When learning to partition the world into categories, peoplerely on a set of assumptions (overhypotheses) about possi-ble category structures. Here we propose that the nature ofthese overhypotheses depends on the presence of a verbal la-bel associated with a given category. We describe a computa-tional model that demonstrates how labels can either acceler-ate or hinder category learning, depending on whether or notthe prior beliefs imposed by their presence align with the truecategory structure. This account provides an explanation forthe phenomena described in prior experimental work (Lupyan,Rakison, & McClelland, 2007; Brojde, Porter, & Colunga,2011) that have remained unexplained by other models. Basedon these results, we argue that the overhypothesis theory of la-bel effects provides a way to formalize and quantify the effectof language on category learning and to develop a more precisedelineation between linguistic and non-linguistic thought.

How does listeners’ perceptual bias influence their interpreta-tion of an ambiguous multiword utterance? We address thisquestion by investigating the relationship between word or-der in a binomial (an expression of type “A and B”) and vi-sual properties of image pairs serving as its potential referents.We found that listeners’ choices were strongly influenced byiconicity and relative salience of images within the pair: par-ticipants preferred referents where the first mentioned imagewas located on the left, as well as pairs where the first im-age was larger than the second image. The effect of image or-der tended to be stronger than the influence of image size, andboth were modulated by participants’ general visual field pref-erences (determined in a separate experimental condition). Wefurther show that binomial phrase interpretation can be simu-lated by a Rational Speech Act model that includes both wordorder effects and utterance-independent preferences of the par-ticipants.

One well-known prediction of linguistic relativity theories is the effect of a noun's grammatical gender on its semantics; for instance, ”key” is feminine in Spanish but masculine in German and thus might be associated with feminine traits for Spanish speakers but with masculine traits for German speakers. Experimental and corpus evidence for these effects has been mixed. In this work, we considered a distributional semantics account of putative grammatical gender effects on semantics and tested its predictions in Spanish, German, and English (control). In Part 1, we hypothesized that grammatical gender of concrete nouns affects the similarity of noun embeddings to embeddings of adjectives semantically associated with men or with women. We found support for this hypothesis in fastText embeddings, showing that nouns with the same meaning but with opposite genders in Spanish and German show opposite attraction effects both for words “man” and “woman” and for adjectives associated with men and women, although the effect size was weaker for German than for Spanish. BERT embeddings also showed consistent effects for Spanish but mixed results for German, suggesting possible variation across languages. In Part 2, we asked whether people systematically choose adjectives associated with women/men for grammatically feminine/masculine nouns, respectively. In a noun-adjective matching experiment (432 participants total), we found predicted grammatical gender effects for Spanish but not for German. Cosine similarity between the noun and the adjectives in fastText embeddings significantly predicted trial-level responses in all 3 languages; however, Spanish showed an additional effect of grammatical gender, indicating that participant noun-adjective associations are not fully explained by distributional semantics.

In rare cases of severe and intractable epilepsy, cerebral hemispherectomy is performed to arrest seizure activity and improve quality of life. The remaining hemisphere is often capable of supporting many cognitive functions post-surgery, although the outcome depends on the underlying etiology, hemisphere removed, and age of resection. The mechanisms underlying this massive reorganization are at present unknown. Here we examined intrinsic functional connectivity of putative language brain networks in four children after left cerebral hemispherectomy using resting-state functional magnetic resonance imaging (rsfMRI). We compared these functional systems to intrinsic language networks in 15 neurotypical controls using region-of-interest (ROI)-based functional connectivity analyses. In three out of four hemispherectomy patients, the ROI placed in the right inferior gyrus revealed a functional network that strongly resembled the right-hemisphere intrinsic language network observed in controls. This network typically comprised inferior frontal gyrus, superior temporal sulcus, and premotor regions. Quantitative ROI-to-ROI analyses revealed that functional connectivity between major nodes of the language network was significantly altered in all 4 examined patients. Overall, our data demonstrate that the pattern of functional connectivity within language networks is at least partially preserved in the intact right hemisphere of patients who underwent left hemispherectomy.

Large language models (LLMs) like OpenAI's GPT-4 (OpenAI et al., 2023), or Google's PaLM (Chowdhery et al., 2022) generate text responses to user-generated text prompts. In contrast to work that evaluates the extent to which model-generated text coheres with linguistic rules (i.e., formal competence) (Chomsky et al., 2023; Piantadosi, 2023), the present symposium discusses the work of cognitive scientists aimed at assessing the extent and manner in which LLMs show effective understanding, reasoning and decision making, capacities associated with human higher cognition (i.e., functional competence) (Binz & Schulz, 2023; Mahowald et al., 2023; Webb et al., 2023). Given both their expertise and their interest in clarifying the nature of human thinking, cognitive scientists are in a unique position both to carefully evaluate LLMs' capacity for thought (Bhatia, 2023; Han et al., 2024; Mitchell, 2023) and to benefit from them as methodological and theoretical tools. This symposium will thus be of interest not only to cognitive scientists concerned with machine intelligence, but also to those looking to incorporate advances in artificial intelligence with their study of human intelligence.

Exercise provides a robust physiological stimulus that evokes cross-talk among multiple tissues that when repeated regularly (i.e., training) improves physiological capacity, benefits numerous organ systems, and decreases the risk for premature mortality. However, a gap remains in identifying the detailed molecular signals induced by exercise that benefits health and prevents disease. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this gap and generate a molecular map of exercise. Preclinical and clinical studies will examine the systemic effects of endurance and resistance exercise across a range of ages and fitness levels by molecular probing of multiple tissues before and after acute and chronic exercise. From this multi-omic and bioinformatic analysis, a molecular map of exercise will be established. Altogether, MoTrPAC will provide a public database that is expected to enhance our understanding of the health benefits of exercise and to provide insight into how physical activity mitigates disease.

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood^1-3. Here, the Molecular Transducers of Physical Activity Consortium⁴ profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository ( https://motrpac-data.org/ ).