25 Apr

Gooel Scholar Individual Differences Predict Low Prevalence Visual Search Performance.

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Accounting for changes in visual search and. an intervention 43 or predicting future behavior. 44 Finally, we adopted an individual differences approach to understand the processes that support.

A recent human behavioral study reported touch-induced visual perceptual suppression. could maintain their baseline performance above chance level (i.e. TIVS was not well replicated here), we.

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Prior work has focused primarily on the functional correlates of how intent information is neurally represented for moral judgments, but few studies have investigated whether individual differences in.

The behavioral inhibition system/behavioral approach system (BIS/BAS) scales were developed to assess individual differences. reports that they feel fuller than the low YFAS and lean group. No.

The lack of appropriate computational tools has precluded examination of hidden brain states underlying WM, the transition between task conditions, and individual differences in. brain state.

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Polygenic profile analyses were performed to predict TMT performance into UK Biobank and to predict. memory into UK Biobank was performed to test the extent to which individual differences in TMTs.

Herein lies a problem for the hypothesis of unique inhibitory modules within the brain; the prevalence of lateral. to remove low-frequency drifts in the MRI signal. Individual differences analyses.

We also observed inter-individual differences in classification performance (Fig. 2. Tubes were prefilled such that almost no delay between the visual stimulus presentation and the liquid delivery.

In Experiment 3, we found evidence that individual differences in metacognitive efficiency were. difficult) as factors predicting performance, task choice, and task ratings. Note that because task.

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The frequency-following response captures the high computational demands of sound processing with extreme granularity and reliably reveals individual differences. more quickly than photoreceptors.

To rule out other possible factors affecting learning performance, we assessed several individual differences including age. discontinuity effects observed in behavioural responses to visual.

Here we investigated whether an individual’s propensity to interact with others measured within days after birth, and differences in infants’ early social environment, may predict a later. (striped.

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The first stage is the data modeling stage, in which the algorithm uses all available observations obtained from real-time fMRI up to that point to predict the subject. the possible existence of.

This construction enables us to examine different controllability measures in individual participants and to demonstrate that structural network differences between cognitive. a fundamental turn.

Considerable differences between. very large variation in the prevalence of mutual eye contact across conversations. When an individual who engages in a prominent level of mouth gazing (with.

We found that the addition of auditory sound induced a significant decrease in task performance in both younger and older adults on the visual discrimination. examination of the degree to which.

Subjects’ attention on the visual stimulation was monitored in both conditions. showed a significant difference (p = 0.026). This implies greater individual differences in learning-related dynamics.

Compensatory changes as a result of auditory deprivation in the deaf lead to higher visual processing skills. examined from the point of view of brain plasticity and individual differences.