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. The model is able to extract a kind of information gain from pairwise differences in simple associations between visual features and categories. Providing this gain as a reentrant signal with bottom-up visual information, and in top-down spatial priority, appropriately influences the initiation of saccades. LAG-1 provides a moment-by-moment simulation of the interactions of learning and gaze, and thus simultaneously produces phenomena on many timescales, from the duration of saccades and gaze fixations, to the response times for trials, to the slow optimization of attention toward task relevant information across a whole experiment. With only three free parameters (learning rate, trial impatience, and fixation impatience) LAG-1 produces qualitatively correct fits for learning, behavioural timing and eye movement measures, and also for previously unmodelled empirical phenomena (e.g., fixation orders showing stimulus-specific attention, and decreasing fixation counts during feedback). Because LAG-1 is built to capture attention and gaze generally, we demonstrate how it can be applied to other phenomena of visual cognition such as the free viewing of visual stimuli, visual search, and covert attention.

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. Glial responses varied extensively across the PARWM with microglial markers significantly higher in the subventricular region compared to either the middle-zone (CD68 p = 0.028, IBA1 p < 0.001, MHC-II p < 0.001) or subcortical region (CD68 p = 0.002, IBA1 p < 0.001, MHC-II p < 0.001). Clasmatodendritic (CD) GFAP+ astrocytes significantly increased from the subcortical to the subventricular region (p < 0.001), whilst GFAP+ stellate astrocytes significantly decreased (p < 0.001). Cellular reactions could be grouped into two distinct patterns: an immune response associated with MHC-II/IBA1 expression and CD astrocytes; and a more innate response characterised by CD68 expression associated with WML. White matter neuroinflammation showed weak relationships to the measures of SVD, but not to the measures of AD neuropathology. In conclusion, glial responses vary extensively across the PARWM with diverse patterns of white matter neuroinflammation. Although these findings support a role for vascular factors in the pathogenesis of age-related white matter neuroinflammation, additional factors other than SVD and AD pathology may drive this. Understanding the heterogeneity in white matter neuroinflammation will be important for the therapeutic targeting of age-associated white matter damage.

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. The first measures are analogous to existing eye-movement studies: fixation frequency, fixation targets, and fixation duration all change as a function of skill, and are commensurate with known properties of eye movements in learning. Actions that require visual attention at moderate skill levels are eventually performed with little visual attention at all. This (a) confirms the generalizability of laboratory studies of attention and learning using eye movements to digital interface use, and (b) suggests that a wide variety of information access behaviors may be considered as a unified set of phenomena.

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. Using these measures, the data suggest that eye-movements are not substantially connected to error in most cases and that aggregate trial-by-trial attention change is generally stable across a number of changing task variables. The data presented here provide a target for computational models that aim to account for changes in overt attentional behaviors across learning.