scholarly journals Spontaneous task structure formation results in a cost to incidental memory of task stimuli

2018 ◽  
Author(s):  
Christina Bejjani ◽  
Tobias Egner
Keyword(s):  
Structure Formation ◽  
Structure Learning ◽  
Learning Task ◽  
Underlying Structure ◽  
Task Structure ◽  
Cognitive Demands ◽  
Task Set ◽  
Stimulus Response ◽  
Attention To Task ◽  
Task Sets

Humans are characterized by their ability to leverage rules for classifying and linking stimuli to context-appropriate actions. Previous studies have shown that when humans learn stimulus-response associations for two-dimensional stimuli, they implicitly form and generalize hierarchical rule structures (task-sets). However, the cognitive processes underlying structure formation are poorly understood. Across four experiments, we manipulated how trial-unique images mapped onto responses to bias spontaneous task-set formation and investigated structure learning through the lens of incidental stimulus encoding. Participants performed a learning task designed to either promote task-set formation (by “motor-clustering” possible stimulus-action rules), or to discourage it (by using arbitrary category-response mappings). We adjudicated between two hypotheses: Structure learning may promote attention to task stimuli, thus resulting in better subsequent memory. Alternatively, building task-sets might impose cognitive demands (for instance, on working memory) that divert attention away from stimulus encoding. While the clustering manipulation affected task-set formation, there were also substantial individual differences. Importantly, structure learning incurred a cost: spontaneous task-set formation was associated with diminished stimulus encoding. Thus, spontaneous hierarchical task-set formation appears to involve cognitive demands that divert attention away from encoding of task stimuli during structure learning.

scholarly journals Dissociable Neural Systems Support the Learning and Transfer of Hierarchical Control Structure

2020 ◽  
Author(s):  
Adam Eichenbaum ◽  
Jason M. Scimeca ◽  
Mark D’Esposito
Keyword(s):  
Control Structure ◽  
Human Subjects ◽  
Hierarchical Control ◽  
Learning Task ◽  
Neural Systems ◽  
Underlying Structure ◽  
Task Structure ◽  
Control Structures ◽  
Stimulus Response ◽  
Rule Structure

AbstractHumans can draw insight from previous experiences in order to quickly adapt to novel environments that share a common underlying structure. Here we combine functional imaging and computational modeling to identify the neural systems that support the discovery and transfer of hierarchical task structure. Human subjects completed multiple blocks of a reinforcement learning task that contained a global hierarchical structure governing stimulus-response action mapping. First, behavioral and computational evidence showed that humans successfully discover and transfer the hierarchical rule structure embedded within the task. Next, analysis of fMRI BOLD data revealed activity across a frontal-parietal network that was specifically associated with the discovery of this embedded structure. Finally, activity throughout a cingulo-opercular network and in caudal frontal cortex supported the transfer and implementation of this discovered structure. Together, these results reveal a division of labor in which dissociable neural systems support the learning and transfer of abstract control structures.

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

2017 ◽  
Author(s):  
Jason Hubbard ◽  
Atsushi Kikumoto ◽  
Ulrich Mayr
Keyword(s):  
Temporal Dynamics ◽  
Dynamic Properties ◽  
Strong Predictor ◽  
Early Period ◽  
High Temporal Resolution ◽  
Task Set ◽  
Stimulus Response ◽  
Functional Relevance ◽  
Electrophysiological Signal ◽  
Task Sets

AbstractModels 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.

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.

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.

Learning of across- and within-task contingencies modulates partial-repetition costs in dual-tasking

2021 ◽  
Author(s):  
Lasse Pelzer ◽  
Christoph Naefgen ◽  
Robert Gaschler ◽  
Hilde Haider
Keyword(s):  
Dual Task ◽  
Discrimination Task ◽  
Experimental Approach ◽  
Feature Binding ◽  
Dual Tasking ◽  
Single Task ◽  
Task Set ◽  
Task Processing ◽  
Tone Discrimination ◽  
Task Sets

AbstractDual-task costs might result from confusions on the task-set level as both tasks are not represented as distinct task-sets, but rather being integrated into a single task-set. This suggests that events in the two tasks are stored and retrieved together as an integrated memory episode. In a series of three experiments, we tested for such integrated task processing and whether it can be modulated by regularities between the stimuli of the two tasks (across-task contingencies) or by sequential regularities within one of the tasks (within-task contingencies). Building on the experimental approach of feature binding in action control, we tested whether the participants in a dual-tasking experiment will show partial-repetition costs: they should be slower when only the stimulus in one of the two tasks is repeated from Trial n − 1 to Trial n than when the stimuli in both tasks repeat. In all three experiments, the participants processed a visual-manual and an auditory-vocal tone-discrimination task which were always presented concurrently. In Experiment 1, we show that retrieval of Trial n − 1 episodes is stable across practice if the stimulus material is drawn randomly. Across-task contingencies (Experiment 2) and sequential regularities within a task (Experiment 3) can compete with n − 1-based retrieval leading to a reduction of partial-repetition costs with practice. Overall the results suggest that participants do not separate the processing of the two tasks, yet, within-task contingencies might reduce integrated task processing.

scholarly journals Analysis and Design of the Battery Initial Energy Level with Task Scheduling for Energy-Harvesting Embedded Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xingyu Miao ◽  
Jiayuan Wei ◽  
Yongqi Ge
Keyword(s):  
Energy Harvesting ◽  
Energy Level ◽  
Initial Energy ◽  
Success Rates ◽  
Worst Case ◽  
Task Set ◽  
Analysis And Design ◽  
Available Energy ◽  
Battery Capacity ◽  
Task Sets

When the energy-harvesting embedded system (EHES) is running, its available energy (harvesting energy and battery storage energy) seems to be sufficient overall. However, in the process of EHES task execution, an energy shortage may occur in the busy period such that system tasks cannot be scheduled. We call this issue the energy deception (ED) of the EHES. Aiming to address the ED issue, we design an appropriate initial energy level of the battery. In this paper, we propose three algorithms to judge the feasibility of the task set and calculate the appropriate initial energy level of the battery. The holistic energy evaluation (HEE) algorithm makes a preliminary judgment of the task set feasibility according to available energy and consumption energy. A worst-case response time-based initial energy level of the battery (WCRT-IELB) algorithm and an accurate cycle-initial energy level of the battery (AC-IELB) algorithm can calculate the proper initial battery capacity. We use the YARTISS tool to simulate the above three algorithms. We conducted 250 experiments on As Late As Possible (ALAP) and As Soon As Possible (ASAP) scheduling with the maximum battery capacities of 50, 100, 200, 300, and 400. The experimental results show that setting a reasonable initial energy level of the battery can effectively improve the feasibility of the task set. Among the 250 task sets, the HEE algorithm filtered 2.8% of them as infeasible task sets. When the battery capacity is set to 400, the WCRT-BIEL algorithm increases the success rates of the ALAP and ASAP by 17.2% and 26.8%, respectively. The AC-BIEL algorithm increases the success rates of the ALAP and ASAP by 18% and 26.8%, respectively.

scholarly journals Contextual Adaptation of Cognitive Flexibility is driven by Task- and Item-Level Learning

2019 ◽  
Author(s):  
Audrey Siqi-Liu ◽  
Tobias Egner
Keyword(s):  
Cognitive Flexibility ◽  
Stimulus Item ◽  
Transfer Task ◽  
Learning Mechanisms ◽  
Behavioral Experiments ◽  
Task Set ◽  
Switch Costs ◽  
Cognitive Stability ◽  
Current Task ◽  
Task Sets

Adaptive behavior requires finding, and adjusting, an optimal tradeoff between focusing on a current task-set (cognitive stability) and updating that task-set when the environment changes (cognitive flexibility). Such dynamic adjustments of cognitive flexibility are observed in cued task-switching paradigms, where switch costs tend to decrease as the proportion of switch trials over blocks increases. However, the learning mechanisms underlying this phenomenon, here referred to as the list-wide proportion switch effect (LWPSE), are currently unknown.We addressed this question across four behavioral experiments. Experiment 1 replicated the basic LWPSE reported in previous studies. Having participants switch between three instead of two tasks, Experiment 2 demonstrated that the LWPSE is preserved even when the specific alternate task to switch to cannot be anticipated. Experiments 3a and 3b tested for the generalization of list-wide switch-readiness to an unbiased “transfer task,” presented equally often as switch and repeat trials, by intermixing the transfertask with biased tasks. Despite the list-wide bias, the LWPSE was only found for biased tasks, suggesting that the modulations of switch costs are task set and/or task stimulus (item)-specific. To evaluate these two possibilities, Experiment 4 employed biased versus unbiased stimuli within biased task sets and found switch-cost modulations for both stimuli sets. These results establish how people adapt their stability-flexibility tradeoff to different contexts. Specifically, our findings show that people learn to associate context appropriate levels of switch readiness with switch-predictive cues, provided by task sets as well as specific task stimuli.

To Choose or to Avoid: Age Differences in Learning from Positive and Negative Feedback

2011 ◽  
Vol 23 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Ben Eppinger ◽  
Jutta Kray
Keyword(s):  
Older Adults ◽  
Learning Task ◽  
Learning Preferences ◽  
Negative Outcomes ◽  
Learning Condition ◽  
Younger Adults ◽  
Stimulus Response ◽  
Learning Impairments ◽  
Error Related Negativity ◽  
Reversal Phase

In this study, we investigated whether older adults learn more from bad than good choices than younger adults and whether this is reflected in the error-related negativity (ERN). We applied a feedback-based learning task with two learning conditions. In the positive learning condition, participants could learn to choose responses that lead to monetary gains, whereas in the negative learning condition, they could learn to avoid responses that lead to monetary losses. To test the stability of learning preferences, the task involved a reversal phase in which stimulus–response assignments were inverted. Negative learners were defined as individuals that performed better in the negative than in the positive learning condition (and vice versa for positive learners). The behavioral data showed strong individual differences in learning from positive and negative outcomes that persisted throughout the reversal phase and were more pronounced for older than younger adults. Older negative learners showed a stronger tendency to avoid negative outcomes than younger negative learners. However, contrary to younger adults, this negative learning bias was not associated with a larger ERN, suggesting that avoidance learning in older negative learners might be decoupled from error processing. Furthermore, older adults showed learning impairments compared to younger adults. The ERP analyses suggest that these impairments reflect deficits in the ability to build up relational representations of ambiguous outcomes.

Sign in / Sign up

Export Citation Format

Share Document