Neurophysiological and phenomenological data on sensorimotor decision making are growing so rapidly that it is now necessary and achievable to capture it in biologically inspired models, for advancing our understanding in both research and clinical settings. However, the main impediment in moving from elegant models with few free parameters to more complex biological models in humans lies in constraining the more numerous parameters with behavioral data (without human single-cell recording). Here we show that a behavioral effect called "saccadic inhibition" (1) is predicted by existing complex (neuronal field) models, (2) constrains crucial temporal parameters of the model, precisely enough to address individual differences, and (3) is not accounted for by current simple decision models, even after significant additions. Visual onsets appearing while an observer plans a saccade knock out a subpopulation of saccadic latencies that would otherwise occur, producing a clear dip in the latency distribution. This overlooked phenomenon is remarkably well time locked across conditions and observers, revealing and characterizing a fast automatic component of visual input to oculomotor competition. The neural field model not only captures this but predicts additional features that are borne out: the dips show spatial specificity, are lawfully modulated in contrast, and occur with S-cone stimuli invisible to the retinotectal route. Overall, we provide a way forward for applying precise neurophysiological models of saccade planning in humans at the individual level.
In most visual search experiments in the laboratory, objects are presented on an isolated, blank background. In most real world search tasks, however, the background is continuous and can be complex. In six experiments, we examine the ability of the visual system to separate search items from a background. The results support a view in which objects are separated from backgrounds in a single, preattentive step. This is followed by a limited-capacity search process that selects objects that might be targets for further identification. Identity information regarding the object's status (target or distractor) then accumulates through a limited capacity parallel process. The main effect of background complexity is to slow the accumulation of information in this later recognition stage. It may be that recognition is slowed because background noise causes the preattentive segmentation stage to deliver less effectively segmented objects to later stages. Only when backgrounds become nearly identical to the search objects does the background have the effect of slowing item-by-item selection.
We studied resting-state oscillatory connectivity using magnetoencephalography in healthy young humans (N = 183) genotyped for APOE-ɛ4, the greatest genetic risk for Alzheimer’s disease (AD). Connectivity across frequencies, but most prevalent in alpha/beta, was increased in APOE-ɛ4 in a set of mostly right-hemisphere connections, including lateral parietal and precuneus regions of the Default Mode Network. Similar regions also demonstrated hyperactivity, but only in gamma (40–160 Hz). In a separate study of AD patients, hypoconnectivity was seen in an extended bilateral network that partially overlapped with the hyperconnected regions seen in young APOE-ɛ4 carriers. Using machine-learning, AD patients could be distinguished from elderly controls with reasonable sensitivity and specificity, while young APOE-e4 carriers could also be distinguished from their controls with above chance performance. These results support theories of initial hyperconnectivity driving eventual profound disconnection in AD and suggest that this is present decades before the onset of AD symptomology.
The underpinning assumption of much research on cognitive individual differences (or group differences) is that task performance indexes cognitive ability in that domain. In many tasks performance is measured by differences (costs) between conditions, which are widely assumed to index a psychological process of interest rather than extraneous factors such as speed–accuracy trade-offs (e.g., Stroop, implicit association task, lexical decision, antisaccade, Simon, Navon, flanker, and task switching). Relatedly, reaction time (RT) costs or error costs are interpreted similarly and used interchangeably in the literature. All of this assumes a strong correlation between RT-costs and error-costs from the same psychological effect. We conducted a meta-analysis to test this, with 114 effects across a range of well-known tasks. Counterintuitively, we found a general pattern of weak, and often no, association between RT and error costs (mean r = .17, range −.45 to .78). This general problem is accounted for by the theoretical framework of evidence accumulation models, which capture individual differences in (at least) 2 distinct ways. Differences affecting accumulation rate produce positive correlation. But this is cancelled out if individuals also differ in response threshold, which produces negative correlations. In the models, subtractions between conditions do not isolate processing costs from caution. To demonstrate the explanatory power of synthesizing the traditional subtraction method within a broader decision model framework, we confirm 2 predictions with new data. Thus, using error costs or RT costs is more than a pragmatic choice; the decision carries theoretical consequence that can be understood through the accumulation model framework.
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