ABSTRACT The location and topography of the first three visual field maps in the human brain, V1-V3, are well agreed upon and routinely measured across most laboratories. The position of 4 th visual field map, ‘hV4’, is identified with less consistency in the neuroimaging literature. Using magnetic resonance imaging (MRI) data, we describe landmarks to help identify the position and borders of hV4. The data consist of anatomical images, visualized as cortical meshes to highlight the sulcal and gyral patterns, and functional data obtained from retinotopic mapping experiments, visualized as eccentricity and angle maps on the cortical surface. Several features of the functional and anatomical data can be found across nearly all subjects and are helpful for identifying the location and extent of the hV4 map. The medial border of hV4 is shared with the posterior, ventral portion of V3, and is marked by a retinotopic representation of the upper vertical meridian. The anterior border of hV4 is shared with the VO-1 map, and falls on a retinotopic representation of the peripheral visual field, usually coincident with the posterior transverse collateral sulcus. The ventro-lateral edge of the map typically falls on the inferior occipital gyrus, where functional MRI artifacts often obscure the retinotopic data. Finally, we demonstrate the continuity of retinotopic parameters between hV4 and its neighbors; hV4 and V3v contain iso-eccentricity lines in register, whereas hV4 and VO-1 contain iso-polar angle lines in register. Together, the multiple constraints allow for a consistent identification of the hV4 map across most human subjects.
The ventral surface of the human occipital lobe contains multiple retinotopic maps. The most posterior of these maps is considered a potential homolog of macaque V4, and referred to as human V4 ("hV4"). The location of the hV4 map, its retinotopic organization, its role in visual encoding, and the cortical areas it borders have been the subject of considerable investigation and debate over the last 25 years. We review the history of this map and adjacent maps in ventral occipital cortex, and consider the different hypotheses for how these ventral occipital maps are organized. Advances in neuroimaging, computational modeling, and characterization of the nearby anatomical landmarks and functional brain areas have improved our understanding of where human V4 is and what kind of visual representations it contains.
Implicit Gender Aftereffects in the Perception of Face Silhouettes Nicolas Davidenko (ndaviden@psych.stanford.edu) Jonathan Winawer (winawer@mit.edu) Nathan Witthoft (witthoft@mit.edu) Michael Ramscar (michael@psych.stanford.edu) Psychology Department, Stanford University, 450 Serra Mall, Building 420, Stanford, CA 94305 USA Introduction Several recent studies have shown figural aftereffects in the perception and categorization of face stimuli (e.g., Leopold, O’Toole, Vetter, & Blanz, 2001, Witthoft, Winawer, & Boroditsky, 2006). In these studies, prolonged exposure to a face with a particular characteristic temporarily biases subsequent perception of faces as having the opposite characteristic. For instance, prolonged exposure to a male face biases subsequent perception of a gender-neutral face as female (Kaping, Bilson, & Webster, 2002). Here we examine whether such gender aftereffects can arise in a brief, implicit adaptation paradigm using parameterized face silhouettes (Davidenko, 2004; see Figure 1a). neutral. 10 Ss rated sequentially presented face silhouettes (displayed every 4 seconds, for 3 seconds each) on race, age, distinctiveness, and gender. Gender ratings were done on the approximately gender-neutral target silhouettes. We varied the number of male or female silhouettes preceding each target silhouette from 0 to 3. Results. We found a large gender aftereffect, modulated by the number of gendered silhouettes preceding the target (Figure 1c). The more female silhouettes that preceded the target, the more likely the target was to be rated as male, and vice versa (r = .89, p < .01), indicating adaptation can be induced by observing only a few silhouettes sequentially. Methods Experiment 1: Simultaneous presentation Stimuli and Procedure. From an analysis of silhouette face space (Davidenko, 2006), we constructed 8 male and 8 female face silhouettes and one target silhouette that was approximately gender-neutral. 121 Ss filled out a questionnaire with 9 silhouettes, the first 8 of which were either all male or all female. Ss rated each silhouette on age, race, attractiveness, or gender. Only the 9 th face (the target) was rated for gender. We hypothesized that the few seconds spent rating each of the first 8 gendered silhouettes would be sufficient to induce gender-specific adaptation, biasing the gender rating of the 9 th face. Figure 1: A parameterized face silhouette (a); results of Experiment 1 (b) and Experiment 2 (c). Discussion Results. Gender ratings of the target silhouette were dramatically affected by the gender of preceding silhouettes (Figure 1b). Of the 59 subjects who adapted to female silhouettes, 97% rated the target silhouette as male, while of the 62 subjects who adapted to male silhouettes, only 39% rated the target silhouette as male. This extends the figural aftereffects previously shown with front- and 3/4-view photographs of faces to silhouettes, and shows that adaptation can be obtained even when subjects are doing tasks orthogonal to the adapting dimension. It also shows how quickly adaptation can occur (many adaptation paradigms begin with a minute or more of adaptation). In Experiment 2 we tested whether the adaptation effect depended on simultaneous contrast (e.g., seeing a gender- neutral face in the context of 8 female faces), or whether it could be achieved from sequential presentation. Experiment 2: Rapid sequential presentation Stimuli and Procedure. We constructed three sets of parameterized face silhouettes: female, male, and gender- Our results show that briefly viewed gendered face silhouettes can implicitly affect the perceived gender of a target silhouette, both in simultaneous and sequential presentation paradigms. References Davidenko, N. (2004). Modeling face-shape representation using silhouetted face profiles [Abstract]. Journal of Vision, 4(8), 436a. Davidenko, N. (2006). Silhouetted face profiles: a methodology for face perception research. Manuscript under review. Kaping, D., Bilson, A. C., & Webster, M. A. (2002). Adaptation and categorical judgments of faces [Abstract]. Journal of Vision, 2(7), 564a. Leopold, D.A., O'Toole, A.J., Vetter, T., & Blanz, V. (2001). Prototype-referenced shape encoding revealed by high-level aftereffects. Nature Neuroscience, 4, 89–94. Witthoft, N., Winawer, J. & L. Boroditsky. How Looking at Someone You Don’t Know Can Help You to Recognize Someone You Do (2006). Proceedings of the 29th Annual Meeting of the Cognitive Science Society.
Visual illusions and other perceptual phenomena can be used as tools to uncover the otherwise hidden constructive processes that give rise to perception. Although many perceptual processes are assumed to be universal, variable susceptibility to certain illusions and perceptual effects across populations suggests a role for factors that vary culturally. One striking phenomenon is seen with two-tone images-photos reduced to two tones: black and white. Deficient recognition is observed in young children under conditions that trigger automatic recognition in adults. Here we show a similar lack of cue-triggered perceptual reorganization in the Pirahã, a hunter-gatherer tribe with limited exposure to modern visual media, suggesting such recognition is experience- and culture-specific.
Exemplar Frequency Affects Unsupervised Learning of Shapes Nathan Witthoft (witthoft@stanford.edu) Department of Psychology, Jordan Hall, 450 Serra Mall, Building 420 Stanford, CA 94305 USA Nicolas Davidenko (ndaviden@psych.stanford.edu) Department of Psychology, Jordan Hall, 450 Serra Mall, Building 420 Stanford, CA 94305 USA Kalanit Grill-Spector (kalanit@stanford.edu) Department of Psychology, Jordan Hall, 450 Serra Mall, Building 420 Stanford, CA 94305 USA Abstract Exposure to the spatiotemporal statistics of the world is thought to have a profound effect on shaping the response properties of the visual cortex and our visual experience. Here we ask whether subjects’ discrimination performance on a set of parameterized shapes changes as a function of the distribution with which the shapes appear in an unsupervised paradigm. During training, subjects performed a fixation task while shapes drawn from a single axis of a parameterized shape space appeared in the background. The frequency with which individual shapes appeared was determined by imposing a normal distribution centered on the middle of the shape axis. Comparison of performance on a shape discrimination task pre and post training showed that subjects' d-prime increased as a function of the frequency with which the exemplars appeared despite the lack of feedback and engagement in a simultaneous task not directed at the shapes. Performance on an untrained set of shapes was largely unchanged across the two testing sessions. This suggests that the visual system may optimize representations by fitting itself to the distribution of experienced exemplars even without feedback, providing the most discriminative power where examples are most likely to occur. Keywords: Unsupervised learning, vision, perceptual learning. Background How people are able to discriminate visually similar items while recognizing the same item across dramatic image transformations is one of the fundamental problems of vision. Experience is thought to play a critical role in forming the underlying cortical representations that support these abilities. One possibility that has been explored in computational and behavioral studies is that the visual system is able to discover and take advantage of statistical regularities in the retinal input via simple unsupervised learning mechanisms (Barlow, 1989a). Our proposal is that unsupervised learning of the frequency of exemplars may fine-tune cortical representations to best match the distribution of exemplars within a category, thus providing the selectivity needed to discriminate between highly similar images where they are most likely to occur. Unsupervised learning is a process whereby the brain receives inputs but obtains neither supervised target outputs, feedback, nor rewards and as a result finds patterns in the data beyond what would be considered random noise (Ghahramani, 2004). The theoretical framework is based on the notion that the brain’s goal is to build representations of the input (even without feedback) that can be used for decision making and predicting future inputs (Poggio et al., 1992). These self-organizing mechanisms could play a crucial role in transforming the continuous flux of retinal stimulation into the stable recognizable objects of our everyday experience. It is important to note that there may be internal reward that guides learning (Seitz and Watanabe, 2005), but this takes place in the absence of explicit feedback on performance. Numerous studies have shown that the visual system adjusts itself as a function of experience even in situations where subjects are uninstructed. Adaptation represents a phenomenon of this kind, where prolonged exposure to some stimulus value can shift the sensitivity of the visual system for a short period of time. For example, viewing rightward motion causes subsequently presented static stimuli to appear as though they are moving to the left (Anstis et al., 1998). Such aftereffects are perceptually compelling, and can be found for a wide variety of visual features ranging from the relatively simple such as line orientation to the very complex such as facial identity (Leopold et al., 2001; Witthoft et al., 2006) and do not require instruction or feedback (though some may require attention; Moradi et al., 2005). With respect to our proposal, it has been argued that adaptation is not just a useful way for psychologists to probe the visual system, but reflects a functional mechanism by which vision increases its sensitivity to changes in recent experience (Webster et al., 2001; Barlow & Foldiak, 1989; Clifford & Rhodes, Studies of perceptual learning also show experience dependent changes, but have often relied on the
In this paper we estimate the minimum prevalence of grapheme-color synesthetes with letter-color matches learned from an external stimulus, by analyzing a large sample of English-speaking grapheme-color synesthetes. We find that at least 6% (400/6588 participants) of the total sample learned many of their matches from a widely available colored letter toy. Among those born in the decade after the toy began to be manufactured, the proportion of synesthetes with learned letter-color pairings approaches 15% for some 5-year periods. Among those born 5 years or more before it was manufactured, none have colors learned from the toy. Analysis of the letter-color matching data suggests the only difference between synesthetes with matches to the toy and those without is exposure to the stimulus. These data indicate learning of letter-color pairings from external contingencies can occur in a substantial fraction of synesthetes, and are consistent with the hypothesis that grapheme-color synesthesia is a kind of conditioned mental imagery.
Prior research on the position sensitivity of category selective regions has shown a coupling between face selectivity and foveal representations and place selectivity and sensitivity to the periphery (Levy 2001), as well as decreased tuning to position as one ascends the ventral stream hierarchy (Schwartzlose 2008). However, most studies have examined position sensitivity by comparing discrete locations in the visual field. We extend this prior work by using continuous retinotopic mapping and measuring population receptive fields (pRFs) in category selective regions. 12 subjects participated in an fMRI experiment that contained both a category localizer as well as retinotopic mapping using flickering checkerboard stimuli. By fitting a pRF model to each voxel, we determined the retinal position and the spatial extent of the visual field which best matched the observed BOLD response within each voxel. Then, in each individual brain we defined category-selective ROIs as well as visual field maps. Category-selective ROIs were subdivided by their intersection with visual field maps. Consistent with Aracaro et al 2009, place-selective voxels in the collateral sulcus overlap at least partially with the anterior ventral visual field maps (VO1-2; PHC1-2). pRF fits to these place-selective voxels show large receptive fields that have a strong bias towards the upper visual field and the periphery. By contrast, face-selective ROIs on the ventral surface (IOG-, pFus-, mFus-faces) did not overlap with known visual field maps, but contain pRFs that are foveally centered and their combined coverage of the visual field coverage is generally contained within the central 10째. Interestingly, pRFs in right hemisphere face selective ROIs show greater coverage of the ipsilateral visual field than those on the left, which may be related to the often-reported right hemisphere dominance for face processing in humans. Meeting abstract presented at VSS 2014
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