The propositional nature of human associative learning

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research

Consensus Message Passing for Layered Graphical Models

Generative models provide a powerful framework for probabilistic reasoning. However, in many domains their use has been hampered by the practical difficulties of inference. This is particularly the case in computer vision, where models of the imaging process tend to be large, loopy and layered. For this reason bottom-up conditional models have traditionally dominated in such domains. We find that widely-used, general-purpose message passing inference algorithms such as Expectation Propagation (EP) and Variational Message Passing (VMP) fail on the simplest of vision models. With these models in mind, we introduce a modification to message passing that learns to exploit their layered structure by passing 'consensus' messages that guide inference towards good solutions. Experiments on a variety of problems show that the proposed technique leads to significantly more accurate inference results, not only when compared to standard EP and VMP, but also when compared to competitive bottom-up conditional models.Comment: Appearing in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 201

identifying archaeological knowledge using multi dimensional scaling and multiple constraint satisfaction

In this thesis, I look at the current state of research in two fields: the cognitive psychology of learning and expertise & the development of Intelligent Tutoring Systems, especially their methods of modelling the users knowledge state. Within these areas I proceed to examine the way that these theories have overlapped in the past and consider their recent divergence, suggesting that this parting of the ways is premature. I then consider other relevent research so as to suggest a hypothesis where a symbolic connectionist approach to the modelling of knowledge states could be a solution to previous difficulties in the field of Intelligent Tutoring. This hypothesis is then used to construct a method for its examination and also a computer program to analyse the collected data. I then undertake experimental work to validate my hypothesis, and compare my results and methods with a pre-established technique for interpreting the data, that of multi-dimensional scaling. Finally the method now shown to be feasible is discussed to indicate the its success and highlight its shortcomings. Further suggestions are also made as to further research avenues

A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization

We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples. Because EM sits on a rigorous statistical foundation and has been thoroughly analyzed, this connection provides a new coherent framework with which to reason about EDAs

Expert Systems and Artificial Neural Networks for Spatial Analysis and Modelling: Essential Components for Knowledge-Based Geographical Information Systems

Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Self-directedness, integration and higher cognition

In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm

Connectionist Modelling of Category Learning

A shortcoming is identified with respect to the ability of exemplar-based connectionist models of category learning to offer accounts of learning about stimuli with variable dimensionality. Models which may simulate these tasks, such as the configural-cue network (Gluck & Bower, 1988b), appear to be unable to accurately simulate certain data well simulated by exemplar-based models such as ALCOVE (Kruschke, 1992). A task in which the advantage of ALCOVE is exemplified is the prediction of human learning rates on the six category structures tested by Shepard, Hovland, and Jenkins (1961). The ability of ALCOVE to simulate the observed order of difficulty depends on its incorporation of selective attention processes (Nosofsky, Gluck, Palmeri, McKinley, & Glauthier, 1994). This thesis focuses on developing configural-cue network models which incorporate these processes. Informed by an information-theoretic approach to modelling the implementation of selective attention using a configural-cue representation, five connectionist models are developed. Each is capable of predicting the order of difficulty reported by Shepard et al. (1961). Two models employ a modular structure, but analysis suggests that these may lack much of the functionality of the basic configural-cue network. The remaining three incorporate dimensional attention schemes. These models appear to offer superior generalisability in relation to the simulation of learning about variable dimensionality stimuli. This generalisability is examined by applying a variant of one of these dimensional attention models, to data collected by Kruschke (1996a) on the inverse base-rate effect and base-rate neglect. The model provides a qualitative fit to this data. The success of these configural-cue models on these two tasks, only successfully modelled previously using two distinct types of representation, indicates that the approach has some potential for further applications. Differences between the models applied, however, indicates that more sophisticated conceptions of the attention process may be required to allow further generalisability.