Processing Speed

Study objectivesTo determine how mid-afternoon naps of differing durations benefit memory encoding, vigilance, speed of processing (SOP), mood, and sleepiness; to evaluate if these benefits extend past 3 hr post-awakening and to examine how sleep macrostructure during naps modulate these benefits.MethodsFollowing short habitual sleep, 32 young adults underwent four experimental conditions in randomized order: wake; naps of 10 min, 30 min, and 60 min duration verified with polysomnography. A 10-min test battery was delivered at a pre-nap baseline, and at 5 min, 30 min, 60 min, and 240 min post-nap. Participants encoded pictures 90 min post-nap and were tested for recognition 210 min later.ResultsNaps ranging from 10 to 60 min increased positive mood and alleviated self-reported sleepiness up to 240 min post-nap. Compared to waking, only naps of 30 min improved memory encoding. Improvements in vigilance were moderate, and benefits for SOP were not observed. Sleep inertia was observed for the 30 min to 60 min naps but was resolved within 30 min after waking. We found no significant associations between sleep macrostructure and memory benefits.ConclusionsWith short habitual sleep, naps ranging from 10 to 60 min had clear and lasting benefits for positive mood and self-reported sleepiness/alertness. Cognitive improvements were moderate, with only the 30 min nap showing benefits for memory encoding. While there is no clear "winning" nap duration, a 30 min nap appears to have the best trade-off between practicability and benefit.Clinical trial idEffects of Varying Duration of Naps on Cognitive Performance and Memory Encoding, https://www.clinicaltrials.gov/ct2/show/NCT04984824, NCT04984824.

BackgroundKnowledge on the nature of post-COVID neurological sequelae often manifesting as cognitive dysfunction and fatigue is still unsatisfactory.ObjectivesWe assumed that cognitive dysfunction and fatigue in post-COVID syndrome are critically linked via hypoarousal of the brain. Thus, we assessed whether tonic alertness as a neurocognitive index of arousal is reduced in these patients and how this relates to the level of central nervous activation and subjective mental fatigue as further indices of arousal.Methods40 post-COVID patients with subjective cognitive dysfunction and 40 matched healthy controls underwent a whole-report paradigm of briefly presented letter arrays. Based on report performance and computational modelling according to the theory of visual attention, the parameter visual processing speed (VPS) was quantified as a proxy of tonic alertness. Pupillary unrest was assessed as a measure of central nervous activation. The Fatigue Assessment Scale was applied to assess subjective mental fatigue using the corresponding subscale.ResultsVPS was reduced in post-COVID patients compared to controls (p = 0.005). In these patients, pupillary unrest (p = 0.029) and mental fatigue (p = 0.001) predicted VPS, explaining 34% of the variance and yielding a large effect with f2 = 0.51.ConclusionIn post-COVID patients with subjective cognitive dysfunction, hypoarousal of the brain is reflected in decreased processing speed which is explained by a reduced level of central nervous activation and a higher level of mental fatigue. In turn, reduced processing speed objectifies mental fatigue as a core subjective clinical complaint in post-COVID patients.

BackgroundVarious populations with chronic conditions are at risk for decreased cognitive performance, making assessment of their cognition important. Formal mobile cognitive assessments measure cognitive performance with greater ecological validity than traditional laboratory-based testing but add to participant task demands. Given that responding to a survey is considered a cognitively demanding task itself, information that is passively collected as a by-product of ecological momentary assessment (EMA) may be a means through which people's cognitive performance in their natural environment can be estimated when formal ambulatory cognitive assessment is not feasible. We specifically examined whether the item response times (RTs) to EMA questions (eg, mood) can serve as approximations of cognitive processing speed.ObjectiveThis study aims to investigate whether the RTs from noncognitive EMA surveys can serve as approximate indicators of between-person (BP) differences and momentary within-person (WP) variability in cognitive processing speed.MethodsData from a 2-week EMA study investigating the relationships among glucose, emotion, and functioning in adults with type 1 diabetes were analyzed. Validated mobile cognitive tests assessing processing speed (Symbol Search task) and sustained attention (Go-No Go task) were administered together with noncognitive EMA surveys 5 to 6 times per day via smartphones. Multilevel modeling was used to examine the reliability of EMA RTs, their convergent validity with the Symbol Search task, and their divergent validity with the Go-No Go task. Other tests of the validity of EMA RTs included the examination of their associations with age, depression, fatigue, and the time of day.ResultsOverall, in BP analyses, evidence was found supporting the reliability and convergent validity of EMA question RTs from even a single repeatedly administered EMA item as a measure of average processing speed. BP correlations between the Symbol Search task and EMA RTs ranged from 0.43 to 0.58 (P0.70) WP reliability. After correcting for unreliability in multilevel models, EMA RTs from most combinations of items showed moderate WP correlations with the Symbol Search task (ranged from 0.29 to 0.58; PConclusionsAssessing the RTs to EMA items (eg, mood) may be a method of approximating people's average levels of and momentary fluctuations in processing speed without adding tasks beyond the survey questions.

Computational capacity and knowledge of humans are more limited than those of large language models (LLMs). However, in buzzer quizzes, human trivia experts can often identify the correct answer even from insufficient information such as only a few words. Investigating how they can make fast and accurate judgments is expected to highlight new characteristics of human intelligence, but little is known about that. In this exploratory and case-based analysis, we predicted that trivia experts and LLMs would differ in which words/phrases in a question are important for identifying the answer, and compared experts' performance with LLMs' performance in Japanese trivia questions through an LLM-as-a-judge approach: We regarded LLMs as evaluators and then used their outputs as comparative tools for experts' evaluations. First, we constructed a quiz question processing system that tokenized question texts based on morphological analysis and then numerically evaluated the importance of each token using GPT-4o/GPT-4.1. Then, we conducted a behavioral experiment wherein actual trivia experts were asked to numerically evaluate the importance of each token, just as LLMs had performed. As a result, trivia experts treated a variety of words/phrases as important, even if each word/phrase did not appear to be strongly associated with the answer. Their evaluation scores tended to accumulate faster than those of the LLMs. This may indicate that trivia experts can make inductive inferences faster (e.g., finding a common concept even from few items). More advanced question-answering systems may be designed by applying trivia experts' cognitive processes to LLMs' information processing, and our findings may provide a scaffolding toward such goals.