Statistical learning modulates the direction of the first head movement in a large-scale search task

Habit-like attentional bias is unlike goal-driven attentional bias against spatial updating

Statistical knowledge of a target's location may benefit visual search, and rapidly understanding the changes in regularity would increase the adaptability in visual search situations where fast and accurate performance is required. The current study tested the sources of statistical knowledge-explicitly-given instruction or experience-driven learning-and whether they affect the speed and location spatial attention is guided. Participants performed a visual search task with a statistical regularity to bias one quadrant ("old-rich" condition) in the training phase, followed by another quadrant ("new-rich" condition) in the switching phase. The "instruction" group was explicitly instructed on the regularity, whereas the "no-instruction" group was not. It was expected that the instruction group would rely on goal-driven attention (using regularities with explicit top-down knowledge), and the no-instruction group would rely on habit-like attention (learning regularities through repetitive experiences) in visual search. Compared with the no-instruction group, the instruction group readjusted spatial attention following the regularity switch more rapidly. The instruction group showed greater attentional bias toward the new-rich quadrant than the old-rich quadrant; however, the no-instruction group showed a similar extent of attentional bias to two rich quadrants. The current study suggests that the source of statistical knowledge can affect attentional allocation. Moreover, habit-like attention, a different type of attentional source than goal-driven attention, is relatively implicit and inflexible.

Location probability learning in 3-dimensional virtual search environments

When a visual search target frequently appears in one target-rich region of space, participants learn to search there first, resulting in faster reaction time when the target appears there than when it appears elsewhere. Most research on this location probability learning (LPL) effect uses 2-dimensional (2D) search environments that are distinct from real-world search contexts, and the few studies on LPL in 3-dimensional (3D) contexts include complex visual cues or foraging tasks and therefore may not tap into the same habit-like learning mechanism as 2D LPL. The present study aimed to establish a baseline evaluation of LPL in controlled 3D search environments using virtual reality. The use of a virtual 3D search environment allowed us to compare LPL for information within a participant's initial field of view to LPL for information behind participants, outside of the initial field of view. Participants searched for a letter T on the ground among letter Ls in a large virtual space that was devoid of complex visual cues or landmarks. The T appeared in one target-rich quadrant of the floor space on half of the trials during the training phase. The target-rich quadrant appeared in front of half of the participants and behind the other half. LPL was considerably greater in the former condition than in the latter. This reveals an important constraint on LPL in real-world environments and indicates that consistent search patterns and consistent egocentric spatial coding are essential for this form of visual statistical learning in 3D environments.

The Difficulty of Effectively Using Allocentric Prior Information in a Spatial Recall Task

Prior information represents the long-term statistical structure of an environment. For example, colds develop more often than throat cancer, making the former a more likely diagnosis for a sore throat. There is ample evidence for effective use of prior information during a variety of perceptual tasks, including the ability to recall locations using an egocentric (self-based) frame. However, it is not yet known if people can use prior information effectively when using an allocentric (world-based) frame. Forty-eight adults were shown sixty sets of three target locations in a sparse virtual environment with three beacons. The targets were drawn from one of four prior distributions. They were then asked to point to the targets after a delay and a change in perspective. While searches were biased towards the beacons, we did not find any evidence that participants successfully exploited the prior distributions of targets. These results suggest that allocentric reasoning does not conform to normative Bayesian models: we saw no evidence for use of priors in our cognitively-complex (allocentric) task, unlike in previous, simpler (egocentric) recall tasks. It is possible that this reflects the high biological cost of processing precise allocentric information.

Context affects implicit learning of spatial bias depending on task relevance

Recent studies on the probability cueing effect have shown that a spatial bias emerges toward a location where a target frequently appears. In the present study, we explored whether such spatial bias can be flexibly shifted when the target-frequent location changes depending on the given context. In four consecutive experiments, participants performed a visual search task within two distinct contexts that predicted the visual quadrant that was more likely to contain a target. We found that spatial attention was equally biased toward two target-frequent quadrants, regardless of context (context-independent spatial bias), when the context information was not mandatory for accurate visual search. Conversely, when the context became critical for the visual search task, the spatial bias shifted significantly more to the target-frequent quadrant predicted by the given context (context-specific spatial bias). These results show that the task relevance of context determines whether probabilistic knowledge can be learned flexibly in a context-specific manner.

Acquisition of habitual visual attention and transfer to related tasks

Extensive research has shown that statistical learning affects perception, attention, and action control; however, few studies have directly linked statistical learning with the formation of habits. Evidence that learning can induce a search habit has come from location probability learning, in which people prioritize locations frequently attended to in the past. Here, using an alternating training-testing procedure, we demonstrated that the initial attentional bias arises from short-term intertrial priming, whereas probability learning takes longer to emerge, first reaching significance in covert orienting (measured by reaction times) after about 48 training trials, and in overt orienting (measured by eye movements) after about 96 training trials. We further showed that location probability learning is persistent after training is discontinued, by transferring from a letter search task to a scene search task-emulating another characteristic feature of habits. By identifying the onset of probability learning and investigating its task specificity, this study provides evidence that probability cuing can induce habitual spatial attention.

Predicting Emotion and Engagement of Workers in Order Picking Based on Behavior and Pulse Waves Acquired by Wearable Devices

Many logistics companies adopt a manual order picking system. In related research, the effect of emotion and engagement on work efficiency and human errors was verified. However, related research has not established a method to predict emotion and engagement during work with high exercise intensity. Therefore, important variables for predicting the emotion and engagement during work with high exercise intensity are not clear. In this study, to clarify the mechanism of occurrence of emotion and engagement during order picking. Then, we clarify the explanatory variables which are important in predicting the emotion and engagement during work with high exercise intensity. We conducted verification experiments. We compared the accuracy of estimating human emotion and engagement by inputting pulse wave, eye movements, and movements to deep neural networks. We showed that emotion and engagement during order picking can be predicted from the behavior of the worker with an accuracy of error rate of 0.12 or less. Moreover, we have constructed a psychological model based on the questionnaire results and show that the work efficiency of workers is improved by giving them clear targets.