Re-evaluating rhythmic attentional switching: Spurious oscillations from shuffling-in-time

Brookshire, Geoffrey

doi:10.1101/2021.05.07.443101

Cited by 12 publications

(10 citation statements)

References 102 publications

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“…Within each condition, for each location separately, we compared the observed data against the surrogate data using the two non‐parametric testing analyses described above ([1] score the group‐averaged power spectrum against a group‐averaged surrogate distribution, and [2] perform a paired‐samples t ‐test of individual power spectra vs. the medians of individual surrogate distributions). We also investigated whether the significant effects at the pooled non‐cued locations in the moderated informative cue condition could not be explained by an autocorrelation in the behavioural time course (Brookshire, 2021). We used the Monte Carlo singular spectrum analysis (SSA) method, originally proposed by Allen and Smith (1996), to differentiate the signal from aperiodic background activity.…”

Section: Methodsmentioning

confidence: 99%

No evidence of rhythmic visuospatial attention at cued locations in a spatial cuing paradigm, regardless of their behavioural relevance

Werf

Oever

Schuhmann

et al. 2021

Eur J of Neuroscience

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Recent evidence suggests that visuospatial attentional performance is not stable over time but fluctuates in a rhythmic fashion. These attentional rhythms allow for sampling of different visuospatial locations in each cycle of this rhythm. However, it is still unclear in which paradigmatic circumstances rhythmic attention becomes evident. First, it is unclear at what spatial locations rhythmic attention occurs. Second, it is unclear how the behavioural relevance of each spatial location determines the rhythmic sampling patterns. Here, we aim to elucidate these two issues. Firstly, we aim to find evidence of rhythmic attention at the predicted (i.e. cued) location under moderately informative predictor value, replicating earlier studies. Secondly, we hypothesise that rhythmic attentional sampling behaviour will be affected by the behavioural relevance of the sampled location, ranging from non‐informative to fully informative. To these aims, we used a modified Egly‐Driver task with three conditions: a fully informative cue, a moderately informative cue (replication condition), and a non‐informative cue. We did not find evidence of rhythmic sampling at cued locations, failing to replicate earlier studies. Nor did we find differences in rhythmic sampling under different predictive values of the cue. The current data does not allow for robust conclusions regarding the non‐cued locations due to the absence of a priori hypotheses. Post‐hoc explorative data analyses, however, clearly indicate that attention samples non‐cued locations in a theta‐rhythmic manner, specifically when the cued location bears higher behavioural relevance than the non‐cued locations.

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Section: Methodsmentioning

confidence: 99%

No evidence of rhythmic visuospatial attention at cued locations in a spatial cuing paradigm, regardless of their behavioural relevance

Werf

Oever

Schuhmann

et al. 2021

Eur J of Neuroscience

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“…Recent behavioral evidence (Venables, 1960;Landau and Fries, 2012;Dugue and VanRullen, 2014;Song et al, 2014) shows that, instead of a smooth and continuous behavior, spatial attention samples the visual environment rhythmically, leading to fluctuating periods of perceptual sensitivity [see VanRullen (2016) for a review]. In other words, these studies suggest that attention and perception might not be attached to a specific location in space (e.g., the cued location), but rather exhibit a temporal rhythmicity between relevant spatial locations [but see Brookshire (2021) for a critical perspective on these observations].…”

Section: Attention Explores the Space Rhythmicallymentioning

confidence: 99%

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Amengual

Hamed

2021

Front. Neural Circuits

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Persistent activity has been observed in the prefrontal cortex (PFC), in particular during the delay periods of visual attention tasks. Classical approaches based on the average activity over multiple trials have revealed that such an activity encodes the information about the attentional instruction provided in such tasks. However, single-trial approaches have shown that activity in this area is rather sparse than persistent and highly heterogeneous not only within the trials but also between the different trials. Thus, this observation raised the question of how persistent the actually persistent attention-related prefrontal activity is and how it contributes to spatial attention. In this paper, we review recent evidence of precisely deconstructing the persistence of the neural activity in the PFC in the context of attention orienting. The inclusion of machine-learning methods for decoding the information reveals that attention orienting is a highly dynamic process, possessing intrinsic oscillatory dynamics working at multiple timescales spanning from milliseconds to minutes. Dimensionality reduction methods further show that this persistent activity dynamically incorporates multiple sources of information. This novel framework reflects a high complexity in the neural representation of the attention-related information in the PFC, and how its computational organization predicts behavior.

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“…However, Brookshire (2021) claims that this does not allow to distinguish between periodic and aperiodic temporal structures. We argue that a correct discrimination between periodic and aperiodic temporal structures can be based on a parametrization of the spectrum.…”

Section: Discussionmentioning

confidence: 99%

“…A recent preprint (Brookshire, 2021) raised the point that testing the observed data against randomization distributions obtained by randomly pairing BRVs and POIs from different trials allows to reject the null hypothesis that there is no consistent temporal structure in the accuracy time course. However, Brookshire (2021) claims that this does not allow to distinguish between periodic and aperiodic temporal structures. We argue that a correct discrimination between periodic and aperiodic temporal structures can be based on a parametrization of the spectrum.…”

Section: Discussionmentioning

confidence: 99%

Quantifying rhythmicity in perceptual reports

Tosato

Rohenkohl

Dowdall

2022

Preprint

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Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the Discrete Fourier Transform (DFT) and the Least Square Spectrum (LSS). DFT and LSS can be applied both on the averaged accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effect) or on the population (random-effect) of simulated participants. Multiple comparisons across frequencies were corrected using False-Discovery-Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated Sensitivity, Specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the averaged-accuracy-time-course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved Sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest Specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effect approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.

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Re-evaluating rhythmic attentional switching: Spurious oscillations from shuffling-in-time

Cited by 12 publications

References 102 publications

No evidence of rhythmic visuospatial attention at cued locations in a spatial cuing paradigm, regardless of their behavioural relevance

No evidence of rhythmic visuospatial attention at cued locations in a spatial cuing paradigm, regardless of their behavioural relevance

Revisiting Persistent Neuronal Activity During Covert Spatial Attention

Quantifying rhythmicity in perceptual reports

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