Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance

Benwell, Christopher S. Y.; Tagliabue, Chiara F.; Veniero, Domenica; Cecere, Roberto; Savazzi, Silvia; Thut, Gregor

doi:10.1523/eneuro.0182-17.2017

Cited by 186 publications

(242 citation statements)

References 77 publications

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“…This finding convincingly ties cortical excitability in our paradigm to a strategically applied criterion shift, as opposed to a change in detection sensitivity. Convergently, other studies also report a link between prestimulus low-frequency EEG activity and subjective perception, but not objective task performance 32,33 .…”

Section: Discussionmentioning

confidence: 93%

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

Kloosterman

Gee

Werkle‐Bergner

et al. 2017

Preprint

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Decision bias is traditionally conceptualized as an internal reference against which sensory evidence is compared. Instead, we show that individuals implement decision bias by shifting the rate of sensory evidence accumulation towards a decision bound. Participants performed a target detection task while we recorded EEG. We experimentally manipulated participants’ decision criterion for reporting targets using different stimulus-response reward contingencies, inducing either a liberal or a conservative bias. Drift diffusion modeling revealed that a liberal strategy biased sensory evidence accumulation towards target-present choices. Moreover, a liberal bias resulted in stronger midfrontal pre-stimulus 2-6 Hz (theta) power and suppression of pre-stimulus 8—12 Hz (alpha) power in posterior cortex. The alpha suppression in turn mediated the output activity of visual cortex, as expressed in 59—100 Hz (gamma) power. These findings show that observers can intentionally control cortical excitability to strategically bias evidence accumulation towards the decision bound that maximizes their reward.

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Section: Discussionmentioning

confidence: 93%

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

Kloosterman

Gee

Werkle‐Bergner

et al. 2017

Preprint

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“…Beta-band power in particular is associated with feedback signals (Bastos et al, 2015), stronger attention and top-down control (Buschman and Miller, 2007;Salazar et al, 2012), and the prioritization of the current brain states over new inputs (Spitzer and Haegens, 2017). In support to this view, stronger low frequency (<30 Hz) oscillations attenuate early evoked sensory responses (Iemi et al, 2019), increase the criterion of perceptual detection (resulting in fewer detections) by modulating baseline excitability (Limbach and Corballis, 2016;Benwell et al, 2017;Craddock et al, 2017;Iemi et al, 2017) and degrade performance in perceptual decisions (Haegens et al, 2011).…”

Section: Discussion (1500)mentioning

confidence: 97%

Brain dynamics for confidence-weighted learning

Meyniel

2019

Preprint

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Learning in a changing and uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions.Bayesian models use this confidence to regulate learning: for a given surprise, the update is smaller when confidence is higher. We explored the human brain dynamics sub-tending such a confidenceweighting using magneto-encephalography. During our volatile probability learning task, subjects' confidence reports conformed with Bayesian inference. Several stimulus-evoked brain responses reflected surprise, and some of them were indeed further modulated by confidence. Confidence about predictions also modulated pupil-linked arousal and beta-range (15-30 Hz) oscillations, which in turn modulated specific stimulus-evoked surprise responses. Our results suggest thus that confidence about predictions modulates intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations. Meyniel et al., 2015b), the weight of evidence (Rohe et al., 2019), the precision of predictions (Iglesias et al., 2013;Mathys et al., 2014;Vossel et al., 2014) discussed at the end of this article.Here, we propose to use optimal Bayesian models as a benchmark to formalize, at a computational level, the learning process. In particular, we formalize the notion of discrepancy between

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“…Recent studies have suggested that pre-stimulus alpha activity may reflect the criterion used to commit a specific response and may hence reflect a perceptual or decisional bias (Limbach & Corballis, 2016;Iemi, Chaumon, Crouzet, & Busch, 2017;Craddock, Poliakoff, El-deredy, Klepousniotou, & Lloyd, 2017;Iemi & Busch, 2018;Rohe, Ehlis, & Noppeney, 2019). Along such a role in perceptual decision making, alpha activity was shown to correlate with subjective awareness (Benwell, et al, 2017;Lange, Oostenveld, & Fries, 2013;Gulbinaite, İlhan, & VanRullen, 2017) and decision confidence (Samaha, Iemi, & Postle, 2017;Wöstmann, Waschke, & Obleser, 2018). Still, it remains unclear whether pre-stimulus activity indeed reflects an individual's intrinsic bias, or reflects processes that facilitate veridical sensory encoding, as previous work did not unambiguously quantify the relation of spontaneous brain activity to idiosyncratic and temporary biases.…”

Section: Introductionmentioning

confidence: 99%

Alpha Activity Reflects the Magnitude of an Individual Bias in Human Perception

Grabot

Kayser

2019

Preprint

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Biases in sensory perception can arise from both experimental manipulations and personal traitlike features. These idiosyncratic biases and their neural underpinnings are often overlooked in studies on the physiology underlying perception. A potential candidate mechanism mediating such idiosyncratic biases is spontaneous alpha activity, a prominent brain rhythm known to influence the processing of upcoming information in general. Using a time perception task, we here tested the hypothesis that alpha power reflects the compensation for an idiosyncratic bias. Importantly, to understand the interplay between idiosyncratic biases and contextual (temporary) biases induced by experimental manipulations, we quantified this relation before and after temporal recalibration. Using EEG recordings in human participants, we found that pre-stimulus alpha power correlates with the tendency to respond relative to an own idiosyncratic bias, with stronger alpha leading to responses matching the bias. In contrast, alpha power did not predict response correctness. These results also held after temporal recalibration and were specific to the alpha band, suggesting that alpha power indexes the cognitive effort to overcome an individual's momentary bias in perception. Significance statementThe brain is a biased organ, frequently generating systematically distorted percepts of the world, leading each of us to evolve in our own subjective reality. However, such biases are often overlooked or considered noise when studying the neural mechanisms underlying perception. We show that spontaneous alpha band activity predicts the degree of biasedness of human choices in a time perception task, suggesting that alpha power indexes the effort needed to overcome an individual's idiosyncratic bias. This result provides a window onto the neural underpinnings of subjective perception, and offers the possibility to quantify or manipulate such priors in future studies.

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Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance

Cited by 186 publications

References 77 publications

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

Brain dynamics for confidence-weighted learning

Alpha Activity Reflects the Magnitude of an Individual Bias in Human Perception

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