A Bio-Logical Theory of Animal Learning

This article provides the foundation for a new predictive theory of animal learning that is based upon a simple logical model. The knowledge of experimental subjects at a given time is described using logical equations. These logical equations are then used to predict a subject’s response when presented with a known or a previously unknown situation. This new theory suc- cessfully anticipates phenomena that existing theories predict, as well as phenomena that they cannot. It provides a theoretical account for phenomena that are beyond the domain of existing models, such as extinction and the detection of novelty, from which “external inhibition” can be explained. Examples of the methods applied to make predictions are given using previously published results. The present theory proposes a new way to envision the minimal functions of the nervous system, and provides possible new insights into the way that brains ultimately create and use knowledge about the world

Is conditioning a useful framework for understanding the development and treatment of phobias?

Despite the prevalence of therapeutic interventions based on conditioning models of fear acquisition, conditioning has been seen by many as a poor explanation of how fears develop: partly because research on conditioning has become less mainstream and models of teaming have become increasingly more complex. This article reviews some of what is now known about conditioning/associative teaming and describes how these findings account for some early criticisms of conditioning models of fear acquisition. It also describes how pathways to fear such as vicarious teaming and fear information can be conceptualised as forms of associative teaming that obey the same teaming rules. Some popular models of conditioning are then described with a view to highlighting the important components in teaming. Finally, suggestions are made about how what we know about conditioning can be applied to improve therapeutic interventions and prevention programs for child anxiety. (c) 2006 Elsevier Ltd. All rights reserved

Category Development in Early Language

Developing knowledge of the vehicle, animal, and fruit categories was traced in six children from 0 to 8. Data from mothers’ language diaries and from bi-monthly sessions with the children were pooled to analyse the growth, content, and internal structure of the three categories over time. The children developed some grasp of most of the focal concepts in each category, but they made fewer differentiations than adults do. Overextension of a single concept term to encompass a cluster of related referents was common. The frequent discrepancies between comprehension and production of concept terms highlighted the importance of examining both modes. The data showed marked individual differences in style of category acquisition

Category Development in Early Language

Developing knowledge of the vehicle, animal, and fruit categories was traced in six children from 0 to 8. Data from mothers’ language diaries and from bi-monthly sessions with the children were pooled to analyse the growth, content, and internal structure of the three categories over time. The children developed some grasp of most of the focal concepts in each category, but they made fewer differentiations than adults do. Overextension of a single concept term to encompass a cluster of related referents was common. The frequent discrepancies between comprehension and production of concept terms highlighted the importance of examining both modes. The data showed marked individual differences in style of category acquisition

Overextension in Early Language Development

This research explored overextension in the early vocabularies of six children, followed in a language diary study from 0 to 8. Results indicated that only one-third of the first 75 words acquired by each child were overextended. A small set of high-frequency, early acquired words accounted for a disproportionate number of overextensions. Overextensions were classified into three types: categorical overinclusions, analogical overextensions and predicate statements. Four types of information served as the bases for word applications: perceptual, action-functional, affective and contextual. The use of words to denote associative complexes of a well-organized, systematic character was discussed as a characteristic form of early word usage

Overextension in Early Language Development

This research explored overextension in the early vocabularies of six children, followed in a language diary study from 0 to 8. Results indicated that only one-third of the first 75 words acquired by each child were overextended. A small set of high-frequency, early acquired words accounted for a disproportionate number of overextensions. Overextensions were classified into three types: categorical overinclusions, analogical overextensions and predicate statements. Four types of information served as the bases for word applications: perceptual, action-functional, affective and contextual. The use of words to denote associative complexes of a well-organized, systematic character was discussed as a characteristic form of early word usage

A Dutch Book Theorem and Converse Dutch Book Theorem for Kolmogorov Conditionalization

This paper discusses how to update one’s credences based on evidence that has initial probability 0. I advance a diachronic norm, Kolmogorov Conditionalization, that governs credal reallocation in many such learning scenarios. The norm is based upon Kolmogorov’s theory of conditional probability. I prove a Dutch book theorem and converse Dutch book theorem for Kolmogorov Conditionalization. The two theorems establish Kolmogorov Conditionalization as the unique credal reallocation rule that avoids a sure loss in the relevant learning scenarios

A Dutch Book Theorem and Converse Dutch Book Theorem for Kolmogorov Conditionalization

This paper discusses how to update one’s credences based on evidence that has initial probability 0. I advance a diachronic norm, Kolmogorov Conditionalization, that governs credal reallocation in many such learning scenarios. The norm is based upon Kolmogorov’s theory of conditional probability. I prove a Dutch book theorem and converse Dutch book theorem for Kolmogorov Conditionalization. The two theorems establish Kolmogorov Conditionalization as the unique credal reallocation rule that avoids a sure loss in the relevant learning scenarios