Visual Search for Type of Motion is Based on Simple Motion Primitives

Assessing visual search performance using a novel dynamic naturalistic scene

Daily activities require the constant searching and tracking of visual targets in dynamic and complex scenes. Classic work assessing visual search performance has been dominated by the use of simple geometric shapes, patterns, and static backgrounds. Recently, there has been a shift toward investigating visual search in more naturalistic dynamic scenes using virtual reality (VR)-based paradigms. In this direction, we have developed a first-person perspective VR environment combined with eye tracking for the capture of a variety of objective measures. Participants were instructed to search for a preselected human target walking in a crowded hallway setting. Performance was quantified based on saccade and smooth pursuit ocular motor behavior. To assess the effect of task difficulty, we manipulated factors of the visual scene, including crowd density (i.e., number of surrounding distractors) and the presence of environmental clutter. In general, results showed a pattern of worsening performance with increasing crowd density. In contrast, the presence of visual clutter had no effect. These results demonstrate how visual search performance can be investigated using VR-based naturalistic dynamic scenes and with high behavioral relevance. This engaging platform may also have utility in assessing visual search in a variety of clinical populations of interest.

Searching for illusory motion

In a series of four experiments, standard visual search was used to explore whether the onset of illusory motion pre-attentively guides vision in the same way that the onset of real-motion is known to do. Participants searched for target stimuli based on Akiyoshi Kitaoka's classic illusions, configured so that they either did or did not give the subjective impression of illusory motion. Distractor items always contained the same elements as target items, but did not convey a sense of illusory motion. When target items contained illusory motion, they popped-out, with flat search slopes that were independent of set size. Search for control items without illusory motion - but with identical structural differences to distractors - was slow and serial in nature (> 200 ms/item). Using a nulling task, we estimated the speed of illusory rotation in our displays to be approximately 2 °/s. Direct comparison of illusory and real-motion targets moving with matched velocity showed that illusory motion targets were detected more quickly. Blurred target items that conveyed a weak subjective impression of illusory motion gave rise to serial but faster (< 100 ms/item) search than control items. Our behavioral findings of parallel detection across the visual field, together with previous imaging and neurophysiological studies, suggests that relatively early cortical areas play a causal role in the perception of illusory motion. Furthermore, we hope to re-emphasize the way in which visual search can be used as a flexible, objective measure of illusion strength.

Shape representation modulating the effect of motion on visual search performance

The effect of motion on visual search has been extensively investigated, but that of uniform linear motion of display on search performance for tasks with different target-distractor shape representations has been rarely explored. The present study conducted three visual search experiments. In Experiments 1 and 2, participants finished two search tasks that differed in target-distractor shape representations under static and dynamic conditions. Two tasks with clear and blurred stimuli were performed in Experiment 3. The experiments revealed that target-distractor shape representation modulated the effect of motion on visual search performance. For tasks with low target-distractor shape similarity, motion negatively affected search performance, which was consistent with previous studies. However, for tasks with high target-distractor shape similarity, if the target differed from distractors in that a gap with a linear contour was added to the target, and the corresponding part of distractors had a curved contour, motion positively influenced search performance. Motion blur contributed to the performance enhancement under dynamic conditions. The findings are useful for understanding the influence of target-distractor shape representation on dynamic visual search performance when display had uniform linear motion.

A saliency-based bottom-up visual attention model for dynamic scenes analysis

This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work adds motion saliency calculations to a neural network model with realistic temporal dynamics [(e.g., building motion salience on top of De Brecht and Saiki Neural Networks 19:1467-1474, (2006)]. The resulting network elicits strong transient responses to moving objects and reaches stability within a biologically plausible time interval. The responses are statistically different comparing between earlier and later motion neural activity; and between moving and non-moving objects. We demonstrate the network on a number of synthetic and real dynamical movie examples. We show that the model captures the motion saliency asymmetry phenomenon. In addition, the motion salience computation enables sudden-onset moving objects that are less salient in the static scene to rise above others. Finally, we include strong consideration for the neural latencies, the Lyapunov stability, and the neural properties being reproduced by the model.

Finding flicker: critical differences in temporal frequency capture attention

Rapid visual flicker is known to capture attention. Here we show slow flicker can also capture attention under reciprocal temporal conditions. Observers searched for a target line (vertical or horizontal) among tilted distractors. Distractor lines were surrounded by luminance modulating annuli, all flickering sinusoidally at 1.3 or 12.1 Hz, while the target’s annulus flickered at frequencies within this range. Search times improved with increasing target/distractor frequency differences. For target–distractor frequency separations >5 Hz reaction times were minimal with high-frequency targets correctly identified more rapidly than low frequency targets (~400 ms). Critically, however, at these optimal frequency separations search times for low and high-frequency targets were unaffected by set size (slow flicker popped out from high flicker, and vice versa), indicating parallel and symmetric search performance when searching for high or low frequency targets. In a “cost” experiment using 1.3 and 12.1 Hz flicker, the unique flickering annulus sometimes surrounded a distractor and, on other trials, surrounded the target. When centered on a distractor, the unique frequency produced a clear and symmetrical search cost. Together, these symmetric pop-out and search costs demonstrate that temporal frequency is a pre-attentive visual feature capable of capturing attention, and that it is relative rather than absolute frequencies that are critical. The shape of the search functions strongly suggest that early visual temporal frequency filters underlie these effects.

Pooling elementary motion signals into perception of global motion direction

Six observers were asked to indicate in which of two opposite directions, to the right or to the left, an entire display appeared to move, based on the proportion of right vs leftward motion elements, each of which was distinctly visible. The performance of each observer was described by Thurstone's discriminative processes and Bernoulli trial models which described empirical psychometric functions equally well. Although formally it was impossible to discriminate between these two models, treating observer as a counting device which measures a randomly selected subsample of all available motion elements had certain advantages. According to the Bernoulli trial model decisions about the global motion direction in a range of 12-800 elements were based on taking into account about 4±2 random moving dot elements. This small number is not due to cancellation of the opposite motion vectors since the motion direction recognition performance did not improve after the compared motion directions were made orthogonal. This may indicate that the motion pooling mechanism studied in our experiment is strongly limited in capacity.