Petzschner, Glasauer, & Stephan, 2015;Press, Kok, & Yon, 2020;Tenenbaum & Griffiths, 2001). Many different effects on perception have been observed in the laboratory, but those most relevant to generalization research are, among others, effects of preceding stimuli (e.g., Chambers & Pressnitzer, 2014;Jones et al, 2006;Petzschner & Glasauer, 2011), expectations (e.g., Wiech et al, 2014;Zaman et al, 2019c), stimulus repetition (e.g., Dong, Gao, Lv, & Bao, 2016;Muenssinger et al, 2013;Pérez-González & Malmierca, 2014), the range of presented stimuli (e.g., Kowal, 1993;Petzschner & Glasauer, 2011), and associative learning at large (e.g., Asutay & Västfjäll, 2012;Resnik, Sobel, & Paz, 2011;Zaman et al, 2018). For instance, Petzschner et al (2015) demonstrated that size estimates were overor underestimated depending on the range of presented stimuli and were able to model such effects using a Bayesian perception model (see below).…”

“…An abundance of research has demonstrated that perception of the same stimulus is not static. In almost all research that investigates how humans perceive stimuli, using categorizations, identifications, judgments, or adjustment tasks, responses vary within and between individuals (e.g., Hoskin et al, 2019;Huang & Sekuler, 2010;Jones, Love, & Maddox, 2006;Li, Howard, Parrish, & Gottfried, 2008;Ons, De Baene, & Wagemans, 2011;Petzschner & Glasauer, 2011;Samaey, Wagemans, & Moors, 2020;Schechtman, Laufer, & Paz, 2010;Tenenbaum & Griffiths, 2001;Zaman, Wiech, & Vlaeyen, 2019c). Ashby and Lee (1993) even suggested that the success of perceptual theories depends on their ability to account for what they called "the axiom of perceptual variability," stating that variations in perceptual representations are fundamental to perception research.…”