Review
Spontaneous Brain Oscillations and Perceptual Decision-Making

https://doi.org/10.1016/j.tics.2020.05.004Get rights and content

Highlights

  • Spontaneous changes in low-frequency oscillatory amplitude bias moment-to-moment perceptual decisions through criterion effects, not sensitivity changes.

  • Models of perceptual decision-making combined with neurophysiological evidence suggest that spontaneous alpha-band oscillations modulate sensory responses without changing the fidelity of stimulus representations.

  • Criteria underlying subjective measures of perception, such as detection, visibility, and confidence, do not adapt to alpha-related changes in sensory processing, leading to dissociations between objective and subjective aspects of perception.

  • There is emerging support for a domain-general effect of alpha on perceptual decisions across sensory modalities.

Making rapid decisions on the basis of sensory information is essential to everyday behaviors. Why, then, are perceptual decisions so variable despite unchanging inputs? Spontaneous neural oscillations have emerged as a key predictor of trial-to-trial perceptual variability. New work casting these effects in the framework of models of perceptual decision-making has driven novel insight into how the amplitude of spontaneous oscillations impact decision-making. This synthesis reveals that the amplitude of ongoing low-frequency oscillations (<30 Hz), particularly in the alpha-band (8–13 Hz), bias sensory responses and change conscious perception but not, surprisingly, the underlying sensitivity of perception. A key model-based insight is that various decision thresholds do not adapt to alpha-related changes in sensory activity, demonstrating a seeming suboptimality of decision mechanisms in tracking endogenous changes in sensory responses.

Section snippets

Neural Oscillations and the Decision Process

The combination of psychophysics, brain activity measurement, and computational modeling has led to many advances in our understanding of the mechanisms that support perceptual decision-making [1,2]. Central to current understandings of decision-making is the notion that the sensory apparatus provides varying levels of evidence that a particular perceptual feature is present (called sensory evidence; see Glossary). For example, the firing rate of neurons in the motion direction-sensitive middle

Behavioral Impact of Spontaneous Fluctuations in Oscillatory Power

Many experiments link trial-by-trial variability in the amplitude of spontaneous oscillations to behavior without recourse to a formal modeling framework. A commonly used approach is the stimulus detection task, where hit rates (Box 2) and, less commonly, false alarm rates, are the primary behavioral measure. Several experiments have also used discrimination tasks, where objective measures of accuracy (e.g., % correct) are the primary metric, often in tandem with subjective reports of confidence

Linking Oscillations and Behavior with Models of Perceptual Decision Making

Most studies reviewed in the previous section have relied on ad hoc interpretations of observable data (e.g., oscillatory power and hit rates) to elucidate the latent mechanisms underlying the perceptual effects of prestimulus brain states. However, decision-making mechanisms are not directly observable in the same way that behavior is. Therefore, theorizing about the effect of oscillations on behavior can be improved by recourse to a formal model of perceptual decision-making that relates

How Does Spontaneous Alpha Activity Change Perceptual Decisions?

Ambiguity can arise because the SDT criterion parameter can change due to multiple factors [34,35]. For example, because criterion is computed as standard deviations relative to the distance from the intersection point between sensory distributions (Box 2), the criterion that one computes from behavior could change as a result of sensory distribution changes or absolute evidence-threshold changes (Figure 2). This makes for difficulty in interpreting criterion effects as being sensory or

Spontaneous Oscillations Bias Subjective Perception and Early Sensory Processing

A recent experiment [40] examined how prestimulus oscillatory power modulates early (~80 ms) visual event-related potentials (ERP) in response to high-contrast stimuli that produce a C1 response (Figure 3), an ERP component thought to reflect the initial afferent volley of activity in the primary visual cortex [41,42]. It was found that states of high prestimulus alpha/beta power led to a suppressed C1, indicating an inhibitory effect on early visual processing [40]. However, a direct link

Implications for Perceptual Decision-Making and Models Thereof

If BSEM is accurate, it would imply a violation of the so-called ‘optimal’ or ‘Bayesian’ confidence hypothesis, which states that confidence is an optimal read-out of the probability that a decision is correct. The optimal view is explicit in numerous computational models of perceptual decision-making [63., 64., 65., 66., 67., 68.]. Given that BSEM proposes that confidence criteria do not (perfectly) adjust to trial-to-trial fluctuations in alpha-driven changes in sensory responses, it can

Attention-Induced Fluctuations in Oscillatory Power

The studies reviewed earlier focused on fluctuations in oscillatory power that are spontaneous in the sense that they were not induced by experimental manipulations of attention. Of course, this does not rule out that ostensibly ‘spontaneous’ fluctuations simply reflect fluctuations in participants' attention, motivation, or other states. When attention is experimentally manipulated, for example by cueing task-relevant locations or stimulus features, alpha power typically decreases (increases)

Concluding Remarks and Future Directions

Here we detailed the relationship between spontaneous oscillatory amplitude, principally in low frequencies encompassing the alpha-band, and subsequent perceptual decisions. Before the application of SDT models, the predominant understanding was that reductions in prestimulus alpha amplitude led to more accurate perceptual decisions (although see [18] for a non-model-based approach that reaches a similar conclusion as our synthesis). Instead, reduced prestimulus alpha biases observers to report

Acknowledgments

This work was supported by a grant from the German Research Foundation (DFG) to N.A.B. (BU 2400/8-1, BU 2400/9-1) and by a Vidi grant from the Netherlands Organization for Scientific Research (NWO 016.Vidi.185.137) to S.H. and by University of California (UC) start-up funds to J.S.

Glossary

Alpha-band
oscillations between 8 and 13 Hz, ubiquitous in sensory systems, cortical, and subcortical structures, and closely linked with neural excitability (Box 1). Many experiments have focused on alpha and perceptual decisions.
Amplitude
magnitude of an oscillation, often measured in volts for electroencephalography (EEG) and tesla for magnetoencephalography (MEG) (power is amplitude squared).
Baseline sensory excitability model
SDT-based model proposed here that describes the relationship

References (121)

  • S.D. Mayhew

    Spontaneous EEG alpha oscillation interacts with positive and negative BOLD responses in the visual–auditory cortices and default-mode network

    NeuroImage

    (2013)
  • F. Meyniel

    Confidence as Bayesian probability: from neural origins to behavior

    Neuron

    (2015)
  • J.I. Sanders

    Signatures of a statistical computation in the human sense of confidence

    Neuron

    (2016)
  • J.M. Beck

    Probabilistic population codes for bayesian decision making

    Neuron

    (2008)
  • M.L. Lőrincz

    Temporal framing of thalamic relay-mode firing by phasic inhibition during the alpha rhythm

    Neuron

    (2009)
  • F. Lopes da Silva

    Neural mechanisms underlying brain waves: from neural membranes to networks

    Electroencephalogr. Clin. Neurophysiol.

    (1991)
  • D.M. Green et al.

    Signal Detection Theory and Psychophysics

    (1966)
  • H. van Dijk

    Prestimulus oscillatory activity in the alpha band predicts visual discrimination ability

    J. Neurosci.

    (2008)
  • N.A. Busch

    The phase of ongoing EEG oscillations predicts visual perception

    J. Neurosci.

    (2009)
  • K.E. Mathewson

    To see or not to see: pre-stimulus alpha phase predicts visual awareness

    J. Neurosci.

    (2009)
  • N.A. Busch et al.

    Spontaneous EEG oscillations reveal periodic sampling of visual attention

    PNAS

    (2010)
  • M. Chaumon et al.

    Prestimulus neural oscillations inhibit visual perception via modulation of response gain

    J. Cogn. Neurosci.

    (2014)
  • K.E. Mathewson

    Dynamics of alpha control: preparatory suppression of posterior alpha oscillations by frontal modulators revealed with combined EEG and event-related optical signal

    J. Cogn. Neurosci.

    (2014)
  • D.M. Roberts

    Prestimulus oscillations in the alpha band of the EEG are modulated by the difficulty of feature discrimination and predict activation of a sensory discrimination process

    J. Cogn. Neurosci.

    (2014)
  • G. Boncompte

    Spontaneous alpha power lateralization predicts detection performance in an un-cued signal detection task

    PLoS One

    (2016)
  • S. Brüers et al.

    Alpha power modulates perception independently of endogenous factors

    Front. Neurosci.

    (2018)
  • V. Romei

    Spontaneous fluctuations in posterior α-band EEG activity reflect variability in excitability of human visual areas

    Cereb. Cortex

    (2008)
  • J. Samaha

    Distinct oscillatory frequencies underlie excitability of human occipital and parietal cortex

    J. Neurosci.

    (2017)
  • J. Lange

    Reduced occipital alpha power indexes enhanced excitability rather than improved visual perception

    J. Neurosci.

    (2013)
  • J. Keil

    Prestimulus beta power and phase synchrony influence the sound-induced flash illusion

    Cereb. Cortex

    (2014)
  • S. Nelli

    Fluctuations in instantaneous frequency predict alpha amplitude during visual perception

    Nat. Commun.

    (2017)
  • B.C. Bays

    Alpha-band EEG activity in perceptual learning

    J. Vis.

    (2015)
  • M.X. Cohen et al.

    Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors

    Cereb. Cortex

    (2013)
  • C.S.Y. Benwell

    Trial-by-trial co-variation of pre-stimulus EEG alpha power and visuospatial bias reflects a mixture of stochastic and deterministic effects

    Eur. J. Neurosci.

    (2018)
  • A. Wutz

    Temporal windows in visual processing: ‘prestimulus brain state’ and ‘poststimulus phase reset’ segregate visual transients on different temporal scales

    J. Neurosci.

    (2014)
  • C.S.Y. Benwell

    Pre-stimulus EEG power predicts conscious awareness but not objective visual performance

    eNeuro

    (2017)
  • M. Kaiser

    Single trial prestimulus oscillations predict perception of the sound-induced flash illusion

    Sci. Rep.

    (2019)
  • K. Limbach et al.

    Prestimulus alpha power influences response criterion in a detection task

    Psychophysiology

    (2016)
  • L. Iemi

    Spontaneous neural oscillations bias perception by modulating baseline excitability

    J. Neurosci.

    (2017)
  • B. van Vugt

    The threshold for conscious report: signal loss and response bias in visual and frontal cortex

    Science

    (2018)
  • N.A. Kloosterman

    Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

    eLife

    (2019)
  • L. Iemi et al.

    Moment-to-moment fluctuations in neuronal excitability bias subjective perception rather than strategic decision-making

    eNeuro

    (2018)
  • Y. Ko et al.

    A detection theoretic explanation of blindsight suggests a link between conscious perception and metacognition

    Philos. Trans. R. Soc. B Biol. Sci.

    (2012)
  • R.N. Denison

    Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence

    PNAS

    (2018)
  • M.A.K. Peters

    Who’s afraid of response bias?

    Neurosci. Conscious.

    (2016)
  • L. Lebovich

    Idiosyncratic choice bias naturally emerges from intrinsic stochasticity in neuronal dynamics

    Nat. Hum. Behav.

    (2019)
  • L. Iemi

    Multiple mechanisms link prestimulus neural oscillations to sensory responses

    eLife

    (2019)
  • F. Di Russo

    Source analysis of event-related cortical activity during visuo-spatial attention

    Cereb. Cortex

    (2003)
  • M.I. Vanegas

    Exploiting individual primary visual cortex geometry to boost steady state visual evoked potentials

    J. Neural Eng.

    (2013)
  • S. Haegens

    Laminar profile and physiology of the α rhythm in primary visual, auditory, and somatosensory regions of neocortex

    J. Neurosci.

    (2015)

    • Cited by (0)

    View full text
    Published by Elsevier Ltd.