The spatial characteristics of plaid-form-selective mechanisms

Contrast Gain Control in Plaid Pattern Detection

A plaid is a combination of two gratings whose orientations are orthogonal to each other with the same or similar contrasts. We used plaid patterns as stimuli to investigate the mechanisms underlying the detection of a plaid to understand how the visual system combines information from orientation-selective channels. We used a masking paradigm in which an observer was required to detect a target (either a spiral or a plaid) superimposed on a pedestal. We measured the target threshold versus pedestal contrast (TvC) functions at 7 pedestal contrasts for various target-pedestal combinations with a temporal 2AFC paradigm and a staircase procedure. All TvC functions, except the one with an orthogonal spiral pedestal, showed a dipper shape, although the position of the dip and the slope varied across conditions. The result can be explained by a multiple-mechanism divisive inhibition model, which contains several orientation-selective mechanisms. The response of each mechanism is the excitation of a linear filter divided by a broadband inhibitory input. The threshold is determined by a nonlinear combination of the responses of those mechanisms. Alternative models with mechanism(s) specific for plaid did not provide a better description of the data. Thus, a plaid pattern is mediated by a combination of orientation-selective mechanisms. An early plaid-specific mechanism is not necessary for plaid detection.

Understanding mid-level representations in visual processing

It is clear that early visual processing provides an image-based representation of the visual scene: Neurons in Striate cortex (V1) encode nothing about the meaning of a scene, but they do provide a great deal of information about the image features within it. The mechanisms of these "low-level" visual processes are relatively well understood. We can construct plausible models for how neurons, up to and including those in V1, build their representations from preceding inputs down to the level of photoreceptors. It is also clear that at some point we have a semantic, "high-level" representation of the visual scene because we can describe verbally the objects that we are viewing and their meaning to us. A huge number of studies are examining these "high-level" visual processes each year. Less well studied are the processes of "mid-level" vision, which presumably provide the bridge between these "low-level" representations of edges, colors, and lights and the "high-level" semantic representations of objects, faces, and scenes. This article and the special issue of papers in which it is published consider the nature of "mid-level" visual processing and some of the reasons why we might not have made as much progress in this domain as we would like.

Gain control in the response of human visual cortex to plaids

A recent intrinsic signal optical imaging study in tree shrew showed, surprisingly, that the population response of V1 to plaid patterns comprising grating components of equal contrast is predicted by the average of the responses to the individual components (MacEvoy SP, Tucker TR, Fitzpatrick D. Nat Neurosci 12: 637-645, 2009). This prompted us to compare responses to plaids and gratings in human visual cortex as a function of contrast and orientation. We found that the functional MRI (fMRI) blood oxygenation level-dependent (BOLD) responses of areas V1-V3 to a plaid comprising superposed grating components of equal contrast are significantly higher than the responses to a single component. Furthermore, the orientation response profile of a plaid is poorly predicted from a linear combination of the responses to its components. Together, these results indicate that the model of MacEvoy et al. (2009) cannot, without modification, account for the fMRI BOLD response to plaids in human visual cortex.

The timing of binding and segregation of two compound aftereffects

Temporal information in a scene is thought to be an important cue for visual grouping of local image features into a single object. The majority of studies on this topic have attempted to determine the conditions that facilitate segregation of a figure from a cluttered background. Here we examine the temporal characteristics of two aftereffects that appear to have roles in visual integration: the curvature aftereffect (CAE; Hancock & Peirce, 2008) and plaid-selective contrast adaptation (Peirce & Taylor, 2006). Both aftereffects used a “compound adaptation” paradigm measuring adaptation to a compound stimulus that cannot be explained by adaptation to its components presented in isolation. The temporal tuning characteristics of the two aftereffects differed in three distinct ways. First, plaid-selective adaptation was very sensitive to temporal phase asynchronies, while the CAE was not. Second, while both aftereffects showed integration of alternating components above 4 Hz, for plaids the overall magnitude of adaptation was less than to synchronous stimuli and was eliminated at the highest frequencies. Finally, plaid-selective adaptation demonstrated a low-pass dependency for temporal flicker frequency of synchronous gratings, whereas the CAE did not. Overall, these results suggest that at least two different mechanisms are involved in the binding/segregation of local signals into compound patterns: one with high temporal resolution that allows rapid parsing of plaid patterns into their components and one with a coarser temporal sensitivity that mediates the CAE.

Ameliorating the combinatorial explosion with spatial frequency-matched combinations of V1 outputs

Little is known about the way in which the outputs of early orientation-selective neurons are combined. One particular problem is that the number of possible combinations of these outputs greatly outweighs the number of processing units available to represent them. Here we consider two of the possible ways in which the visual system might reduce the impact of this problem. First, the visual system might ameliorate the problem by collapsing across some low-level feature coded by previous processing stages, such as spatial frequency. Second, the visual system may combine only a subset of available outputs, such as those with similar receptive field characteristics. Using plaid-selective contrast adaptation and the curvature aftereffect, we found no evidence for the former solution; both aftereffects were clearly tuned to the spatial frequency of the adaptor relative to the test probe. We did, however, find evidence for the latter with both aftereffects; when the components forming our compound stimuli were dissimilar in spatial frequency, the effects of adapting to them were substantially reduced. This has important implications for mid-level visual processing, both for the combinatorial explosion and for the selective "binding" of common features that are perceived as coming from a single visual object.