Prestimulus alpha-band power biases visual discrimination confidence, but not accuracy

Abstract

The magnitude of power in the alpha-band (8–13 Hz) of the electroencephalogram (EEG) prior to the onset of a near threshold visual stimulus predicts performance. Together with other findings, this has been interpreted as evidence that alpha-band dynamics reflect cortical excitability. We reasoned, however, that non-specific changes in excitability would be expected to influence signal and noise in the same way, leaving actual discriminability unchanged. Indeed, using a two-choice orientation discrimination task, we found that discrimination accuracy was unaffected by fluctuations in prestimulus alpha power. Decision confidence, on the other hand, was strongly negatively correlated with prestimulus alpha power. This finding constitutes a clear dissociation between objective and subjective measures of visual perception as a function of prestimulus cortical excitability. This dissociation is predicted by a model where the balance of evidence supporting each choice drives objective performance but only the magnitude of evidence supporting the selected choice drives subjective reports, suggesting that human perceptual confidence can be suboptimal with respect to tracking objective accuracy.

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

“…Indeed, due to lack of a time dimension to track evidence accumulation, SDT cannot distinguish between these two possibilities 3,4 . Thus far, trial-to-trial variations in the criterion have been shown to correspond to spontaneous fluctuations in neural excitability, as measured in prestimulus oscillatory cortical activity in the 8-12 Hz (alpha) band [4][5][6] . Alpha oscillations, in turn, have been proposed to be involved in the gating of task-relevant sensory information 7 , in line with adaptive regulation of evidence accumulation.…”

Tuned normalization in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior

“…Although dominant models suggest that confidence reflects an optimal readout of the probability that a decision is correct (Ratcliff and Rouder, 1998;Ratcliff and McKoon, 2008;Pleskac and Busemeyer, 2010;Tsetsos et al, 2012;Fetsch et al, 2014;Kiani et al, 2014;Pouget et al, 2016;Sanders et al, 2016;Zylberberg et al, 2016), it appears challenging for such models to account for counterintuitive behaviors in which confidence and accuracy do not covary (Rahnev et al, 2011(Rahnev et al, , 2012a(Rahnev et al, , 2012bKoizumi et al, 2015;Maniscalco et al, 2016;Samaha et al, 2016). An alternative hypothesis suggesting that confidence reflects a heuristic reliance on decision-congruent evidence (Zylberberg et al, 2012;Koizumi et al, 2015;Maniscalco et al, 2016;Samaha et al, 2016Samaha et al, , 2017 captures many of these behaviors, and is supported by human intracranial electrophysiology (Peters et al, 2017b).…”

“…In light of this discussion, and of the empirically observed tuned normalization in cortical areas, a biologically plausible model of sensory evidence accumulation ought to implement more than one level of lateral inhibition and consider how such tuned normalization may affect a neuron's role in the circuitry. Further, such stratification of tuned normalization could provide a neural mechanism to explain findings that confidence judgments rely on the magnitude of decisioncongruent evidence (Zylberberg et al, 2012;Aitchison et al, 2015;Koizumi et al, 2015;Maniscalco et al, 2016;Peters et al, 2017b;Samaha et al, 2017). Specifically, the output of less normalized 'detection' neurons could be used to index decision-congruent evidence and therefore be used for confidence rating.…”

“…These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by capitalizing on the diversity of neural resources available. Samaha et al, 2016Samaha et al, , 2017. In other words, to compute confidence the system uses a suboptimal heuristic that overly relies on decision-congruent evidence magnitude rather than optimal computations.…”

Unraveling the relation between EEG-correlates of attentional orienting and sound localization performance: a diffusion model approach

“…It is relatively undisputed that alpha power lateralization tracks the locus and timing of spatial attention (Bae & Luck, 2018;Foster et al, 2017;Samaha, Iemi, & Postle, 2017). In addition, a growing body of evidence supports the notion that the alpha rhythm as a correlate of spatial attention, so far predominantly investigated in the visual attention literature, analogously operates in different modalities (Haegens et al, 2011;Klatt et al, 2018aKlatt et al, , 2018bThorpe, D'Zmura, & Srinivasan, 2012;Wöstmann et al, 2016Wöstmann et al, , 2018.…”