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Creativity is typically defined as the generation of novel and useful ideas or artifacts. This generative capacity is crucial to everyday problem solving, technological innovation, scientific discovery, and the arts. A central concern of cognitive scientists is to understand the processes that underlie human creative thinking. We review evidence that one process contributing to human creativity is the ability to generate novel representations of unfamiliar situations by completing a partially-specified relation or an analogy. In particular, cognitive tasks that trigger generation of relational similarities between dissimilar situations—distant analogies—foster a kind of creative mindset. We discuss possible computational mechanisms that might enable relation-driven generation, and hence may contribute to human creativity, and conclude with suggested directions for future research. 

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.

 

Dyadic interactions can sometimes elicit a disconcerting response from viewers, generating a sense of “awkwardness.” Despite the ubiquity of awkward social interactions in daily life, it remains unknown what visual cues signal the oddity of human interactions and yield the subjective impression of awkwardness. In the present experiments, we focused on a range of greeting behaviors (handshake, fist bump, high five) to examine both the inherent objectivity and impact of contextual and kinematic information in the social evaluation of awkwardness. In Experiment 1, participants were asked to discriminate whether greeting behaviors presented in raw videos were awkward or natural, and if judged as awkward, participants provided verbal descriptions regarding the awkward greeting behaviors. Participants showed consensus in judging awkwardness from raw videos, with a high proportion of congruent responses across a range of awkward greeting behaviors. We also found that people used social-related and motor-related words in their descriptions for awkward interactions. Experiment 2 employed advanced computer vision techniques to present the same greeting behaviors in three different display types. All display types preserved kinematic information, but varied contextual information: (1) patch displays presented blurred scenes composed of patches; (2) body displays presented human body figures on a black background; and (3) skeleton displays presented skeletal figures of moving bodies. Participants rated the degree of awkwardness of greeting behaviors. Across display types, participants consistently discriminated awkward and natural greetings, indicating that the kinematics of body movements plays an important role in guiding awkwardness judgments. Multidimensional scaling analysis based on the similarity of awkwardness ratings revealed two primary cues: motor coordination (which accounted for most of the variability in awkwardness judgments) and social coordination. We conclude that the perception of awkwardness, while primarily inferred on the basis of kinematic information, is additionally affected by the perceived social coordination underlying human greeting behaviors. 

Analogical reasoning is an active topic of investigation across education, artificial intelligence (AI), cognitive psychology, and related fields. In all fields of inquiry, explicit analogy problems provide useful tools for investigating the mechanisms underlying analogical reasoning. Such sets have been developed by researchers working in the fields of educational testing, AI, and cognitive psychology. However, these analogy tests have not been systematically made accessible across all the relevant fields. The present paper aims to remedy this situation by presenting a working inventory of verbal analogy problem sets, intended to capture and organize sets from diverse sources.