Aging affects the balance between goal-guided and habitual spatial attention

Peripheral vision contributes to implicit attentional learning: Findings from the "mouse-eye" paradigm

The central visual field is essential for activities like reading and face recognition. However, the impact of peripheral vision loss on daily activities is profound. While the importance of central vision is well established, the contribution of peripheral vision to spatial attention is less clear. In this study, we introduced a "mouse-eye" method as an alternative to traditional gaze-contingent eye tracking. We found that even in tasks requiring central vision, peripheral vision contributes to implicit attentional learning. Participants searched for a T among Ls, with the T appearing more often in one visual quadrant. Earlier studies showed that participants' awareness of the T location probability was not essential for their ability to learn. When we limited the visible area around the mouse cursor, only participants aware of the target's location probability showed learning; those unaware did not. Adding placeholders in the periphery did not restore implicit attentional learning. A control experiment showed that when participants were allowed to see all items while searching and moving the mouse to reveal the target's color, both aware and unaware participants acquired location probability learning. Our results underscore the importance of peripheral vision in implicitly guided attention. Without peripheral vision, only explicit, but not implicit, attentional learning prevails.

Stability of individual differences in implicitly guided attention

Daily activities often occur in familiar environments, affording us an opportunity to learn. Laboratory studies have shown that people readily acquire an implicit spatial preference for locations that frequently contained a search target in the past. These studies, however, have focused on group characteristics, downplaying the significance of individual differences. In a pre-registered study, we examined the stability of individual differences in two variants of an implicit location probability learning (LPL) task. We tested the possibility that individual differences were stable in variants that shared the same search process, but not in variants involving different search processes. In Experiment 1, participants performed alternating blocks of T-among-Ls and 5-among-2s search tasks. Unbeknownst to them, the search target appeared disproportionately often in one region of space; the high-probability regions differed between the two tasks. LPL transferred between the two tasks. In addition, individuals who showed greater LPL in the T-task also did so in the 5-task and vice versa. In Experiment 2, participants searched for either a camouflaged-T against background noise or a well-segmented T among well-segmented Ls. These two tasks produced task-specific learning that did not transfer between tasks. Moreover, individual differences in learning did not correlate between tasks. Thus, LPL is associated with stable individual differences across variants, but only when the variants share common search processes.

Proportional Changes in Cognitive Subdomains During Normal Brain Aging

Background: Neuroscience lacks a reliable method of screening the early stages of dementia. Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains. Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes. Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age. Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

Simulated central vision loss impairs implicit location probability learning

Some eye diseases, especially macular degeneration, can cause central vision loss (CVL), impairing goal-driven guidance of attention. Does CVL also affect implicit, experience-driven attention? We investigated how simulated central scotomas affected young adults' ability to prioritize locations frequently containing visual search targets (location probability learning). Participants searched among distractor letter 'L's for a target 'T' that appeared more often in one screen quadrant than others. To dissociate potential impairments to statistical learning of target locations and attentional guidance, two experiments each included search with and without simulated scotomas. Experiment 1 successfully induced probability learning in a no-scotoma phase. When participants later searched both with and without simulated scotomas, they showed persistent, statistically equivalent spatial biases in both no-scotoma and scotoma search. Experiment 2 trained participants with a central scotoma. While Experiment 1's participants acquired probability learning regardless of their self-reported awareness of the target's location probability, in Experiment 2 only aware participants learned to bias attention to the high probability region. Similarly, learning with a scotoma affected search with no scotoma in aware but not unaware participants. Together, these results show that simulated central vision loss interferes with the acquisition of implicitly learned location probability learning, supporting a role of central vision in implicit spatial attentional biases.

Experience-Driven Auditory Attention

In addition to conscious goals and stimulus salience, an observer's prior experience also influences selective attention. Early studies demonstrated experience-driven effects on attention mainly in the visual modality, but increasing evidence shows that experience drives auditory selection as well. We review evidence for a multiple-levels framework of auditory attention, in which experience-driven attention relies on mechanisms that acquire control settings and mechanisms that guide attention towards selected stimuli. Mechanisms of acquisition include cue-target associative learning, reward learning, and sensitivity to prior selection history. Once acquired, implementation of these biases can occur either consciously or unconsciously. Future research should more fully characterize the sources of experience-driven auditory attention and investigate the neural mechanisms used to acquire and implement experience-driven auditory attention.

Age-related changes in Bayesian belief updating during attentional deployment and motor intention

Predicting upcoming events using past observations is a crucial component of an efficient allocation of attentional resources. Therefore, the deployment of attention is sensitive to different types of cues predicting upcoming events. Here we investigated probabilistic inference abilities in spatial and feature-based attentional, as well as in motor-intentional subsystems, focusing specifically on the age-related changes in these abilities. In two behavioral experiments, younger and older adults (20 younger and 20 older adults for each experiment) performed three versions of a cueing paradigm, where spatial, feature, or motor cues predicted the location, color, or motor response of a target stimulus. The percentage of cue validity (i.e., the probability of the cue being valid) changed over time, thereby creating a volatile environment. A Bayesian hierarchical model was used to estimate trial-wise beliefs concerning the cue validity from reaction times and to derive a subject-specific belief updating parameter ω in each task version. We also manipulated task difficulty: participants performed an easier version of the task in Experiment 1 and a more difficult version in Experiment 2. Results from Experiment 1 suggested a preserved ability of older adults to use the three different cues to generate predictions. However, the increased task demands of Experiment 2 uncovered a difference in belief updating between the two age groups, indicating moderate evidence for a reduction of the ability to update predictions with motor intention cues in older adults. These results point at a distinction of attentional and motor-intentional subsystems, with age-related differences tackling especially the motor-intentional subsystem.

Habitual versus goal-driven attention

Recent research has expanded the list of factors that control spatial attention. Beside current goals and perceptual salience, statistical learning, reward, motivation and emotion also affect attention. But do these various factors influence spatial attention in the same manner, as suggested by the integrated framework of attention, or do they target different aspects of spatial attention? Here I present evidence that the control of attention may be implemented in two ways. Whereas current goals typically modulate where in space attention is prioritized, search habits affect how one moves attention in space. Using the location probability learning paradigm, I show that a search habit forms when people frequently find a visual search target in one region of space. Attentional cuing by probability learning differs from that by current goals. Probability cuing is implicit and persists long after the probability cue is no longer valid. Whereas explicit goal-driven attention codes space in an environment-centered reference frame, probability cuing is viewer-centered and is insensitive to secondary working memory load and aging. I propose a multi-level framework that separates the source of attentional control from its implementation. Similar to the integrated framework, the multi-level framework considers current goals, perceptual salience, and selection history as major sources of attentional control. However, these factors are implemented in two ways, controlling where spatial attention is allocated and how one shifts attention in space.

Explicit goal-driven attention, unlike implicitly learned attention, spreads to secondary tasks

To what degree does spatial attention for one task spread to all stimuli in the attended region, regardless of task relevance? Most models imply that spatial attention acts through a unitary priority map in a task-general manner. We show that implicit learning, unlike endogenous spatial cuing, can bias spatial attention within one task without biasing attention to a spatially overlapping secondary task. Participants completed a visual search task superimposed on a background containing scenes, which they were told to encode for a later memory task. Experiments 1 and 2 used explicit instructions to bias spatial attention to one region for visual search; Experiment 3 used location probability cuing to implicitly bias spatial attention. In location probability cuing, a target appeared in one region more than others despite participants not being told of this. In all experiments, search performance was better in the cued region than in uncued regions. However, scene memory was better in the cued region only following endogenous guidance, not after implicit biasing of attention. These data support a dual-system view of top-down attention that dissociates goal-driven and implicitly learned attention. Goal-driven attention is task general, amplifying processing of a cued region across tasks, whereas implicit statistical learning is task-specific. (PsycINFO Database Record