Verbal analogy problem sets: An inventory of testing materials.

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

Impaired global, and compensatory local, biological motion processing in people with high levels of autistic traits

People with Autism Spectrum Disorder (ASD) are hypothesized to have poor high-level processing but superior low-level processing, causing impaired social recognition, and a focus on non-social stimulus contingencies. Biological motion perception provides an ideal domain to investigate exactly how ASD modulates the interaction between low and high-level processing, because it involves multiple processing stages, and carries many important social cues. We investigated individual differences among typically developing observers in biological motion processing, and whether such individual differences associate with the number of autistic traits. In Experiment 1, we found that individuals with fewer autistic traits were automatically and involuntarily attracted to global biological motion information, whereas individuals with more autistic traits did not show this pre-attentional distraction. We employed an action adaptation paradigm in the second study to show that individuals with more autistic traits were able to compensate for deficits in global processing with an increased involvement in local processing. Our findings can be interpreted within a predictive coding framework, which characterizes the functional relationship between local and global processing stages, and explains how these stages contribute to the perceptual difficulties associated with ASD

Emergent Analogical Reasoning in Large Language Models

The recent advent of large language models has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training. In human cognition, this capacity is closely tied to an ability to reason by analogy. Here, we performed a direct comparison between human reasoners and a large language model (the text-davinci-003 variant of GPT-3) on a range of analogical tasks, including a novel text-based matrix reasoning task closely modeled on Raven's Progressive Matrices. We found that GPT-3 displayed a surprisingly strong capacity for abstract pattern induction, matching or even surpassing human capabilities in most settings. Our results indicate that large language models such as GPT-3 have acquired an emergent ability to find zero-shot solutions to a broad range of analogy problems

Functional form of motion priors in human motion perception

It has been speculated that the human motion system combines noisy measurements with prior expectations in an optimal, or rational, manner. The basic goal of our work is to discover experimentally which prior distribution is used. More specifically, we seek to infer the functional form of the motion prior from the performance of human subjects on motion estimation tasks. We restricted ourselves to priors which combine three terms for motion slowness, first-order smoothness, and second-order smoothness. We focused on two functional forms for prior distributions: L2-norm and L1-norm regularization corresponding to the Gaussian and Laplace distributions respectively. In our first experimental session we estimate the weights of the three terms for each functional form to maximize the fit to human performance. We then measured human performance for motion tasks and found that we obtained better fit for the L1-norm (Laplace) than for the L2-norm (Gaussian). We note that the L1-norm is also a better fit to the statistics of motion in natural environments. In addition, we found large weights for the second-order smoothness term, indicating the importance of high-order smoothness compared to slowness and lower-order smoothness. To validate our results further, we used the best fit models using the L1-norm to predict human performance in a second session with different experimental setups. Our results showed excellent agreement between human performance and model prediction – ranging from 3% to 8% for five human subjects over ten experimental conditions – and give further support that the human visual system uses an L1-norm (Laplace) prior

Zero-shot visual reasoning through probabilistic analogical mapping

Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive direct training on visual reasoning tasks, and yield limited generalization to problems with novel content. In contrast, a long tradition of research in cognitive science has focused on elucidating the computational principles underlying human analogical reasoning; however, this work has generally relied on manually constructed representations. Here we present visiPAM (visual Probabilistic Analogical Mapping), a model of visual reasoning that synthesizes these two approaches. VisiPAM employs learned representations derived directly from naturalistic visual inputs, coupled with a similarity-based mapping operation derived from cognitive theories of human reasoning. We show that without any direct training, visiPAM outperforms a state-of-the-art deep learning model on an analogical mapping task. In addition, visiPAM closely matches the pattern of human performance on a novel task involving mapping of 3D objects across disparate categories

Aesthetic preferences for prototypical movements in human actions

A commonplace sight is seeing other people walk. Our visual system specializes in processing such actions. Notably, we are not only quick to recognize actions, but also quick to judge how elegantly (or not) people walk. What movements appear appealing, and why do we have such aesthetic experiences? Do aesthetic preferences for body movements arise simply from perceiving others’ positive emotions? To answer these questions, we showed observers different point-light walkers who expressed neutral, happy, angry, or sad emotions through their movements and measured the observers’ impressions of aesthetic appeal, emotion positivity, and naturalness of these movements. Three experiments were conducted. People showed consensus in aesthetic impressions even after controlling for emotion positivity, finding prototypical walks more aesthetically pleasing than atypical walks. This aesthetic prototype effect could be accounted for by a computational model in which walking actions are treated as a single category (as opposed to multiple emotion categories). The aesthetic impressions were affected both directly by the objective prototypicality of the movements, and indirectly through the mediation of perceived naturalness. These findings extend the boundary of category learning, and hint at possible functions for action aesthetics