Dynamics of task-set carry-over: evidence from eye-movement analyses

Kikumoto, Atsushi; Hubbard, Jason; Mayr, Ulrich

doi:10.3758/s13423-015-0944-y

Cited by 15 publications

(17 citation statements)

References 21 publications

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“…For example, the timing of the target/distractor location representations is highly consistent with recent work using eye-tracking to assess the dynamics of attentional allocation to task-relevant and irrelevant features (12,23). More importantly, our results also reveal the time course for both cue and task representations.…”

Section: Discussionsupporting

confidence: 89%

“…After initial preprocessing and identification of artifacts (see SM), the single-trial EEG data were decomposed into a time-frequency representation via wavelet decomposition (for details see SM). For simplicity we focused on frequency bands that are most often presented in the literature: delta (2-3 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12), and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31). For each frequency band, we averaged the power signal across the range of interest.…”

Section: Eeg Decoding Analysesmentioning

confidence: 99%

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EEG Decoding Reveals the Temporal Dynamics and Functional Relevance of Goal-Relevant Representations

Hubbard

Kikumoto

Mayr

2017

Preprint

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Abstract:Models of action control assume that abstract task-set settings regulate lower-level stimulus/response representations. Yet, we know little about the functional and dynamic properties of task-set representations in humans. Using a cued task-switching paradigm, we show that information about task sets and lower-level stimulus/response aspects can be extracted through decoding analyses from the scalp electrophysiological signal (EEG) on the single-trial level and with high temporal resolution. Task-sets are active throughout the entire processing cascade and trial-to-trial variations in task-set strength emerges as a remarkably strong predictor of variability in performance, both within and between individuals. Also, taskset strength is related to stimulus representation strength at an early period and to the strength of response representations at a later period, consistent with the notion that task-sets coordinate successive, lower-level representations in a concurrent manner. These results demonstrate a powerful approach towards uncovering stages of information processing and their relative importance for performance. Keywords: Executive control, Task switching, EEG. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/219741 doi: bioRxiv preprint first posted online Nov. 15, 2017; 3 EEG Decoding Reveals the Temporal Dynamics and Functional Relevance of Goal-Relevant Representations in HumansThe efficiency of information processing differs both moment to moment, and from one individual to the next. Such variability could reflect the quality of low-level, stimulus or response representations. Alternatively, it may arise from the strength of abstract, task-set representations that instantiate or control lower-level processes (1-3). For example, in the experimental paradigm we used in the current work (see Figure 1a), participants were informed on each trial through auditory cues, which of two tasks to perform (4, 5). For the color task, they attended to the color singleton within the array of objects and responded via button press whether the exact color was orange or purple. Similarly, for the orientation task, participants attended the orientation singleton and responded whether the line tilted to the left or to the right.In this situation, successful performance requires lower-level representations of the task cue, of the target location, and of the task-relevant feature/response. However, it may also require abstract task-set representations that differentiate between the color task context and the orientation task context and that ensure an adequate configuration of lower-level

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Section: Discussionsupporting

confidence: 89%

Section: Eeg Decoding Analysesmentioning

confidence: 99%

EEG Decoding Reveals the Temporal Dynamics and Functional Relevance of Goal-Relevant Representations

Hubbard

Kikumoto

Mayr

2017

Preprint

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“…We used a cued task switching paradigm that was closely modelled after a paradigm that we had previously used in the context of eye-tracking experiments 12,26 . On each trial, an auditory cue indicated which of the tasks, the Color task or the Orientation task, participants had to complete.…”

Section: Methodsmentioning

confidence: 99%

“…The experiment began with two single-task practice blocks (one for each task, order counter-balanced), and a task-switching practice block (20 trials), followed by 22 test blocks of 64 trials each. In order to incentivize participants to respond quickly and accurately, they were rewarded a small amount (0.5 cents) for each trial where they were faster than the 75 th % percentile of their RT distribution up to that point, but only if they maintained at least 90% accuracy for a given block 26 . At the end of each block, subjects were given feedback about their average RT and accuracy for that block.…”

Section: Methodsmentioning

confidence: 99%

EEG Decoding Reveals the Strength and Temporal Dynamics of Goal-Relevant Representations

Hubbard

Kikumoto

Mayr

2019

Sci Rep

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Models of action control assume that attentional control settings regulate the processing of lower-level stimulus/response representations. Yet, little is known about how exactly control and sensory/response representations relate to each other to produce goal-directed behavior. Addressing this question requires time-resolved information about the strength of the different, potentially overlapping representations, on a trial-by-trial basis. Using a cued task-switching paradigm, we show that information about relevant representations can be extracted through decoding analyses from the scalp electrophysiological signal (EEG) with high temporal resolution. Peaks in representational strength—indexed through decoding accuracy—proceeded from superficial task cues, to stimulus locations, to features/responses. In addition, attentional-set representations were prominent throughout almost the entire processing cascade. Trial-by-trial analyses provided detailed information about when and to what degree different representations predict performance, with attentional settings emerging as a strong and consistent predictor of within-individual and across-individual variability in performance. Also, the strength of attentional sets was related to target representations early in the post-stimulus period and to feature/response representations at a later period, suggesting control of successive, lower-level representations in a concurrent manner. These results demonstrate a powerful approach towards uncovering different stages of information processing and their relative importance for performance.

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“…Specifically, disengagement may support breaking mental set by allowing novel combinations of information to be generated (Oberauer et al, 2007). Higher AC may improve the integrity of this process by mitigating attentional capture at a time when PM is susceptible to intrusion from SM (Cosman & Vecera, 2013;Dreisbach & Wenke, 2011;Kikumoto, Hubbard, & Mayr, 2016;Mayr, Kuhns, & Hubbard, 2014;Richter & Yeung, 2012;Robison & Unsworth, 2017). Therefore, it was hypothesized that PM would be positively related to breaking mental set, but only for individuals with higher AC.…”

Section: Discussionmentioning

confidence: 99%

Functional opponency in working memory capacity predicts cognitive flexibility in problem solving.

Stockum¹,

Charles²

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Cognitive flexibility is a hallmark of individuals with higher working memory capacity (WMC). Yet, research demonstrates that higher WMC individuals are sometimes more likely to adopt rigid problem-solving approaches. The present research examines a novel account for these contradictory findings-that different WMC mechanisms interact in ways that both support and constrain cognitive flexibility. Across three studies, participants completed the water jug task-a problem-solving task requiring them to first establish and then break mental set using a complex strategy. Participants then completed measures targeting three WMC mechanisms: attention control, primary memory, and secondary memory. Study 1 demonstrated that primary memory and secondary memory predict breaking mental set in opposite directions. Study 2 replicated these findings while also demonstrating that attention control moderates these effects. Study 3 replicated these results using a less restrictive sampling procedure (i.e., participants were provided the complex strategy). The present research supports the proposed theory of functional opponency in WMC.

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Dynamics of task-set carry-over: evidence from eye-movement analyses

Cited by 15 publications

References 21 publications

EEG Decoding Reveals the Temporal Dynamics and Functional Relevance of Goal-Relevant Representations

EEG Decoding Reveals the Temporal Dynamics and Functional Relevance of Goal-Relevant Representations

EEG Decoding Reveals the Strength and Temporal Dynamics of Goal-Relevant Representations

Functional opponency in working memory capacity predicts cognitive flexibility in problem solving.

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