Nicholas Ichien

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task‐specific knowledge acquired from a wealth of prior experience, or is it based on the domain‐general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three‐dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task‐specific knowledge to analogical reasoning. We also developed a new model using part‐based comparison (PCM) by applying a domain‐general mapping procedure to learned representations of cars and their component parts. Across four‐term analogies (Experiment 1) and open‐ended analogies (Experiment 2), the domain‐general PCM model, but not the task‐specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human‐like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity.

Using poetic metaphors in the Serbian language, we identified systematic variations in the impact of fluid and crystalized intelligence on comprehension of metaphors that varied in rated aptness and familiarity. Overall, comprehension scores were higher for metaphors that were high rather than low in aptness, and high rather than low in familiarity. A measure of crystalized intelligence was a robust predictor of comprehension across the full range of metaphors, but especially for those that were either relatively unfamiliar or more apt. In contrast, individual differences associated with fluid intelligence were clearly found only for metaphors that were low in aptness. Superior verbal knowledge appears to be particularly important when trying to find meaning in novel metaphorical expressions, and also when exploring the rich interpretive potential of apt metaphors. The broad role of crystalized intelligence in metaphor comprehension is consistent with the view that metaphors are largely understood using semantic integration processes continuous with those that operate in understanding literal language.