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A Bayesian perspective on sensory and cognitive integration in pain perception and placebo analgesia.

Anchisi D, Zanon M - PLoS ONE (2015)

Bottom Line: The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists.Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data.Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical and Biological Sciences, Universit degli Studi di Udine, Udine, Italy.

ABSTRACT
The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists. We propose that placebo analgesia is a facet of pain perception, others being the modulating effects of emotions, cognition and past experience, and we suggest that a computational understanding of pain may provide a unifying explanation of these phenomena. Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data. Our model not only agrees with placebo analgesia, but also predicts that learning can affect pain perception in other unexpected ways, which experimental evidence supports. Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.

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Related in: MedlinePlus

Model predictions of pain rating with and without cues, after conditioning.(A) 100 random draws (top) from posterior probability distributions (bottom) for high (noCSh), low (noCSl) and intermediate (noCSm) stimulus intensities, paired with no cue. (B) Probability distributions of pain rating obtained with different effectiveness of conditioning (w = weight factor attributed to conditioning), and 20 random draws from each probability distribution. Predictions for intermediate stimuli paired with no cue (midblue stimuli, left) are displayed aside those for high stimuli (right) paired with green (green circles and curves, placebo condition) and red (red circles and curves, overt no-treatment condition) cues. ◊ = means of each sample.
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pone.0117270.g005: Model predictions of pain rating with and without cues, after conditioning.(A) 100 random draws (top) from posterior probability distributions (bottom) for high (noCSh), low (noCSl) and intermediate (noCSm) stimulus intensities, paired with no cue. (B) Probability distributions of pain rating obtained with different effectiveness of conditioning (w = weight factor attributed to conditioning), and 20 random draws from each probability distribution. Predictions for intermediate stimuli paired with no cue (midblue stimuli, left) are displayed aside those for high stimuli (right) paired with green (green circles and curves, placebo condition) and red (red circles and curves, overt no-treatment condition) cues. ◊ = means of each sample.

Mentions: Probability distributions before (A, B) and after (A, B, C) conditioning. (A) Posterior probability distributions of pain rating given the stimulus intensity. The color scale codes for relative probabilities (scaled so the maximum equals 1). Orange curves indicate maxima (most probable pain rating), also reported in (B). Vertical lines highlight some of the distributions shown, with same colors and line types, in (C) and in Figs. 4C and 5A. (B) Most probable rating given a stimulus, for each possible stimulus: before training (pre), and after training. Values after training are shown for stimuli paired with a cue (Cg: green cue; Cr: red cue) or not (noCue). Horizontal lines indicate the estimated pain rating for high stimuli paired with red (red dotted line, overt no-treatment) and green (green dashed line, placebo condition) cues, and for low stimuli paired with green (green dotted line, overt treatment) and red (brown dashed line, nocebo condition) cues. (C) Prior probability distribution (prior), and posterior probability distributions conditioned on the high stimulus (Sh), on the green cue (Cg), and on both the high stimulus and the green cue together (CgSh, placebo).


A Bayesian perspective on sensory and cognitive integration in pain perception and placebo analgesia.

Anchisi D, Zanon M - PLoS ONE (2015)

Model predictions of pain rating with and without cues, after conditioning.(A) 100 random draws (top) from posterior probability distributions (bottom) for high (noCSh), low (noCSl) and intermediate (noCSm) stimulus intensities, paired with no cue. (B) Probability distributions of pain rating obtained with different effectiveness of conditioning (w = weight factor attributed to conditioning), and 20 random draws from each probability distribution. Predictions for intermediate stimuli paired with no cue (midblue stimuli, left) are displayed aside those for high stimuli (right) paired with green (green circles and curves, placebo condition) and red (red circles and curves, overt no-treatment condition) cues. ◊ = means of each sample.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4321992&req=5

pone.0117270.g005: Model predictions of pain rating with and without cues, after conditioning.(A) 100 random draws (top) from posterior probability distributions (bottom) for high (noCSh), low (noCSl) and intermediate (noCSm) stimulus intensities, paired with no cue. (B) Probability distributions of pain rating obtained with different effectiveness of conditioning (w = weight factor attributed to conditioning), and 20 random draws from each probability distribution. Predictions for intermediate stimuli paired with no cue (midblue stimuli, left) are displayed aside those for high stimuli (right) paired with green (green circles and curves, placebo condition) and red (red circles and curves, overt no-treatment condition) cues. ◊ = means of each sample.
Mentions: Probability distributions before (A, B) and after (A, B, C) conditioning. (A) Posterior probability distributions of pain rating given the stimulus intensity. The color scale codes for relative probabilities (scaled so the maximum equals 1). Orange curves indicate maxima (most probable pain rating), also reported in (B). Vertical lines highlight some of the distributions shown, with same colors and line types, in (C) and in Figs. 4C and 5A. (B) Most probable rating given a stimulus, for each possible stimulus: before training (pre), and after training. Values after training are shown for stimuli paired with a cue (Cg: green cue; Cr: red cue) or not (noCue). Horizontal lines indicate the estimated pain rating for high stimuli paired with red (red dotted line, overt no-treatment) and green (green dashed line, placebo condition) cues, and for low stimuli paired with green (green dotted line, overt treatment) and red (brown dashed line, nocebo condition) cues. (C) Prior probability distribution (prior), and posterior probability distributions conditioned on the high stimulus (Sh), on the green cue (Cg), and on both the high stimulus and the green cue together (CgSh, placebo).

Bottom Line: The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists.Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data.Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical and Biological Sciences, Universit degli Studi di Udine, Udine, Italy.

ABSTRACT
The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists. We propose that placebo analgesia is a facet of pain perception, others being the modulating effects of emotions, cognition and past experience, and we suggest that a computational understanding of pain may provide a unifying explanation of these phenomena. Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data. Our model not only agrees with placebo analgesia, but also predicts that learning can affect pain perception in other unexpected ways, which experimental evidence supports. Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.

Show MeSH
Related in: MedlinePlus