Critical dynamics of endogenous fluctuations predict cognitive flexibility in the Go/NoGo task

Simola, Jaana; Zhigalov, Alexander; Morales‐Muñoz, Isabel; Palva, Satu

doi:10.1038/s41598-017-02750-9

Cited by 43 publications

(86 citation statements)

References 46 publications

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“…However, these indicated moderate reliability at most (α = .61 for circle drawing and .56 for tapping). Simola et al (2017) ran their task twice on each participant, changing only a taskirrelevant feature of the stimulus (colour), and found no significant differences in either the autocorrelation, the PSD, or the DFA between time 1 and 2. However, nonsignificant results are difficult to interpret, and this analysis does not give an estimate of the extent of repeatability.…”

Section: Individual Differencesmentioning

confidence: 99%

“…Two recent studies have aimed to link temporal structures to individual differences in performance. Firstly, Simola et al (2017) extracted the DFA slopes from the RT of a Go/No-Go task. They found that participants with steeper DFA slopes made less commission errors on the task (r-value = -.35), but found no correlation with mean RT or standard deviation of RT.…”

Section: Individual Differencesmentioning

confidence: 99%

“…Despite the different findings, their interpretations rely on similar constructs. On the one hand, Simola et al (2017) interpret their negative correlation between temporal structure and performance as evidence that brains which operate closer to the critical point show higher long-term correlations and allow for the mental flexibility needed to perform the Go/No-Go task. On the other hand, Irrmischer et al (2017) interpret their positive correlation as evidence that brains which operate closer to the critical point show higher long-term correlations and allow for the successful dynamics of switching attention from task-related to task-unrelated thoughtsand that the amount of attentional switching is affected by mood.…”

Section: Individual Differencesmentioning

confidence: 99%

“…properties have been underacknowledged. It has been suggested that the structures show individual differences (Madison, 2004;Torre et al, 2011;Simola et al, 2017), and that they may be used to distinguish healthy individuals from those with attentional dysfunction (Gilden & Hancock, 2007). However, the reliability of these individual differences remains largely unknown.…”

Section: Intra-individual Repeatability Of Temporal Dependenciesmentioning

confidence: 99%

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Temporal structure in sensorimotor variability: a stable trait, but what for?

Perquin¹,

Bompas²

2019

Preprint

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Human performance shows substantial endogenous variability over time. Such variability has been known to show temporal structures: Performance from action to action is not independent, but shows correlation with itself over time. While the existence of such dependencies has been frequently reported on, its measurement and interpretation come with a number of controversies, and its potential benefit for studying individual differences remains unclear. Two recent studies have linked temporal structures to individual differences in task performance, but with contrasting results. In the current study, we aim to investigate the intra-individual repeatability of these temporal structures in endogenous performance on the Metronome Task (25 participants, tested in two sessions ~45 minutes apart). Secondly, we examine the inter-individual correlates of the temporal structures (83 participants), specifically looking at: 1) task performance, 2) meta-cognitive ratings of attentional state, and 3) self-assessed personality traits (ADHD tendencies, mind wandering, and impulsivity).Rather than using one analysis method (as is common in the literature), we consistently compare all the frequently-used analysis methodsallowing us to investigate the structures without an a prior assumption on the underlying time scales.Results indicate that autocorrelation at lag 1 and Power Spectra Density slope showed the most intra-individual repeatability, ARFIMA(1,d,1) parameters showed the least, and evidence for Detrended Fluctuation Analysis was indeterminate with a moderate effect size at best. Overall, the autocorrelation at lag 1 seemed the best measure for studying individual differences, due to its high reliability and ease of use. Furthermore, with exception of the ARFIMA parameters, temporal structure was correlated with performance but not with subjective attentional state or self-assessed personality traits between participants.

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Section: Individual Differencesmentioning

confidence: 99%

Section: Individual Differencesmentioning

confidence: 99%

Section: Individual Differencesmentioning

confidence: 99%

Section: Intra-individual Repeatability Of Temporal Dependenciesmentioning

confidence: 99%

See 2 more Smart Citations

Temporal structure in sensorimotor variability: a stable trait, but what for?

Perquin¹,

Bompas²

2019

Preprint

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“…In particular, the reverberating regime covers a specific range ideal baseline [25] from which brain areas or neural circuits can 357 adapt to meet task demands [35,[46][47][48][49][50][51][52].…”

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confidence: 99%

A unified picture of neuronal avalanches arises from the understanding of sampling effects

Neto

Spitzner

Priesemann

2019

Preprint

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To date, it is still impossible to sample the entire mammalian brain with single-neuron precision. This forces one to either use spikes (focusing on few neurons) or to use coarse-sampled activity (averaging over many neurons, e.g. LFP). Naturally, the sampling technique impacts inference about collective properties. Here, we emulate both sampling techniques on a spiking model to quantify how they alter observed correlations and signatures of criticality. We discover a general effect: when the inter-electrode distance is small, electrodes sample overlapping regions in space, which increases the correlation between the signals. For coarse-sampled activity, this can produce power-law distributions even for non-critical systems. In contrast, spikes enable one to distinguish the underlying dynamics. This explains why coarse measures and spikes have produced contradicting results in the past -that are now all consistent with a slightly subcritical regime. Introduction 1For more than two decades, it has been argued that the cor-2 tex might operate at a critical point [1][2][3][4][5][6]. The criticality hy-3 pothesis states that by operating at a critical point, neuronal 4 networks could benefit from optimal information-processing 5 properties. Properties maximized at criticality include the cor-6 relation length [7], the autocorrelation time [6], the dynamic 7 range [8] and the richness of spatio-temporal patterns [9, 10]. 8 Evidence for criticality in the brain often derives from mea-9 surements of neuronal avalanches. Neuronal avalanches are 10 cascades of neuronal activity that spread in space and time. If a 11 system is critical, the probability distribution of avalanche size 12 ( ) follows a power law ( ) ∼ − [7, 11]. Such power-13 law distributions have been observed repeatedly in experiments 14 since they were first reported by Beggs & Plenz in 2003 [1]. 15 However, not all experiments have produced power laws 16 and the criticality hypothesis remains controversial. It turns 17 out that results for cortical recordings in vivo differ systemati-18 cally: 19 Studies that use what we here call coarse-sampled activity 20 typically produce power-law distributions [1, 12-21]. In con-21 trast, studies that use sub-sampled activity typically do not [14, 22 22-26]. Coarse-sampled activity include LFP, M/EEG, fMRI 23 and potentially calcium imaging, while sub-sampled activity is 24 front-most spike recordings. We hypothesize that the apparent 25 contradiction between coarse-sampled (LFP-like) data and sub-26 sampled (spike) data can be explained by the differences in the 27 recording and analysis procedures.28In general, the analysis of neuronal avalanches is not 29 straightforward. In order to obtain avalanches, one needs to de-30 fine discrete events. While spikes are discrete events by nature, 31 a coarse-sampled signal has to be converted into a binary form. 32This conversion hinges on thresholding the signal, which can be 33 problematic [27][28][29][30]. Furthermore, events have to be grouped 34 into avalanches, and th...

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Temporal complexity measure of reaction time series: Operational versus event time

et al. 2023

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Introduction Detrended fluctuation analysis (DFA) is a well‐established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number. Methods Herein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t). The DFA algorithm was then applied to the X(t) time series to evaluate scaling indices. The dataset analyzed is based on a Go–NoGo shooting task that was performed by 30 participants under low and high time‐stress conditions in each of six repeated sessions over a 3‐week period. Results This new perspective leads to quantitatively better results in (1) differentiating scaling indices between low versus high time‐stress conditions and (2) predicting task performance outcomes. Conclusion We show that by changing from operational time to event time, the DFA allows discrimination of time‐stress conditions and predicts performance outcomes.

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Critical dynamics of endogenous fluctuations predict cognitive flexibility in the Go/NoGo task

Cited by 43 publications

References 46 publications

Temporal structure in sensorimotor variability: a stable trait, but what for?

Temporal structure in sensorimotor variability: a stable trait, but what for?

A unified picture of neuronal avalanches arises from the understanding of sampling effects

Temporal complexity measure of reaction time series: Operational versus event time

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