Boundary effects of expectation in human pain perception

Abstract

Perception of sensory stimulation is influenced by numerous psychological variables. One example is placebo analgesia, where expecting low pain causes a painful stimulus to feel less painful. Yet, because pain evolved to signal threats to survival, it should be maladaptive for highly-erroneous expectations to yield unrealistic pain experiences. Therefore, we hypothesised that a cue followed by a highly discrepant stimulus intensity, which generates a large prediction error, will have a weaker influence on the perception of that stimulus. To test this hypothesis we collected two independent pain-cueing datasets. The second dataset and the analysis plan were preregistered ( https://osf.io/5r6z7/ ). Regression modelling revealed that reported pain intensities were best explained by a quartic polynomial model of the prediction error. The results indicated that the influence of cues on perceived pain decreased when stimulus intensity was very different from expectations, suggesting that prediction error size has an immediate functional role in pain perception.

Figure 1

Graphical representation of the hypothesised polynomial relationship between PE and PE_sub. As the discrepancy between cued intensity and stimulus intensity (PE) increases (from the origin towards both extremes of the X axis), the discrepancy between stimulus intensity and pain intensity rating (PE_sub) also increases, as expectations influence pain perception. The ‘tipping point’ (red marker) is reached where stimulus intensity is so unexpected that the influence of expectations begins to decrease. Positive PE, where pain was greater than expected, is plotted on the right side of the plot. For example, a PE of +2 would reflect a cued expectation of 2 on the NPS, but an actual stimulus intensity of 4 NPS (4 NPS–2 NPS); as this is a small PE, the cued expectation influences perception, resulting in a perception of 3 NPS and a PE_sub of −1 (3 NPS–4 NPS). A PE of +5 would reflect the same cued intensity, 2 NPS, but an actual stimulus intensity of 7 NPS; as this is a large PE, beyond the perceptual ‘tipping point’, the influence of expectation on perceived pain is decreased, resulting in a perception of 6.5 NPS, and PE_sub is decreased to −0.5 (6.5 NPS–7 NPS). This hypothetical relationship is also plotted for pain that is lower than expected, associated with negative PE, on the left side of the plot. Across positive and negative PE Trials, the hypothesised relationship between PE and PE_sub would be best expressed by a cubic polynomial.

Figure 2

Average influence of cued intensity and stimulus intensity on NPS rating. A cue predicting low intensity pain (Cue 2) decreases the perceived intensity of a painful stimulus compared to baseline (veridical cue), and a cue predicting high intensity pain (Cue 8) increases the perceived intensity compared to baseline. Error bars represent the standard error of the mean.

Figure 3

Initial fitted models per dataset, both of which show a cubic relationship between PE and PE_sub,. This relationship is particularly clear in Dataset 2.

Figure 4

Polynomial relationship between PE and PE_sub per dataset; Trial 1 (red solid curve), Trial 60 (blue dashed curve), Trial 120 (green dotted curve)

Figure 5

Trial timeline. After viewing a fixation cross, participants viewed a number from 2 to 8 which depicted the cued intensity for that Trial. After a blank screen, participants received the stimulus, followed by another blank screen. A rating screen was presented which prompted participants to rate the pain on a NPS scale.