Higher performers upregulate brain signal variability in response to more feature-rich visual input
| Autor: | Maike Kleemeyer, Samira M. Epp, Thad A. Polk, Douglas D. Garrett, Ulman Lindenberger |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Male
Computer science Cognitive Neuroscience media_common.quotation_subject Speech recognition Signal 050105 experimental psychology lcsh:RC321-571 03 medical and health sciences 0302 clinical medicine Cognition Perception Feature (machine learning) Reaction Time Humans 0501 psychology and cognitive sciences Computer Simulation Visual Pathways Effects of sleep deprivation on cognitive performance lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry media_common Aged Visual Cortex Brain Mapping 05 social sciences Brain Middle Aged Magnetic Resonance Imaging Oxygen Sensory input Neurology Feature (computer vision) Signal variability Neural differentiation Female Psychology Neuroscience Facial Recognition 030217 neurology & neurosurgery Photic Stimulation Psychomotor Performance |
| Zdroj: | NeuroImage NeuroImage, Vol 217, Iss, Pp 116836-(2020) |
| DOI: | 10.1101/249029 |
| Popis: | The extent to which brain responses differ across varying cognitive demands is referred to as “neural differentiation,” and greater neural differentiation has been associated with better cognitive performance in older adults. An emerging approach has examined within-person neural differentiation using moment-to-moment brain signal variability. A number of studies have found that brain signal variability differs by cognitive state; however, the factors that cause signal variability to rise or fall on a given task remain understudied. We hypothesized that top performers would modulate signal variability according to the complexity of sensory input, upregulating variability when processing more feature-rich stimuli. In the current study, 46 older adults passively viewed face stimuli and house stimuli during fMRI. Low-level analyses of our stimuli showed that house images were more feature-rich than faces, and subsequent computational modelling of ventral visual stream responses (HMAX) revealed that houses were more feature-rich especially in V1/V2-like model layers. Notably, we then found that participants exhibiting greater face-to-house upregulation of brain signal variability in V1/V2 (higher for house relative to face stimuli) also exhibited more accurate, faster, and more consistent behavioral performance on a battery of offline visuo-cognitive tasks. Further, control models revealed that face-house modulation of mean brain signal was relatively insensitive to offline cognition, providing further evidence for the importance of brain signal variability for understanding human behavior. We conclude that the ability to align brain signal variability to the complexity of perceptual input may mark heightened trait-level behavioral performance in older adults. |
| Databáze: | OpenAIRE |
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