Prediction and Situational Option Generation in Soccer

Paul Ward, Michigan Technological University Naturalistic models of decision making, such as the Recognition- Primed Decision (RPD) model (e.g., Klein, Calderwood, & Clinton-Cirocco, 1986; Klein, 1997), suggest that as individuals become more experienced within a domain they automatically recognize situational patterns as familiar which, in turn, activates an associated situational response. Typically, this results in a workable course of action being generated first, and subsequent options generated only if the initial option proves ineffective

Towards a model of how designers mentally categorise design information

This study aims to explore how designers mentally categorise design information during the early sketching performed in the generative phase. An action research approach is particularly appropriate for identifying the various sorts of design information and the cognitive operations involved in this phase. Thus, we conducted a protocol study with eight product designers based on a descriptive model derived from cognitive psychological memory theories. Subsequent protocol analysis yielded a cognitive model depicting the mental categorisation of design information processing performed by designers. This cognitive model included a structure for design information (high, middle, and low levels) and linked cognitive operations (association and transformation). Finally, this paper concludes by discussing directions for future research on the development of new computational tools for designers

LIDA: A Working Model of Cognition

In this paper we present the LIDA architecture as a working model of cognition. We argue that such working models are broad in scope and address real world problems in comparison to experimentally based models which focus on specific pieces of cognition. While experimentally based models are useful, we need a working model of cognition that integrates what we know from neuroscience, cognitive science and AI. The LIDA architecture provides such a working model. A LIDA based cognitive robot or software agent will be capable of multiple learning mechanisms. With artificial feelings and emotions as primary motivators and learning facilitators, such systems will ‘live’ through a developmental period during which they will learn in multiple ways to act in an effective, human-like manner in complex, dynamic, and unpredictable environments. We discuss the integration of the learning mechanisms into the existing IDA architecture as a working model of cognition

The typical developmental trajectory of social and executive functions in late adolescence and early adulthood.

Executive functions and social cognition develop through childhood into adolescence/early adulthood and are important for adaptive goal-oriented behaviour (Apperly, Samson & Humphreys, 2009; Blakemore & Choudhury, 2006). These functions are attributed to frontal networks known to undergo protracted maturation into early adulthood (Barker, Andrade, Morton, Romanowski & Bowles, 2010; Lebel, Walker, Leemans, Phillips & Beaulieu, 2008) although social cognition functions are also associated with widely distributed networks. Previously, non-linear development has been reported around puberty on an emotion match to sample task (McGivern, Andersen, Byrd, Mutter & Reilly, 2002) and for IQ in mid adolescence (Ramsden et al., 2011). However, there are currently little data on the typical development of social and executive functions in late adolescence and early adulthood. In a cross sectional design, 98 participants completed tests of social cognition and executive function, Wechsler Abbreviated Scale of Intelligence (Wechsler, 1999), Positive and Negative Affect Scale (Watson, Clark & Tellegan, 1988), Hospital Anxiety and Depression Scale (Zigmond & Snaith, 1983) and measures of pubertal development and demographics at age 17, 18 and 19. Non-linear age differences for letter fluency and concept formation executive functions were found, with a trough in functional ability in 18 year olds compared to other groups. There were no age group differences on social cognition measures. Gender accounted for differences on one scale of concept formation, one dynamic social interaction scale and two empathy scales. The clinical, developmental and educational implications of these findings are discussed

A Cognitive Science Based Machine Learning Architecture

In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, humanlike learning mechanisms:\ud 1) perceptual learning, the learning of new objects, categories, relations, etc.,\ud 2) episodic learning of events, the what, where, and when,\ud 3) procedural learning, the learning of new actions and action sequences with which to accomplish new tasks. The paper argues for the use of modular components, each specializing in implementing individual facets of human and animal cognition, as a viable approach towards achieving general intelligence

Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics

We introduce a dynamic neural algorithm called Dynamic Neural (DN) SARSA(\lambda) for learning a behavioral sequence from delayed reward. DN-SARSA(\lambda) combines Dynamic Field Theory models of behavioral sequence representation, classical reinforcement learning, and a computational neuroscience model of working memory, called Item and Order working memory, which serves as an eligibility trace. DN-SARSA(\lambda) is implemented on both a simulated and real robot that must learn a specific rewarding sequence of elementary behaviors from exploration. Results show DN-SARSA(\lambda) performs on the level of the discrete SARSA(\lambda), validating the feasibility of general reinforcement learning without compromising neural dynamics.Comment: Sohrob Kazerounian, Matthew Luciw are Joint first author

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg