Abstract

ABSTRACTHigh-level judgement and decision-making tasks display dynamic bidirectional relationships in which salient cues determine how responses are evaluated by decision-makers, and these responses in turn determine the cues that are considered. In this paper, we propose Kosko's bidirectional associative memory network, a minimal two-layer recurrent neural network, as a mathematically tractable toy model with which the properties of existing bidirectional models, and the behavioural implications of these properties, can be studied. We first derive results regarding the dynamics of the BAM network, and then show how these results can be used to provide an analytically sound explanation for a number of important findings, including coherence shifts in judgement and choice, anchoring effects, and reference point effects.