Biomedical subjects

Jacqueline M Fulvio

Publications and source records attributed to Jacqueline M Fulvio.

3 recordsLinked to original sources

PubMed2006

Combining achromatic and chromatic cues to transparency.

We investigated how achromatic and chromatic cues interact to produce transparency. Observers were shown six-region stimulus displays similar to those used by R. Kasrai and F. A. A. Kingdom (2001) and made adjustments of the color and luminance attributes of one of the filter regions to achieve the best percept of transparency. The dependent measure of primary interest was setting reliability, the reciprocal of setting variance. We wished to determine whether the combination of chromatic and achromatic information leads to enhanced reliability of perceived transparency. In Experiment 1, we measured reliability for achromatic, L, superimposed luminance with color, L + C, and superimposed luminance with polarity-reversing color, L + iC. We found that observers' reliability was highest for the L + C condition, consistent with effective cue combination. In a second experiment, we compared setting reliability for L, L + C, and a new chromatic-only condition C. In the L + C condition, observers were asked to make separate and iterative settings of luminance and color to achieve the best percept of transparency. We compared their settings in L with the luminance settings in L + C and their settings in C with their color settings in L + C. Color adjustments were more reliable when accompanied by luminance information but not vice versa. In Experiment 3, we manipulated the transmittance of the achromatic and chromatic filters separately and investigated how this influences the settings made for each attribute. No systematic influence of filter transmittance on the settings made for perceived transparency was found.

Color↗

PubMed2006

Surface geometry influences the shape of illusory contours.

Geometric and neural models of illusory-contour (IC) synthesis currently use only local contour geometry to derive the shape of ICs. Work on the visual representation of shape, by contrast, points to the importance of both contour and surface geometry. We investigated the influence of surface-based geometric factors on IC shape. The local geometry of inducing-contour pairs was equated in stereoscopic IC displays, and the shape of the enclosed surface was varied by manipulating sign of curvature, cross-axial shape width, and medial-axis geometry. IC shapes were measured using a parametric shape-adjustment task (Experiment 1) and a dot-adjustment task (Experiment 2). Both methods revealed large influences of surface geometry. ICs enclosing locally concave regions were perceived to be systematically more angular than those enclosing locally convex regions. Importantly, the influence of sign of curvature was modulated significantly by shape width and medial-axis geometry: IC shape difference between convex and concave inducers was greater for narrow shapes than wider ones, and greater for shapes with straight axis and symmetric contours (diamond versus bowtie), than those with curved axis and parallel contours (bent tubes). Even at the level of illusory "contours," there is a contribution of region-based geometry which is sensitive to nonlocal shape properties involving medial geometry and part decomposition. Models of IC synthesis must incorporate the role of nonlocal region-based geometric factors in a way that parallels their role in organizing visual shape representation more generally.

Depth Perception↗

PubMed2005

Visual extrapolation of contour geometry.

Computing the shapes of object boundaries from fragmentary image contours poses a formidable problem for the visual system. We investigated the extrapolation of contour shape by human vision. Measurements of extrapolation position and orientation were taken at six distances from the point of occlusion, thereby yielding a detailed representation of the extrapolated contours. Analyses of these measurements revealed that: (i) extrapolation curvature increases linearly with the curvature of the inducing contour, although there is individual bias in the slope; (ii) the precision with which an extrapolated contour is represented is roughly constant, in angular terms, with increasing distance from the point of occlusion; (iii) there is a substantial cost of curvature, in that the overall precision of an extrapolated contour decreases systematically with curvature; (iv) the shapes of visually extrapolated contours are characterized by a nonlinear decrease in curvature, asymptoting to zero; and (v) this decaying pattern of curvature is explained by a Bayesian model in which, with increasing distance from the point of occlusion, the prior tendency to minimize curvature gradually dominates the likelihood tendency to minimize variation in curvature.

Bayes Theorem↗