CALM - A Process Model of Category Generalization, Abstraction and Structuring

R Schlegelmilch; AJ Wills; Helversen B von

CALM - A Process Model of Category Generalization, Abstraction and Structuring

R Schlegelmilch, AJ Wills, Helversen B von

School of Psychology

Research output: Contribution to conference › Conference paper (not formally published) › peer-review

Abstract

In this paper, we introduce CALM, a process model that is designed to abstract solutions in simple and complex category learning tasks. The model includes strong assumptions about the interaction of processes driving learning behavior, typically addressed in terms of feature attention, stimulus generalization, rule abstraction and knowledge partitioning. We present simulations of CALM, showing that the model can account both for systematic variations in Type II category difficulty, and for individual differences in extrapolation of an XOR category structure.

Original language	English
Publication status	Published - 1 Jan 2018

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title = "CALM - A Process Model of Category Generalization, Abstraction and Structuring",

abstract = "In this paper, we introduce CALM, a process model that is designed to abstract solutions in simple and complex category learning tasks. The model includes strong assumptions about the interaction of processes driving learning behavior, typically addressed in terms of feature attention, stimulus generalization, rule abstraction and knowledge partitioning. We present simulations of CALM, showing that the model can account both for systematic variations in Type II category difficulty, and for individual differences in extrapolation of an XOR category structure.",

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AU - Schlegelmilch, R

AU - Wills, AJ

AU - von, Helversen B

PY - 2018/1/1

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N2 - In this paper, we introduce CALM, a process model that is designed to abstract solutions in simple and complex category learning tasks. The model includes strong assumptions about the interaction of processes driving learning behavior, typically addressed in terms of feature attention, stimulus generalization, rule abstraction and knowledge partitioning. We present simulations of CALM, showing that the model can account both for systematic variations in Type II category difficulty, and for individual differences in extrapolation of an XOR category structure.

AB - In this paper, we introduce CALM, a process model that is designed to abstract solutions in simple and complex category learning tasks. The model includes strong assumptions about the interaction of processes driving learning behavior, typically addressed in terms of feature attention, stimulus generalization, rule abstraction and knowledge partitioning. We present simulations of CALM, showing that the model can account both for systematic variations in Type II category difficulty, and for individual differences in extrapolation of an XOR category structure.

M3 - Conference paper (not formally published)

ER -

CALM - A Process Model of Category Generalization, Abstraction and Structuring

Abstract

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