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1. Car expertise does not compete with face expertise during ensemble coding

   https://doi.org/10.3758/s13414-020-02188-8  

   Sun, Jisoo; Gauthier, Isabel (January 2020, Attention, Perception, & Psychophysics)

   null (Ed.)

   When objects from two categories of expertise (e.g., faces and cars in dual car/face experts) are processed simultaneously, competition occurs across a variety of tasks. Here, we investigate whether competition between face and car processing also occurs during ensemble coding. The relationship between single object recognition and ensemble coding is debated, but if ensemble coding relies on the same ability as object recognition, we expect cars to interfere with ensemble coding of faces as a function of car expertise. We measured the ability to judge the variability in identity of arrays of faces, in the presence of task irrelevant distractors (cars or novel objects). On each trial, participants viewed two sequential arrays containing four faces and four distractors, judging which array was the more diverse in terms of face identity. We measured participants’ car expertise, object recognition ability, and face recognition ability. Using Bayesian statistics, we found evidence against competition as a function of car expertise during ensemble coding of faces. Face recognition ability predicted ensemble judgments for faces, regardless of the category of task-irrelevant distractors. The result suggests that ensemble coding is not susceptible to competition between different domains of similar expertise, unlike single-object recognition. 

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2. Knowledge clustering and the acquisition of creative expertise

   https://doi.org/10.1016/j.plrev.2023.08.004  

   Loui, Psyche; Margulis, Elizabeth Hellmuth (December 2023, Physics of Life Reviews)
3. Transdisciplinary Philosophy of Science: Meeting the Challenge of Indigenous Expertise

   https://doi.org/10.1017/psa.2023.127  

   Ludwig, David; El-Hani, Charbel N; Gatti, Fabio; Kendig, Catherine; Kramm, Matthias; Neco, Lucia; Delgado, Abigail Nieves; Poliseli, Luana; Renck, Vitor; Ressiore_C, Adriana; et al (January 2024, Philosophy of Science)

   Abstract Transdisciplinary research knits together knowledge from diverse epistemic communities in addressing social-environmental challenges, such as biodiversity loss, climate crises, food insecurity, and public health. This article reflects on the roles of philosophy of science in transdisciplinary research while focusing on Indigenous and other subjugated forms of knowledge. We offer a critical assessment of demarcationist approaches in philosophy of science and outline a constructive alternative of transdisciplinary philosophy of science. While a focus on demarcation obscures the complex relations between epistemic communities, transdisciplinary philosophy of science provides resources for meeting epistemic and political challenges of collaborative knowledge production. 

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4. New genres and obsolete expertise in the new textiles economy

   https://doi.org/10.1080/17530350.2022.2087719  

   Lynch, Caitrin; Coppola, Adam; Holmes, Andrew; Rosner, Margaret (September 2022, Journal of Cultural Economy)

   This paper examines the role of distinctions between genres in conflicts over expertise in an American textile mill. Riverway is a struggling 150-year-old New England textile mill where members of a diverse workforce focus on producing high quality fabric in a context that demands daily adaptive expertise. Based on fieldwork from 2015–19, we examine how Riverway workers interacted with documentation systems at the mill. They engaged with managers and coworkers around new forms of technology and shifting valuations about skill and expertise, and, in the process, made sense of their roles as workers in a declining industry where their skills and forms of expertise were nearly obsolete. Through analysis of conflicts in the mill, we examine how members of the mill's community of practice made sense of a changing genre ecology of tools for communicating, recording information, communicating asynchronously, and asserting expertise. We show how technologies for making fabric and communicating about fabric production became symbols of valuations tangentially linked to production. These technologies became lightning rods for debates about expertise, skill, and knowledge, and for everyday distinctions that workers and managers made about the value of their work during a time of economic change marked by mill closures. 

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5. Visual Expertise Explains Image Inversion Effects

   Gahl, Martha; Kulkarni, Shubham; Pathak, Nikhil; Russell, Alex; Cottrell, Garrison W (November 2023, Proceedings of Machine Learning Research Volume 243)

   Fumero, Marco; Rodolà, Emanuele; Domine, Clementine; Locatello, Francesco; Dziugaite, Gintare Karolina; Caron, Mathilde (Ed.)

   We present an anatomically-inspired neurocomputational model, including a foveated retina and the log-polar mapping from the visual field to the primary visual cortex, that recreates image inversion effects long seen in psychophysical studies. We show that visual expertise, the ability to discriminate between subordinate-level categories, changes the performance of the model on inverted images. We first explore face discrimination, which, in humans, relies on configural information. The log-polar transform disrupts configural information in an inverted image and leaves featural information relatively unaffected. We suggest this is responsible for the degradation of performance with inverted faces. We then recreate the effect with other subordinate-level category discriminators and show that the inversion effect arises as a result of visual expertise, where configural information becomes relevant as more identities are learned at the subordinate-level. Our model matches the classic result: faces suffer more from inversion than mono-oriented objects, which are more disrupted than non-mono-oriented objects when objects are only familiar at a basic-level, and simultaneously shows that expert-level discrimination of other subordinate-level categories respond similarly to inversion as face experts. 

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