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Hauptman, M.

Publications and source records attributed to Hauptman, M..

5 recordsLinked to original sources

Expertise modifies neural responses to causal inference during language comprehension

Linguistic communication relies on shared common ground, yet different individuals in a community have specialized semantic knowledge. As a key example, physicians acquire rich knowledge about the causes of illness. An open question is whether such "causal expertise" influences the neural basis of language comprehension. Here we tested the hypothesis that when understanding everyday language like, "Holly used a grimy faucet during her overseas plane ride. Now she has the stomach flu," medical experts retrieve richer representations in semantic networks of the brain. Physicians (n=16) and age- and education-matched controls (n=16) undergoing fMRI read two-sentence vignettes that elicited implicit causal inferences about illness or object mechanics as well as control vignettes that did not elicit causal inferences. Vignettes were linguistically matched across conditions and were accessible to experts and non-experts alike. Across both groups, causal vignettes about illness engaged a temporoparietal semantic network previously implicated in thinking about animates, i.e., people and animals (precuneus and left temporoparietal junction), whereas causal vignettes about object mechanics engaged a semantic network associated with places and objects. Critically, physicians' responses in the animacy network were more robust and self-similar, suggesting the activation of richer semantic knowledge. Medical expertise effects were specific to biological inferences rather than mechanical or mental state inferences. By contrast, individually localized frontotemporal language and frontoparietal reasoning networks did not respond preferentially to causally connected sentences or show effects of expertise. Our findings suggest that causal expertise alters neural responses to causal inferences that unfold automatically during language comprehension.

neuroscience↗

Accelerating Insight Discovery in Large Biomedical Text with Scalable Processing Framework

Large language models are increasingly being used by dermatology professionals to support diagnostic investigation, patient education, and medical research. While these models can help manage information overload and improve efficiency, concerns persist regarding their accuracy and potential reliance on dubious sources. We introduce Quanta, a hybrid system that combines large language models with established evaluation metrics, such as cosine similarity, to enable efficient summarization and interpretation of curated research corpora. This methodology ensures that synthesized insights remain domain-specific and contextually relevant, thereby supporting clinicians and researchers in navigating the expanding digital landscape of dermatology literature. Deployed within an interactive chatbot, the tool delivers direct answers to user queries, provides cross-publication insights, and can suggest new directions for research. Comparative evaluations on benchmark datasets demonstrate improvements in accuracy, efficiency, and computational cost, with the curated document approach enhancing reliability and reducing misinformation risk.

bioinformatics↗

Inferring illness causes recruits the animacy semantic network

Inferring the causes of illness is a culturally universal example of causal thinking. We tested the hypothesis that implicit causal inferences about biological processes (e.g., illness) depend on the animacy semantic network. Participants (n=20) undergoing fMRI read two-sentence vignettes that elicited causal inferences across sentences, either about the emergence of illness or about the mechanical breakdown of inanimate objects, in addition to noncausal control vignettes. All vignettes were about people and were linguistically matched. The same participants performed localizer tasks: language, logical reasoning, and mentalizing. Inferring illness causes, relative to all control conditions, selectively engaged a portion of the precuneus (PC) previously implicated in the semantic representation of animates (e.g., people, animals). Neural responses to causal inferences about illness were adjacent to but distinct from responses to mental state inferences, suggesting a neural mind/body distinction. We failed to find evidence for domain-general responses to causal inference. Implicit causal inferences are supported by content-specific semantic networks that encode causal knowledge.

neuroscience↗

Neural specialization for 'visual' concepts emerges in the absence of vision

Vision provides a key source of information about many concepts, including living things (e.g., tiger) and visual events (e.g., sparkle). According to a prominent theoretical framework, neural specialization for different conceptual categories is shaped by sensory features, e.g., living things are neurally dissociable from navigable places because living things concepts depend more on visual features. We tested this framework by comparing the neural basis of visual concepts across sighted (n=22) and congenitally blind (n=21) adults. Participants judged the similarity of words varying in their reliance on vision while undergoing fMRI. We compared neural responses to living things nouns (birds, mammals) and place nouns (natural, manmade). In addition, we compared visual event verbs (e.g., sparkle) to non-visual events (sound emission, hand motion, mouth motion). People born blind exhibited distinctive univariate and multivariate responses to living things in a temporo-parietal semantic network activated by nouns, including the precuneus (PC). To our knowledge, this is the first demonstration that neural selectivity for living things does not require vision. We additionally observed preserved neural signatures of visual light events in the left middle temporal gyrus (LMTG+). Across a wide range of semantic types, neural representations of sensory concepts develop independent of sensory experience. Significance StatementVision offers a key source of information about major conceptual categories, including animals and light emission events. Comparing neural signatures of concepts in congenitally blind and sighted people tests the contribution of visual experience to conceptual representation. Sighted and congenitally blind participants heard visual nouns (e.g., tiger) and verbs (e.g., sparkle), as well as less visual nouns (e.g., barn) and verbs (e.g., squeak) while undergoing fMRI. Contrary to previous claims, both univariate and multivariate approaches reveal similar representations of animals and light emission verbs across groups. Across a broad range of semantic types, visual concepts develop independent of visual experience. These results challenge theories that emphasize the role of sensory information in conceptual representation.

neuroscience↗

Non-literal language processing is jointly supported by the language and Theory of Mind networks: Evidence from a novel meta-analytic fMRI approach

Going beyond the literal meaning of utterances is key to communicative success. However, the mechanisms that support non-literal inferences remain debated. Using a novel meta-analytic approach, we evaluate the contribution of linguistic, social-cognitive, and executive mechanisms to non-literal interpretation. We identified 74 fMRI experiments (n=1,430 participants) from 2001-2021 that contrasted non-literal language comprehension with a literal control condition, spanning ten phenomena (e.g., metaphor, irony, indirect speech). Applying the activation likelihood estimation approach to the 825 activation peaks yielded six left-lateralized clusters. We then evaluated the locations of both the individual-study peaks and the clusters against probabilistic functional atlases (cf. macroanatomy, as is typically done) for three candidate brain networks--the language-selective network (Fedorenko et al., 2011), which supports language processing, the Theory of Mind (ToM) network (Saxe & Kanwisher, 2003), which supports social inferences, and the domain-general Multiple-Demand (MD) network (Duncan, 2010), which supports executive control. These atlases were created by overlaying individual activation maps of participants who performed robust and extensively validated localizer tasks that target each network in question (n=806 for language; n=198 for ToM; n=691 for MD). We found that both the individual-study peaks and the ALE clusters fell primarily within the language network and the ToM network. These results suggest that non-literal processing is supported by both i) mechanisms that process literal linguistic meaning, and ii) mechanisms that support general social inference. They thus undermine a strong divide between literal and non-literal aspects of language and challenge the claim that non-literal processing requires additional executive resources.

neuroscience↗