Simulated central vision loss does not impair implicit location probability learning when participants search through simple displays

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.

Simulation of visual impairment in persons with normal vision for scientific research

Simulation of visual impairment in healthy eyes has multiple applications in students' training, research and product development. However, due to the absence of an existing standard protocol, the method of simulation was left to the discretion of the researcher. This review aimed to outline the various methods of simulating visual impairment and categorising them. A scoping review of the relevant publications was conducted. Of the 1593 articles originally retrieved from the databases, 103 were included in the review. The characteristics of the participants, the method for simulation of the visual impairment in persons with normal vision and the level or type of visual impairment that was simulated were extracted from the papers. None of the methods of simulation can be judged as being superior to the others. However, electronic displays produced the most consistent form of visual impairment simulation.

Contribution of peripheral vision to attentional learning

Attention is tuned towards locations that frequently contain a visual search target (location probability learning; LPL). Peripheral vision, covering a larger field than the fovea, often receives information about the target. Yet what is the role of peripheral vision in attentional learning? Using gaze-contingent eye tracking, we examined the impact of simulated peripheral vision loss on location probability learning. Participants searched for a target T among distractor Ls. Unbeknownst to them, the T appeared disproportionately often in one quadrant. Participants searched with either intact vision or "tunnel vision," restricting the visible search items to the central 6.7º (in diameter) of the current gaze. When trained with tunnel vision, participants in Experiment 1 acquired LPL, but only if they became explicitly aware of the target's location probability. The unaware participants were not faster finding the target in high-probability than in low-probability locations. When trained with intact vision, participants in Experiment 2 successfully acquired LPL, regardless of whether they were aware of the target's location probability. Thus, whereas explicit learning may proceed with central vision alone, implicit LPL is strengthened by peripheral vision. Consistent with Guided Search (Wolfe, 2021), peripheral vision supports a nonselective pathway to guide visual search.

Statistical learning of target location and distractor location rely on different mechanisms during visual search

More studies have demonstrated that people have the capacity to learn and make use of environmental regularities. This capacity is known as statistical learning (SL). Despite rich empirical findings, it is not clear how the two forms of SL (SL of target location and SL of distractor location) influence visual search and whether they rely on the shared cognitive mechanism. In Experiment 1 and Experiment 2, we manipulated the probability of target location and the probability of distractor location, respectively. The results suggest that attentional guidance (they referred to overt attention) may mainly contribute to the SL effect of the target location and the distractor location, which is in line with the notion of priority mapping. To a small extent, facilitation of response selection may also contribute to the SL effect of the target location but does not contribute to the SL effect of the distractor location. However, the main difference between the two kinds of SL occurred in the early stage (it involved covert attention). Together, our findings indicate that the two forms of SL reflect partly shared and partly independent cognitive mechanisms.