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The role of alpha oscillations in temporal binding within and across the senses

Nature Human Behaviour volume 6, pages 732–742 (2022)Cite this article

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Abstract

An intriguing notion in cognitive neuroscience posits that alpha oscillations mould how the brain parses the constant influx of sensory signals into discrete perceptual events. Yet, the evidence is controversial and the underlying neural mechanism unclear. Further, it is unknown whether alpha oscillations influence observers’ perceptual sensitivity (that is, temporal resolution) or their top-down biases to bind signals within and across the senses. Combining electroencephalography, psychophysics and signal detection theory, this multi-day study rigorously assessed the impact of alpha frequency on temporal binding of signals within and across the senses. In a series of two-flash discrimination experiments, 20 human observers were presented with one or two flashes together with none, one or two sounds. Our results provide robust evidence that pre-stimulus alpha frequency as a dynamic neural state and an individual’s trait index do not influence observers’ perceptual sensitivity or bias for two-flash discrimination in any of the three sensory contexts. These results challenge the notion that alpha oscillations have a profound impact on how observers parse sensory inputs into discrete perceptual events.

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Fig. 1: Experimental design, example trials, alpha frequency predictions and behavioural results.
Fig. 2: Influence of pre-stimulus alpha frequency on d′ (see also Supplementary Tables 5 and 7).
Fig. 3: Influence of pre-stimulus alpha frequency on Biascentre (see also Supplementary Tables 6 and 7).
Fig. 4: Psychometric functions and trait alpha peak frequency correlations with perceptual threshold (see also Supplementary Table 13).

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Data availability

All data relevant for reproducing the analyses in this manuscript are available upon request in the University of Birmingham eData repository for research purposes: https://doi.org/10.25500/edata.bham.00000729. Because of size limits of the repository, the uploaded data is low-pass filtered at 28 Hz and down-sampled to 76 Hz. The data are available to other researchers upon request, because of constraints imposed by the ethics approval under which this study was conducted.

Code availability

The code used for conducting the experiment, pre-processing and analysing the original data, and producing the figures is available on GitHub: https://github.com/sbuergers/dfi_publish.

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Acknowledgements

We thank all the participants for committing to such a long research study, A. Mihalik and M. Aller for help in setting up the experiment, M. Aller, J. Zumer, D. Meijer and G. Degano for valuable discussions about data analysis and C. Hickey, T. Van Viegen and A. Ferrari for helpful suggestions on the manuscript and figure design. This study was funded by the European Research Council starter grant ERC-multsens (U.N.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Computational Neuroscience and Cognitive Robotics Centre, University of Birmingham, Birmingham, UK

    Steffen Buergers & Uta Noppeney

  2. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands

    Uta Noppeney

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  1. Steffen Buergers
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Contributions

S.B. and U.N. conceived the study. S.B. and U.N. designed the experiments. S.B. collected the data. S.B. and U.N. analysed the data. S.B. and U.N. interpreted the data. S.B. and U.N. wrote the manuscript. U.N. provided direct supervision at all stages of the process.

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Correspondence to Steffen Buergers.

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Nature Human Behaviour thanks Nathan Weisz, Daniel Senkowski and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Typical study schedule.

Supplementary Fig. 1 The standard testing schedule lasted 5 consecutive days. Each day included two experimental runs of approximately 70 minutes task performance organized in ~6 min blocks with self-paced breaks in-between blocks. Additionally, 2–3 minutes of awake eyes-closed EEG activity were recorded prior to a day’s EEG recording of task related activity. Note that in the two-interval forced choice (2IFC) experiment no EEG was recorded.

Extended Data Fig. 2 Results from EEG source analysis: Influence of pre-stimulus alpha frequency on perceptual sensitivity (cf. Supplementary Tables 10, 12).

Supplementary Fig. 2a, Perceptual sensitivity (across-participants’ mean ± 1 within-subject SEM) is shown for low (1st tercile, dashed) and high (3rd tercile, solid) pre-stimulus alpha frequency in the visual source ROI across pre-stimulus time for experimental design (rows) x sensory context (columns). We observed only two significant effects of pre-stimulus alpha frequency on perceptual sensitivity in the ‘yes-no SOA’ experiment: In the ‘1 sound’ condition for the temporally resolved analysis (column 2: p = 0.015) and in the ‘0 sound’ condition for the time collapsed analysis (column 4: t20 = 3.286, p = 0.004, d = 0.270, 95% CI = [0.092, 0.416]). Neither effect was replicated in the ‘yes-no threshold’ experiment. Bayes factors (BF) show the evidence for HA relative to H0 plotted on a log10-scaled ordinate. Purple lines indicate thresholds for moderate evidence favouring H0 (< 1/3) or Ha (> 3)77. b, Within subject consistency of perceptual sensitivity relationship with alpha frequency over tasks. The difference in sensitivity between low and high alpha frequency (Δd’ = d’low – d’high) was not significantly correlated between the ‘yes-no SOA’ and the ‘yes-no threshold’ experiment over participants for any sensory context. SEM, Standard error of the mean. Paired t-test p < 0.01 (**).

Extended Data Fig. 3 Results from EEG source analysis: Influence of pre-stimulus alpha frequency on Biascentre (cf. Supplementary Tables 11, 12).

Supplementary Fig. 3a, Biascentre (across-participants’ mean ± 1 within-subject SEM) is shown for low (1st tercile, dashed) and high (3rd tercile, dashed) pre-stimulus alpha frequency in the visual source ROI across pre-stimulus time for experimental design (rows) x sensory context (columns). We observed no significant effect of pre-stimulus alpha frequency on bias in any of the experiments (all p > 0.05; columns 1–3, two-sided cluster randomization tests, column 4, paired t-tests). Bayes factors (BF) show the evidence for HA relative to H0 plotted on a log10-scaled ordinate. Purple lines indicate thresholds for moderate evidence favouring H0 (< 1/3) or Ha (> 3)77. b, Within subject consistency of bias relationship with alpha frequency over tasks. The difference in bias between low and high alpha frequency (Δbias = biaslow – biashigh) was significantly correlated between the ‘yes-no SOA’ and the ‘yes-no threshold’ experiments over participants in the 1 sound (see Supplementary Table 12) context. SEM, Standard error of the mean.

Extended Data Fig. 4 Source space trait alpha peak frequency correlations with perceptual threshold (cf. Supplementary Table 15).

Supplementary Fig. 4 Source space pre-stimulus trait alpha peak frequency correlations with temporal binding window estimates obtained from each experiment. In the ‘yes-no threshold’ experiment the binding window size is given as the asynchrony yielding approximately 50% performance accuracy for ‘2 flash + 0 sound’, ‘2 flash + 1 sound’, and ‘1 flash + 2 sound’ trials. The alpha temporal resolution hypothesis, based on previous studies, predicts a negative correlation between alpha frequency and temporal binding window length30,31,32. Bayes factors (BF) show the evidence for HA relative to H0 plotted on a log10-scaled ordinate. Purple lines indicate thresholds for moderate evidence favouring H0 (< 1/3) or Ha (> 3)77. SEM, Standard error of the mean.

Extended Data Fig. 5 Sensor space within subject analysis: Differences in pre-stimulus alpha frequency for one flash and two flash percepts.

Supplementary Fig. 5 In line with previous research41, we compared pre-stimulus alpha frequency (−0.6 to −0.1 s relative to first stimulus onset) between perceptual outcomes (“see one flash” or “see two flashes” responses) for each ‘2 flash’ condition using cluster-based randomization tests. The alpha band definition was 6–14 Hz, and the electrodes used were O2, PO4 and PO8, as in the signal detection analyses of sensitivity and bias. Please note that this approach cannot distinguish between perceptual sensitivity and bias. We did not observe any significant differences in alpha frequency for “one flash” versus “two flashes” reports. Error bars denote ± 1 within subject SEM. Bayes factors (BF) show the evidence for HA relative to H0 plotted on a log10-scaled ordinate. Purple lines indicate thresholds for moderate evidence favouring H0 (< 1/3) or Ha (> 3)77. SEM, Standard error of the mean.

Extended Data Fig. 6 Sensor space within subject analysis: The influence of pre-stimulus alpha power on perceptual sensitivity and bias.

Supplementary Fig. 6 Because alpha power and frequency are intimately related, we assessed the effect of pre-stimulus power on d’ and Biascentre. a, b, Perceptual sensitivity (a, across-participants’ mean ± 1 within-subject SEM) and bias (b, across-participants’ mean ± 1 within-subject SEM) are shown for low (1st tercile, orange) and high (3rd tercile, purple) pre-stimulus alpha power across pre-stimulus time in experimental design (rows) x sensory context (columns). We observed no significant effect of pre-stimulus alpha power on perceptual sensitivity and only one significant effect for bias in the 1 sound condition of the ‘yes-no threshold’ experiment (p = 0.011). Bayes factors (BF) show the evidence for HA relative to H0 plotted on a log10-scaled ordinate. Purple lines indicate thresholds for moderate evidence favouring H0 (< 1/3) or Ha (> 3)77. SEM, Standard error of the mean.

Extended Data Fig. 7 Consistency of individual perceptual thresholds across ‘yes-no SOA’, ‘yes-no threshold’ and ‘2IFC’ experiments (cf. Supplementary Table 4).

Supplementary Fig. 7 Left (columns 1–3): Pairwise correlations of perceptual window estimates (in ms) between different experiments (rows), separately for no sound, one sound and two sound contexts (columns). Right (column 4): Bayes factors show the evidence for HA relative to H0 plotted on a log10-scaled ordinate. Purple lines indicate thresholds for substantial evidence favouring H0 (< 1/3) or Ha (> 3)77. Separately for each sound context, we assessed the pairwise correlations between the perceptual thresholds from the ‘yes-no SOA’, the ‘yes-no threshold’ and the ‘2IFC’ experiments. In the ‘yes-no SOA’ and the ‘2IFC’ experiments the thresholds were obtained from psychometric functions fitted to percent correct scores as a function of SOA. In the ‘yes-no threshold’ experiment, they were obtained from adaptive staircases. Critically, only the threshold obtained from the ‘2IFC’ paradigm can be interpreted as a measure of sensitivity, because it depends only on the variance parameter of the underlying signal distribution, but not on a particular criterion. By contrast, the threshold obtained from ‘yes-no SOA’ or ‘yes-no threshold’ experiments depends on observers’ criterion (or bias) and the variance of the underlying signal distribution. Moreover, it is important to emphasize that the adaptive staircases of the ‘yes-no threshold’ experiment adjusted the SOA of the two flashes or sounds individually for each participant to match their “one flash” and “two flash” reports independently for the stimulus combinations: ‘2 flash + 0 sound’; ‘2 flash + 1 sound’; ‘1 flash + 2 sound’. Thus, while the thresholds from the ‘yes-no SOA’ experiment depended jointly on the ‘1 flash’ and the ‘2 flash’ conditions, the thresholds from the adaptive staircases depended either on the ‘1 flash’ or the ‘2 flash’ condition alone in a particular sound context. In the ‘0 sound’ and ‘1 sound’ conditions this difference is not critical for understanding the relationship between the thresholds for the ‘yes-no SOA’ and the ‘yes-no threshold’ experiments, because the ‘1 flash + 0 sound’ and ‘1 flash + 1 sound’ conditions do not vary across SOAs. Hence, we simply add the same constant to the accuracy values across different SOA levels. However, for the ‘2 sound’ context, the percent correct score of the ‘2 flash + 2 sound’ conditions varies across SOAs thereby affecting the shape of the psychometric function for the ‘yes-no SOA’ paradigm. Moreover, consistent with previous research32, we observed substantial inter-participant variability for the ‘1 flash + 2 sound’ condition of the ‘yes-no SOA’ experiment. Some observers almost never experienced the double flash illusion irrespective of SOA. Others experienced the double flash illusion on almost every trial despite SOAs of 200 ms. Some participants were more likely to perceive the double flash illusion for large relative to small SOAs. Further, in several participants the thresholds (or perceptual windows) estimated from the psychometric function were at the bounds. These patterns illustrate that threshold parameters cannot be reliably estimated for the ‘2 sound’ condition in yes-no paradigms, because the experience of the double flash illusion is very susceptible to various sorts of biases. By contrast, the ‘2IFC’ paradigm presents on each trial one flash and two flashes in separate intervals together with 0, 1 or 2 sounds (that is the sound is uninformative about the number of flashes), so that observers decisions are not affected by these biases. In summary, because the thresholds obtained from the different experiments have been estimated differently, we would not expect strong correlations, particularly not for the ‘2 sound’ conditions, for which only the ‘2IFC’ task can estimate reliable thresholds. Consistent with this conjecture, we observed strong pairwise correlations for the ‘0 sound’ and ‘1 sound’ contexts. The correlations were particularly strong between the thresholds from the ‘yes-no SOA’ and the ‘yes-no threshold’ experiments that are both influenced by sensitivity and bias. The thresholds for the ‘2IFC’ experiment that reflect only sensitivity correlate slightly less but still significantly with those from the yes-no experiments. In contrast to the strong correlations in the ’0 sound’ and ‘1 sound’ conditions, the threshold parameters estimated for the ‘2 sound’ conditions did not correlate across the ‘yes-no SOA’, ‘yes-no threshold’ or ‘2IFC’ experiment as indicated by Bayes Factors (BFs; column 4; for more detailed statistics see Supplementary Table 4). This lack of consistency for the ’2 sound’ condition mainly arises from the fact that observers’ “two flash” reports on ‘1 flash + two sound’ trials is susceptible to shifts in criterion within and across participants (see above), so that threshold could be reliably estimated only in the ‘2IFC’ paradigm.

Extended Data Fig. 8 Supplementary Fig. 8. Results from beamformer source analysis.

a. Post-stimulus source power is overlaid on three sections of a canonical brain in MNI space: \(\frac{{Var_{post\;[100\;ms,300\;ms]} - Var_{pre[ - 600\;ms, - 100\;ms]}}}{{Var_{pre[ - 600\;ms, - 100\;ms]}}}\). As expected, we observed a peak in source power in occipital regions contralateral to flash stimulus. b. The source activity extracted for the 7 grid points in the right occipital ROI is shown as a function of time together with the grand average ERP (pooled over O2, PO4 and PO8) for the 1-flash 0-sound trials.

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Buergers, S., Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nat Hum Behav 6, 732–742 (2022). https://doi.org/10.1038/s41562-022-01294-x

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