Learning must be constrained for it to lead to productive generalizations. Although biology is undoubtedly an important source of constraints, prior experience may be another, leading learners to represent input in ways that are more conducive to some generalizations than others, and/or to upand downweight features when entertaining generalizations. In two experiments, 4-month-old and 7-month-old infants were familiarized with sequences of musical chords or tones adhering either to an AAB pattern or an ABA pattern. In both cases, the 4-month-olds learned the generalization, but the 7-month-olds did not. The success of the 4-month-olds appears to contradict an account that this type of pattern learning is the provenance of a language-specific rule-learning module. It is not yet clear what drives the age-related change, but plausible candidates include differential experience with language and music, as well as interactions between general cognitive development and stimulus complexity.

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Infants have been shown to generalize from a small number of input examples. However, existing studies allow two possible means of generalization. One is via a process of noting similarities shared by several examples. Alternatively, generalization may reflect an implicit desire to explain the input. The latter view suggests that generalization might occur when even a single input example is surprising, given the learner’s current model of the domain. To test the possibility that infants are able to generalize based on a single example, we familiarized 9-month-olds with a single three-syllable input example that contained either one surprising feature (syllable repetition, Exp. 1) or two features (repetition and a rare syllable, Exp. 2). In both experiments, infants generalized only to new strings that maintained all of the surprising features from familiarization. This research suggests that surprise can promote very rapid generalization.

The language of space and spatial relations is a rich source of abstract semantic structure. We develop a probabilistic model that learns to understand utterances that describe spatial configurations of objects in a tabletop scene by seeking the meaning that best explains the sentence chosen. The inference problem is simplified by assuming that sentences express symbolic representations of (latent) semantic relations between referents and landmarks in space, and that given these symbolic representations, utterances and physical locations are conditionally independent. As such, the inference problem factors into a symbolgrounding component (linking propositions to physical locations) and a symbol-translation component (linking propositions to parse trees). We evaluate the model by eliciting production and comprehension data from human English speakers and find that our system recovers the referent of spatial utterances at a level of proficiency approaching human performance. I. INTRODUCTIONImagine that a friend asks you to "Bring me the thing toward the far corner of the table." This simple request requires fairly sophisticated cognitive processing. You must first identify that she is referring to something on a table, in particular, one with corners. Then, you must orient the table to distinguish "far" vs. "near" corners. Finally, if there is more than one object near a "far" corner, but one is also very near the edge, you might tend to favor the other one, reasoning that it would have been easy to ask for "the one near the edge".We model a version of this problem, with a particular focus on recovering the referent of spatial utterances like "the thing toward the far corner of the table", where the only information available about the intended object is its position relative to a landmark in the scene. Beginning with no knowledge about the meanings of words, but equipped with a small vocabulary of spatial relations (e.g., containment, proximity, ordering in cardinal directions) and abstract representations of objects and their parts (e.g., a table can be represented as a line with ends and a middle, or as a rectangle with corners, quadrants and edges), our model learns probabilistic correspondences between sentences and abstract spatial relations between referents and landmarks by "observing" a teacher repeatedly generating an utterance and pointing to a location in space.Clearly a method that involved supervised learning based on observed propositional semantics would not be developmentally plausible, as children do not get to observe symbolic meaning directly, and so crucially, the abstract relations are never made overt to our learner. Rather, the locations are probabilistically assigned to abstract landmark-relation pairs

We develop a Bayesian modeling approach for tracking people in 3D from monocular video with unknown cameras. Modeling in 3D provides natural explanations for occlusions and smoothness discontinuities that result from projection, and allows priors on velocity and smoothness to be grounded in physical quantities: meters and seconds vs. pixels and frames. We pose the problem in the context of data association, in which observations are assigned to tracks. A correct application of Bayesian inference to multitarget tracking must address the fact that the model's dimension changes as tracks are added or removed, and thus, posterior densities of different hypotheses are not comparable. We address this by marginalizing out the trajectory parameters so the resulting posterior over data associations has constant dimension. This is made tractable by using (a) Gaussian process priors for smooth trajectories and (b) approximately Gaussian likelihood functions. Our approach provides a principled method for incorporating multiple sources of evidence; we present results using both optical flow and object detector outputs. Results are comparable to recent work on 3D tracking and, unlike others, our method requires no pre-calibrated cameras.