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Functional Parts
Previous work in visual cognition has extensively explored the power of parts-based representations of objects for recognition, categorization, and functional reasoning. We propose a novel, parts-based representation of objects, where the parts of an object are found by grouping together object elements that move together over a set of images. The distribution of object configurations is then succinctly described in terms of these functional parts and an orthogonal set of modal transformations of these parts. If the distribution has a natural set of principal axes, the computed modes are stable and functionally significant. Moreover, the representation is always unique and robustly computable because it does not rely critically on the properties of any particular element in any particular instance of the object. Most importantly, the representation provides a set of direct cues to object functionality without making any assumptions about object geometry or invoking any high-level domain knowledge. This robustness and functional transparency may be contrasted with standard representations based on geometric parts, such as generalized cylinders (Marr and Nishihara, 1978) or geons (Biederman, 1987), which are sensitive to accidental ahgnments and occlusions (Biederman, 1987), and which only support functional reasoning in conjunction with high-level domain knowledge (Tversky and Hemenway, 1984)
Variability, negative evidence, and the acquisition of verb argument constructions
We present a hierarchical Bayesian framework for modeling the acquisition of verb argument constructions. It embodies a domain-general approach to learning higher-level knowledge in the form of inductive constraints (or overhypotheses), and has been used to explain other aspects of language development such as the shape bias in learning object names. Here, we demonstrate that the same model captures several phenomena in the acquisition of verb constructions. Our model, like adults in a series of artificial language learning experiments, makes inferences about the distributional statistics of verbs on several levels of abstraction simultaneously. It also produces the qualitative learning patterns displayed by children over the time course of acquisition. These results suggest that the patterns of generalization observed in both children and adults could emerge from basic assumptions about the nature of learning. They also provide an example of a broad class of computational approaches that can resolve Baker's Paradox
When Computer Vision Gazes at Cognition
Joint attention is a core, early-developing form of social interaction. It is
based on our ability to discriminate the third party objects that other people
are looking at. While it has been shown that people can accurately determine
whether another person is looking directly at them versus away, little is known
about human ability to discriminate a third person gaze directed towards
objects that are further away, especially in unconstraint cases where the
looker can move her head and eyes freely. In this paper we address this
question by jointly exploring human psychophysics and a cognitively motivated
computer vision model, which can detect the 3D direction of gaze from 2D face
images. The synthesis of behavioral study and computer vision yields several
interesting discoveries. (1) Human accuracy of discriminating targets
8{\deg}-10{\deg} of visual angle apart is around 40% in a free looking gaze
task; (2) The ability to interpret gaze of different lookers vary dramatically;
(3) This variance can be captured by the computational model; (4) Human
outperforms the current model significantly. These results collectively show
that the acuity of human joint attention is indeed highly impressive, given the
computational challenge of the natural looking task. Moreover, the gap between
human and model performance, as well as the variability of gaze interpretation
across different lookers, require further understanding of the underlying
mechanisms utilized by humans for this challenging task.Comment: Tao Gao and Daniel Harari contributed equally to this wor
Higher order inference in verb argument structure acquisition
Successful language learning combines generalization and
the acquisition of lexical constraints. The conflict is particularly clear for verb argument structures, which may
generalize to new verbs (John gorped the ball to Bill ->John gorped Bill the ball), yet resist generalization with certain lexical items (John carried the ball to Bill -> *John carried Bill the ball). The resulting learnability “paradox” (Baker 1979) has received great attention in the acquisition literature.
Wonnacott, Newport & Tanenhaus 2008 demonstrated that adult learners acquire both general and verb-specific
patterns when acquiring an artificial language with two
competing argument structures, and that these same
constraints are reflected in real time processing. The current work follows up and extends this program of research in two new experiments. We demonstrate that the results are consistent with a hierarchical Bayesian model, originally developed by Kemp, Perfors & Tenebaum (2007) to capture the emergence of feature biases in word learning
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