Representation of Shape in Individuals From a Culture With Minimal Exposure to Regular, Simple Artifacts: Sensitivity to Nonaccidental Versus Metric Properties

Many of the phenomena underlying shape recognition can be derived from the greater sensitivity to nonaccidental properties of an image (e.g., whether a contour is straight or curved), which are invariant to orientation in depth, than to the metric properties of an image (e.g., a contour's degree of curvature), which can vary with orientation. What enables this sensitivity? One explanation is that it derives from people's immersion in a manufactured world in which simple, regular shapes distinguished by nonaccidental properties abound (e.g., a can, a brick), and toddlers are encouraged to play with toy shape sorters. This report provides evidence against this explanation. The Himba, a seminomadic people living in a remote region of northwestern Namibia where there is little exposure to regular, simple artifacts, were virtually identical to Western observers in their greater sensitivity to nonaccidental properties than to metric properties of simple shapes

Predicting the psychophysical similarity of faces and non-face complex shapes by image-based measures

AbstractShape representation is accomplished by a series of cortical stages in which cells in the first stage (V1) have local receptive fields tuned to contrast at a particular scale and orientation, each well modeled as a Gabor filter. In succeeding stages, the representation becomes largely invariant to Gabor coding (Kobatake & Tanaka, 1994). Because of the non-Gabor tuning in these later stages, which must be engaged for a behavioral response (Tong, 2003; Tong et al., 1998), a V1-based measure of shape similarity based on Gabor filtering would not be expected to be highly correlated with human performance when discriminating complex shapes (faces and teeth-like blobs) that differ metrically on a two-choice, match-to-sample task. Here we show that human performance is highly correlated with Gabor-based image measures (Gabor simple and complex cells), with values often in the mid 0.90s, even without discounting the variability in the speed and accuracy of performance not associated with the similarity of the distractors. This high correlation is generally maintained through the stages of HMAX, a model that builds upon the Gabor metric and develops units for complex features and larger receptive fields. This is the first report of the psychophysical similarity of complex shapes being predictable from a biologically motivated, physical measure of similarity. As accurate as these measures were for accounting for metric variation, a simple demonstration showed that all were insensitive to viewpoint invariant (nonaccidental) differences in shape

Greater sensitivity to nonaccidental than metric shape properties in preschool children

AbstractNonaccidental properties (NAPs) are image properties that are invariant over orientation in depth and allow facile recognition of objects at varied orientations. NAPs are distinguished from metric properties (MPs) that generally vary continuously with changes in orientation in depth. While a number of studies have demonstrated greater sensitivity to NAPs in human adults, pigeons, and macaque IT cells, the few studies that investigated sensitivities in preschool children did not find significantly greater sensitivity to NAPs. However, these studies did not provide a principled measure of the physical image differences for the MP and NAP variations. We assessed sensitivity to NAP vs. MP differences in a nonmatch-to-sample task in which 14 preschool children were instructed to choose which of two shapes was different from a sample shape in a triangular display. Importantly, we scaled the shape differences so that MP and NAP differences were roughly equal (although the MP differences were slightly larger), using the Gabor-Jet model of V1 similarity (Lades & et al., 1993). Mean reaction times (RTs) for every child were shorter when the target shape differed from the sample in a NAP than an MP. The results suggest that preschoolers, like adults, are more sensitive to NAPs, which could explain their ability to rapidly learn new objects, even without observing them from every possible orientation

Perceptual Context in Cognitive Hierarchies

Cognition does not only depend on bottom-up sensor feature abstraction, but also relies on contextual information being passed top-down. Context is higher level information that helps to predict belief states at lower levels. The main contribution of this paper is to provide a formalisation of perceptual context and its integration into a new process model for cognitive hierarchies. Several simple instantiations of a cognitive hierarchy are used to illustrate the role of context. Notably, we demonstrate the use context in a novel approach to visually track the pose of rigid objects with just a 2D camera

A cross-cultural study of the representation of shape: Sensitivity to generalized cone dimensions

Many of the phenomena underlying shape recognition can be derived from an assumption that the representation of simple parts can be understood in terms of independent dimensions of generalized cones, e.g., whether the axis of a cylinder is straight or curved or whether the sides are parallel or nonparallel. What enables this sensitivity? One explanation is that the representations derive from our immersion in a manufactured world of simple objects, e.g., a cylinder and a funnel, where these dimensions can be readily discerned independent of other stimulus variations. An alternative explanation is that genetic coding and/or early experience with extended contours - a characteristic of all naturally varying visual worlds - would be sufficient to develop the appropriate representations. The Himba, a seminomadic people in a remote region of Northwestern Namibia with little exposure to regular, simple artifacts, were virtually identical to western observers in representing generalized-cone dimensions of simple shapes independently. Thus immersion in a world of simple, manufactured shapes is not required for the development of a representation that specifies these dimensions independently

A cognitive framework for object recognition with application to autonomous vehicles

Autonomous vehicles or self-driving cars are capable of sensing the surrounding environment so they can navigate roads without human input. Decisions are constantly made on sensing, mapping and driving policy using machine learning techniques. Deep Learning – massive neural networks that utilize the power of parallel processing – has become a popular choice for addressing the complexities of real time decision making. This method of machine learning has been shown to outperform alternative solutions in multiple domains, and has an architecture that can be adapted to new problems with relative ease. To harness the power of Deep Learning, it is necessary to have large amounts of training data that are representative of all possible situations the system will face. To successfully implement situational awareness in driverless vehicles, it is not possible to exhaust all possible training examples. An alternative method is to apply cognitive approaches to perception, for situations the autonomous vehicles will face. Cognitive approaches to perception work by mimicking the process of human intelligence – thereby permitting a machine to react to situations it has not previously experienced. This paper proposes a novel cognitive approach for object recognition. The proposed cognitive object recognition algorithm, referred to as Recognition by Components, is inspired by the psychological studies pertaining to early childhood development. The algorithm works by breaking down images into a series of primitive forms such as square, triangle, circle or rectangle and memory based aggregation to identify objects. Experimental results suggest that Recognition by Component algorithm performs significantly better than algorithms that require large amounts of training data

Recognition of non face objects, designed to require the same stimulus processing as that for faces, show only minimal effects of differences in contrast polarity or orientation direction

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