Distinct mechanisms mediate visual detection and identification

Abstract

A core organizing principle for studies of the brain is that distinct neural pathways mediate distinct behavioral tasks [1, 2]. When two related tasks are mediated by a common pathway, studies of one are likely to generalize to the other. Here, we test whether performance on two laboratory tasks that model object detection and identification are mediated by common mechanisms of visual adaptation. Although both tasks rely on the luminance pattern in images, their demands on visual processing are quite different. Object detection requires discriminating image luminance differences associated with the light reflected from adjacent objects. To encode these differences reliably, neurons adapt their limited dynamic range to prevailing viewing conditions [3-6]. Object identification, on the other hand, demands a fixed response to light reflected from an object independent of illumination [7]. We compared performance in discrimination and identification tasks for simulated surfaces. In striking contrast to studies with less structured contexts, we found clear evidence that distinct processes mediate judgments in the two tasks. These results challenge models that account for perceived lightness entirely through the action of image-encoding mechanisms.

Figure 1. Images of the Shadowed and Painted Checkerboards

The two images shown here are physically identical except that the penumbra of the shadow has been replaced by a sharp edge coinciding with the checkers. In the latter case, it tends to look like the checkers along the negative diagonal have been painted with a darker paint. Despite the physical similarity of the two images, most people see a greater difference in the appearance of the spots in the shadow than in the paint image. The fact that the appearance effect in the paint image is lesser than in the shadow image suggests that the apparent lightness of the spots is influenced, perhaps implicitly, by consideration of the causal structure of the images: In effect, the visual system infers that there is less light reaching the shadowed region of the checkerboard and compensates for this illumination difference to create a stable representation of surface reflectance.

Figure 2. Discrimination and Matching Results for Eight Participants in the Paint and Shadow Conditions

The top left and right panels show just-noticeable differences (JNDs) for tests located outside and inside the shadowed and painted regions, respectively (as indicated in the images in the lower right). The x axis is labeled as “Pedestal Intensity” and refers to the fixed spot intensity, I_p. Participants’ task was to discriminate I_p shown in one interval from I_p+ΔI shown in the other interval. For each participant the increments, ΔI, were normalized by detection thresholds (i.e., discrimination threshold for I_p = 0) measured at the test location outside the shadow. We pooled and fitted the data with cumulative Gaussians to estimate JNDs defined as the 75% correct point. The JNDs plotted in the top two panels were rescaled by the average absolute threshold. Diamonds and circles represent data from the shadow and paint conditions, respectively. Error bars are 95% confidence intervals. Data from the two participants who only observed in the paint condition are not included here. Asymmetric matches from the appearance task are plotted in the bottom-left panel. The mean intensity of participants’ settings is plotted against the fixed test intensities. Standard errors were smaller than the symbols used here. Matches were performed with the fixed test in both locations: Open and closed symbols represent, respectively, settings when the adjustable test was outside and inside the shadowed or painted region. In the paint condition, deviations from the physical identity of the test and matches are very small. In the shadow condition, matches occurred when the spot intensity in the shadow was lower than the spot intensity outside the shadow.

Figure 3. Common-Mechanism Model

The red and black curves in the central panel represent the intensity-response function of a typical mechanism in two states of adaptation. The black and red curves in the lower panel represent the just-noticeable differences (JNDs) associated with these two states of adaptation. Each point on each curve in the lower panel represents a predicted 75% correct discrimination threshold for the response curves in the middle panel. Note that JNDs are lowest where the response curves are steepest and become infinite when the response curves saturate. These JND curves were derived under the assumption that responses of the encoding mechanism are corrupted by additive, normally distributed noise of fixed variance. This is formally equivalent to Fechner’s proposal that discrimination thresholds correspond to a set difference in the neural response to the targets presented [34]. The blue and orange points in the central and lower panel demonstrate the logic for one test intensity. The intensity difference between the blue and orange points (x axis) in the central panel yields a response difference (y axis) that permits correct discrimination of intensity increment 75% of the time. In the lower panel, the test intensity is again plotted on the x axis, and the intensity difference (one JND) between the blue and orange points is represented on the y axis. Comparison of the red and black curves in the central and lower panels shows that low JNDs correspond to tests located at steep parts of the response functions, and high JNDs correspond to tests located at shallow parts. The top panel shows the matches predicted across the states of adaptation (i.e., two contexts) shown in the central panel. To predict apparent matches, we assume that the targets in the two contexts look the same when the response of the mechanism is the same in the two contexts. For a target presented in the first context with intensity indicated by the black point (central panel), a target with intensity indicated by the red point produces the same response the other context. These would therefore be considered a match. The target intensities that produce matches for the states of adaptation in the central panel are shown by the red line in the top panel. The faint gray line in this panel represents physical identity of the targets. In the case shown, the difference between the two intensity-response functions is described by a change in the gain parameter of Equation 1, so that the predicted matches plot along a line with unity slope in the log-log plot. Other shapes of the predicted matching function may be obtained when other parameters of Equation 1 vary with adaptation.