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.

Efficient allocation of attentional sensitivity gain in visual cortex reduces foveal sensitivity in visual search.

The human visual system has a high-resolution fovea and a low-resolution periphery. When actively searching for a target, humans perform a covert search during each fixation, and then shift fixation (the fovea) to probable target locations. Previous studies of covert search under carefully controlled conditions provide strong evidence that for simple and small search displays, humans process all potential target locations with the same efficiency that they process those locations when individually cued on each trial. Here, we extend these studies to the case of large displays, in which the target can appear anywhere within the display. These more natural conditions reveal an attentional effect in which sensitivity in the fovea and parafovea is greatly diminished. We show that this "foveal neglect" is the expected consequence of efficiently allocating a fixed total attentional sensitivity gain across the retinotopic map in the visual cortex. We present a formal theory that explains our findings and the previous findings.

Detection of occluding targets in natural backgrounds.

Detection of target objects in the surrounding environment is a common visual task. There is a vast psychophysical and modeling literature concerning the detection of targets in artificial and natural backgrounds. Most studies involve detection of additive targets or of some form of image distortion. Although much has been learned from these studies, the targets that most often occur under natural conditions are neither additive nor distorting; rather, they are opaque targets that occlude the backgrounds behind them. Here, we describe our efforts to measure and model detection of occluding targets in natural backgrounds. To systematically vary the properties of the backgrounds, we used the constrained sampling approach of Sebastian, Abrams, and Geisler (2017). Specifically, millions of calibrated gray-scale natural-image patches were sorted into a 3D histogram along the dimensions of luminance, contrast, and phase-invariant similarity to the target. Eccentricity psychometric functions (accuracy as a function of retinal eccentricity) were measured for four different occluding targets and 15 different combinations of background luminance, contrast, and similarity, with a different randomly sampled background on each trial. The complex pattern of results was consistent across the three subjects, and was largely explained by a principled model observer (with only a single efficiency parameter) that combines three image cues (pattern, silhouette, and edge) and four well-known properties of the human visual system (optical blur, blurring and downsampling by the ganglion cells, divisive normalization, intrinsic position uncertainty). The model also explains the thresholds for additive foveal targets in natural backgrounds reported in Sebastian et al. (2017).

Mechanisms of saccadic decision making while encoding naturalistic scenes.

Saccadic eye movements are the primary vehicle by which human gaze is brought in alignment with vital visual information present in naturalistic scenes. Although numerous studies using the double-step paradigm have demonstrated that saccade preparation is subject to modification under certain conditions, this has yet to be studied directly within a naturalistic scene-viewing context. To reveal characteristic properties of saccade programming during naturalistic scene viewing, we contrasted behavior across three conditions. In the Static condition of the main experiment, double-step targets were presented following a period of stable fixation on a central cross. In a Scene condition, targets were presented while participants actively explored a naturalistic scene. During a Noise condition, targets were presented during active exploration of a 1/f noise-filtered scene. In Experiment 2, we measure saccadic responses in three Static conditions (Uniform, Scene, and Noise) in which the backgrounds are the same as Experiment 1 but scene exploration is no longer permitted. We find that the mechanisms underlying saccade modification generalize to both dynamic conditions. However, we show that a property of saccade programming known as the saccadic dead time (SDT), the interval prior to saccade onset during which a saccade may not be canceled or modified, is lower in the Static task than it is in the dynamic tasks. We also find a trend toward longer SDT in the Scene as compared with Noise conditions. We discuss the implication of these results for computational models of scene viewing, reading, and visual search tasks.

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.

Extremely selective attention: eye-tracking studies of the dynamic allocation of attention to stimulus features in categorization.

Humans have an extremely flexible ability to categorize regularities in their environment, in part because of attentional systems that allow them to focus on important perceptual information. In formal theories of categorization, attention is typically modeled with weights that selectively bias the processing of stimulus features. These theories make differing predictions about the degree of flexibility with which attention can be deployed in response to stimulus properties. Results from 2 eye-tracking studies show that humans can rapidly learn to differently allocate attention to members of different categories. These results provide the first unequivocal demonstration of stimulus-responsive attention in a categorization task. Furthermore, the authors found clear temporal patterns in the shifting of attention within trials that follow from the informativeness of particular stimulus features. These data provide new insights into the attention processes involved in categorization.