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Expectation in perceptual decision making: neural and computational mechanisms

An Erratum to this article was published on 22 October 2014

Key Points

  • Visual stimuli in the real world are often highly predictable on the basis of spatial context, transition probabilities and prior information from previous glances.

  • Normative Bayesian models dictate how expectations (that is, the prior) should be combined with incoming sensory evidence (that is, the likelihood) for optimal perceptual inference.

  • Prior information about upcoming percepts modulates baseline neural activity in sensory neurons encoding the expected stimulus and in decision-related neurons that integrate the sensory evidence.

  • Predictive coding is a neurobiologically plausible computational framework that seeks to explain how top-down priors and bottom-up inputs are combined.

  • Expectation and attention are often entangled but they are conceptually distinct. Expectation relates to the probability of a sensory event, whereas selective attention pertains to the relevance of a sensory event. A stimulus can be probable or improbable, irrespective of its behavioural relevance.

  • During decision making, expectation can alter the gain of information processing towards stimuli that are expected to occur.

Abstract

Sensory signals are highly structured in both space and time. These structural regularities in visual information allow expectations to form about future stimulation, thereby facilitating decisions about visual features and objects. Here, we discuss how expectation modulates neural signals and behaviour in humans and other primates. We consider how expectations bias visual activity before a stimulus occurs, and how neural signals elicited by expected and unexpected stimuli differ. We discuss how expectations may influence decision signals at the computational level. Finally, we consider the relationship between visual expectation and related concepts, such as attention and adaptation.

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Figure 1: Expectations in visual perception.
Figure 2: Decision-theoretic approaches to understanding expectation.
Figure 3: Biasing of neural signals by expectation at different processing stages.
Figure 4: Repetition suppression and expectation suppression.

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Acknowledgements

The authors are grateful to M. Usher, T. Egner, T. Donner, S. van Gaal and P. Kok for comments on the manuscript. This work was supported by a European Research Council Starter Award to C.S., and a James S. McDonnell Fund award to F.d.L.

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Glossary

Bayes' rule

Bayes' rule describes how the relative probability (or odds) of two possible hypotheses changes (from prior odds ratio to posterior odds ratio) as new evidence is acquired. Formally, the posterior odds ratio is calculated by multiplying the prior odds ratio by the likelihood ratio (also known as the Bayes factor), which is the probability of observing the new evidence, given the two hypotheses.

N1

A negative evoked potential (measured by electro-encephalography or magneto-encephalography) that peaks between 80 ms and 120 ms after the onset of a sensory stimulus (which can be visual, auditory or somatosensory). This potential is sensitive to manipulations of stimulus predictability.

P3b

A large positive evoked potential that peaks between 250 ms and 500 ms after the presentation of a stimulus. The P3b is associated with decision processes and strongly reacts to rare, surprising events.

Oddball

In the oddball paradigm, participants are subjected to frequent 'standard' stimuli interspersed with rare 'oddball' stimuli that require a response. Oddball stimuli elicit enhanced sensory and decision-related neural responses.

Selective attention

The cognitive function by which information is selected for further processing on the basis of its salience or relevance to a current task or goal.

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Summerfield, C., de Lange, F. Expectation in perceptual decision making: neural and computational mechanisms. Nat Rev Neurosci 15, 745–756 (2014). https://doi.org/10.1038/nrn3838

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