Cognitive complexity explains processing asymmetry in judgments of similarity versus difference.

Human judgments of similarity and difference are sometimes asymmetrical, with the former being more sensitive than the latter to relational overlap, but the theoretical basis for this asymmetry remains unclear. We test an explanation based on the type of information used to make these judgments (relations versus features) and the comparison process itself (similarity versus difference). We propose that asymmetries arise from two aspects of cognitive complexity that impact judgments of similarity and difference: processing relations between entities is more cognitively demanding than processing features of individual entities, and comparisons assessing difference are more cognitively complex than those assessing similarity. In Experiment 1 we tested this hypothesis for both verbal comparisons between word pairs, and visual comparisons between sets of geometric shapes. Participants were asked to select one of two options that was either more similar to or more different from a standard. On unambiguous trials, one option was unambiguously more similar to the standard; on ambiguous trials, one option was more featurally similar to the standard, whereas the other was more relationally similar. Given the higher cognitive complexity of processing relations and of assessing difference, we predicted that detecting relational difference would be particularly demanding. We found that participants (1) had more difficulty detecting relational difference than they did relational similarity on unambiguous trials, and (2) tended to emphasize relational information more when judging similarity than when judging difference on ambiguous trials. The latter finding was replicated using more complex story stimuli (Experiment 2). We showed that this pattern can be captured by a computational model of comparison that weights relational information more heavily for similarity than for difference judgments.

Relational and lexical similarity in analogical reasoning and recognition memory: Behavioral evidence and computational evaluation.

We examined the role of different types of similarity in both analogical reasoning and recognition memory. On recognition tasks, people more often falsely report having seen a recombined word pair (e.g., flower: garden) if it instantiates the same semantic relation (e.g., is a part of) as a studied word pair (e.g., house: town). This phenomenon, termed relational luring, has been interpreted as evidence that explicit relation representations-known to play a central role in analogical reasoning-also impact episodic memory. We replicate and extend previous studies, showing that relation-based false alarms in recognition memory occur after participants encode word pairs either by making relatedness judgments about individual words presented sequentially, or by evaluating analogies between pairs of word pairs. To test alternative explanations of relational luring, we implemented an established model of recognition memory, the Generalized Context Model (GCM). Within this basic framework, we compared representations of word pairs based on similarities derived either from explicit relations or from lexical semantics (i.e., individual word meanings). In two experiments on recognition memory, best-fitting values of GCM parameters enabled both similarity models (even the model based solely on lexical semantics) to predict relational luring with comparable accuracy. However, the model based on explicit relations proved more robust to parameter variations than that based on lexical similarity. We found this same pattern of modeling results when applying GCM to an independent set of data reported by Popov, Hristova, and Anders (2017). In accord with previous work, we also found that explicit relation representations are necessary for modeling analogical reasoning. Our findings support the possibility that explicit relations, which are central to analogical reasoning, also play an important role in episodic memory.

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.

Advancing with age: Older adults excel in comprehension of novel metaphors.

Older adults may experience certain forms of cognitive decline, but some forms of semantic memory remain intact in older age. To address how metaphor comprehension changes with age and whether metaphor comprehension relies more heavily on analogical reasoning (supported by fluid intelligence) or on conceptual combination (supported by crystalized intelligence), we compared performance of younger and older adults. In two experiments, healthy older adults (54-88 years) scored lower on a measure of fluid intelligence (Ravens Progressive Matrices) but higher on a measure of crystalized intelligence (Mill Hill Vocabulary Test) relative to younger adults (18-34 years). Groups were equally successful in comprehending relatively easy metaphors (Study 1), but older adults showed a striking advantage over younger adults for novel literary metaphors (Study 2). Mixed-effects modeling showed that measures of fluid and crystalized intelligence each made separable contributions to metaphor comprehension for both groups, but older adults relied more on crystalized intelligence than did younger adults. These age-related dissociations clarify cognitive effects of aging and highlight the importance of crystalized intelligence for metaphor comprehension in both younger and older adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Two Computational Approaches to Visual Analogy: Task-Specific Models Versus Domain-General Mapping.

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.

Probabilistic analogical mapping with semantic relation networks.

The human ability to flexibly reason using analogies with domain-general content depends on mechanisms for identifying relations between concepts, and for mapping concepts and their relations across analogs. Building on a recent model of how semantic relations can be learned from nonrelational word embeddings, we present a new computational model of mapping between two analogs. The model adopts a Bayesian framework for probabilistic graph matching, operating on semantic relation networks constructed from distributed representations of individual concepts and of relations between concepts. Through comparisons of model predictions with human performance in a novel mapping task requiring integration of multiple relations, as well as in several classic studies, we demonstrate that the model accounts for a broad range of phenomena involving analogical mapping by both adults and children. We also show the potential for extending the model to deal with analog retrieval. Our approach demonstrates that human-like analogical mapping can emerge from comparison mechanisms applied to rich semantic representations of individual concepts and relations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Predicting patterns of similarity among abstract semantic relations.

Although models of word meanings based on distributional semantics have proved effective in predicting human judgments of similarity among individual concepts, it is less clear whether or how such models might be extended to account for judgments of similarity among relations between concepts. Here we combine an individual-differences approach with computational modeling to predict human judgments of similarity among word pairs instantiating a variety of abstract semantic relations (e.g., contrast, cause-effect, part-whole). A measure of cognitive capacity predicted individual differences in the ability to discriminate among distinct relations. The human pattern of relational similarity judgments, both at the group level and for individual participants, was best predicted by a model that takes representations of word meanings based on distributional semantics as its inputs and uses them to learn an explicit representation of relations. These findings indicate that although the meanings of abstract semantic relations are not directly coded in the meanings of individual words, important aspects of relational similarity can be derived from distributional semantics. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Causal actions enhance perception of continuous body movements.

Our experience of motion depends not only on spatiotemporal features of stimuli, but also on our recognition of seemingly higher-level properties, as when we see an actor's body movements as goal-directed. Here, we examined how the perception of social causation in human actions guides the perceptual interpolation of motion in the observation of body movements. Natural human-object interactions were recorded for videos in which a person prepared to catch a ball thrown by another person. We manipulated the number of image frames between key postures to yield a short clip with different frame rates, and asked participants to judge whether the catcher's action showed smooth movements or sudden changes. In the causal condition, the catcher faced toward the ball and the thrower to preserve an intention-based causal relation between the ball's movement and the catcher's action in which the former causes the catcher's intention to act. In the non-causal condition, the catcher performed the same movements to raise their hands to catch a ball, except that they faced away from the ball, creating the impression of either a psychic reaction or coincidental non-goal-directed behavior, which makes movements of the ball appear to be an implausible cause of the catcher's intention to act. Across four experiments, we found that humans were more likely to judge the catcher's body movements to be continuous in the causal condition than in the non-causal condition. The effect was maintained as long as the intention-based causal relation was present, even when only part of the chain of causal events was observed. These findings indicate that intention-based cause-effect relations in human actions guide perceptual interpolation of body movements.

Understanding the visual perception of awkward body movements: How interactions go awry.

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.

Mothers know best: redirecting adolescent reward sensitivity toward safe behavior during risk taking.

Despite being one of the healthiest developmental periods, morbidity and mortality rates increase dramatically during adolescence, largely due to preventable, risky behaviors. Heightened reward sensitivity, coupled with ineffective cognitive control, has been proposed to underlie adolescents' risk taking. In this study, we test whether reward sensitivity can be redirected to promote safe behavior. Adolescents completed a risk-taking task in the presence of their mother and alone during fMRI. Adolescents demonstrated reduced risk-taking behavior when their mothers were present compared with alone, which was associated with greater recruitment of the ventrolateral prefrontal cortex (VLPFC) when making safe decisions, decreased activation in the ventral striatum following risky decisions and greater functional coupling between the ventral striatum and VLPFC when making safe decisions. Importantly, the very same neural circuitry (i.e. ventral striatum) that has been linked to greater risk-taking can also be redirected toward thoughtful, more deliberative and safe decisions.