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1. Four dimensions characterize attributions from faces using a representative set of English trait words

   https://doi.org/10.1038/s41467-021-25500-y  

   Lin, Chujun; Keles, Umit; Adolphs, Ralph (December 2021, Nature Communications)

   null (Ed.)

   Abstract People readily (but often inaccurately) attribute traits to others based on faces. While the details of attributions depend on the language available to describe social traits, psychological theories argue that two or three dimensions (such as valence and dominance) summarize social trait attributions from faces. However, prior work has used only a small number of trait words (12 to 18), limiting conclusions to date. In two large-scale, preregistered studies we ask participants to rate 100 faces (obtained from existing face stimuli sets), using a list of 100 English trait words that we derived using deep neural network analysis of words that have been used by other participants in prior studies to describe faces. In study 1 we find that these attributions are best described by four psychological dimensions, which we interpret as “warmth”, “competence”, “femininity”, and “youth”. In study 2 we partially reproduce these four dimensions using the same stimuli among additional participant raters from multiple regions around the world, in both aggregated and individual-level data. These results provide a comprehensive characterization of trait attributions from faces, although we note our conclusions are limited by the scope of our study (in particular we note only white faces and English trait words were included). 

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2. Children use polysemy to structure new word meanings

   Srinivasan, Mahesh; Berner, Catherine; Rabagliati, Hugh (April 2018, Journal of experimental psychology. General)

   It is well-known that children rapidly learn words, following a range of heuristics. What is less well appreciated is that – because most words are polysemous and have multiple meanings (e.g., ‘glass’ can label a material and drinking vessel) – children will often be learning a new meaning for a known word, rather than an entirely new word. Across four experiments we show that children flexibly adapt a well-known heuristic – the shape bias – when learning polysemous words. Consistent with previous studies, we find that children and adults preferentially extend a new object label to other objects of the same shape. But we also find that when a new word for an object (‘a gup’) has previously been used to label the material composing that object (‘some gup’), children and adults override the shape bias, and are more likely to extend the object label by material (Experiments 1 and 3). Further, we find that, just as an older meaning of a polysemous word constrains interpretations of a new word meaning, encountering a new word meaning leads learners to update their interpretations of an older meaning (Experiment 2). Finally, we find that these effects only arise when learners can perceive that a word’s meanings are related, not when they are arbitrarily paired (Experiment 4). Together, these findings show that children can exploit cues from polysemy to infer how new word meanings should be extended, suggesting that polysemy may facilitate word learning and invite children to construe categories in new ways. 

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3. Visual resemblance and interaction history jointly constrain pictorial meaning

   https://doi.org/10.1038/s41467-023-37737-w  

   Hawkins, Robert D.; Sano, Megumi; Goodman, Noah D.; Fan, Judith E. (April 2023, Nature Communications)

   Abstract How do drawings—ranging from detailed illustrations to schematic diagrams—reliably convey meaning? Do viewers understand drawings based on how strongly they resemble an entity (i.e., as images) or based on socially mediated conventions (i.e., as symbols)? Here we evaluate a cognitive account of pictorial meaning in which visual and social information jointly support visual communication. Pairs of participants used drawings to repeatedly communicate the identity of a target object among multiple distractor objects. We manipulated social cues across three experiments and a full replication, finding that participants developed object-specific and interaction-specific strategies for communicating more efficiently over time, beyond what task practice or a resemblance-based account alone could explain. Leveraging model-based image analyses and crowdsourced annotations, we further determined that drawings did not drift toward “arbitrariness,” as predicted by a pure convention-based account, but preserved visually diagnostic features. Taken together, these findings advance psychological theories of how successful graphical conventions emerge. 

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4. Observing gesture at learning enhances subsequent phonological and semantic processing of L2 words: An N400 study

   https://doi.org/10.1016/j.bandl.2023.105327  

   Morett, Laura M (November 2023, Brain and Language)

   This study employed the N400 event-related potential (ERP) to investigate how observing different types of gestures at learning affects the subsequent processing of L2 Mandarin words differing in lexical tone by L1 English speakers. The effects of pitch gestures conveying lexical tones (e.g., upwards diagonal movements for rising tone), semantic gestures conveying word meanings (e.g., waving goodbye for to wave), and no gesture were compared. In a lexical tone discrimination task, larger N400s for Mandarin target words mismatching vs. matching Mandarin prime words in lexical tone were observed for words learned with pitch gesture. In a meaning discrimination task, larger N400s for English target words mismatching vs. matching Mandarin prime words in meaning were observed for words learned with pitch and semantic gesture. These findings provide the first neural evidence that observing gestures during L2 word learning enhances subsequent phonological and semantic processing of learned L2 words. 

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5. Avenues into Integration: Communicating taxonomic intelligence from sender to recipient

   https://doi.org/10.3897/biss.4.59006  

   Sterner, Beckett; Upham, Nathan; Sen, Atriya; Franz, Nico (October 2020, Biodiversity Information Science and Standards)

   null (Ed.)

   “What is crucial for your ability to communicate with me… pivots on the recipient’s capacity to interpret—to make good inferential sense of the meanings that the declarer is able to send” (Rescher 2000, p148). Conventional approaches to reconciling taxonomic information in biodiversity databases have been based on string matching for unique taxonomic name combinations (Kindt 2020, Norman et al. 2020). However, in their original context, these names pertain to specific usages or taxonomic concepts, which can subsequently vary for the same name as applied by different authors. Name-based synonym matching is a helpful first step (Guala 2016, Correia et al. 2018), but may still leave considerable ambiguity regarding proper usage (Fig. 1). Therefore, developing "taxonomic intelligence" is the bioinformatic challenge to adequately represent, and subsequently propagate, this complex name/usage interaction across trusted biodiversity data networks. How do we ensure that senders and recipients of biodiversity data not only can share messages but do so with “good inferential sense” of their respective meanings? Key obstacles have involved dealing with the complexity of taxonomic name/usage modifications through time, both in terms of accounting for and digitally representing the long histories of taxonomic change in most lineages. An important critique of proposals to use name-to-usage relationships for data aggregation has been the difficulty of scaling them up to reach comprehensive coverage, in contrast to name-based global taxonomic hierarchies (Bisby 2011). The Linnaean system of nomenclature has some unfortunate design limitations in this regard, in that taxonomic names are not unique identifiers, their meanings may change over time, and the names as a string of characters do not encode their proper usage, i.e., the name “Genus species” does not specify a source defining how to use the name correctly (Remsen 2016, Sterner and Franz 2017). In practice, many people provide taxonomic names in their datasets or publications but not a source specifying a usage. The information needed to map the relationships between names and usages in taxonomic monographs or revisions is typically not presented it in a machine-readable format. New approaches are making progress on these obstacles. Theoretical advances in the representation of taxonomic intelligence have made it increasingly possible to implement efficient querying and reasoning methods on name-usage relationships (Chen et al. 2014, Chawuthai et al. 2016, Franz et al. 2015). Perhaps most importantly, growing efforts to produce name-usage mappings on a medium scale by data providers and taxonomic authorities suggest an all-or-nothing approach is not required. Multiple high-profile biodiversity databases have implemented internal tools for explicitly tracking conflicting or dynamic taxonomic classifications, including eBird using concept relationships from AviBase (Lepage et al. 2014); NatureServe in its Biotics database; iNaturalist using its taxon framework (Loarie 2020); and the UNITE database for fungi (Nilsson et al. 2019). Other ongoing projects incorporating taxonomic intelligence include the Flora of Alaska (Flora of Alaska 2020), the Mammal Diversity Database (Mammal Diversity Database 2020) and PollardBase for butterfly population monitoring (Campbell et al. 2020). 

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