Dataset Information

Using Signal Detection Theory to Better Understand Cognitive Fatigue.

ABSTRACT: When we are fatigued, we feel that our performance is worse than when we are fresh. Yet, for over 100 years, researchers have been unable to identify an objective, behavioral measure that covaries with the subjective experience of fatigue. Previous work suggests that the metrics of signal detection theory (SDT)-response bias (criterion) and perceptual certainty (d')-may change as a function of fatigue, but no work has yet been done to examine whether these metrics covary with fatigue. Here, we investigated cognitive fatigue using SDT. We induced fatigue through repetitive performance of the n-back working memory task, while functional magnetic resonance imaging (fMRI) data was acquired. We also assessed cognitive fatigue at intervals throughout. This enabled us to assess not only whether criterion and d' covary with cognitive fatigue but also whether similar patterns of brain activation underlie cognitive fatigue and SDT measures. Our results show that both criterion and d' were correlated with changes in cognitive fatigue: as fatigue increased, subjects became more conservative in their response bias and their perceptual certainty declined. Furthermore, activation in the striatum of the basal ganglia was also related to cognitive fatigue, criterion, and d'. These results suggest that SDT measures represent an objective measure of cognitive fatigue. Additionally, the overlap and difference in the fMRI results between cognitive fatigue and SDT measures indicate that these measures are related while also separate. In sum, we show the relevance of SDT measures in the understanding of fatigue, thus providing researchers with a new set of tools with which to better understand the nature and consequences of cognitive fatigue.

SUBMITTER: Wylie GR

PROVIDER: S-EPMC7844088 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Using Signal Detection Theory to Better Understand Cognitive Fatigue.

Wylie Glenn R GR Yao Bing B Sandry Joshua J DeLuca John J

Frontiers in psychology 20210115

When we are fatigued, we feel that our performance is worse than when we are fresh. Yet, for over 100 years, researchers have been unable to identify an objective, behavioral measure that covaries with the subjective experience of fatigue. Previous work suggests that the metrics of signal detection theory (SDT)-response bias (criterion) and perceptual certainty (<i>d</i>')-may change as a function of fatigue, but no work has yet been done to examine whether these metrics covary with fatigue. Her ...[more]

PMID: 33519595

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Project description:INTRODUCTION:Models from signal detection theory are commonly used to score neuropsychological test data, especially tests of recognition memory. Here we show that certain item response theory models can be formulated as signal detection theory models, thus linking two complementary but distinct methodologies. We then use the approach to evaluate the validity (construct representation) of commonly used research measures, demonstrate the impact of conditional error on neuropsychological outcomes, and evaluate measurement bias. METHOD:Signal detection-item response theory (SD-IRT) models were fitted to recognition memory data for words, faces, and objects. The sample consisted of U.S. Infantry Marines and Navy Corpsmen participating in the Marine Resiliency Study. Data comprised item responses to the Penn Face Memory Test (PFMT; N = 1,338), Penn Word Memory Test (PWMT; N = 1,331), and Visual Object Learning Test (VOLT; N = 1,249), and self-report of past head injury with loss of consciousness. RESULTS:SD-IRT models adequately fitted recognition memory item data across all modalities. Error varied systematically with ability estimates, and distributions of residuals from the regression of memory discrimination onto self-report of past head injury were positively skewed towards regions of larger measurement error. Analyses of differential item functioning revealed little evidence of systematic bias by level of education. CONCLUSIONS:SD-IRT models benefit from the measurement rigor of item response theory-which permits the modeling of item difficulty and examinee ability-and from signal detection theory-which provides an interpretive framework encompassing the experimentally validated constructs of memory discrimination and response bias. We used this approach to validate the construct representation of commonly used research measures and to demonstrate how nonoptimized item parameters can lead to erroneous conclusions when interpreting neuropsychological test data. Future work might include the development of computerized adaptive tests and integration with mixture and random-effects models.

Dataset Information

Using Signal Detection Theory to Better Understand Cognitive Fatigue.

Publications

Using Signal Detection Theory to Better Understand Cognitive Fatigue.

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