Visually, we can extract a statistical summary of sets of elements efficiently. However, our visual system has a severe limitation in that the ability to recognize an object is remarkably impaired when it is surrounded by other objects. The goal of this study was to investigate whether the crowding effect obstructs the calculation of the mean size of objects. First, we verified that the crowding effect occurs when comparing the sizes of circles (Experiment 1). Next, we manipulated the distances between circles and measured the sensitivity when circles were on or off the limitation of crowding (Experiment 2). Participants were asked to compare the mean sizes of the circles in the left and right visual fields and to judge which was larger. Participants' sensitivity to mean size difference was lower when the circles were located in the nearer distance. Finally, we confirmed that crowding is responsible for the observed results by showing that displays without a crowded object eliminated the effects (Experiment 3). Our results indicate that the statistical information of size does not circumvent the bottleneck of crowding.

Recent studies have sought to determine which levels of categories are processed first in visual scene categorization and have shown that the natural and man-made superordinate-level categories are understood faster than are basic-level categories. The current study examined the robustness of the superordinate-level advantage in a visual scene categorization task. A go/no-go categorization task was evaluated with response time distribution analysis using an ex-Gaussian template. A visual scene was categorized as either superordinate or basic level, and two basic-level categories forming a superordinate category were judged as either similar or dissimilar to each other. First, outdoor/ indoor groups and natural/man-made were used as superordinate categories to investigate whether the advantage could be generalized beyond the natural/man-made boundary. Second, a set of images forming a superordinate category was manipulated. We predicted that decreasing image set similarity within the superordinate-level category would work against the speed advantage. We found that basic-level categorization was faster than outdoor/indoor categorization when the outdoor category comprised dissimilar basic-level categories. Our results indicate that the superordinate-level advantage in visual scene categorization is labile across different categories and category structures.

We can rapidly and efficiently recognize many types of objects embedded in complex scenes. What information supports this object recognition is a fundamental question for understanding our visual processing. We investigated the eccentricity-dependent role of shape and statistical information for ultrarapid object categorization, using the higher-order statistics proposed by Portilla and Simoncelli (2000). Synthesized textures computed by their algorithms have the same higher-order statistics as the originals, while the global shapes were destroyed. We used the synthesized textures to manipulate the availability of shape information separately from the statistics. We hypothesized that shape makes a greater contribution to central vision than to peripheral vision and that statistics show the opposite pattern. Results did not show contributions clearly biased by eccentricity. Statistical information demonstrated a robust contribution not only in peripheral but also in central vision. For shape, the results supported the contribution in both central and peripheral vision. Further experiments revealed some interesting properties of the statistics. They are available for a limited time, attributable to the presence or absence of animals without shape, and predict how easily humans detect animals in original images. Our data suggest that when facing the time constraint of categorical processing, higher-order statistics underlie our significant performance for rapid categorization, irrespective of eccentricity.

It is known that unpleasant images capture our attention. However, the causes of the emotions evoked by these images can vary. Trypophobia is the fear of clustered objects. A recent study claimed that this phobia is elicited by the specific power spectrum of such images. In the present study, we measured saccade trajectories to examine how trypophobic images possessing a characteristic power spectrum affect visual attention. The participants' task was to make a saccade in the direction that was indicated by a cue. Four irrelevant images with different emotional content were presented as periphery distractors at 0 ms, 150 ms, and 450 ms in terms of cue-image onset asynchrony. The irrelevant images consisted of trypophobic, fearful, or neutral scenes. The presence of saccade trajectory deviations induced by trypophobic images suggest that intact trypophobic images oriented attention to their location. Moreover, when the images were phase scrambled, the saccade curved away from the trypophobic images, suggesting that trypophobic power spectra also triggered attentional capture, which was weak and then led to inhibition. These findings suggest that not only the power spectral characteristics but also the gist of a trypophobic image affect attentional deployment.