Visual Learning in Multiple-Object Tracking

Tracking moving objects in space is important for the maintenance of spatiotemporal continuity in everyday visual tasks. In the laboratory, this ability is tested using the Multiple Object Tracking (MOT) task, where participants track a subset of moving objects with attention over an extended period of time. The ability to track multiple objects with attention is severely limited. Recent research has shown that this ability may improve with extensive practice (e.g., from action videogame playing). However, whether tracking also improves in a short training session with repeated trajectories has rarely been investigated. In this study we examine the role of visual learning in multiple-object tracking and characterize how varieties of attention interact with visual learning.Participants first conducted attentive tracking on trials with repeated motion trajectories for a short session. In a transfer phase we used the same motion trajectories but changed the role of tracking targets and nontargets. We found that compared with novel trials, tracking was enhanced only when the target subset was the same as that used during training. Learning did not transfer when the previously trained targets and nontargets switched roles or mixed up. However, learning was not specific to the trained temporal order as it transferred to trials where the motion was played backwards.These findings suggest that a demanding task of tracking multiple objects can benefit from learning of repeated motion trajectories. Such learning potentially facilitates tracking in natural vision, although learning is largely confined to the trajectories of attended objects. Furthermore, we showed that learning in attentive tracking relies on relational coding of all target trajectories. Surprisingly, learning was not specific to the trained temporal context, probably because observers have learned motion paths of each trajectory independently of the exact temporal order

The consequences of preparing for informative or distracting stimuli

How do people prepare for upcoming distractors? It has been recently suggested that not only observers do not inhibit distractors before their appearance, but they are rather more alert at those moments. Interestingly, a similar effect was found when observers were expecting task-relevant, informative stimuli, supporting a mandatory "attend-all" mechanism. However, thus far, the preparation effect was only demonstrated in speeded dot-probe tasks and it is yet to be determined whether preparing for distractors merely facilitates motor preparation or whether it has other outcomes, such as modifying early perceptual processes. Replacing the dot-probe task with a four-letter memory encoding task revealed that memory was enhanced when the letters appeared when informative stimuli were expected. Memory was also enhanced, although to a lesser extent, when observers were expecting distracting stimuli to appear. These results indicate that the preparation effect has perceptual consequences and that it is more flexible than previously thought

The effect of working memory maintenance on long-term memory

Initially inspired by the Atkinson and Shiffrin model, researchers have spent a half century investigating whether actively maintaining an item in working memory (WM) leads to improved subsequent long-term memory (LTM). Empirical results have been inconsistent, and thus the answer to the question remains unclear. We present evidence from 13 new experiments as well as a meta-analysis of 61 published experiments. Both the new experiments and meta-analysis show clear evidence that increased WM maintenance of a stimulus leads to superior recognition for that stimulus in subsequent LTM tests. This effect appears robust across a variety of experimental design parameters, suggesting that the variability in prior results in the literature is probably due to low power and random chance. The results support theories on which there is a close link between WM and LTM mechanisms, while challenging claims that this relationship is specific to verbal memory and evolved to support language acquisition.NSF (Grants 0345525 and 5F32HD072748

Early and late selection: Effects of load, dilution and salience

Our visual system is constantly bombarded by a variety of stimuli, of which only a small part is relevant to the task at hand. As a result, goal-directed behavior requires a high degree of selectivity at some point in the processing stream. The precise point at which selection takes place has been the focus of much debate. Early selection advocates argue that the locus of selection is at early stages of processing and that therefore, unattended stimuli are not fully processed. In contrast, late selection theorists argue that attention operates only after stimuli have been fully processed. Evidence supporting both sides has been accumulated over the years and the debate played a central role in the attention literature for decades. Perceptual load theory was put forward as an intermediate solution: the locus of selective attention depends on task requirements. When load is high, selection is early. When load is low, selection is late. This solution has been widely accepted and the early/late debate has been, for the most part, set aside. However, recently, perceptual load theory has been challenged on both theoretical and methodological grounds. It has been argued that it is not load, but rather perceptual dilution salience and other perceptual factors that determine the efficacy of attentional selection, which would call for a reevaluation of the current status of both perceptual load theory and its proposed alternatives, and more broadly, the early/late selection debate. The goal of this Research Topic is to provide an up-to-date overview of both empirical evidence and theoretical views on these key questions

An illustration of the conditions tested in Experiment 1.

<p>The numbers 1 to 16 correspond to 16 random motion trajectories.</p

Results from Experiment 2.

<p>Left: training phase. Right: transfer phase. Error bars show ±1s.e.</p