A classic finding reported in Beck (1966a) is that observers tend to indicate a more natural texture break between a set of T’s and tilted T’s than between a set of T’s and backward L’s. This finding has played a prominent role in discussions about the properties of texture segmentation and in the development of computational theories of texture segmentation. Due to the small sample size of the original study, we replicated the original experiment with a larger sample. Regrettably, we (...) discovered that the description in Beck (1966a) is insufficient to allow us to reproduce the stimuli. For our replication, we created stimuli that seem consistent with the spirit of the original study. The results of the replication study partly matchBeck’s original data (r = .59), but conclusions indicated by these results deviate from some of Beck’s key conclusions.We also further explored the influential claim derived from a second experiment in Beck (1966a) that texture segmentation was not related to the perceived similarity of elements. Contrary to this claim, a re-analysis of Beck’s data indicates the counterintuitive conclusion that observers tend to indicate stronger texture segmentation between regions that are rated as being more similar. In a replication of the second experiment, this relation flipped so that the replication observers indicated stronger texture segmentation for regions with lower similarity ratings. Despite being influential, we conclude that there are substantial problems with the stimuli, analyses, and conclusions in Beck (1966a). (shrink)

In a series of articles, Jacob Beck proposed that a variety of texture segmentation phenomena occurs due to emergent features that arise from “links” between elements with appropriate local properties, such as alignment, orientation, and proximity. His findings and ideas guided theoretical and computational models, and some of his demonstrations became textbook knowledge about visual perception. We build on this work in two ways. First, we provide a modern replication of a classic texture segmentation study using a much larger sample (...) size. Overall, the replication agrees with Beck’s original findings, although there are some quantitative differences. Second, we show how to apply a quantitative model of visual cortex to Beck’s experiment and demonstrate that the model can explain many aspects of Beck’s findings. Key to the model’s success is cognitive control of connections between individual elements (akin to Beck’s “links” between elements) and a selection mechanism that makes it easy to identify how well elements within a region connect and how well different regions are disconnected. Overall, the model supports Beck’s claim that local properties can facilitate patterns of connections between stimulus elements and that some connection patterns allow an observer to easily distinguish textures. (shrink)

A fundamental characteristic of human visual perception is the ability to group together disparate elements in a scene and treat them as a single unit. The mechanisms by which humans create such groupings remain unknown, but grouping seems to play an important role in a wide variety of visual phenomena, and a good understanding of these mechanisms might provide guidance for how to improve machine vision algorithms. Here, we build on a proposal that some groupings are the result of connections (...) in cortical area V2 that join disparate elements, thereby allowing them to be selected and segmented together. In previous instantiations of this proposal, connection formation was based on the anatomy (e.g., extent) of receptive fields, which made connection formation obligatory when the stimulus conditions stimulate the corresponding receptive fields. We now propose dynamic circuits that provide greater flexibility in the formation of connections and that allow for top-down control of perceptual grouping. With computer simulations we explain how the circuits work and show how they can account for a wide variety of Gestalt principles of perceptual grouping, texture segmentation tasks, amodal illusory contours, and ratings of perceived groupings. We propose that human observers use such top-down control to implement task-dependent connection strategies that encourage particular groupings of stimulus elements in order to promote performance on various visual tasks. (shrink)

Although perceptual grouping has been widely studied, its mechanisms remain poorly understood. We propose a neural model of grouping that, through top-down control of its circuits, implements a grouping strategy involving both a connection strategy (which elements to connect) and a selection strategy (that defines spatiotemporal properties of a selection signal to segment target elements and facilitate identification). We apply the model to a letter discrimination task that investigated relationships among uniform connectedness and the grouping principles of proximity and shape (...) similarity. Participants reported whether small circles formed a global letter E or H, and these circles could be connected by a line or be embedded in a matrix of squares. In the model, a good grouping strategy for this task consists of a connection strategy that connects circles but not squares for all conditions and a selection strategy that uses a selection signal of varying size, depending on whether squares were present. Consistent with empirical results, which were verified in two replication studies, model performance is worse with distractor squares, and line connectors improve performance only in the condition with squares. Rather than relying on abstract grouping principles, we show how the empirical results can be explained in terms of observers implementing a task-dependent grouping strategy that promotes overall performance. (shrink)