The neural response to a stimulus diminishes with repeated presentations, a phenomenon known as repetition suppression. We here use neuroimaging to demonstrate that repetition suppression appears to be a special case of &#x22;prediction suppression&#x22;--that is, the brain shows diminishing activity when subsequent stimuli in a train are predictable. This demonstration supports the hypothesis that the brain dynamically leverages prediction to minimize energy consumption

David M. Eagleman

Sara J. Churchill

Vani Pariyadath

English

Nature Precedings

Table 1 | Example stimuli from the three categories (Ordered sequence, Scrambled sequence and Non-sequence) 
presented during the experiment. 
 
4 7 9 1 61 2 3 4 5Numbers
C J A V LG H I J KLetters
Tuesday Thursday Sunday Monday 
Wednesday
Wednesday Thursday Friday 
Saturday Sunday
Days
February April January October JulyFebruary March April May JuneMonths
Scrambled sequenceOrdered sequence
Chair Table Desk Bed SofaFurniture
Mercedes Honda Toyota Lexus FordCars
Dog Cat Bear Monkey RatAnimals
Pear Peach Banana Apple GrapeFruit
Non-sequence
 
Predictability engenders more 
efficient neural responses 
 
Vani Pariyadath1, Sara J Churchill1 and David M 
Eagleman1,2 
1Department of Neuroscience, 2Department of Psychiatry, 
Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 
77030  
 
The neural response to a stimulus diminishes with 
repeated presentations, a phenomenon known as 
repetition suppression. We here use neuroimaging to 
demonstrate that repetition suppression appears to 
be a special case of “prediction suppression”—that is, 
the brain shows diminishing activity when subsequent 
stimuli in a train are predictable. This demonstration 
supports the hypothesis that the brain dynamically 
leverages prediction to minimize energy consumption. 
  
Repetition suppression can be measured in neural firing 
rates using single cell electrophysiology1,2, in event 
related potentials using electroencephalography 3, in the 
BOLD response using functional magnetic resonance 
imaging 4 and using PET5 and MEG 6. Explanations for 
such stimulus-specific adaptation include neural fatigue 
and sharpening of neural network representations7. 
    Not only the neural response, but the perceived 
duration of a stimulus as well is found to contract with 
repetition8-11. We have previously hypothesized that this 
correlation is not accidental, but that subjective duration 
is a direct reflection of the amplitude of neural 
responses8,9. Interestingly, duration contractions can be 
demonstrated using non-repeating stimuli that are 
nonetheless predictable8, which suggested the possibility 
that repetition suppression is a special case of what we 
term “prediction suppression.” To test this hypothesis, we 
performed a neuroimaging experiment using 
presentations of predictable and unpredictable stimuli. 
Participants viewed series of 5 words that belonged to a 
sequential category (e.g. months of the year, days of the 
week), presented either in their natural order (Ordered 
condition, e.g. March, April, May, June, July) or in a 
scrambled order (Scrambled condition, e.g. May, 
February, October, March, January). In control trials, 
participants were presented with words that belonged to a 
non-sequential category (Non-sequential condition, e.g. 
Peach, Plum, Apple, Pear, Banana). Stimuli in the first 2 
conditions were letters, numbers, days of the week or 
months of the year. Stimuli for the non-sequential 
condition were names of fruits, makes of car, types of 
furniture, or animals (Table 1 and Supplementary 
Methods). 
    We localized cortical regions that were selectively 
involved in processing sequential stimuli (random-effects 
contrasts between the Non-sequential trials and either the 
Ordered or Scrambled sequences; see Supplementary 
Methods for details). The regions comprised a cluster in 
the right temporoparietal junction (TPJ) and a cluster in 
the right middle temporal gyrus (MTG). Within these 
right hemisphere regions we find that the neural response 
to predictable stimuli (“Ordered sequence”) spans a 
smaller volume than the response to unpredictable 
stimuli (“Scrambled sequence”; Fig. 1). Importantly, the 
peak coordinates remain the same while only the size of 
the activated voxel cluster changes. This difference in 
cluster sizes is not an artifact of the chosen statistical 
threshold, as the effect is clear across a range of 
threshold choices (Supplementary Fig. 1). Two other 
clusters appear in the Scrambled condition (left 
temporoparietal junction, peak activation -60, -24, 16 and 
left inferior frontal gyrus, peak activation, -52, 8, 16), but 
not in the Ordered condition, suggesting that they too 
drop out of the active cortical network with increasing 
predictability. 
This finding that scrambled sequences result in a 
greater volume of neural activation than unscrambled 
sequences supports the idea that predictable stimuli, like 
repeated stimuli, induce more efficient coding7, perhaps 
via sharper tuning of neural populations12 and/or dynamic 
tuning of synaptic strengths13. Our results are in 
agreement with other studies indicating that repetition 
suppression is a manifestation of satisfied perceptual 
predictions14. While it has recently been shown that 
suppression can be modulated by predictions based on 
immediate context14, we present here evidence that such 
predictions can also be founded from past experience. 
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Further, note that the TPJ shows increased activation in 
response to viewing a movie in reverse, a condition in 
which predictability is degraded15.  
Traditional repetition suppression experiments 
involve the repetition of a single stimulus, and are thus 
unable to rule out low level properties of the stimulus 
(such as details of the visual form). By using stimuli that 
change each time, either predictably or unpredictably, we 
are led to suggest that the diminishing neural response 
seen with stimulus repetition may be a special case of a 
more general phenomenon of prediction suppression. 
 
 
 
 
 
 
 
 
ACKNOWLEDGMENTS 
This work was supported by NIH R01 grant 
NS053960 (DME). 
 
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Figure 1 | Processing of predictable stimuli recruits smaller cortical
networks. Participants viewed 5 words that were presented serially
while in an fMRI scanner. The words belonged to a sequential
category in their natural order (“Ordered sequence”) or in a
scrambled order (“Scrambled sequence”), or to a non-sequential
category (“Non-sequential”). (A) Sagittal (x=64) and coronal (y=-56)
sections showing increased activation in the right temporoparietal
junction (TPJ, peak activation 64,-44,20) and right middle temporal
gyrus (MTG, peak activation 56,-64,0) in response to sequential
stimuli (whether ordered or scrambled). The figure overlays two
contrasts: Ordered sequence greater than non-sequence (blue),
and Scrambled sequence greater than non-sequence (green).  (B)
Although the peak cluster activation remained the same, a greater
volume of voxels was activated in response to the Scrambled
sequence (unpredictable) condition than the Ordered sequence
(predictable) condition. Clusters p<0.005 with at least 20
continuous voxels; Supplementary Fig. 1 demonstrates the same
effect over a variety of parameter settings. 
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Predictability engenders more efficient neural responses

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Predictability engenders more efficient neural responses

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