Alpha Oscillations Shape Sensory Representation and Perceptual Sensitivity
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Evaluation Summary:
This work investigates how prestimulus alpha neural oscillations differentially modulate sensory signal and noise during visual detection and demonstrates that alpha power correlates with the subject's perceptual discriminability but not with decision criterion, supporting that alpha power modulates sensory signals more strongly than noise. The key conceptual claim is directly related to existing claims in the literature, although this is an unusually elegant experimental demonstration of the phenomenon.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Abstract
Alpha activity (8–14 Hz) is the dominant rhythm in the awake brain and is thought to play an important role in setting the internal state of the brain. Previous work has associated states of decreased alpha power with enhanced neural excitability. However, evidence is mixed on whether and how such excitability enhancement modulates sensory signals of interest versus noise differently, and what, if any, are the consequences for subsequent perception. Here, human subjects (male and female) performed a visual detection task in which we manipulated their decision criteria in a blockwise manner. Although our manipulation led to substantial criterion shifts, these shifts were not reflected in prestimulus alpha band changes. Rather, lower prestimulus alpha power in occipital-parietal areas improved perceptual sensitivity and enhanced information content decodable from neural activity patterns. Additionally, oscillatory alpha phase immediately before stimulus presentation modulated accuracy. Together, our results suggest that alpha band dynamics modulate sensory signals of interest more strongly than noise.
SIGNIFICANCE STATEMENT The internal state of our brain fluctuates, giving rise to variability in perception and action. Neural oscillations, most prominently in the alpha band, have been suggested to play a role in setting this internal state. Here, we show that ongoing alpha band activity in occipital-parietal regions predicts the quality of visual information decodable in neural activity patterns and subsequently the human observer's sensitivity in a visual detection task. Our results provide comprehensive evidence that visual representation is modulated by ongoing alpha band activity and advance our understanding on how, when faced with unchanging external stimuli, internal neural fluctuations influence perception and behavior.
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Evaluation Summary:
This work investigates how prestimulus alpha neural oscillations differentially modulate sensory signal and noise during visual detection and demonstrates that alpha power correlates with the subject's perceptual discriminability but not with decision criterion, supporting that alpha power modulates sensory signals more strongly than noise. The key conceptual claim is directly related to existing claims in the literature, although this is an unusually elegant experimental demonstration of the phenomenon.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Reviewer #1 (Public Review):
1: The authors formulate competing hypotheses on the behavioral impact of alpha oscillations using signal detection theory (SDT) (Intro and Fig. 1). SDT is indeed well suited for this, as it is used to compute the orthogonal behavioral metrics d' (discriminability) and criterion (bias). However, soon the authors write:
"The higher d' for conservative trials may be due to the more skewed mapping between the false alarm (FA) rate to its Z-value in our d' computation. Specifically, when criterion (or the decision boundary) intersects the noise distribution at its right tail, small changes in FA rate are nonlinearly exaggerated after Z-transformation. As we did not observe a difference in accuracy between conservative and liberal trials, which is a more robust measure of perceptual discriminability when target …
Reviewer #1 (Public Review):
1: The authors formulate competing hypotheses on the behavioral impact of alpha oscillations using signal detection theory (SDT) (Intro and Fig. 1). SDT is indeed well suited for this, as it is used to compute the orthogonal behavioral metrics d' (discriminability) and criterion (bias). However, soon the authors write:
"The higher d' for conservative trials may be due to the more skewed mapping between the false alarm (FA) rate to its Z-value in our d' computation. Specifically, when criterion (or the decision boundary) intersects the noise distribution at its right tail, small changes in FA rate are nonlinearly exaggerated after Z-transformation. As we did not observe a difference in accuracy between conservative and liberal trials, which is a more robust measure of perceptual discriminability when target presence rate equals 50%, we argue that the observed statistically significant d' difference is equivocal."
And also:
"For the binning analyses, we mainly focused on the percentage correct (i.e., accuracy),
and hit and FA rates, because these metrics scale linearly (as opposed to d', which scales
nonlinearly as the hit rate increases or FA rate decreases linearly) and are well defined for both
behavioral data and MVPA outputs."And indeed from Fig. 3 onwards they do not really use SDT anymore, which is confusing given the Introduction and Fig. 1. I think it's also problematic, as accuracy, hit-rate and fa-rate are not orthogonal and are therefore much less suited to arbitrate between their competing hypotheses. As a result, I'm not convinced the paper accomplishes what it sets out to do in the Introduction.
2: Related, if indeed the authors choose to deviate from SDT, they should put the metric "% yes-choices" on equal footing with accuracy. For example, in Fig. 3A, we can see that alpha oscillations predict a reduction of hit-rate as well as fa-rate; this suggest that the main effect is actually on choice bias (% yes-choices) rather than accuracy. If that's true, then the title of this manuscript is misleading.
3: Have the authors considered to test for non-monotonic effects of alpha oscillations and cortical computation and behavior?
4: The authors use challenging and sophisticated methods, but these are introduced very casually. For example:
"To obtain a more fine-grained picture of the alpha power modulation of behavior, we applied generalized linear mixed models (GLMMs; see Methods) to account for both between-subjects and within-subject trial-by-trial response variability, and to estimate the effects of alpha oscillatory power on d' and criterion simultaneously."
And:
"To evaluate the quality of visual information coding, we used multivariate pattern analysis (MVPA), operationalizing the quality of visual representation as the neural classifier's classification performance. We used the priming trials to train binary classifiers to classify target-present vs. absent trials in a time-resolved manner, [...]".
It would help a lot if the authors could unpack their rationale some more. For example, why did they consider between-subjects effects, and could they show some scatter plots with between-subjects correlations before turning to the GLMM? Also, what is the question the authors wanted to answer that required training the classifier in a time-resolved manner (which I like, on a personal note)?
5: Throughout, the label "liberal trials" is odd, given that group-average criterion > 0 on those trials (Fig. 2C).
6: It would be nice to explicitly bridge to the literature on (pupil-linked) arousal predicted shifts in decision-making, and to findings on the relationship between alpha oscillations and (pupil-linked) arousal.
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Reviewer #2 (Public Review):
Zhou et al. investigates whether alpha-band (8-14 Hz) neural oscillations differentially modulate sensory signal and noise during visual detection. The authors reason that the preferential modulation of signal predicts a relationship between alpha power and the subject's perceptual discriminability but not decision criterion, and conversely, that the similar modulation of signal and noise predicts a relationship between alpha power and the subject's decision criterion but not perceptual discriminability. The authors find that alpha power in early visual cortex does not correlate with the block-wise changes in the subjects' decision criterion. However, trial-to-trial variations in visual cortical alpha power during the trial period before stimulus presentation correlate inversely with the subject's perceptual …
Reviewer #2 (Public Review):
Zhou et al. investigates whether alpha-band (8-14 Hz) neural oscillations differentially modulate sensory signal and noise during visual detection. The authors reason that the preferential modulation of signal predicts a relationship between alpha power and the subject's perceptual discriminability but not decision criterion, and conversely, that the similar modulation of signal and noise predicts a relationship between alpha power and the subject's decision criterion but not perceptual discriminability. The authors find that alpha power in early visual cortex does not correlate with the block-wise changes in the subjects' decision criterion. However, trial-to-trial variations in visual cortical alpha power during the trial period before stimulus presentation correlate inversely with the subject's perceptual discriminability. Moreover, lower prestimulus alpha power in visual areas is associated with enhanced information that can be decoded about the visual stimulus from recorded neural activity. Finally, the subject's accuracy depends on the phase of the alpha oscillations in parietal and frontal regions. Based on these findings, the authors conclude that alpha power modulates sensory signals more strongly than noise.
The question is interesting, the task design and priming procedures are rigorous and clever, and the analyses are sophisticated.
The conclusions of the paper would be more strongly supported if the concerns below could be addressed:
1. A potential strength of the manuscript consists in correlating alpha band power with not only the subject's accuracy (i.e. percentage of trials correct) but with the indices of d' and decision criterion from signal detection theory. Because any difference in accuracy can depend on a difference in only d', only criterion, or both, using d' and criterion provide a more precise quantification of behavior. However, this strength is undermined by the unquantified statistical bias in the estimates of d' and criterion as a result of limited sample size of certain categories of trials. This is an issue to which the authors themselves allude (at the bottom of page 4 and top of page 5) and is well documented (Macmillan and Creelman, 2004), but it is not addressed quantitatively in the manuscript. This issue therefore raises concern about measurements of d' and criterion throughout the manuscript. Because the manuscript's conclusions depend critically on the measurements of the subject's d' and criterion, therefore concern is also raised about the manuscript's conclusions.
2. Another potential strength of the manuscript involves shifting of the subject's decision criterion between blocks without changes in the subject's d' to isolate the relationship between decision criterion and alpha oscillations. However, the analyses in Figure 2C indicate that the subject's d' changed between blocks, and the brief argument that this difference in d' should be ignored is not quite convincing without detailed quantitative analyses. Because the possibility remains that d' changed between priming conditions, and yet no difference in alpha power could be detected between conditions, the result appears to be inconsistent with the finding that lower alpha power is related to enhanced d'.
3. The interpretations of the data at four places in the manuscript seem to be overly focused on a limited set of conditions or time windows, while not taking into account other conditions or time windows. This makes the interpretations appear incomplete and weakens confidence in the conclusions. (A) If trial-to-trial variations in alpha power are inversely correlated with d', then one should expect to see this relationship not only in the conservative priming condition but also in the liberal priming condition (Figure 3D). The absence of a significant relationship in the liberal priming condition appears to be inconsistent with the conclusions of the paper and is not addressed. (B) While the trial-to-trial prestimulus alpha power is explored for its relationship with either d' or criterion, the trial-to-trial alpha power during other task periods, in particular during stimulus presentation or during mask presentation, is not examined for its relationship with d' or criterion. (C) A significant relationship between the subject's accuracy and the phase of the alpha oscillations in visual ROIs is detectable at multiple brief time points before stimulus onset (Figure 5A), yet the authors state in the discussion that alpha phase in visual areas does not modulate the subject's accuracy. Instead, the authors focus on the relationship between the subject's accuracy and the alpha phase in parietal and frontal areas. (D) The prestimulus alpha power in visual cortex is significantly related to the criterion of the MVPA classifier during the conservative priming condition (page 7, "beta_c = 0.0096, p = 0.016"). This appears inconsistent with the conclusion that alpha power is independent of decision criterion.
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Reviewer #3 (Public Review):
In this paper, the authors considered how prestimulus alpha oscillations affect stimulus processing and perception, a classic problem that has attracted many prior studies. The authors introduced some manipulations to prime the subject and to modulate decision criterion. The main findings are that low prestimulus alpha is better for stimulus processing and perception and prestimulus alpha phase has a role in stimulus processing and perception.
This work, like similar studies in the past, proposes a linear relationship between prestimulus alpha and stimulus processing and perception, namely, lower alpha is better for stimulus processing and perception. There are also reports of a nonlinear relationship. See, for example, Linkenkaer-Hansen K, Nikulin VV, Palva S, Ilmoniemi RJ and Palva J M (2004). Prestimulus …
Reviewer #3 (Public Review):
In this paper, the authors considered how prestimulus alpha oscillations affect stimulus processing and perception, a classic problem that has attracted many prior studies. The authors introduced some manipulations to prime the subject and to modulate decision criterion. The main findings are that low prestimulus alpha is better for stimulus processing and perception and prestimulus alpha phase has a role in stimulus processing and perception.
This work, like similar studies in the past, proposes a linear relationship between prestimulus alpha and stimulus processing and perception, namely, lower alpha is better for stimulus processing and perception. There are also reports of a nonlinear relationship. See, for example, Linkenkaer-Hansen K, Nikulin VV, Palva S, Ilmoniemi RJ and Palva J M (2004). Prestimulus oscillations enhance psychophysical performance in humans. Journal of Neuroscience 24:10186-10190. There should be some discussion on which of the two relationships are theoretically more plausible. It could be that detecting the nonlinear relationship is more difficult.
The work is rigorous and elegantly designed but although the authors frame the study in terms of decision making criteria and how these may shift on a trial-by-trial basis, the data analysis do not directly address these claims, instead focussing on the linear relation between alpha and perception. A more detailed analysis of the data centred on the decision variables may yield interesting new insights.
On a technical note, defining alpha range to be 6 - 14 Hz is too wide. Both high theta and low beta is included in this range. This makes the attribution of observed effects exclusively to alpha difficult.
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