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This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive science. Building from an existing human-generated dataset, we show that machine-verified norms capture aspects of conceptual structure beyond what is expressed in human norms alone, and better explain human judgments of semantic similarity amongst items that are distally related. The results suggest that LLMs can greatly enhance traditional methods of semantic feature norm verification, with implications for our understanding of conceptual representation in humans and machines., To appear as a Tiny Paper at ICLR 2023

Neural network models of language have long been used as a tool for developing hypotheses about conceptual representation in the mind and brain. For many years, such use involved extracting vector-space representations of words and using distances among these to predict or understand human behavior in various semantic tasks. In contemporary language AIs, however, it is possible to interrogate the latent structure of conceptual representations using methods nearly identical to those commonly used with human participants. The current work uses two common techniques borrowed from cognitive psychology to estimate and compare lexical-semantic structure in both humans and a well-known AI, the DaVinci variant of GPT-3. In humans, we show that conceptual structure is robust to differences in culture, language, and method of estimation. Structures estimated from AI behavior, while individually fairly consistent with those estimated from human behavior, depend much more upon the particular task used to generate behavior responses--responses generated by the very same model in the two tasks yield estimates of conceptual structure that cohere less with one another than do human structure estimates. The results suggest one important way that knowledge inhering in contemporary AIs can differ from human cognition.

Cognitive models of learning often propose that belief formation is rational: new experience is combined with prior belief to support statistically optimal inferences. Why then are false beliefs so ubiquitous and difficult to correct? Prior work using a novel category-learning paradigm suggests that false beliefs may arise from the way that learners, when presented with contradictory information, choose to weight different information sources. When at least one source provides evidence highly consistent with the learner’s current belief, other sources are strongly discounted and little belief change is observed; but when evidence from the most agreeable source is more discrepant with the learner, other sources receive proportionally more weight and effect larger belief change. The current work assesses whether a similar model explains patterns of belief change, not in a novel lab-based task, but for propositional real-world beliefs held by participants prior to coming into the lab. In a paradigm analogous to the previous category-learning study, participants known to hold a polarized opinion on a controversial topic read arguments varying in agreement and/or opposition to that opinion. The discrepancy between the participant’s existing belief and that expressed by the arguments was experimentally varied. The pattern of belief change was remarkably similar to that observed in the novel category-learning task, suggesting that a similar underlying model can explain belief elasticity even for existing real-world beliefs.

Understanding how an individual changes its attitude, belief, and opinion due to other people's social influences is vital because of its wide implications. A core methodology that is used to study the change of attitude under social influences is agent-based model (ABM). The goal of this review paper is to compare and contrast existing ABMs, which I classify into two families, the deductive ABMs and the inductive ABMs. The former subsumes social simulation studies, and the latter involves human experiments. To facilitate the comparison between ABMs of different formulations, I propose a general unified formulation, in which all ABMs can be viewed as special cases. In addition, I show the connections between deductive ABMs and inductive ABMs, and point out their strengths and limitations. At the end of the paper, I identify underexplored areas and suggest future research directions., Comment: 57 pages

Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human labor. Large language models (LLMs) offer a novel avenue for the automatic generation of such feature lists, but are prone to significant error. Here, we present a new method for combining a learned model of human lexical-semantics from limited data with LLM-generated data to efficiently generate high-quality feature norms., Comment: To be published in the ICLR TinyPaper track

How are our conceptual representations organized, and how does this semantic structure differ in individuals or groups? We included 36 faces from the Chicago Face Database (Ma et al., 2015) varying in race, gender, and facial expression in a triplet judgement task, where participants were showed a target face and asked which of two other faces is most similar to the target face expression. Each of our 71 participants completed 1,012 triplet judgements. Using their judgements, we fit individualized embeddings capturing distances between faces in semantic space, resulting in 71 embeddings. We calculated the Procrustes similarity between all pairs of embeddings and clustered similarities, revealing four clusters of respondents. Each cluster organized faces differently, indicating group-wide differences in semantic organization of expression. At the individual level, we find that each participant’s responses for held-out items are better predicted by their own embedding than by other participants’ embeddings, p < .00001.

We employ a reverse-engineering approach to illuminate the neurocomputational building blocks that combine to support controlled semantic cognition: the storage and context-appropriate use of conceptual knowledge. By systematically varying the structure of a computational model and assessing the functional consequences, we identified the architectural properties that best promote some core functions of the semantic system. Semantic cognition presents a challenging test case, as the brain must achieve two seemingly contradictory functions: abstracting context-invariant conceptual representations across time and modalities, while producing specific context-sensitive behaviours appropriate for the immediate task. These functions were best achieved in models possessing a single, deep multimodal hub with sparse connections from modality-specific regions, and control systems acting on peripheral rather than deep network layers. The reverse-engineered model provides a unifying account of core findings in the cognitive neuroscience of controlled semantic cognition, including evidence from anatomy, neuropsychology and functional brain imaging. By varying the presence of different building blocks in a computational model, Jackson et al. reverse-engineer the architecture for controlled semantic cognition and test this model against evidence from anatomy, neuropsychology and functional imaging.

Drawing is a powerful cognitive technology for creating external representations of thought. While drawings have long provided inspiration to researchers in many areas of cognitive science, including psychology, machine learning, and neuroscience, these communities have not generally had opportunities to interact and share insights. The goal of this workshop is to bring together perspectives from multiple disciplines to explore the question of how humans use drawings to communicate knowledge, catalyzing new opportunities for multidisciplinary collaboration. We are introducing a novel ``flipped'' format wherein we will hold three virtual seminars in the weeks leading up to CogSci 2022, each highlighting insights from Machine Learning, Neuroscience, and Developmental Science, respectively. Holding these thematic seminars in advance will enable us to attract a broader audience for our event and focus on promoting informal interaction among workshop attendees at the on-site event.

To investigate how basic aspects of perception are shaped by acquired knowledge about the world, we assessed colour perception and cognition in patients with semantic dementia (SD), a disorder that progressively erodes conceptual knowledge. We observed a previously undocumented pattern of impairment to colour perception and cognition characterized by: (i) a normal ability to discriminate between only subtly different colours but an impaired ability to group different colours into categories, (ii) normal perception and memory for the colours red, green, and blue but impaired perception and memory for colours lying between these regions of a fully-saturated and luminant spectrum, and (iii) normal naming of polar colours in the opponent-process colour system (red, green, blue, yellow, white, and black) but impaired naming of other basic colours (brown, gray, pink, and orange). The results suggest that fundamental aspects of perception can be shaped by acquired knowledge about the world, but only within limits., Highlights • Study of colour perception and cognition in patients with semantic dementia. • Colour discrimination is normal but categorization is impaired. • Patients and controls show opposing accuracy patterns in memory and perception. • Naming is more impaired for secondary than primary colours.

We present a case-series comparison of patients with cross-modal semantic impairments consequent on either (a) bilateral anterior temporal lobe atrophy in semantic dementia (SD) or (b) left-hemisphere fronto-parietal and/or posterior temporal stroke in semantic aphasia (SA). Both groups were assessed on a new test battery designed to measure how performance is influenced by concept familiarity, typicality and specificity. In line with previous findings, performance in SD was strongly modulated by all of these factors, with better performance for more familiar items (regardless of typicality), for more typical items (regardless of familiarity) and for tasks that did not require very specific classification, consistent with the gradual degradation of conceptual knowledge in SD. The SA group showed significant impairments on all tasks but their sensitivity to familiarity, typicality and specificity was more variable and governed by task-specific effects of these factors on controlled semantic processing. The results are discussed with reference to theories about the complementary roles of representation and manipulation of semantic knowledge., Highlights • Case-series comparison of semantic dementia (SD) and semantic aphasia (SA). • Effects of concept familiarity, typicality and specificity across four tasks. • SD performance was better for familiar/typical items and less specific classification. • In SA, effects of these factors varied across tasks reflecting the control demands. • Data are consistent with interacting semantic representations and control processes.

Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials.

Funder: European Research Council; FundRef: http://dx.doi.org/10.13039/501100000781; Grant(s): GAP: 502670428 - BRAIN2MIND_NEUROCOMP, How does the human brain encode semantic information about objects? This paper reconciles two seemingly contradictory views. The first proposes that local neural populations independently encode semantic features; the second, that semantic representations arise as a dynamic distributed code that changes radically with stimulus processing. Combining simulations with a well-known neural network model of semantic memory, multivariate pattern classification, and human electrocorticography, we find that both views are partially correct: information about the animacy of a depicted stimulus is distributed across ventral temporal cortex in a dynamic code possessing feature-like elements posteriorly but with elements that change rapidly and nonlinearly in anterior regions. This pattern is consistent with the view that anterior temporal lobes serve as a deep cross-modal 'hub' in an interactive semantic network, and more generally suggests that tertiary association cortices may adopt dynamic distributed codes difficult to detect with common brain imaging methods.