Three experiments of visual search for a cube (for a square pillar in Experiment 3) with an odd conjunction of orientation of faces and color (a cube with a red top face and a green right face among cubes with a green top face and a red right face, for example) showed that the search is made more efficient by arranging cubes (or square pillars) so that their top faces lie in a horizontal surface defined by pictorial cues. This effect shows the same asymmetry as that of the surface defined by the disparity cue did [Perception and Psychophysics, 62 (2000) 540], implying that the effect is independent of the three-dimensional cue and the global surface structure influences the control of attention during the search.

BackgroundIn the human visual system, different attributes of an object, such as shape, color, and motion, are processed separately in different areas of the brain. This raises a fundamental question of how are these attributes integrated to produce a unified perception and a specific response. This “binding problem” is computationally difficult because all attributes are assumed to be bound together to form a single object representation. However, there is no firm evidence to confirm that such representations exist for general objects.Methodology/Principal FindingsHere we propose a paired-attribute model in which cognitive processes are based on multiple representations of paired attributes. In line with the model's prediction, we found that multiattribute stimuli can produce an illusory perception of a multiattribute object arising from erroneous integration of attribute pairs, implying that object recognition is based on parallel perception of paired attributes. Moreover, in a change-detection task, a feature change in a single attribute frequently caused an illusory perception of change in another attribute, suggesting that multiple pairs of attributes are stored in memory.Conclusions/SignificanceThe paired-attribute model can account for some novel illusions and controversial findings on binocular rivalry and short-term memory. Our results suggest that many cognitive processes are performed at the level of paired attributes rather than integrated objects, which greatly facilitates the binding problem and provides simpler solutions for it.

Every day we perceive pictures on our mobile phones and scroll through images within a limited space. At present, however, visual perception via image scrolling is not well understood. This study investigated the nature of visual perception within a small window frame. It compared visual search efficiency using three modes: scrolling, moving-window, and free-viewing. The item number and stimulus size varied. Results showed variations in search efficiency depending on search mode. The slowest search occurred under the scrolling condition, followed by the moving-window condition, and the fastest search occurred under the no-window condition. For the scrolling condition, the response time increased the least sharply in proportion to item number but most sharply in proportion to the stimulus size compared to the other two conditions. Analysis of the trace of scan revealed frequent pauses interjected with small and fast stimulus shifts for the scrolling condition, but slow and continuous window movements interjected with a few pauses for the moving-window condition. We concluded that searching via scrolling was less efficient than searching via a moving-window, reflecting differences in dynamic properties of participants’ scan.

Self-correction for crowdsourced tasks is a two-stage setting that allows a crowd worker to review the task results of other workers; the worker is then given a chance to update his/her results according to the review.Self-correction was proposed as an approach complementary to statistical algorithms in which workers independently perform the same task. It can provide higher-quality results with few additional costs. However, thus far, the effects have only been demonstrated in simulations, and empirical evaluations are needed. In addition, as self-correction gives feedback to workers, an interesting question arises: whether perceptual learning is observed in self-correction tasks. This paper reports our experimental results on self-corrections with a real-world crowdsourcing service.The empirical results show the following: (1) Self-correction is effective for making workers reconsider their judgments. (2) Self-correction is more effective if workers are shown task results produced by higher-quality workers during the second stage. (3) Perceptual learning effect is observed in some cases. Self-correction can give feedback that shows workers how to provide high-quality answers in future tasks.The findings imply that we can construct a positive loop to improve the quality of workers effectively.We also analyze in which cases perceptual learning can be observed with self-correction in crowdsourced microtasks.