Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations with seven established (...) similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation‐metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category‐specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity. (shrink)

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. (shrink)