Donders is dead: cortical traveling waves and the limits of mental chronometry in cognitive neuroscience

Alexander, David; Trengove, Chris; Leeuwen, Cees van

doi:10.1007/s10339-015-0662-4

Cited by 23 publications

(29 citation statements)

References 41 publications

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“…The traditional view of mental chronectomy from Donders (1969) [ 81 ] suggests that brain states are additive and therefore separable in both space and time. This assumption, which fundamentally underlies cognitive neuroscience, supports the notion that one can identify and compare cognitive processes using linear (difference or subtraction) methods [ 82 ]: one can take one cognitive process and compare a second process that differs in only one important respect, and conclude that the difference between the two cognitive processes is revealed in the difference between the two sets of measurements associated with them [ 83 ]. Critically, this approach enables one to study supposed individual task elements, even if these elements can never be experienced in isolation.…”

Section: Discussionmentioning

confidence: 74%

A Functional Cartography of Cognitive Systems

et al. 2015

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One of the most remarkable features of the human brain is its ability to adapt rapidly and efficiently to external task demands. Novel and non-routine tasks, for example, are implemented faster than structural connections can be formed. The neural underpinnings of these dynamics are far from understood. Here we develop and apply novel methods in network science to quantify how patterns of functional connectivity between brain regions reconfigure as human subjects perform 64 different tasks. By applying dynamic community detection algorithms, we identify groups of brain regions that form putative functional communities, and we uncover changes in these groups across the 64-task battery. We summarize these reconfiguration patterns by quantifying the probability that two brain regions engage in the same network community (or putative functional module) across tasks. These tools enable us to demonstrate that classically defined cognitive systems—including visual, sensorimotor, auditory, default mode, fronto-parietal, cingulo-opercular and salience systems—engage dynamically in cohesive network communities across tasks. We define the network role that a cognitive system plays in these dynamics along the following two dimensions: (i) stability vs. flexibility and (ii) connected vs. isolated. The role of each system is therefore summarized by how stably that system is recruited over the 64 tasks, and how consistently that system interacts with other systems. Using this cartography, classically defined cognitive systems can be categorized as ephemeral integrators, stable loners, and anything in between. Our results provide a new conceptual framework for understanding the dynamic integration and recruitment of cognitive systems in enabling behavioral adaptability across both task and rest conditions. This work has important implications for understanding cognitive network reconfiguration during different task sets and its relationship to cognitive effort, individual variation in cognitive performance, and fatigue.

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Section: Discussionmentioning

confidence: 74%

A Functional Cartography of Cognitive Systems

et al. 2015

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“…The mechanism underlying this effect requires further exploration. The lack of a similar effect in the power peak may be due to averaging across epochs, which may obscure latency differences across the scalp (Alexander, Trengove, & van Leeuwen, 2015).…”

Section: Saccade Vs Fixation Onsetmentioning

confidence: 92%

“…On the other hand, reliable estimation of evoked potentials is possible only after averaging of many trials. In the averaging, task-relevant information may be lost (Alexander et al, 2013(Alexander et al, , 2015. In contrast, the time-frequency analysis can reliably be done on single trials, which allows to study how experimental stimulation modulates ongoing brain activity.…”

Section: Merits Of Frequency Vs Time Domain Analysismentioning

confidence: 97%

Combining EEG and eye movement recording in free viewing: Pitfalls and possibilities

Nikolaev

Meghanathan

Leeuwen

2016

Brain and Cognition

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104

151

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Co-registration of EEG and eye movement has promise for investigating perceptual processes in free viewing conditions, provided certain methodological challenges can be addressed. Most of these arise from the self-paced character of eye movements in free viewing conditions. Successive eye movements occur within short time intervals. Their evoked activity is likely to distort the EEG signal during fixation. Due to the non-uniform distribution of fixation durations, these distortions are systematic, survive across-trials averaging, and can become a source of confounding. We illustrate this problem with effects of sequential eye movements on the evoked potentials and time-frequency components of EEG and propose a solution based on matching of eye movement characteristics between experimental conditions. The proposal leads to a discussion of which eye movement characteristics are to be matched, depending on the EEG activity of interest. We also compare segmentation of EEG into saccade-related epochs relative to saccade and fixation onsets and discuss the problem of baseline selection and its solution. Further recommendations are given for implementing EEG-eye movement co-registration in free viewing conditions. By resolving some of the methodological problems involved, we aim to facilitate the transition from the traditional stimulus-response paradigm to the study of visual perception in more naturalistic conditions.

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“…In contrast, difference measures assume that cognitive processes are additive and that experimental conditions vary in all but a single cognitive process (Donders, 1868(Donders, , 1969). Yet, it is likely that cognitive processes are not additive and that inserting an additional cognitive process interacts with other cognitive processes required in the task (Alexander, Trengove, & van Leeuwen, 2015;Friston et al, 1996;Schubert et al, 2015).…”

Section: Performance In Ef Tasks: What Does It Measure?mentioning

confidence: 99%