The Implicit Association Test (IAT) was used to measure population levels of conceptual alignment among two polar sensory metaphors and clusters of concepts to which they are commonly applied. A total of 873 participants were tested online, to compare within- and between-cluster alignments of concepts associated with two different polar sensory metaphors (up/down and black/white). IAT results were sensitive to semantic alignments that were also picked up by Latent Semantic Analysis (LSA) using a large-scale corpus of English. However, even with (...) these semantic alignments taken into account, the dual categorization results demonstrated strong metaphor-cluster alignments over and above the predictions of LSA. It is proposed that, rather than ontogenetic development of metaphoric concepts based on sensorimotor experience, conventionalized sensorimotor metaphors in language may be cognitive tools that provide language learners with insights supported by conceptual properties of a phylogenetically evolved perceptual interface. (shrink)

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. (shrink)

Why have core knowledge? Standard answers typically emphasize the difficulty of learning core knowledge from experience, or the benefits it confers for learning about the world. Here, we suggest a complementary reason: Core knowledge is critical for learning not just about the external world, but about the mind itself.