Kenneth R. Livingston

On the interaction of prior knowledge and stimulus structure in category learning.

Contemporary theories of categorization propose that concepts are coherent in virtue of being embedded in a network of theories about the world. Those theories function to pick out some of the many possible features of a set of objects as most salient for purposes of classification, a process that is complex and still poorly understood (Murphy & Medin, 1985). Part of what makes this account incomplete is a lack of information as to (1) what makes a feature salient on a given occasion and (2) how feature salience interacts with category structure to determine the course of learning. We report on the results of three studies of category learning using complex schematic drawings to show that (1) the contrast set defined by ones initial encounters with category exemplars can be a source of individual differences in feature salience assignments; (2) such effects are short-lived in the face of clear evidence about actual feature diagnosticity; and (3) more robust prior hypotheses interact with category structure to either enhance learning or impede it. The enhancement occurs when the hypothesis emphasizes category-relevant features, even if the hypothesis is in fact incorrect. A hypothesis that assigns high salience to irrelevant features impedes learning. Learning does occur as feedback concerning category structure leads to enhanced salience for relevant features. Salience of irrelevant features remains high, however, suggesting that such learning as occurs involves augmentation and not total revision of the (incorrect) prior hypothesis.

An electric ray inspired Biomimetic Autonomous Underwater Vehicle

The development of a novel Biologically-inspired (or Biomimetic) Autonomous Underwater Vehicle (BAUV) inspired by the Pacific electric ray is addressed. The design and hardware implementation of experimental prototypes of the “RayBot” BAUV are described. Extensive observations of live electric rays provided key biological inspirations in the development of the BAUV. As part of the effort, a six degree-of-freedom impulse-based multi-body approach for modeling and simulation of BAUVs was also developed and validated through comparison with experimental data.

Category learning in the context of co-presented items

A series of four studies explore how the presentation of multiple items on each trial of a categorization task affects the course of category learning. In a three-category supervised classification task involving multi-dimensionally varying artificial organism-like stimuli, learners are shown a target plus two context items on every trial, with the context items’ category membership explicitly identified. These triads vary in whether one, two, or all three categories are represented. This presentation context can support within-category comparison and/or between-category contrast. The most successful learning occurs when all categories are represented in each trial. This pattern occurs across two different underlying category structures and across variations in learners’ prior knowledge of the relationship between the target and context items. These results appear to contrast with some other recent findings and make clear the potential importance of context-based inter-item evaluation in human category learning, which has implications for psychological theory and for real-world learning environments.

Evolving Robot Morphology Facilitates the Evolution of Neural Modularity and Evolvability

Although recent work has demonstrated that modularity can increase evolvability in non-embodied systems, it remains to be seen how the morphologies of embodied agents influences the ability of an evolutionary algorithm to find useful and modular controllers for them. We hypothesize that a modular control system may enable different parts of a robots body to sense and react to stimuli independently, enabling it to correctly recognize a seemingly novel environment as, in fact, a composition of familiar percepts and thus respond appropriately without need of further evolution. Here we provide evidence that supports this hypothesis: We found that such robots can indeed be evolved if (1) the robots morphology is evolved along with its controller, (2) the fitness function selects for the desired behavior and (3) also selects for conservative and robust behavior. In addition, we show that if constraints (1) and (3) are relaxed, or structural modularity is selected for directly, the robots have too little or too much modularity and lower evolvability. Thus, we demonstrate a previously unknown relationship between modularity and embodied cognition: evolving morphology and control such that robots exhibit conservative behavior indirectly selects for appropriate modularity and, thus, increased evolvability.

A multi-body approach for 6DOF modeling of Biomimetic Autonomous Underwater Vehicles with simulation and experimental results

We propose a six degree-of-freedom multi-body approach for modeling and simulation of Biologically-inspired (or Biomimetic) Autonomous Underwater Vehicles (BAUVs), i.e., artificial fish. The proposed approach is based on considering the BAUV as comprised of multiple rigid bodies interlinked through joints; the external force and torque on each rigid body in the BAUV is expressed using quasi-steady aerodynamic theory and the joint constraints are imposed through an impulse-based technique. A BAUV simulation platform has been implemented based on the proposed modeling framework and has been applied to analyze a specific BAUV inspired by the electric ray. The hardware implementation of the electric ray inspired BAUV is also presented. Finally, sample simulation results and validation against experimental data collected from the electric ray inspired BAUV are also presented.

Morphological Modularity Can Enable the Evolution of Robot Behavior to Scale Linearly with the Number of Environmental Features

In evolutionary robotics, populations of robots are typically trained in simulation before one or more of them are instantiated as physical robots. However, in order to evolve robust behavior, each robot must be evaluated in multiple environments. If an environment is characterized by

Modularity and Sparsity: Evolution of Neural Net Controllers in Physically Embodied Robots

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The Neurocomputational mind meets normative epistemology 1

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What Fodor means: Some thoughts on reading Jerry Fodor's a theory of content and other essays

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Epigenetic Operators and the Evolution of Physically Embodied Robots

features, each robot must be evaluated in all