ERPs dissociate the effects of switching task sets and task cues

2006 ◽  
Vol 1095 (1) ◽  
pp. 107-123 ◽  
Author(s):  
Rebecca Nicholson ◽  
Frini Karayanidis ◽  
Elizabeth Bumak ◽  
Dane Poboka ◽  
Patricia T. Michie
Keyword(s):  
Switching Task ◽  
Task Sets ◽  
And Task

scholarly journals No-go trials in task switching: effects on the task-set and task-space level

2021 ◽  
Author(s):  
Juliane Scheil ◽  
Thomas Kleinsorge
Keyword(s):  
Task Switching ◽  
The Other ◽  
Specific Task ◽  
Task Space ◽  
Response Level ◽  
Task Set ◽  
Common Marker ◽  
Task Sets ◽  
And Task

AbstractA common marker for inhibition processes in task switching are n − 2 repetition costs. The present study aimed at elucidating effects of no-go trials on n − 2 repetition costs. In contrast to the previous studies, no-go trials were associated with only one of the three tasks in the present two experiments. High n − 2 repetition costs occurred if the no-go task had to be executed in trial n − 2, irrespective of whether a response had to be withheld or not. In contrast, no n − 2 repetition costs were visible if the other two tasks were relevant in n − 2. Whereas this n − 2 effect was unaffected by whether participants could reliably exclude a no-go trial or not, effects of no-gos in trial n were determined by this knowledge. The results differ from effects of no-go trials that are not bound to a specific task. It is assumed that the present no-go variation exerted its effect not on the response level, but on the level of task sets, resulting in enhanced salience of the no-go task that leads to higher activation and, as a consequence, to stronger inhibition. The dissociation of the effects on no-gos in trials n − 2 and n as a function of foreknowledge suggests that the balance between activation and inhibition is shifted not only for single trials and tasks, but for the whole task space.

Effects of Working Memory Capacity, Task Switching, and Task Difficulty on Multitasking Performance

2016 ◽  
Vol 60 (1) ◽  
pp. 502-506 ◽  
Author(s):  
Shijing Liu ◽  
Amy Wadeson ◽  
Na Young Kim ◽  
Chang S. Nam
Keyword(s):  
Working Memory ◽  
Task Switching ◽  
Task Difficulty ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Trail Making ◽  
Human Operators ◽  
Switching Task ◽  
Different Levels ◽  
And Task

Multitasking requires human operators to handle the demands of multiple tasks through task switching at the same time and this ability is required in many jobs. Previous studies showed that different levels of working memory capacity (WMC) and task switching abilities can lead to differences on multitasking performance. With increased complexity of tasks, maintaining task performance is challenging. This study sought to find the relations of WMC, task switching, task difficulty, and multitasking performance. Multi-Attribute Task Battery II (MATB-II) was employed in this study as a platform to assess multitasking. Automated OSPAN and Trail Making Tasks (TMT) were used to assess WMC and the task switching ability, respectively. Results indicated that there were significant effects of these three parameters on multitasking performance. Other dimensions of multitasking performance will be addressed in future studies.

Switching task sets creates event boundaries in memory

Cognition ◽  
2022 ◽  
Vol 221 ◽  
pp. 104992
Author(s):  
Yuxi Candice Wang ◽  
Tobias Egner
Keyword(s):  
Switching Task ◽  
Task Sets

Towards a Theory of Working Memory

2020 ◽  
pp. 116-149 ◽  
Author(s):  
Klaus Oberauer
Keyword(s):  
Working Memory ◽  
Computational Models ◽  
Long Term Memory ◽  
Reproduction Task ◽  
Term Memory ◽  
Immediate Serial Recall ◽  
Span Tasks ◽  
Task Sets ◽  
And Task

Working memory provides a medium for building and manipulating new representations that control our thoughts and actions. To fulfil this function, a working memory system needs to meet six requirements: (1) it must have a mechanism for rapidly forming temporary bindings to combine elements into new structures; (2) it needs a focus of attention for selectively accessing individual elements for processing; (3) it must hold both declarative representations of what is the case, and procedural representations of how to act on the current situation; (4) it needs a process for rapid updating, including rapid removal of outdated contents. Moreover, contents of working memory (5) need to be shielded from interference from long-term memory, while (6) working memory should be able to use information in long-term memory when it is useful. This chapter summarizes evidence in support of these mechanisms and processes. It presents three computational models that each implement some of these mechanisms, and explains different subsets of empirical findings about working memory: the SOB-CS model accounts for behaviour in tests of immediate serial recall, including complex-span tasks. The interference model explains data from a common test of visual working memory, the continuous-reproduction task. The set-selection model explains how people learn memory sets and task sets, how these sets are retrieved from long-term memory, and how these mechanisms enable switching between memory sets and task sets.

scholarly journals Evidence for a multicomponent hierarchical representation of dual tasks

2020 ◽  
Author(s):  
Patricia Hirsch ◽  
Clara Roesch ◽  
Iring Koch
Keyword(s):  
Dual Task ◽  
Task Interference ◽  
Dual Tasks ◽  
Hierarchical Representation ◽  
Switch Costs ◽  
Task Processing ◽  
The Individual ◽  
Task Sets ◽  
Dual Task Interference ◽  
And Task

Abstract Recent dual-task studies observed worse performance in task-pair switches than in task-pair repetitions and interpreted these task-pair switch costs as evidence that the identity of the two individual tasks performed within a dual task is jointly represented in a single mental representation, termed “task-pair set.” In the present study, we conducted two experiments to examine (a) whether task-pair switch costs are due to switching cues or/and task pairs and (b) at which time task-pair sets are activated during dual-task processing. In Experiment 1, we used two cues per task-pair and found typical dual-task interference, indicating that performance in the individual tasks performed within the dual task deteriorates as a function of increased temporal task overlap. Moreover, we observed cue switch costs, possibly reflecting perceptual cue priming. Importantly, there were also task-pair switch costs that occur even when controlling for cue switching. This suggests that task-pair switching per se produces a performance cost that cannot be reduced to costs of cue switching. In Experiment 2, we employed a go/no-go-like manipulation and observed task-pair switch costs after no-go trials where subjects prepared for a task-pair, but did not perform it. This indicates that task-pair sets are activated before performing a dual task. Together, the findings of the present study provide further evidence for a multicomponent hierarchical representation consisting of a task-pair set organized at a hierarchically higher level than the task sets of the individual tasks performed within a dual task.

scholarly journals ADHD and attentional control: Impaired segregation of task positive and task negative brain networks

2018 ◽  
Vol 2 (2) ◽  
pp. 200-217 ◽  
Author(s):  
Brian D. Mills ◽  
Oscar Miranda-Dominguez ◽  
Kathryn L. Mills ◽  
Eric Earl ◽  
Michaela Cordova ◽  
...  
Keyword(s):  
Brain Networks ◽  
Adhd Group ◽  
Continuous Performance Task ◽  
Resting State Functional Connectivity ◽  
Group Status ◽  
Children With Adhd ◽  
System Integrity ◽  
Correlated Activity ◽  
Task Sets ◽  
And Task

In children with attention deficit hyperactivity disorder (ADHD) difficulty maintaining task focus may relate to the coordinated, negatively correlated activity between brain networks that support the initiation and maintenance of task sets (task positive networks) and networks that mediate internally directed processes (i.e., the default mode network). Here, resting-state functional connectivity MRI between these networks was examined in ADHD, across development, and in relation to attention. Children with ADHD had reduced negative connectivity between task positive and task negative networks ( p = 0.002). Connectivity continues to become more negative between these networks throughout development (7–15 years of age) in children with ADHD ( p = 0.005). Regardless of group status, females had increased negative connectivity ( p = 0.003). In regards to attentional performance, the ADHD group had poorer signal detection (d′) on the continuous performance task (CPT) ( p < 0.0001), more so on easy than difficult d′ trials ( p < 0.0001). The reduced negative connectivity in children with ADHD also relates to their attention, where increased negative connectivity is related to better performance on the d′ measure of the CPT ( p = 0.008). These results highlight and further strengthen prior reports underscoring the role of segregated system integrity in ADHD.

scholarly journals Temporal chunking as a mechanism for unsupervised learning of task-sets

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Flora Bouchacourt ◽  
Stefano Palminteri ◽  
Etienne Koechlin ◽  
Srdjan Ostojic
Keyword(s):  
Reaction Times ◽  
Neural Correlates ◽  
Behavioral Performance ◽  
Bold Signal ◽  
Improve Performance ◽  
Task Set ◽  
Neural Data ◽  
Stimulus Response ◽  
Task Sets ◽  
And Task

Depending on environmental demands, humans can learn and exploit multiple concurrent sets of stimulus-response associations. Mechanisms underlying the learning of such task-sets remain unknown. Here we investigate the hypothesis that task-set learning relies on unsupervised chunking of stimulus-response associations that occur in temporal proximity. We examine behavioral and neural data from a task-set learning experiment using a network model. We first show that task-set learning can be achieved provided the timescale of chunking is slower than the timescale of stimulus-response learning. Fitting the model to behavioral data on a subject-by-subject basis confirmed this expectation and led to specific predictions linking chunking and task-set retrieval that were borne out by behavioral performance and reaction times. Comparing the model activity with BOLD signal allowed us to identify neural correlates of task-set retrieval in a functional network involving ventral and dorsal prefrontal cortex, with the dorsal system preferentially engaged when retrievals are used to improve performance.

scholarly journals Temporal Chunking as a Mechanism for Unsupervised Learning of Task-Sets

2019 ◽  
Author(s):  
Flora Bouchacourt ◽  
Stefano Palminteri ◽  
Etienne Koechlin ◽  
Srdjan Ostojic
Keyword(s):  
Reaction Times ◽  
Neural Correlates ◽  
Behavioral Performance ◽  
Bold Signal ◽  
Improve Performance ◽  
Task Set ◽  
Neural Data ◽  
Stimulus Response ◽  
Task Sets ◽  
And Task

AbstractDepending on environmental demands, humans can learn and exploit multiple concurrent sets of stimulus-response associations. Mechanisms underlying the learning of such task-sets remain unknown. Here we investigate the hypothesis that task-set learning relies on unsupervised chunking of stimulus-response associations that occur in temporal proximity. We examine behavioral and neural data from a task-set learning experiment using a network model. We first show that task-set learning can be achieved provided the timescale of chunking is slower than the timescale of stimulus-response learning. Fitting the model to behavioral data confirmed this expectation and led to specific predictions linking chunking and task-set retrieval that were borne out by behavioral performance and reaction times. Comparing the model activity with BOLD signal allowed us to identify neural correlates of task-set retrieval in a functional network involving ventral and dorsal prefrontal cortex, with the dorsal system preferentially engaged when retrievals are used to improve performance.

When Hearing Does Not Mean Understanding: On the Neural Processing of Syntactically Complex Sentences by Listeners With Hearing Loss

2021 ◽  
pp. 1-13
Author(s):  
Margreet Vogelzang ◽  
Christiane M. Thiel ◽  
Stephanie Rosemann ◽  
Jochem W. Rieger ◽  
Esther Ruigendijk
Keyword(s):  
Hearing Loss ◽  
Sentence Processing ◽  
Secondary Task ◽  
Sentence Comprehension ◽  
Inferior Frontal Gyrus ◽  
Normal Hearing ◽  
Cognitive Demands ◽  
Sentence Complexity ◽  
Complex Sentences ◽  
And Task

Purpose Adults with mild-to-moderate age-related hearing loss typically exhibit issues with speech understanding, but their processing of syntactically complex sentences is not well understood. We test the hypothesis that listeners with hearing loss' difficulties with comprehension and processing of syntactically complex sentences are due to the processing of degraded input interfering with the successful processing of complex sentences. Method We performed a neuroimaging study with a sentence comprehension task, varying sentence complexity (through subject–object order and verb–arguments order) and cognitive demands (presence or absence of a secondary task) within subjects. Groups of older subjects with hearing loss ( n = 20) and age-matched normal-hearing controls ( n = 20) were tested. Results The comprehension data show effects of syntactic complexity and hearing ability, with normal-hearing controls outperforming listeners with hearing loss, seemingly more so on syntactically complex sentences. The secondary task did not influence off-line comprehension. The imaging data show effects of group, sentence complexity, and task, with listeners with hearing loss showing decreased activation in typical speech processing areas, such as the inferior frontal gyrus and superior temporal gyrus. No interactions between group, sentence complexity, and task were found in the neuroimaging data. Conclusions The results suggest that listeners with hearing loss process speech differently from their normal-hearing peers, possibly due to the increased demands of processing degraded auditory input. Increased cognitive demands by means of a secondary visual shape processing task influence neural sentence processing, but no evidence was found that it does so in a different way for listeners with hearing loss and normal-hearing listeners.

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