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

Author(s):  
Juliane Scheil ◽  
Thomas Kleinsorge

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.

2021 ◽  
pp. 174702182110315
Author(s):  
Motonori Yamaguchi ◽  
Husnain H. Shah ◽  
Bernhard Hommel

Two different variations of joint task switching led to different conclusions as to whether co-acting individuals share the same task-sets. The present study aimed at bridging this gap by replicating the version in which two actors performed two different tasks. Experiment 1 showed switch costs across two actors in a joint condition, which agreed with previous studies, but also yielded even larger switch costs in a solo condition, which contradicted the claim that actors represent an alternative task as their own when it is carried out by the co-actor but not when no one carries it out. Experiments 2 and 3 further examined switch costs in the solo condition with the aim to rule out possible influences of task instructions for and experiences with the other task that was not assigned to the actor. Before participants were instructed on the second of the two tasks, switch costs were still obtained without a co-actor when explicit task names (“COLOUR” and “SHAPE”) served as go/nogo signals (Experiment 2), but not when arbitrary symbols (“XXXX” and “​​​​”) served as go/nogo signals (Experiment 3). The results thus imply that switch costs depend on participants’ knowledge of task cues being assigned to two different tasks, but not on whether the other task is performed by a co-actor. These findings undermine the assumption that switch costs in the joint conditions reflect shared task-sets between co-actors in this procedure.


2016 ◽  
Vol 170 ◽  
pp. 66-73 ◽  
Author(s):  
Patricia Hirsch ◽  
Tina Schwarzkopp ◽  
Mathieu Declerck ◽  
Stefanie Reese ◽  
Iring Koch

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Heike Elchlepp ◽  
Stephen Monsell ◽  
Aureliu Lavric

2021 ◽  
Author(s):  
Motonori Yamaguchi ◽  
Bernhard Hommel

Two different variations of joint task switching led to different conclusions as to whether co-acting individuals share the same task-sets. The present study aimed at bridging this gap by replicating the version in which two actors performed two different tasks. Experiment 1 showed switch costs across two actors in a joint condition, which agreed with previous studies, but also yielded even larger switch costs in a solo condition, which contradicted the claim that actors represent an alternative task as their own when it is carried out by the co-actor but not when no one carries it out. Experiments 2 and 3 further examined switch costs in the solo condition with the aim to rule out possible influences of task instructions for and experiences with the other task that was not assigned to the actor. Before participants were instructed on the second of the two tasks, switch costs were still obtained without a co-actor when explicit task names (“COLOUR” and “SHAPE”) served as go/nogo signals (Experiment 2), but not when arbitrary symbols (“XXXX” and “++++”) served as go/nogo signals (Experiment 3). The results thus imply that switch costs depend on participants’ knowledge of task cues being assigned to two different tasks, but not on whether the other task is performed by a co-actor. These findings undermine the assumption that switch costs in the joint conditions reflect shared task-sets between co-actors in this procedure.


2014 ◽  
Vol 14 (10) ◽  
pp. 710-710
Author(s):  
M. Dodd ◽  
M. Mills ◽  
E. Dalmaijer ◽  
S. Van der Stigchel

2020 ◽  
Author(s):  
Michael Imburgio ◽  
Joseph M Orr

Most theories describing the cognitive processes underlying task switching allow for contributions of active task-set reconfiguration and task set inertia. Manipulations of the Cue-to-Stimulus-Interval (CSI) are generally thought to influence task set reconfiguration, while Response-to-Stimulus Interval (RSI) manipulations are generally thought to influence task set inertia (i.e., proactive interference from the previous task-set). However, these theories do not adequately account for the processes underlying voluntary task selection, because a participant can theoretically prepare for an upcoming trial at any point. To this end we used drift diffusion models to examine the contributions of reconfiguration and task set inertia in 216 undergraduate students who performed either cued or voluntary task switching paradigms. In both task versions, longer CSIs allowed for better preparation on all trial types. For the voluntary condition, but not the explicit condition, longer RSIs also reduced the effect of switching on preparation when CSIs were short. Further, when given enough time to prepare, participants in the voluntary version prepared more efficiently for switches than repeats. Together, these results indicate the use of a more proactive strategy when participants chose to switch in the voluntary version. In both paradigms, RSI manipulations produced the expected effect on switch costs; however, they consistently slowed repeat performance and generally did not affect performance on switch trials. The results suggest that drift diffusion models can quantify differences in strategy across voluntary and explicit task switching as well as measure contributions of inertia and preparation to voluntary task switching performance, including identifying preparation that occurs outside of the CSI in voluntary switching. The results also suggest that reductions in switch cost caused by reduced inertia might be more related to impeding repeat performance rather than facilitating switch performance. Future work should extend the current findings with manipulations of proactive vs. reactive strategies and other manipulations of inertia.


eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Flora Bouchacourt ◽  
Stefano Palminteri ◽  
Etienne Koechlin ◽  
Srdjan Ostojic

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.


Sign in / Sign up

Export Citation Format

Share Document