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. The process of perceptual inference can be well illustrated by the phenomenon of bistable apparent motion in the Ternus display, in which subjective perception spontaneously alternates between element motion (EM) and group motion (GM) percepts depending on whether two consecutively presented frames are grouped over time or not. The frequency of alpha-band oscillations has long been hypothesized to gate the temporal window of perceptual grouping over time. Under this hypothesis, variation in the intrinsic alpha frequency should predict perceptual outcome of the bistable Ternus display. Moreover, we hypothesize that the perception system employs this prior knowledge on intrinsic alpha frequency to resolve perceptual ambiguity, by shifting perceptual inference towards the predicted percepts. Using electroencephalography and intracranial recordings, we showed that both between and within subjects, lower prestimulus alpha frequencies (PAFs) predicted the EM percepts since the two frames fell in the same alpha cycle and got temporally integrated, while higher PAFs predicted the GM percepts since the two frames fell in different alpha cycles. Multivariate decoding analysis between the EM percepts with lower PAFs and the GM percepts with higher PAFs further revealed a representation of the subsequently reported bistable percept in the neural signals shortly before the actual appearance of the second frame. Therefore, perceptual inference, based on variation in intrinsic PAFs, biases poststimulus neural representations by inducing preactivation of the predicted percepts. In addition, enhanced prestimulus blood-oxygen-level–dependent (BOLD) signals and network dynamics in the frontoparietal network, together with reduced prestimulus alpha power, upon perceiving the EM percepts suggest that temporal grouping is an attention-demanding process.

Understanding Neural Oscillations in the Human Brain: From Movement to Consciousness and Vice Versa

“…Interestingly, Parkkonen et al (2008) showed that the perception of vase or face in the Rubin's picture is accompanied by a respective modulation of 12 and 15 Hz for magnetoencephalographic oscillations. Shen et al (2019) demonstrated by means of EEG and intracranial recordings that variation in the intrinsic frequency peak of the alpha oscillation predicts the perceptive consciousness of the bistable Ternus display (He and Ooi, 1999).…”

“…Electroencephalography (EEG) (Haegens et al, 2010; Braboszcz and Delorme, 2011; Baird et al, 2014; Horschig et al, 2014; Shafto and Pitts, 2015; Koivisto et al, 2016; Ye et al, 2019) and functional magnetic resonance imaging (fMRI) (Kucyi, 2018; Kucyi et al, 2018; Demertzi et al, 2019; Golkowski et al, 2019; Liégeois et al, 2019; Yin et al, 2019) are commonly used to study general attention and consciousness in humans. Although the high temporal resolution of the EEG (timing in milliseconds range) and high spatial resolution of the fMRI (location in millimeters range) are viewed as complementary for understanding neural processes (Bréchet et al, 2019; Shen et al, 2019), recent evidence (Itthipuripat et al, 2019) demonstrated that hemodynamic attentional modulations measured in the early sensory cortex are differentially related to evoked EEG potentials, as they are linked more to later than early evoked potentials.…”

Perception of the difference between past and present stimulus: A rare orientation illusion may indicate incidental access to prediction error-like signals

“…As demonstrated in recent simulations that coupled predictive coding schemes to intrinsic brain dynamics, such a computational strategy ensures that conditional expectation maintains uncertainty, which in turn allows for flexible representations of contingencies and ambiguities in the world [67]. As our experiments imposed neither specific demand on the task, nor provided additional contextual influence, further studies may unravel how the presently low frequency of the illusory percept varies with attention [11,12], with spontaneous activity [68], or with global changes in brain state [69]. We here propose a speculative scheme of predictive coding, where error signals, emerging most probably from first steps of cortical processing dynamics, are not explained away but maintained and pushed through the perceptual hierarchy to potentially benefit flexible context-dependent and time-critical post-perceptual decisions [31,[70][71][72][73][74][75] at higher cortical entities.…”

No Evidence for a Single Oscillator Underlying Discrete Visual Percepts

“…It could still be that discrete sampling theory is true and is neurally instantiated via oscillations, but that these two illusions rely on different frequencies which are themselves uncorrelated. A large body of work has linked alpha-band oscillations to temporal windows of processing (Cecere et al, 2015; S. Coffin & Ganz, 1977; Cooke et al, 2019; Grabot et al, 2017; Gray & Emmanouil, 2020; Kristofferson, 1967b; Minami & Amano, 2017; Samaha & Postle, 2015; Shen et al, 2019; Varela et al, 1981; Wutz et al, 2018) and even specifically to the flash-lag illusion (Chakravarthi & VanRullen, 2012; Chota & VanRullen, 2019). On the other hand, lower-frequency oscillations in the theta range are also often implicated in establishing temporal windows of perception (Nakayama et al, 2018; Wutz et al, 2016; for review see VanRullen, 2016).…”

“…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; S. Coffin & Ganz, 1977; Stephen 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, 2017; Sherman et al, 2016).…”