An automatic vigilance hypothesis states that humans preferentially attend to negative stimuli, and this attention to negative valence disrupts the processing of other stimulus properties. Thus, negative words typically elicit slower color naming, word naming, and lexical decisions than neutral or positive words. Larsen, Mercer, and Balota analyzed the stimuli from 32 published studies, and they found that word valence was confounded with several lexical factors known to affect word recognition. Indeed, with these lexical factors covaried out, Larsen et al. found no evidence of automatic vigilance. The authors report a more sensitive analysis of 1011 words. Results revealed a small but reliable valence effect, such that negative words (e.g., "shark") elicit slower lexical decisions and naming than positive words (e.g., "beach"). Moreover, the relation between valence and recognition was categorical rather than linear; the extremity of a word's valence did not affect its recognition. This valence effect was not attributable to word length, frequency, orthographic neighborhood size, contextual diversity, first phoneme, or arousal. Thus, the present analysis provides the most powerful demonstration of automatic vigilance to date.
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http://dx.doi.org/10.1037/1528-3542.8.4.441 | DOI Listing |
J Exp Psychol Gen
January 2025
Department of Psychology, Yale University.
Our ability to maintain a consistent attentional state is essential to many aspects of daily life. Still, despite our best efforts, attention naturally fluctuates between more and less vigilant states. Previous work has shown that offering performance-based rewards or incentives can help to buffer against attentional lapses.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
The Medical Big Data Research Center, Northwest University, Xi'an, 710127 China.
Insomnia, as a common sleep disorder, is the most common complaints in medical practice affecting a large proportion of the population on a situational, recurrent or chronic basis. It has been demonstrated that, during wakefulness, patients with insomnia exhibit increased EEG power in theta, beta, and gamma band. However, the relevant mechanisms underlying such power changes are still lack of understanding.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep's impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming.
View Article and Find Full Text PDFAppl Ergon
December 2024
Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, France; CLLE, Université de Toulouse, CNRS, UT2J & ENAC, Toulouse, France.
Cockpit automation has brought significant benefits in terms of mental workload and fatigue. However, the way primary flight instruments are monitored by pilots may be negatively affected by the high confidence in systems. We examined the effects of automation level on mental workload, manual flight performance and visual strategies.
View Article and Find Full Text PDFComput Biol Med
February 2025
Clinical Research Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China. Electronic address:
Sleep disorders have become a significant health concern in modern society. To investigate and diagnose sleep disorders, sleep analysis has emerged as the primary research method. Conventional polysomnography primarily relies on cerebral electroencephalography (EEG) and electromyography (EMG) for sleep stage scoring, but manual scoring is time-consuming and subjective.
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