The present study used event-related potentials and dipole source modeling to investigate dimension specificity in attentional control. Subjects performed cued attention tasks in which the task-relevant information (a) was always the same, (b) varied between features within the same dimension, or (c) varied between features of two different dimensions. Thus, both demands on control processes in...

Stability and flexibility are fundamental to an intelligent cognitive system. Here, we examined the relationship between stability in selective attention and explicit control of flexible attention. Preschoolers were tested on the Dimension Preference (DP) task, which measures the stability of selective attention to an implicitly primed dimension, and the Dimension Change Card Sort (DCCS) task, ...

While most research in the area of human-information behavior has focused on a single dimension—either the psychological or the social—this case study demonstrated the importance of a multidimensional approach. The Cognitive Work Analysis framework guided this field study of one event of collaborative information retrieval (CIR) carried out by design engineers at Microsoft, including observatio...

Chromatin is a major nuclear component, and it is an active matter of debate to understand its different levels of spatial organization, as well as its implication in gene regulation. Measurements of nuclear chromatin compaction were recently used to understand how DNA is folded inside the nucleus and to detect cellular dysfunctions such as cancer. Super-resolution imaging opens new possibiliti...

To investigate whether attentional capture by salient visual stimuli is mediated by current task sets, we measured the N2pc component as a marker of the spatial locus of visual attention during visual search. In each trial, a singleton stimulus that could either be a target (color task: red circle; shape task: green diamond) or a nontarget (blue circle or green square) was presented among unifo...

Evidence regarding the ability of attention to bias neural processing at the level of single features has been gathering steadily, but most of the experiments to date used arrays with multiple objects and locations, making it difficult to rule out indirect influences from object or spatial attention. To investigate feature-specific selective attention, we have assessed the ability to select and...

The Vapnik-Chervonenkis dimension is a combinatorial parameter that reflects the ”complexity” of a set of sets (a.k.a. concept classes). It has been introduced by Vapnik and Chervonenkis in their seminal paper [1] and has since found many applications, most notably in machine learning theory and in computational geometry. Arguably the most influential consequence of the VC analysis is the funda...