Īlthough artificial intelligence-based perception (AIP) using deep neural networks (DNN) has achieved near human level performance, its well-known limitations are obstacles to the safety assurance needed in autonomous applications. Supplementary materials are available at. We further introduce a web-based tool ( ) for easily calculating and visualising sensorimotor distance between words, featuring coverage of nearly 800 million word pairs. Moreover, sensorimotor distance is equally effective for both concrete and abstract concepts. We demonstrate that, in modelling human similarity judgements, sensorimotor distance has comparable explanatory power to other measures of semantic similarity, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both grounded and computationally efficient. We present a new measure of sensorimotor distance between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor Norms. CBOW), all of which are theoretically problematic in their lack of grounding in sensorimotor experience. WordNet), databases of participant-produced semantic features, or corpus-derived linguistic distributional similarity (e.g. Traditional measures of semantic similarity are typically derived from distance in taxonomic databases (e.g. These findings are consistent with related research in other cognitive domains, such as risky choices, and add to growing evidence that time pressure and other forms of cognitive load do not necessarily alter core cognitive processes themselves but rather affect the precision of response selection.Įxperimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between concepts. We find that the variability of participants’ behavior increases with time pressure, to a point where participants are unlikely to make inferences anymore but instead start choosing readily available response options repeatedly. Computational cognitive modeling following an exemplar-similarity framework showed that the behavior of most participants under time pressure is in line with a lower choice sensitivity, this means less precise response selection, especially when people make similarity judgments. We conducted three experiments (two of them preregistered) in which we manipulated time pressure: one was a categorization task, which was designed based on optimal experimental design principles, and the other two involved a similarity judgment task. The simpler psychological similarity considers the number of matching features but ignores the actual feature value differences. Specifically, we test if people under time pressure attend to fewer object features (attention focus), if they respond less precisely (lower choice sensitivity), or if they simplify a psychological similarity function (simplified similarity). This article compares three psychological mechanisms to make multi-attribute inferences under time pressure in the domains of categorization and similarity judgments. (PsycINFO Database Record (c) 2012 APA, all rights reserved) These questions are rooted in a desire to connect the study of similarity to cognition as a whole. To provide a partial balance to our largely historic focus on similarity, we conclude by raising some unanswered questions for the field. A brief survey of the major approaches to, and models of, similarity is presented. Another argument for the importance of similarity in cognition is simply that it plays a significant role in psychological accounts of problem solving, memory, prediction, and categorization. For example, if people are asked to make an inference about an anatomical property, then anatomical similarities have more influence than behavioral similarities. Empirically, Heit and Rubinstein (1994) showed that if we do know about the property, then this knowledge, rather than a one-size-fits-all similarity, is used to guide our inferences. This relation assumes we have no special knowledge related to property X. As the similarity between A and B increases, so does the probability of correctly inferring that B has X upon knowing that A has X (Tenenbaum, 1999). From this perspective, psychological assessments of similarity are valuable to the extent that they provide grounds for predicting as many important aspects of our world as possible (Holland, Holyoak, Nisbett, & Thagard, 1986 see Dunbar & Fugelsang, Chap. Similarity plays a crucial role in making predictions because similar things usually behave similarly. Human assessments of similarity are fundamental to cognition because similarities in the world are revealing.
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