Folkbiology of freshwater fish

Medin, Douglas L.; Roß, Norbert; Atran, Scott; Cox, Douglas; Coley, John D.; Proffitt, Julia Beth; Blok, Sergey V.

doi:10.1016/j.cognition.2003.12.005

Cited by 129 publications

(143 citation statements)

References 28 publications

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“…Finally, Ross & Murphy showed that both taxonomic and situation-based category labels prime retrieval of category members, and that both kinds of categories can guide inferences in inductive reasoning. Similar results have been obtained by researchers investigating the domains of biology (Shafto & Coley, 2003;Boster & Johnson, 1989;Medin et al, 2005;Proffitt, Coley, & Medin, 2000) and person categorization (Nelson & Miller, 1995;Smith, Fazio, & Cejka, 1996;Zarate & Smith, 1990), and in research with children (Nguyen & Murphy, 2003;Nguyen, 2007). Together, these results provide a compelling demonstration that people spontaneously produce, consistently agree about, and reason based on multiple systems of categories.…”

Section: Empirical Evidence For Cross-cutting Systems Of Categoriessupporting

confidence: 80%

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A probabilistic model of cross-categorization

et al. 2011

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Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that crosscategorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models' predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities.People explain different aspects of everyday objects in different ways. For example, steak is high in iron because it is a meat; however, it is often served with wine because it is a dinner food. The different ways of thinking about steak underscore different ways of thinking about the domain of foods: as a system of taxonomic categories including meats and vegetables, or as a system of

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Section: Empirical Evidence For Cross-cutting Systems Of Categoriessupporting

confidence: 80%

“…Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976;Ross & Murphy, 1999;Medin et al, 2005), or understanding category learning using artificially constructed stimulus sets (e.g. Shepard et al, 1961).…”

Section: Empirical Evidence For Cross-cutting Systems Of Categoriesmentioning

confidence: 99%

A probabilistic model of cross-categorization

et al. 2011

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“…Medin et al, 2006). For example, Medin, Lynch, Coley, and Atran (1997) showed that, while some experts were very similar in their approaches to the task (namely, parks maintenance personnel vs. scientific taxonomists in categorizing trees) other experts differed in their approach to the same task (namely, landscapers vs. scientific taxonomists).…”

Section: Expertise Differences In Perceiving and Interpreting Complexmentioning

confidence: 99%

“…For example, Medin, Lynch, Coley, and Atran (1997) showed that, while some experts were very similar in their approaches to the task (namely, parks maintenance personnel vs. scientific taxonomists in categorizing trees) other experts differed in their approach to the same task (namely, landscapers vs. scientific taxonomists). Moreover, Medin et al (2006) showed in another study that even in well-structured domains, like the categorization of freshwater fish, expertise did not lead to common conceptualizations. The authors concluded that even if the outcome of a categorization process is similar across experts the underlying processes are not necessarily so.…”

Section: Expertise Differences In Perceiving and Interpreting Complexmentioning

confidence: 99%

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In the eyes of the beholder: How experts and novices interpret dynamic stimuli

Jarodzka

Scheiter

Gerjets

et al. 2010

Learning and Instruction

320

190

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Jarodzka, H., Scheiter, K., Gerjets, P., & Van Gog, T. (2010). In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction, 20, 146-154. doi:10.1016/j.learninstruc.2009.02.019Tasks with a complex, dynamic visual component require not only the acquisition of conceptual/procedural but also of perceptual/attentional skills. This study examined expertise differences in perceiving and interpreting complex, dynamic visual stimuli on a performance and on a process level, including perceptual and conceptual strategies. Performance, eye movement, and verbal report data were obtained from seven experts and 14 novices. Results show that experts compared to novices attend more to relevant aspects of the stimulus, use more heterogeneous task approaches, and use knowledge-based shortcuts. Implications for instructional design for the acquisition of perceptual skills are discussed

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Cognitive Studies in Ethnobiology: What Can We Learn About the Mind as Well as Human Environmental Interaction?

Roß

Revilla‐Minaya

2011

Ethnobiology

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Folkbiology of freshwater fish

Cited by 129 publications

References 28 publications

A probabilistic model of cross-categorization

A probabilistic model of cross-categorization

In the eyes of the beholder: How experts and novices interpret dynamic stimuli

Cognitive Studies in Ethnobiology: What Can We Learn About the Mind as Well as Human Environmental Interaction?

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