Susan G. Wardle

Breaking camouflage: Binocular disparity reduces contrast masking in natural images

To study the effect of blur adaptation on accommodative variability, accommodative responses and pupil diameters in myopes (n = 22) and emmetropes (n = 19) were continuously measured before, during, and after exposure to defocus blur. Accommodative and pupillary response measurements were made by an autorefractor during a monocular reading exercise. The text was presented on a computer screen at 33 cm viewing distance with a rapid serial visual presentation paradigm. After baseline testing and a 5-min rest, blur was induced by wearing either an optimally refractive lens, or a +1.0 DS or a +3.0 DS defocus lens. Responses were continuously measured during a 5-min period of adaptation. The lens was then removed, and measurements were again made during a 5-min post-adaptation period. After a second 5-min rest, a final post-adaptation period was measured. No significant change of baseline accommodative responses was found after the 5-min period of adaptation to the blurring lenses (p > 0.05). Compared to the pre-adaptation level, both refractive groups had similar and significant increases in accommodative variability right after blur adaptation to both defocus lenses. After the second rest period, the accommodative variability in both groups returned to the pre-adaptation level. The results indicate that blur adaptation has a short-term effect on the accommodative system to elevate instability of the accommodative response. Mechanisms underlying the increase in accommodative variability by blur adaptation and possible influences of the accommodation stability on myopia development were discussed.

Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time series neuroimaging data

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain–computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to “decode” different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

Flavor evaluative conditioning and contingency awareness

The relationship between flavor evaluative conditioning and contingency awareness was examined in two experiments using flavored drinks. In Experiment 1, one flavor was always paired with sugar and the other with bitter tween (polysorbate20) during conditioning. In a subsequent test phase, participants tasted the two flavors, and their evaluative ratings indicated an overall preference for the sugar-paired flavor. Moreover, participants were generally able to report which flavor had been paired with sugar and which with tween. This finding was replicated and confirmed in Experiment 2A. Furthermore, in both experiments, evaluative conditioning was seen only in those participants who were aware of the contingencies. Experiment 2B demonstrated that evaluative conditioning does not occur to colors, although participants are contingency aware. The differences between the present findings and prior studies, in which apparently unaware flavor conditioning has been found, are discussed.

Do Reaction Times in the Perruchet Effect Reflect Variations in the Strength of an Associative Link

In 3 experiments, we examined Perruchet, Cleeremans, and Destrebecqzs (2006) double dissociation of cued reaction time (RT) and target expectancy. In this design, participants receive a tone on every trial and are required to respond as quickly as possible to a square presented on 50% of those trials (a partial reinforcement schedule). Participants are faster to respond to the square following many recent tone-square pairings and slower to respond following many tone-alone presentations. Of importance, expectancy of the square is highest when performance on the RT task is poorest-following many tone-alone trials. This finding suggests that RT performance is determined by the strength of a tone-square link and that this link is the product of a non-expectancy-based learning mechanism. The present experiments, however, provide evidence that the speeded RTs are not the consequence of the strengthening and weakening of a tone-square link. Thus, the RT Perruchet effect does not provide evidence for a non-expectancy-based link-formation mechanism.

Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions

ABSTRACT Visual object recognition is a complex, dynamic process. Multivariate pattern analysis methods, such as decoding, have begun to reveal how the brain processes complex visual information. Recently, temporal decoding methods for EEG and MEG have offered the potential to evaluate the temporal dynamics of object recognition. Here we review the contribution of M/EEG time‐series decoding methods to understanding visual object recognition in the human brain. Consistent with the current understanding of the visual processing hierarchy, low‐level visual features dominate decodable object representations early in the time‐course, with more abstract representations related to object category emerging later. A key finding is that the time‐course of object processing is highly dynamic and rapidly evolving, with limited temporal generalisation of decodable information. Several studies have examined the emergence of object category structure, and we consider to what degree category decoding can be explained by sensitivity to low‐level visual features. Finally, we evaluate recent work attempting to link human behaviour to the neural time‐course of object processing. HighlightsA review of understanding object recognition using time‐series decoding methods.Timing differences are consistent with stages of the visual processing hierarchy.Temporal decoding provides insights into the representation of object categories.Linking dynamic brain representations to behaviour validates decodable information.Future studies are required to understand temporal complexity of object processing.

Edge-Related Activity Is Not Necessary to Explain Orientation Decoding in Human Visual Cortex.

Multivariate pattern analysis is a powerful technique; however, a significant theoretical limitation in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. This is exemplified by the continued controversy over the source of orientation decoding from fMRI responses in human V1. Recently Carlson (2014) identified a potential source of decodable information by modeling voxel responses based on the Hubel and Wiesel (1972) ice-cube model of visual cortex. The model revealed that activity associated with the edges of gratings covaries with orientation and could potentially be used to discriminate orientation. Here we empirically evaluate whether “edge-related activity” underlies orientation decoding from patterns of BOLD response in human V1. First, we systematically mapped classifier performance as a function of stimulus location using population receptive field modeling to isolate each voxels overlap with a large annular grating stimulus. Orientation was decodable across the stimulus; however, peak decoding performance occurred for voxels with receptive fields closer to the fovea and overlapping with the inner edge. Critically, we did not observe the expected second peak in decoding performance at the outer stimulus edge as predicted by the edge account. Second, we evaluated whether voxels that contribute most to classifier performance have receptive fields that cluster in cortical regions corresponding to the retinotopic location of the stimulus edge. Instead, we find the distribution of highly weighted voxels to be approximately random, with a modest bias toward more foveal voxels. Our results demonstrate that edge-related activity is likely not necessary for orientation decoding. SIGNIFICANCE STATEMENT A significant theoretical limitation of multivariate pattern analysis in neuroscience is the ambiguity in interpreting the source of decodable information used by classifiers. For example, orientation can be decoded from BOLD activation patterns in human V1, even though orientation columns are at a finer spatial scale than 3T fMRI. Consequently, the source of decodable information remains controversial. Here we test the proposal that information related to the stimulus edges underlies orientation decoding. We map voxel population receptive fields in V1 and evaluate orientation decoding performance as a function of stimulus location in retinotopic cortex. We find orientation is decodable from voxels whose receptive fields do not overlap with the stimulus edges, suggesting edge-related activity does not substantially drive orientation decoding.

Phantom surfaces in da Vinci stereopsis

In binocular viewing of natural three-dimensional scenes, occlusion relationships between objects at different depths create regions of the background that are visible to only one eye. These monocular regions can support depth perception. There are two viewing conditions in which a monocular region can be on the nasal side of a binocular surface--(a) when a background surface is viewed through an aperture and (b) when a region is camouflaged against the background in one eyes view. We created stimuli with a monocular region using complex textures in which camouflage was not possible, and for which there was no physical aperture. For these stimuli, observers perceived a strong phantom contour in near depth at the edge of the monocular region, with the monocular texture perceived behind at the depth of the binocular surface. Depth-matching with a probe showed that the depth of the phantom occluding surface was as precise as for stimuli with regular binocular disparity. Monocular regions of texture on the opposite (temporal) side of the binocular surface were perceived behind, as predicted by occlusion geometry, and there was no phantom surface. We discuss the implications for models of da Vinci stereopsis and stereoscopic edge processing, and consider the involvement of a form of Panums limiting case. We conclude that the visual system uses a combination of occlusion geometry and complex matching to precisely locate edges in depth that lack a luminance contour.

Evidence for speed sensitivity to motion in depth from binocular cues.

Motion in depth can be perceived from binocular cues alone, yet it is unclear whether these cues support speed sensitivity in the absence of the monocular cues that normally co-occur in natural viewing. We measure threshold contours in space-time for the discrimination of three-dimensional (3D) motion to determine whether observers use speed to discriminate a test 3D motion from two identical standards. We compare thresholds for random-dot stereograms (RDS) containing both binocular cues to 3D motion-interocular velocity difference and changing disparity over time-with performance for dynamic random-dot stereograms (DRDS), which contain only the second cue. Threshold contours are tilted along the axis of constant velocity in space-time for RDS stimuli at slow speeds (0.5 m/s), evidence for speed sensitivity. However, for higher speeds (1.5 m/s) and DRDS stimuli, observers rely on the component cues of duration and disparity. In a second experiment, noise of constant velocity is added to the standards to degrade the reliability of these separate components. Again there is evidence for speed tuning for RDS, but not for DRDS. Considerable variation is observed in the ability of individual observers to use the different cues in both experiments, however, in general the results emphasize the importance of interocular velocity difference as a critical cue for speed sensitivity to motion in depth, and suggest that speed sensitivity to stereomotion from binocular cues is restricted to relatively slow speeds.

Can Object Category-Selectivity in the Ventral Visual Pathway Be Explained by Sensitivity to Low-Level Image Properties?

While object recognition typically feels effortless, it is one of the most computationally impressive feats performed by the human visual system. Due to its importance as an end stage of visual processing, a great deal of research has focused on characterizing those regions of the brain responsible

Gradients of relative disparity underlie the perceived slant of stereoscopic surfaces

Perceived stereoscopic slant around a vertical axis is strongly underestimated for isolated surfaces, suggesting that neither uniocular image compression nor linear gradients of absolute disparity are very effective cues. However, slant increases to a level close to geometric prediction if gradients of relative disparity are introduced, for example by placing flanking frontal-parallel surfaces at the horizontal boundaries of the slanted surface. Here we examine the mechanisms underlying this slant enhancement by manipulating properties of the slanted surface or the flanking surfaces. Perceived slant was measured using a probe bias method. In Experiment 1, an outlined surface and a randomly textured surface showed similar slant underestimation when presented in isolation, but the enhancement in slant produced by flankers was significantly greater for the textured surface. In Experiment 2, we degraded the relative disparity gradient by (a) reducing overall texture density, (b) reducing flanker width, or (c) adding disparity noise to the flankers. Density had no effect while adding noise to the flankers, or reducing their width significantly decreased perceived slant of the central surface. These results support the view that the enhancement of slant produced by adding flanking surfaces is attributable to the presence of a relative disparity gradient and that the flanker effect can spread to regions of the surface not directly above or below the gradient.