Comment on "Nonadjacent dependency processing in monkeys, apes, and humans".

We comment on the technical interpretation of the study of Watson et al. and caution against their conclusion that the behavioral evidence in their experiments points to nonhuman animals' ability to learn syntactic dependencies, because their results are also consistent with the learning of phonological dependencies in human languages.

GEARing smart environments for pediatric motor rehabilitation.

BACKGROUND: There is a lack of early (infant) mobility rehabilitation approaches that incorporate natural and complex environments and have the potential to concurrently advance motor, cognitive, and social development. The Grounded Early Adaptive Rehabilitation (GEAR) system is a pediatric learning environment designed to provide motor interventions that are grounded in social theory and can be applied in early life. Within a perceptively complex and behaviorally natural setting, GEAR utilizes novel body-weight support technology and socially-assistive robots to both ease and encourage mobility in young children through play-based, child-robot interaction. This methodology article reports on the development and integration of the different system components and presents preliminary evidence on the feasibility of the system. METHODS: GEAR consists of the physical and cyber components. The physical component includes the playground equipment to enrich the environment, an open-area body weight support (BWS) device to assist children by partially counter-acting gravity, two mobile robots to engage children into motor activity through social interaction, and a synchronized camera network to monitor the sessions. The cyber component consists of the interface to collect human movement and video data, the algorithms to identify the children's actions from the video stream, and the behavioral models for the child-robot interaction that suggest the most appropriate robot action in support of given motor training goals for the child. The feasibility of both components was assessed via preliminary testing. Three very young children (with and without Down syndrome) used the system in eight sessions within a 4-week period. RESULTS: All subjects completed the 8-session protocol, participated in all tasks involving the selected objects of the enriched environment, used the BWS device and interacted with the robots in all eight sessions. Action classification algorithms to identify early child behaviors in a complex naturalistic setting were tested and validated using the video data. Decision making algorithms specific to the type of interactions seen in the GEAR system were developed to be used for robot automation. CONCLUSIONS: Preliminary results from this study support the feasibility of both the physical and cyber components of the GEAR system and demonstrate its potential for use in future studies to assess the effects on the co-development of the motor, cognitive, and social systems of very young children with mobility challenges.

Statistical Relational Learning With Unconventional String Models.

This paper shows how methods from statistical relational learning can be used to address problems in grammatical inference using model-theoretic representations of strings. These model-theoretic representations are the basis of representing formal languages logically. Conventional representations include a binary relation for order and unary relations describing mutually exclusive properties of each position in the string. This paper presents experiments on the learning of formal languages, and their stochastic counterparts, with unconventional models, which relax the mutual exclusivity condition. Unconventional models are motivated by domain-specific knowledge. Comparison of conventional and unconventional word models shows that in the domains of phonology and robotic planning and control, Markov Logic Networks With unconventional models achieve better performance and less runtime with smaller networks than Markov Logic Networks With conventional models.

Learning models of Human-Robot Interaction from small data.

This paper offers a new approach to learning discrete models for human-robot interaction (HRI) from small data. In the motivating application, HRI is an integral part of a pediatric rehabilitation paradigm that involves a play-based, social environment aiming at improving mobility for infants with mobility impairments. Designing interfaces in this setting is challenging, because in order to harness, and eventually automate, the social interaction between children and robots, a behavioral model capturing the causality between robot actions and child reactions is needed. The paper adopts a Markov decision process (MDP) as such a model, and selects the transition probabilities through an empirical approximation procedure called smoothing. Smoothing has been successfully applied in natural language processing (NLP) and identification where, similarly to the current paradigm, learning from small data sets is crucial. The goal of this paper is two-fold: (i) to describe our application of HRI, and (ii) to provide evidence that supports the application of smoothing for small data sets.

What complexity differences reveal about domains in language.

An important distinction between phonology and syntax has been overlooked. All phonological patterns belong to the regular region of the Chomsky Hierarchy, but not all syntactic patterns do. We argue that the hypothesis that humans employ distinct learning mechanisms for phonology and syntax currently offers the best explanation for this difference.

Modeling the contribution of phonotactic cues to the problem of word segmentation.

How do infants find the words in the speech stream? Computational models help us understand this feat by revealing the advantages and disadvantages of different strategies that infants might use. Here, we outline a computational model of word segmentation that aims both to incorporate cues proposed by language acquisition researchers and to establish the contributions different cues can make to word segmentation. We present experimental results from modified versions of Venkataraman's (2001) segmentation model that examine the utility of: (1) language-universal phonotactic cues; (2) language-specific phonotactic cues which must be learned while segmenting utterances; and (3) their combination. We show that the language-specific cue improves segmentation performance overall, but the language-universal phonotactic cue does not, and that their combination results in the most improvement. Not only does this suggest that language-specific constraints can be learned simultaneously with speech segmentation, but it is also consistent with experimental research that shows that there are multiple phonotactic cues helpful to segmentation (e.g. Mattys, Jusczyk, Luce & Morgan, 1999; Mattys & Jusczyk, 2001). This result also compares favorably to other segmentation models (e.g. Brent, 1999; Fleck, 2008; Goldwater, 2007; Johnson & Goldwater, 2009; Venkataraman, 2001) and has implications for how infants learn to segment.