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As mental representations are standardly thought to underlie all cognitive processes, a major goal of cognitive science has been to uncover representations. Methods for representation learning from behavioral data often model choice or reaction time data alone, but not jointly, leaving out potentially useful information. Here we develop two models of choice and RT in the odd-one-out task, including one based on the Linear Ballistic Accumulator. Parameter recovery simulations show joint modeling of choice and RT with LBA recovers representations more accurately than modeling choice alone with softmax. However, on two empirical datasets of images and words, joint models performed no better than choice-only models, despite a significant correlation of reaction time with two measures of similarity and choice difficulty in both datasets. We speculate on reasons for the unrealized promise of joint modeling of RT and choice in representation learning.

We introduce a new empirical paradigm for studying naturalistic active learning, as well as new computational tools for jointly modeling algorithmic and rational theories of information search. Subjects in our task can ask questions and learn about hundreds of everyday items, but must retrieve queried items from memory. In order to maximize information gain, subjects need to retrieve sequences of dissimilar items. We find that subjects are not able to do this. Instead, associative memory mechanisms lead to the successive retrieval of similar items, an established memory effect known as semantic congruence. The extent of semantic congruence (and thus suboptimality) is unaffected by task instructions and incentives, though subjects are able to identify efficient query sequences when given a choice. Overall, our results indicate that subjects can distinguish between optimal and suboptimal search if explicitly asked to do so, but have difficulty implementing optimal search from memory. We conclude that associative memory processes place critical restrictions on people’s ability to ask good questions in naturalistic active learning tasks.

We present a general framework for building formal models of naturalistic memory-based decision making. Our frame-work implements established theories of memory search and decision making within a single integrated cognitive system,and uses computational language models to quantify the thoughts over which memory and decision processes operate. Itcan thus describe both the content of the information that is sampled from memory, as well as the processes involved inretrieving and evaluating this information in order to make a decision. The models within our framework can be fit torecall and choice data, and can quantitatively predict choice probability, length of deliberation, retrieved thoughts, andthe effects of decision context. We showcase the power and generality of our framework by applying it to study riskperception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice,and judgments about well-being, society, and culture.

Despite its centrality to cognition, similarity is expensive tomeasure, spurring development of techniques like the SpatialArrangement Method (SpAM), wherein participants placeitems on a 2-dimensional plane such that proximity reflectssimilarity. While SpAM hastens similarity measurement, itssuitability for higher-dimensional stimuli is unknown. InStudy 1, we collected SpAM data for eight differentcategories composed of 20-30 words each. Participant-aggregated SpAM distances correlated strongly (r=.71) withpairwise similarity judgments, although below SpAM andpairwise judgment split-half reliabilities (r’s>.9), and cross-validation with multidimensional scaling fits at increasingdimensionalities suggested that aggregated SpAM datafavored higher dimensional solutions for 7 of the 8 categories.In study 2, we showed that SpAM can recover the Big Fivefactor space of personality traits, and that cross-validationfavors a four- or five-dimension solution on this dataset. Weconclude that SpAM is an accurate and reliable method ofmeasuring similarity for high-dimensional items.

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations with seven established similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation-metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category-specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity.

The complex judgments we make about the innumerable ob-jects in the world are made on the basis of our representa-tion of those objects. Thus a model of judgment should spec-ify (a) our representation of the many objects in the world,and (b) how we use this knowledge for making judgments.Here we show that word embeddings, vector representationsfor words derived from statistics of word use in corpora, proxythis knowledge, and that accurate models of judgment can betrained by regressing human judgment ratings (e.g., femininityof traits) directly on word embeddings. This method achieveshigher out-of-sample accuracy than a vector similarity-basedbaseline and compares favorably to human inter-rater relia-bility. Word embeddings can also identify the concepts mostassociated with observed judgments, and can thus shed lighton the psychological substrates of judgment. Overall, we pro-vide new methods and insights for predicting and understand-ing high-level human judgment.

Optimality in active learning is under intense debate in numerous disciplines. We introduce a new empirical paradigm for studying naturalistic active learning, as well as new computational tools for jointly modeling algorithmic and rational theories of information search. Participants in our task can ask questions and learn about hundreds of everyday items but must retrieve queried items from memory. To maximize information gain, participants need to retrieve sequences of dissimilar items. In eight experiments (N= 795), we find that participants are unable to do this. Instead, associative memory mechanisms lead to the successive retrieval of similar items, an established memory effect known as semantic congruence. The extent of semantic congruence (and thus suboptimality in question asking) is unaffected by task instructions and incentives, though participants can identify efficient query sequences when given a choice between query sequences. Overall, our results indicate that participants can distinguish between optimal and suboptimal search if explicitly asked to do so, but have difficulty implementing optimal search from memory. We conclude that associative memory processes may place critical restrictions on people's ability to ask good questions in naturalistic active learning tasks. [ABSTRACT FROM AUTHOR]

We present a computational model capable of simulating aspects of human knowledge for thousands of real-world concepts. Our approach involves a pretrained transformer network that is further fine-tuned on large data sets of participant-generated feature norms. We show that such a model can successfully extrapolate from its training data, and predict human knowledge for new concepts and features. We apply our model to stimuli from 25 previous experiments in semantic cognition research and show that it reproduces many findings on semantic verification, concept typicality, feature distribution, and semantic similarity. We also compare our model against several variants, and by doing so, establish the model properties that are necessary for good prediction. The success of our approach shows how a combination of language data and (laboratory-based) psychological data can be used to build models with rich world knowledge. Such models can be used in the service of new psychological applications, such as the modeling of naturalistic semantic verification and knowledge retrieval, as well as the modeling of real-world categorization, decision-making, and reasoning. [ABSTRACT FROM AUTHOR]

New languages emerge through interactions among people, yet the role of social network structure in language emergence is not clear, despite research from experimental semiotics, observational fieldwork, and computational modeling. To better understand the effects of social network structure on the formation of conventional referring expressions, we use a silent gesture paradigm that combines the methodological control of experimental semiotics and computational simulations with the naturalistic affordances of the human body, physical environment, and interpersonal communication. We elicited gestural referring expressions from hearing participants randomly assigned to either a richly- or sparsely-connected communicative network. Results demonstrate greater conventionalization among participants in the richly-connected condition, although this effect disappears after accounting for between-condition differences in overall number of communicative interactions. These results provide the first experimental demonstration that communicative network structure causally impacts the conventionalization of referring expressions in human participants, using a communicative modality in which human language naturally arises. [ABSTRACT FROM AUTHOR]