Perceptual inference employs intrinsic alpha frequency to resolve perceptual ambiguity

Abstract

The brain uses its intrinsic dynamics to actively predict observed sensory inputs, especially under perceptual ambiguity. However, it remains unclear how this inference process is neurally implemented in biasing perception of ambiguous inputs towards the predicted percepts. Using electroencephalography and intracranial recordings, we first show that the alpha-band frequency defines a unified time window for perceptual grouping across both space and time: information segments, either spatially or temporally segregated, will be integrated if they fall within the same alpha cycle. Moreover, predictions employ this prior knowledge on intrinsic alpha frequency to shift perceptual inference towards the most possibly observed percepts. Multivariate decoding analysis showed that perceptual inference, based on variance in prestimulus alpha frequency (PAF), biases post-stimulus neural representations by inducing preactivation of the predicted percepts. fMRI results additionally showed that prestimulus activity and intrinsic organization status in the frontoparietal attentional network predict perceptual outcomes, probably by modulating occipitoparietal PAFs.

No evidence for a single oscillator underlying discrete visual percepts

“…At the neural level, oscillations in brain activity have long been speculated to be involved in discrete perceptual sampling. For instance, within‐ and between‐subject variation in alpha‐band frequency (7–14 Hz) is predictive of temporal properties of visual (Baumgarten et al, 2018; Coffin & Ganz, 1977; Coffin, 1977; Gray & Emmanouil, 2020; Gulbinaite et al, 2017; Kristofferson, 1967b; Minami & Amano, 2017; Ro, 2019; Samaha & Postle, 2015; Shen et al, 2019) and cross‐modal perception (Cecere et al, 2015; Cooke et al, 2019; Keil & Senkowski, 2017), with higher‐frequency oscillations being associated with finer‐grained temporal resolution. Moreover, the phase of ongoing alpha activity predicts perception of near‐threshold visual stimuli (Alexander et al, 2020; Busch et al, 2009; Dugué et al, 2011; Mathewson et al, 2009; Samaha et al, 2015; Samaha et al, 2017; Sherman et al, 2016).…”

EEG Alpha Power Predicts the Temporal Sensitivity of Multisensory Integration

“…Importantly, in light of the increasing popularity of instantaneous frequency as a measure in EEG research (e.g. Babu Henry Samuel et al, 2018;Samaha & Postle, 2015;Shen et al, 2018;Wutz et al, 2018), our results caution against an interpretation of such effects in terms of genuine frequency shifts involving one oscillator, without controlling for the possibility of asymmetric power shifts over multiple oscillators.…”

Direct brain recordings reveal occipital cortex involvement in memory development

“…Whereas instantaneous alpha frequency reflects the timing of neuronal spiking and an oscillatory mechanism that is directly relevant to sensory perception (Cohen, 2014;Haegens et al, 2011;Klimesch et al, 2007;VanRullen, 2016), alpha desynchronization is directly relevant to memory encoding (Hanslmayr et al, 2016(Hanslmayr et al, , 2012. Thus, alpha frequency relates more to visual perception (Samaha and Postle, 2015;Shen et al, 2019;Wutz et al, 2018) than to visual memory performance. Indeed, we showed that alpha desynchronization, but not alpha frequency, was associated with individual differences in memory performance.…”

“…Electrophysiological evidence suggests that visual perception happens in a cyclic manner such that particular phases of the alpha cycle are associated with increased neural activity (Haegens et al, 2011;Klimesch et al, 2007) and thus more efficient sensory perception than others (Busch et al, 2009;Busch and VanRullen, 2010;Mathewson et al, 2009). Therefore, time-resolved peak alpha frequency, measured here by instantaneous frequency (Cohen, 2014), may index the temporal resolution of perception (Samaha and Postle, 2015;Shen et al, 2019;Wutz et al, 2018).…”

“…In addition, alpha power and instantaneous frequency may reflect dissociable properties of visual information processing. Whereas broadband alpha power may reflect the rate of neuronal spiking and predict memory outcomes (Hanslmayr et al, 2016(Hanslmayr et al, , 2012, instantaneous alpha frequency may reflect the timing of neuronal spiking and predict perceptual outcomes (Cohen, 2014;Samaha and Postle, 2015;Shen et al, 2019;Wutz et al, 2018). Thus, we anticipated that alpha power and frequency would show complementary but dissociable relationships with memory performance.…”