LAG-1: A dynamic, integrative model of learning, attention, and gaze.

It is clear that learning and attention interact, but it is an ongoing challenge to integrate their psychological and neurophysiological descriptions. Here we introduce LAG-1, a dynamic neural field model of learning, attention and gaze, that we fit to human learning and eye-movement data from two category learning experiments. LAG-1 comprises three control systems: one for visuospatial attention, one for saccadic timing and control, and one for category learning.

Heterogeneity of cellular inflammatory responses in ageing white matter and relationship to Alzheimer's and small vessel disease pathologies.

White matter lesions (WML) are common in the ageing brain, often arising in a field effect of diffuse white matter abnormality. Although WML are associated with cerebral small vessel disease (SVD) and Alzheimer's disease (AD), their cause and pathogenesis remain unclear. The current study tested the hypothesis that different patterns of neuroinflammation are associated with SVD compared to AD neuropathology by assessing the immunoreactive profile of the microglial (CD68, IBA1 and MHC-II) and astrocyte (GFAP) markers in ageing parietal white matter (PARWM) obtained from the Cognitive Function and Ageing Study (CFAS), an ageing population-representative neuropathology cohort.

Digit eyes: Learning-related changes in information access in a computer game parallel those of oculomotor attention in laboratory studies.

Active sensing theory is founded upon the dynamic relationship between information sampling and an observer's evolving goals. Oculomotor activity is a well studied method of sampling; a mouse or a keyboard can also be used to access information past the current screen. We examine information access patterns of StarCraft 2 players at multiple skill levels.

Learning-induced changes in attentional allocation during categorization: a sizable catalog of attention change as measured by eye movements.

Learning how to allocate attention properly is essential for success at many categorization tasks. Advances in our understanding of learned attention are stymied by a chicken-and-egg problem: there are no theoretical accounts of learned attention that predict patterns of eye movements, making data collection difficult to justify, and there are not enough datasets to support the development of a rich theory of learned attention. The present work addresses this by reporting five measures relating to the overt allocation of attention across 10 category learning experiments: accuracy, probability of fixating irrelevant information, number of fixations to category features, the amount of change in the allocation of attention (using a new measure called Time Proportion Shift - TIPS), and a measure of the relationship between attention change and erroneous responses.