Social structure and underwater behavior of harbor seals in southern Monterey Bay, California

To understand harbor seal social and mating strategies, I examined site fidelity, seasonal abundance and distribution, herd integrity, and underwater behavior of individual harbor seals in southern Monterey Bay. Individual harbor seals (n = 444) were identified by natural markings and represented greater than 80% of an estimated 520 seals within this community. Year to year fidelity of individual harbor seals to southern Monterey Bay coastline was 84% (n = 388), and long-term associations (>2 yrs) among individuals were common (>40%). Consistent with these long-term associations, harbor seals were highly social underwater throughout the year. Underwater social behavior included three primary types: (1) visual and acoustic displays, such as vocalizing, surface splashing, and bubble-blowing; (2) playful or agonistic social behavior such as rolling, mounting, attending, and biting; and (3) signal gestures such as head-thrusting, fore-flipper scratch~ng, and growling. Frequency of these types of behavior was related to seal age, gender, season, and resource availability. Underwater behavior had a variety of functions, including promotion of learning and social development, reduction of aggression and preservation of social bonds by maintaining social hierarchy, and facilitation of mate selection during breeding season. Social behavior among adult males was significantly correlated with vocalization characteristics (r = 0.99, X2 = 37.7, p = 0.00087), indicating that seals may assess their competition based on underwater vocalization displays and adopt individual strategies for attracting females during breeding season based on social status. Individual mating strategies may include defending underwater territories, using scramble tactics, and developing social alliances. (PDF contains 105 pages)

Learning optimal adaptation strategies in unpredictable motor tasks.

Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.

Learning the Structure of Deep Sparse Graphical Models

Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single infinitely wide hidden layer. In this paper, we introduce the cascading Indian buffet process (CIBP), which provides a nonparametric prior on the structure of a layered, directed belief network that is unbounded in both depth and width, yet allows tractable inference. We use the CIBP prior with the nonlinear Gaussian belief network so each unit can additionally vary its behavior between discrete and continuous representations. We provide Markov chain Monte Carlo algorithms for inference in these belief networks and explore the structures learned on several image data sets.

Natural actor and belief critic: Reinforcement algorithm for learning parameters of dialogue systems modelled as POMDPs

This article presents a novel algorithm for learning parameters in statistical dialogue systems which are modeled as Partially Observable Markov Decision Processes (POMDPs). The three main components of a POMDP dialogue manager are a dialogue model representing dialogue state information; a policy that selects the system's responses based on the inferred state; and a reward function that specifies the desired behavior of the system. Ideally both the model parameters and the policy would be designed to maximize the cumulative reward. However, while there are many techniques available for learning the optimal policy, no good ways of learning the optimal model parameters that scale to real-world dialogue systems have been found yet. The presented algorithm, called the Natural Actor and Belief Critic (NABC), is a policy gradient method that offers a solution to this problem. Based on observed rewards, the algorithm estimates the natural gradient of the expected cumulative reward. The resulting gradient is then used to adapt both the prior distribution of the dialogue model parameters and the policy parameters. In addition, the article presents a variant of the NABC algorithm, called the Natural Belief Critic (NBC), which assumes that the policy is fixed and only the model parameters need to be estimated. The algorithms are evaluated on a spoken dialogue system in the tourist information domain. The experiments show that model parameters estimated to maximize the expected cumulative reward result in significantly improved performance compared to the baseline hand-crafted model parameters. The algorithms are also compared to optimization techniques using plain gradients and state-of-the-art random search algorithms. In all cases, the algorithms based on the natural gradient work significantly better. © 2011 ACM.

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics, however, the representations can be engaged based on visual context, and are updated by a single-rate process.

Detour behavior changes associated with prenatal morphine exposure in 11-day-old chicks

The central nervous system exhibits remarkable plasticity in early life. Prenatal morphine exposure may induce adverse behavioral effects on the neonate and the developing offspring. In the present study, we investigated the effect of prenatal morphine exposure (daily from embryonic days 12-16, 20 mg/kg) on 11-day-old chicks using two forms of spatial paradigms: one trial detour behavior task in which animals must bypass an obstacle to reach the desired goal without any training and detour learning task which required several trials of training to reach the detour criterion. The results showed that, on the condition that chicks could successfully detour in the first trial, morphine exposed chicks exhibited longer detour latency to finish the task, coupled by a preference for turning right versus turning left. In contrast, no significant difference in learning and memory was found in detour learning task between morphine exposed chicks and saline chicks. These findings suggest specific behavioral changes associated with prenatal exposure to opioids during mid to late gestation, also raise attention to the possible health hazard from pregnancy drug use in everyday life. (C) 2010 ISDN. Published by Elsevier Ltd. All rights reserved.

Learning legged locomotion

Legged locomotion of biological systems can be viewed as a self-organizing process of highly complex system-environment interactions. Walking behavior is, for example, generated from the interactions between many mechanical components (e.g., physical interactions between feet and ground, skeletons and muscle-tendon systems), and distributed informational processes (e.g., sensory information processing, sensory-motor control in central nervous system, and reflexes) [21]. An interesting aspect of legged locomotion study lies in the fact that there are multiple levels of self-organization processes (at the levels of mechanical dynamics, sensory-motor control, and learning). Previously, the self-organization of mechanical dynamics was nicely demonstrated by the so-called Passive Dynamic Walkers (PDWs; [18]). The PDW is a purely mechanical structure consisting of body, thigh, and shank limbs that are connected by passive joints. When placed on a shallow slope, it exhibits natural bipedal walking dynamics by converting potential to kinetic energy without any actuation. An important contribution of these case studies is that, if designed properly, mechanical dynamics can generate a relatively complex locomotion dynamics, on the one hand, and the mechanical dynamics induces self-stability against small disturbances without any explicit control of motors, on the other. The basic principle of the mechanical self-stability appears to be fairly general that there are several different physics models that exhibit similar characteristics in different kinds of behaviors (e.g., hopping, running, and swimming; [2, 4, 9, 16, 19]), and a number of robotic platforms have been developed based on them [1, 8, 13, 22]. © 2009 Springer London.

Effect of information transmission on cooperative behavior

Considering the fact, in the real world, that information is transmitted with a time delay, we study an evolutionary spatial prisoner's dilemma game where agents update strategies according to certain information that they have learned. In our study, the game dynamics are classified by the modes of information learning as well as game interaction, and four different combinations, i.e. the mean-field case, case I, case II and local case, are studied comparatively. It is found that the time delay in case II smoothes the phase transition from the absorbing states of C (or D) to their mixing state, and promotes cooperation for most parameter values. Our work provides insights into the temporal behavior of information and the memory of the system, and may be helpful in understanding the cooperative behavior induced by the time delay in social and biological systems.

Sparse Representations for Fast, One-Shot Learning

Humans rapidly and reliably learn many kinds of regularities and generalizations. We propose a novel model of fast learning that exploits the properties of sparse representations and the constraints imposed by a plausible hardware mechanism. To demonstrate our approach we describe a computational model of acquisition in the domain of morphophonology. We encapsulate phonological information as bidirectional boolean constraint relations operating on the classical linguistic representations of speech sounds in term of distinctive features. The performance model is described as a hardware mechanism that incrementally enforces the constraints. Phonological behavior arises from the action of this mechanism. Constraints are induced from a corpus of common English nouns and verbs. The induction algorithm compiles the corpus into increasingly sophisticated constraints. The algorithm yields one-shot learning from a few examples. Our model has been implemented as a computer program. The program exhibits phonological behavior similar to that of young children. As a bonus the constraints that are acquired can be interpreted as classical linguistic rules.

Growth of Motor Coordination in Early Robot Learning

M. H. Lee and Q. Meng, Growth of Motor Coordination in Early Robot Learning, IJCAI-05, 2005.

Critical incident technique and explicitation interviewing in studies of information behavior

Urquhart, C., Light, A., Thomas, R., Barker, A., Yeoman, A., Cooper, J., Armstrong, C., Fenton, R., Lonsdale, R. & Spink, S. (2003). Critical incident technique and explicitation interviewing in studies of information behavior. Library and Information Science Research, 25(1), 63-88. Sponsorship: JISC (for JUSTEIS element)

Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 1

Rowley, J.& Urquhart, C. (2007). Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 1. Journal of the American Society for Information Science and Technology, 58(8), 1162-1174. Sponsorship: JISC

Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 2

Urquhart, C. & Rowley, J. (2007). Understanding student information behavior in relation to electronic information services: lessons from longitudinal monitoring and evaluation Part 2. Journal of the American Society for Information Science and Technology, 58(8), 1188-1197. Sponsorship: JISC

Staged Competence Learning in Developmental Robotics

M.H. Lee, Q. Meng and F. Chao, 'Staged Competence Learning in Developmental Robotics', Adaptive Behavior, 15(3), pp 241-255, 2007. the full text will be available in September 2008

Adaptive capacity and learning to learn as leverage for social-ecological resilience

Ioan Fazey, John A. Fazey, Joern Fischer, Kate Sherren, John Warren, Reed F. Noss, Stephen R. Dovers (2007) Adaptive capacity and learning to learn as leverage for social?ecological resilience. Frontiers in Ecology and the Environment 5(7),375-380. RAE2008