Search PubMed⌕ Search

PubMed · 15698827

Exploring task-set reconfiguration with random task sequences.

Abstract

Switching between two different tasks normally results in an impairment in people's performance known as a switch cost, typically measured as an increase in reaction time (RT) and errors compared to a situation in which no task switch is required. Researchers in task switching have suggested that this switch cost is the behavioural manifestation of the task set reconfiguration processes that are necessary to perform the upcoming task. However, an examination of the literature in task switching reveals apparently contradictory results about the nature of task set reconfiguration processes. In Experiment 1, we addressed this issue by comparing participants' performance in two different experimental conditions: predictable task switching and random task switching. In the predictable switch condition the switch cost completely vanished after the first repetition of the new task. However, in the random switch condition, while the difference between switch and repetition trials was not significant, we observed a significant reduction in RT between the first and second repetition of the new task. In Experiment 2, we further investigated the pattern of task set reconfiguration in the random switch situation. The results showed a progressive reduction of participants' response latencies across repetitions of the same task. The present study demonstrates that, whereas the results in predictable switching conditions are compatible with an exogenous-reconfiguration hypothesis, random task switching produces a more gradual, decay-like switch cost reduction with task repetition.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Emilio G Milán, Daniel Sanabria, Francisco Tornay, Antonio González. 2004-11-25. Exploring task-set reconfiguration with random task sequences.. https://doi.org/10.1016/j.actpsy.2004.10.015

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Molecular heterochrony and the evolution of sociality in bumblebees (Bombus terrestris).

Sibling care is a hallmark of social insects, but its evolution remains challenging to explain at the molecular level. The hypothesis that sibling care evolved from ancestral maternal care in primitively eusocial insects has been elaborated to involve heterochronic changes in gene expression. This elaboration leads to the prediction that workers in these species will show patterns of gene expression more similar to foundress queens, who express maternal care behaviour, than to established queens engaged solely in reproductive behaviour. We tested this idea in bumblebees (Bombus terrestris) using a microarray platform with approximately 4500 genes. Unlike the wasp Polistes metricus, in which support for the above prediction has been obtained, we found that patterns of brain gene expression in foundress and queen bumblebees were more similar to each other than to workers. Comparisons of differentially expressed genes derived from this study and gene lists from microarray studies in Polistes and the honeybee Apis mellifera yielded a shared set of genes involved in the regulation of related social behaviours across independent eusocial lineages. Together, these results suggest that multiple independent evolutions of eusociality in the insects might have involved different evolutionary routes, but nevertheless involved some similarities at the molecular level.

Analysis of Variance↗

Comparing the small sample performance of several variance estimators under competing risks.

We examine several variance estimators for cumulative incidence estimators that have been proposed over time, some of which are derived from asymptotic martingale or counting process theory, and some of which are developed from the moments of the multinomial distribution. There is little published work comparing these variance estimators, largely because the variance estimators are algebraically complex and difficult to interpret and all but one have yet to be programmed for a standard statistical package. Through simulation and application to real data, we compare the performance of six variance estimators in relation to each other and the bootstrap in order to confirm earlier reports of their performance and to provide future direction toward their application. We find that the multinomial-moment-based estimators have performance close to that of the bootstrap, and are quite accurate for estimating the variance, even in samples of 20 subjects. All but one of the martingale theory-based estimators tend to perform poorly in small samples, tending to either overestimate or underestimate the empirical variance in samples of fewer than 100 subjects.

Analysis of Variance↗