Despite an obvious demand for a variety of statistical tests adapted to classification images, few have been proposed. We argue that two statistical tests based on random field theory (RFT) satisfy this need for smooth classification images. We illustrate these tests on classification images representative of the literature from F. Gosselin and P. G. Schyns (2001) and from A. B. Sekuler, C. M. Gaspar, J. M. Gold, and P. J. Bennett (2004). The necessary computations are performed using the Stat4Ci Matlab toolbox.

The authors examined spatial frequency (SF) tuning of upright and inverted face identification using an SF variant of the Bubbles technique (F. Gosselin & P. G. Schyns, 2001). In Experiment 1, they validated the SF Bubbles technique in a plaid detection task. In Experiments 2a-c, the SFs used for identifying upright and inverted inner facial features were investigated. Although a clear inversion effect was present (mean accuracy was 24% higher and response times 455 ms shorter for upright faces), SF tunings were remarkably similar in both orientation conditions (mean r ϭ .98; an SF band of 1.9 octaves centered at 9.8 cycles per face width for faces of about 6°). In Experiments 3a and b, the authors demonstrated that their technique is sensitive to both subtle bottom-up and top-down induced changes in SF tuning, suggesting that the null results of Experiments 2a-c are real. The most parsimonious explanation of the findings is provided by the quantitative account of the face inversion effect: The same information is used for identifying upright and inverted inner facial features, but processing has greater sensitivity with the former.

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. Studies of reaction-time distributions provide a useful quantitative approach to understand decision processes at the neural level and at the behavioral level. A strong relationship between the spread of latencies and the median is generally accepted even though there has been no attempt to disentangle experimentally these two parameters. Here we test the ability to independently control the median and the variability in reaction times. Reaction times were measured in human subjects instructed to make a discrimination between a target and a distractor in a 2AFC task. In a first experiment, saccadic latencies were measured. In a second experiment, we used manual response reaction times. Subjects were trained to produce four different reaction-time distributions. A reinforcing feedback was given depending on both the variability and the median of the latency distributions. When low variability was reinforced, the standard deviation (SD) of reactiontime distributions were reduced by a factor of two and when high variability was reinforced, the SD returned to baseline level. Our procedure independently affected the spread and the median of the distribution patterns. By fitting the latency distributions using the Reddi and Carpenter LATER model, we found that these effects could be simulated by changing the distribution of the noise affecting the decision process. Our results demonstrate that learned contingencies can affect reaction time variability and support the view that the so-called noise level in decision processes can undergo long-term changes. I N T R O D U C T I O NSaccades are voluntary eye movements used to acquire the retinal image of an object on the fovea, the high-acuity region of the retina. Saccadic reaction time is usually short and the appearance of a target in the visual field typically elicits saccades with a latency averaging 200 ms. However, it appears that this figure is surprisingly long when compared with the neural delays associated with visual and motor processing (Reddi and Carpenter 2000). In fact, a cascade of events takes place between a visual event and a motor response, a functional description of which usually includes three stages: visual processing, response selection, and motor processing (Glimcher 2003;Schall 2001). Because of this, the study of saccadic latency provides a useful quantitative tool for studying how the brain makes decisions.A number of studies have demonstrated that low-level (i.e., visual) and high-level factors influence the average time it takes to respond to a visual stimulus. Interestingly, saccade latencies also change on a trial-bytrial basis, even though the procedure remains unchanged. This observation has been reported in a number of studies: over a series of trials, latency varies unpredictably over a wide range, resulting in typically skewed distributions. At a neural level, it has also been reported that responses of single neurons vary from trial to trial (Schall and Bichot 1998;Thompson et al. 1996). Moreover, most models of decision making, su...

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The visual perception of human faces by man is fast and efficient compared to that of other categories of objects. Using a saccadic choice task, recent studies showed that participants were able to initiate fast reliable saccades in just 100-110ms toward an image of a human face, when this was presented alongside another image without a face. This extremely fast saccadic reaction time is barely predicted using classical models of visual perception. Thus, the present research investigates whether this result might be explained by the low spatial frequency content of images. Using the same paradigm, with two images simultaneously presented to the left or right visual fields, participants were asked to make a saccade towards a target image. The target was defined as an image belonging to one category: human face, animal or vehicle. The other image corresponded to the distractor and belongs to the other categories. We compared performance to saccade toward one category of target. The two images were displayed either in color, gray-level, low-pass filtered or high-pass filtered. As previous studies, we found that the shortest SRT was observed for saccades towards faces rather than towards animals or vehicles. Analysis of saccadic reaction time distributions showed that, in 130-140ms, participants were able to make more correct than incorrect saccades towards faces for unfiltered (color and gray-level) and low-pass filtered images whereas they needed more time for high-pass filtered images. In contrast, the minimum time participants needed to correctly saccade towards animals and vehicles was longer for low-pass and high-pass filtered than for unfiltered images. The analysis of the image statistics in the Fourier domain revealed that the amplitude spectrum of faces was mainly contained in the low spatial frequencies. Consistent with a coarse-to-fine processing of visual information, our results suggest that extremely fast saccades towards faces could be initiated by low spatial frequencies.