Creative association is inherent and essential to creativity and insight. Here we utilised a Chinese compound Remote Associates Task (cRAT) to identify the potential impact of word order (i.e., solution position hereinafter) and word frequency on creative association across two behavioural experiments. Experiment 1 identified the effects of (a) word order and word frequency on cRAT-induced association without considering the specific strategies used during solving such problems and (b) their interaction not only on performance in solving the cRAT, including solution (...) time and accuracy, but also on difficulty rating. Following a coarse-to-fine approach, Experiment 2 examined the effect of both factors on creative association determined according to each participant’s trial-by-trial reports regarding insight solution strategies. Main effects of word order and word frequency, and a two-way interaction, were found on the accuracy and self-rated difficulty. These findings shed light on creative association, multiply-constrained problem solving and constructing compounds. (shrink)

Cognitive Science, Volume 46, Issue 6, June 2022.

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two‐player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether similarity metrics derived from two large databases of human free association norms, the University of South Florida norms and the Small World of Words norms, and two distributional semantic models based on large language (...) corpora (word2vec and GloVe) predict performance in this game. Participant dyads were presented with 20‐item word boards with word pairs of varying relatedness. The speaker received a word pair from the board (e.g., exam‐algebra) and generated a one‐word semantic clue (e.g., math), which was used by the guesser to identify the word pair on the board across three attempts. Response times to generate the clue, as well as accuracy and latencies for the guessed word pair, were strongly predicted by the cosine similarity between word pairs and clues in random walk‐based associative models, and to a lesser degree by the distributional models, suggesting that conceptual representations activated during free association were better able to capture search and retrieval processes in the game. Further, the speaker adjusted subsequent clues based on the first attempt by the guesser, who in turn benefited from the adjustment in clues, suggesting a cooperative influence in the game that was effectively captured by both associative and distributional models. These results indicate that both associative and distributional models can capture relatively unconstrained search processes in a cooperative game setting, and Connector is particularly suited to examine communication and semantic search processes. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 54-77, January 2022.

Search can be found in almost every cognitive activity, ranging across vision, memory retrieval, problem solving, decision making, foraging, and social interaction. Because of its ubiquity, research on search has a tendency to fragment into multiple areas of cognitive science. The proposed topic aims at providing integrative discussion of the central role of search from multiple perspectives. We focus on controlled search processes, which require a goal, uncertainty about the nature, location, or acquisition method of the objects to be searched (...) for, and a method for sampling through the search environment. While this definition of search is general and applicable to different domains, the specific mechanisms in the search process will likely differ. The goal of this issue is to compare and contrast how these search processes are similar and differ in different cognitive activities, with the goal of understanding the general nature of search in terms of the three characteristics stated above. We expect that given its cross-domain nature, the topic on search will be of broad interest to cognitive scientists, including psychologists, behavioral ecologists, computer scientists, neuroscientists, linguists, and sociologists. (shrink)

Semantic memory encompasses one's knowledge about the world. Distributional semantic models, which construct vector spaces with embedded words, are a proposed framework for understanding the representational structure of human semantic knowledge. Unlike some classic semantic models, distributional semantic models lack a mechanism for specifying the properties of concepts, which raises questions regarding their utility for a general theory of semantic knowledge. Here, we develop a computational model of a binary semantic classification task, in which participants judged target words for the (...) referent's size or animacy. We created a family of models, evaluating multiple distributional semantic models, and mechanisms for performing the classification. The most successful model constructed two composite representations for each extreme of the decision axis (e.g., one averaging together representations of characteristically big things and another of characteristically small things). Next, the target item was compared to each composite representation, allowing the model to classify more than 1,500 words with human‐range performance and to predict response times. We propose that when making a decision on a binary semantic classification task, humans use task prompts to retrieve instances representative of the extremes on that semantic dimension and compare the probe to those instances. This proposal is consistent with the principles of the instance theory of semantic memory. (shrink)