How expectation and attention interact to process pain in the brain
Expectation violations and focused attention both amplify pain processing by boosting the excitability of sensory-processing neurons in the somatosensory cortex.
Source
Expectation violation and attention to pain jointly modulate neural gain in somatosensory cortex
What they did
Researchers recorded brain activity using magnetoencephalography in 22 participants while delivering painful electrical pulses to their hands. To test how expectation affected pain, they used a sequence where the pain switched hands unexpectedly after 3 to 7 repetitions of the same location. To test attention, they had participants either focus on the pain to count location switches or focus on a visual cross, while receiving 1000 painful stimuli.
What they found
Both focused attention and unexpected pain switches increased the neural gain—specifically, by decreasing self-inhibition—of superficial pyramidal cells in the primary and secondary somatosensory cortices. However, while attention had a symmetrical effect, unexpected pain primarily boosted neural gain contralaterally. Additionally, unexpected pain increased feedforward and feedback communication between somatosensory, frontal, and parietal regions.
The limits
What it doesn't show
A major limitation of the study is the lack of a behavioral measure of expectation violation, meaning the researchers could not directly assess how surprise itself altered subjective pain ratings. Additionally, the experimental design only tested spatial expectation violations, leaving it unclear if these neural mechanisms generalize to temporal expectations or changes in pain intensity. The findings are also limited to a relatively small sample of healthy young volunteers.
Key terms
- Dynamic Causal Modelling (DCM)
- A method used to estimate and compare the effective connectivity and communication between different brain regions from neuroimaging data.
- Superficial Pyramidal Cells
- Neurons located in the upper layers of the cerebral cortex that, under predictive coding frameworks, are thought to compute and propagate prediction errors to higher-order brain regions.
- Predictive Coding
- A theory of brain function suggesting the brain actively generates predictions about sensory inputs and updates these predictions based on discrepancies (prediction errors) from actual sensations.
- Neural Gain
- The sensitivity or excitability of a neuronal population, which determines how strongly it responds to incoming inputs.
- Roving Oddball Paradigm
- An experimental design where a stream of identical stimuli (standards) is occasionally and unpredictably interrupted by a deviant stimulus, after which the deviant becomes the new standard.
- Somatosensory Cortex
- The region of the brain responsible for processing sensory inputs from the body, including touch, temperature, and pain.
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Quiz yourself
Under predictive coding, how is the precision or confidence of ascending prediction error signals represented in cortical dynamics?
Common questions
Why did the researchers use electrical stimulation to study pain?
They used specialized intra-epidermal electrodes to deliver brief electrical pulses that specifically target nociceptive (pain-sensing) fibers in the skin, allowing them to precisely control the timing and location of painful sensations during MEG recording.
What is the difference between expectation violation and top-down attention in this study?
Expectation violation is a bottom-up process triggered automatically when the physical location of the pain changes unexpectedly, while top-down attention is a goal-directed process where participants actively choose to focus on or ignore the pain.
How did the researchers confirm that the attention task actually worked?
They collected subjective pain ratings and found that participants consistently rated the pain as less intense when they were instructed to ignore it and focus on the visual task.
What is 'precision weighting' in the context of predictive coding?
It is a mechanism where the brain dynamically adjusts the 'volume' or gain of neural signals based on how reliable or informative they are, allowing surprising or highly attended stimuli to have a stronger influence on perception.
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