Abstract

AbstractThis chapter argues that associative learning is the obvious mechanism to simulate judgements of relative frequency. PASS (Probability ASSociator), the specific associationist model proposed, consists of two parts, FEN (Frequency Encoding Network), a neural network, and the CA (Cognitive Algorithms)-module, which operates on the output of the neural network. FEN encodes events, including their contexts, by their featural description and builds up a representation of the frequency with which features co-occur. The CA-module consists of only two algorithms that suffice to model the results usually found in studies on relative frequency estimates as well as on confidence judgements about such estimates. Several extensions of PASS that allow judgements of absolute frequencies and the simulation of biased estimates are suggested, and PASS is compared to competing models that have been used to simulate relative frequency judgements.

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