Illustration the alpha inhibition theory.

A, the alpha inhibition theory suggests that alpha inhibits sensory information processing in a phasic manner. If alpha activity is high, it suggests that the whole area is inhibited and thereby disengaged (Foxe et al., 1998; Jensen & Mazaheri, 2010; Klimesch et al., 2007). We propose a revision of this theory, whereby alpha activity exerts its phasic inhibition to regulate downstream information transfer, creating enhanced signal packages of prioritised information (see also Yang et al., 2023; Zhigalov & Jensen, 2020; Zumer et al., 2014). B, Illustration of the cross-model discrimination task. LEFT: in the EEG-experiment, trials were separated by a 4 s interval, in which a fixation cross was displayed. A brief central presentation of the cue (100 ms) initiated the trial, signalizing the target modality (see figure above from left to right: auditory, unspecified, visual). In the cue-to-target interval, the fixation cross was frequency-tagged at 36 Hz. At the same time, a sound was displayed over headphones, which was frequency-tagged at 40 Hz. Both tones and fixation cross contained no task-relevant information. The target consisting either of a Gabor patch or a tone was presented for 25 ms, Participants had to differentiate between 3 different Gabor rotations or tone pitches, respectively. In 50% of auditory and visually cued trials, a distractor in form of a random pitch or rotation of the un-cued modality was presented alongside the target. RIGHT: The MEG-experiment followed an almost identical setup. This time, trials were separated by a 1 s interval followed by a random jitter of 0 to 300 ms. The visual task was adjusted to now require discrimination between 3 different Gabor patch frequencies for the visual task. Lastly, a blocked design was incorporated where in block 1, no distractors were presented while in block 2, a random distractor from the stimulus pool of the non-target modality was always presented.

Analysis of task accuracy and reaction time indicates increased difficulty of auditory targets in the EEG study, and comparable difficulties in the MEG study.

A, Task accuracy compared between all 6 experimental conditions reveals a drop in accuracy for responses to auditory targets. B, reaction times of correct trials compared between all 6 experimental conditions. The slowest reaction times are observable following auditory targets alongside visual distractors. C, Task accuracy differences were only observable in the first block without distractors. D, reaction times to visual targets in the first block were strongly decreased compared to all other conditions. In the second block, no significant difference in reaction times was observable. EEG Study: N = 22; MEG-Study: N = 27; *** sig < .001; ** sig. < .01; * sig. < .05;

Post-cue modality specific early visual modulation of alpha power in anticipation of an auditory versus a visual target

A-B, The time course of post-cue alpha power. Cluster permutation analysis resulted in two condition effects, both indicating heightened alpha activity when expecting an auditory compared to a visual target (C: p < .01; D: p < .01). C-D, Time-frequency representation of power in the cue-to-target interval. A greater increase in alpha power was observed when expecting an auditory target (average over significant electrodes for the condition difference in A). E, The time course of post-cue alpha power. Cluster permutation analysis resulted in a condition effects, indicating heightened alpha activity when expecting an auditory compared to a visual target (p = .034). F, source localization of the condition difference between expecting an auditory versus a visual target, revealing a significant cluster in early visual areas with stronger effects on the right hemisphere (p < .01). G-H, Time-frequency representation of power in the cue-to-target interval. A greater increase in alpha power was observed when expecting an auditory target (average over electrodes that showed maximal condition difference in E). A, B, E, Cluster electrodes are marked in white. Shading represents standard error from the mean; Δ / ∑ represents (a-b)/(a+b) normalization.

Increase in amplitude of both visual and auditory frequency tagged responses when anticipating visual or auditory targets

Event-related potentials and scalp topographies reveal distinct modality specific responses at the tagged frequencies. A) auditory steady-state evoked potential (ASSEP) averaged over 6 central electrodes displaying the highest 40 Hz power (Fz, FC1, FC2, F11, F2, FCz). B, visual steady-state evoked potential (VSSEP) averaged over 4 occipital electrodes displaying the highest 36 Hz power (POz, O1, O2, Oz C, auditory steady-state evoked fields (ASSEF) averaged over 20 temporal sensors displaying the highest 40 Hz power (10 right, 10 left). D, visual steady-state evoked fields (VSSEF) averaged over 10 occipital sensors, displaying the highest 36 Hz power. E, ASSEF source localization revealed a significant positive cluster in the right-hemispheric early auditory cortex (p < .001). F, VSSEF source localization revealed a significant positive cluster in the early visual cortex (p < .001). G,I, In both the EEG and the MEG study, the Hilbert-envelope of the 40 Hz ASSEP/ASSEF reveals an increase shortly before target onset when anticipating an auditory compared to a visual target (EEG: p = .041; MEG: p = .043 ); H,J), The Hilbert-envelope of the 36 Hz VSSEP/VSSEF likewise reveals an increase shortly before target onset when anticipating an auditory compared to a visual target, both in the EEG as well as the MEG study (EEG: p = .014; MEG: p = .019).K, Condition differences in the 40 Hz ASSEF response did not reach significance in sensor space. L, Condition differences in the 36 Hz VSSEF response were significant over several areas of the visual stream, including most strongly the medial occipital cortex, the calcarine fissure, and the precuneus (p = .047); note: Cluster electrodes are marked in white. Shading represents standard error from the mean. Δ / ∑ represents (a-b)/(a+b) normalization.

Relationship between cue induced alpha modulation and amplitude of frequency tagged responses.

Previously obtained alpha clusters (see Fig. 3) were correlated over trials with 40 Hz and 36 Hz clusters (see Fig. 4), where alpha electrodes/sensors were applied as seeds. The analysis was performed using a cluster-permutation approach, testing a correlation model against a 0-correlation model. Clusters significantly diverging from the 0-correlation model are presented topographically. Additionally, median splits between high and low alpha trials as well as correlation coefficients of these clusters are displayed for all participants A-B, a positive correlation is visible between alpha activity in the last 400 ms and steady state potentials shortly before target onset when expecting a visual target (36 HZ: p = .013; 40 Hz: p = .009). D, when expecting an auditory target, there is a positive correlation visible between alpha activity in the last 400 ms and 36 Hz activity shortly before target onset (p = .010). E, the correlation between alpha activity 400 ms and 36 Hz activity shortly before target onset changes its direction depending on whether an auditory or a visual target is expected (p = .037). C, a positive correlation is also visible between alpha activity as early as ∼1200 ms to 400 ms and 36 Hz activity shortly before target onset when expecting a visual target (p = .016). F-H, a positive correlation is visible between alpha activity in the last 500 ms as well as alpha activity in the last 1500ms–1000 ms and steady state potentials shortly before target onset when expecting a visual target (36 HZ late: p = .013; 40 Hz late: p = .009; 40 Hz early: p = 002). I-K, when expecting an auditory target, there is a positive correlation between alpha activity in the last 500 ms as well as alpha activity in the last 1500ms–1000 ms and steady state potentials shortly before target onset (36 HZ late: p < .001; 40 Hz late: p = .005; 36Hz early: p = 011). A-K, EEG: N = 22; MEG: N = 27; *** sig < .001; ** sig. < .01; * sig. < .05. + sig. < .1

Steady-state response in the intermodulation frequency and its behavioural relevance.

A, the Hilbert-envelope of the 4 Hz steady-state response reveals an increase shortly before target onset when anticipating an auditory compared to a visual target (p < .01). B, there is a trial-by-trial correlation between 4 Hz activity and reaction time when a visual target without distractor was presented. The correlation is further illustrated by a median split between fast and slow reaction time trials as well as by correlation coefficients for each participant. C, replication of the results presented in (A) in our MEG-study (p = .006). D, source localization showed activity over auditory sensory areas, but did not reach significance. EEG: N = 22; MEG: N = 27; ** sig. < .01;

Distractor cost and attentional benefit.

A-B, Illustration of distractor cost: mean performance over trials with distractors was subtracted from mean performance over trials without distractors. Distractor effects were observable for accuracy as well as reaction time; A, accuracy: auditory- – auditory +: M = 10.0 %; SD = 7.3; p < .001; t(21) = 7.32; visual- – visual+: M = 1.5%; SD = 3.06; p = .02). The effect was stronger for auditory than for visual target trials (p < .001; t(21) = 7.67). Reaction time: (auditory- - auditory+: M = -108.1 ms; SD = 84.8; p < .001; t(21) = -5.98; visual- - visual+: M = 123.6 ms; SD = 76.3; p < .001; t(21) = 7.60). auditory distracters decreased response time to visual targets (p < .001; t(21) = -11.99). B, (accuracy: auditory- – auditory +: M = 7.2 %; SD = 7.5; p = .001; t(25) = 4.9; visual- – visual+: M = -7.6%; SD = 10.80; p < .01; t(25) = -3.59; Reaction time: auditory- – auditory +: M = -20.64 ms; SD = 57.6; n.s.: p = .08; t(25) = -1.83; visual- – visual+: M = 60.1 ms ; SD = 58.52; p < .001; t(25) = 5.23). C, Illustration of attentional benefit: mean performance over unspecified trials was subtracted from mean performance over modality-cued trials without distractor. attentional benefit auditory: unspecifically cued auditory targets - informatively cued auditory targets = M = 81.2 ms; SD = 54.9; p < .001; t(21) = 6.94; attentional benefit visual: unspecifically cued visual targets - informatively cued visual targets -; M = 54.4 ms; SD = 41.1 ; p < .001; t(21) = 5.19). The magnitude of the effect on reaction time also differed between conditions (p = .043; t(21) = 2.16), with stronger attentional benefit for auditory target cues. Attentional cues did not affect response accuracy, neither in auditory nor visual target conditions (auditory: p = 0.49; visual: p = 0.32). EEG Study: N = 22; MEG-Study: N = 27; *** sig < .001; ** sig. < .01; * sig. < .05;

Timecourse of alpha activity and frequency-tagging responses for the ambivalent compared to the visually-cued condition.

A, alpha activity compared between expecting a visual target and having received an ambivalent cue. B, 36 Hz frequency-tagging response between expecting a visual target and having received an ambivalent cue. C, 40 Hz frequency-tagging response between expecting a visual target and having received an ambivalent cue.

Correlation of prestimulus alpha change from baseline with reaction time in the MEG study.

A, The analysis was performed using a cluster-permutation approach, testing a correlation model against a 0-correlation model. Clusters significantly diverging from the 0-correlation model are presented topographically (p = .037). Additionally, median splits between fast and slow reaction time trials (p = .013; t(25) = -2.67) as well as correlation coefficients (p = .003; t(25) = -3.34) of these clusters are displayed for all participants. A negative correlation is visible between alpha modulation and reaction times in the last 500 ms before target onset when expecting a visual target. B, Correlation between alpha modulation and reaction time for each participant. Black diamonds represent trials from the first block (without distractor) and blue dots represent trials from the second block (with auditory distractor).

Correlation of 36 Hz change from baseline with reaction time in the MEG study.

A, The analysis was performed using a cluster-permutation approach, testing a correlation model against a 0-correlation model. Clusters significantly diverging from the 0-correlation model are presented topographically (p = .040). Additionally, median splits between fast and slow reaction time trials (p = .005; t(25) = -3.10) as well as correlation coefficients (p = .002; t(25) = -3.46) of these clusters are displayed for all participants. A negative correlation is visible between 36 Hz modulation and reaction times in the last 500 ms before target onset when expecting a visual target. B, Correlation between 36 Hz modulation and reaction time for each participant. Black diamonds represent trials from the first block (without distractor) and blue dots represent trials from the second block (with auditory distractor).

Illustration of eye-tracking during the cue-to-target interval (2.5 – 0 s before target onset).

All datapoints of eye-positions during the cue-to-trial interval for all trials and all participants were plotted with 5% visibility on top of each other. Only 3% of datapoints showed eye-movement larger than 3 degrees of visual angle away from the fixation cross.

Individual ERP power spectra of the cue-to-target interval when anticipating an auditory target in the MEG-study.

Fast-fourier transformation was applied to the averaged trials using a dynamic hanning-tapered sliding time-window of 7 cycles per frequency. The Dotted line represents 40 Hz (auditory frequency-tagging).

Individual ERP power spectra of the cue-to-target interval when anticipating a visual target in the MEG-study.

Fast-fourier transformation was applied to the averaged trials using a dynamic hanning-tapered sliding time-window of 7 cycles per frequency. The Dotted line represents 36 Hz (auditory frequency-tagging).

Individual ERP power spectra of the cue-to-target interval when anticipating an auditory target in the EEG-study.

Fast-fourier transformation was applied to the averaged trials using a dynamic hanning-tapered sliding time-window of 7 cycles per frequency. The Dotted line represents 40 Hz (auditory frequency-tagging).

Individual ERP power spectra of the cue-to-target interval when anticipating a visual target in the EEG-study.

Fast-fourier transformation was applied to the averaged trials using a dynamic hanning-tapered sliding time-window of 7 cycles per frequency. The Dotted line represents 36 Hz (auditory frequency-tagging).

Exemplary illustration of the correlation between alpha power (0.5 – 0 s before target onset) and 36 Hz steady-state response (0.5 – 0 s before target onset) for each participant.

Alpha activity was averaged over the significant group difference cluster for alpha condition differences (seed cluster). Frequency-tagging activity was averaged over the significant cluster in the correlation with the alpha seed activity.