Feature inference learning and eyetracking

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

doi:10.1016/j.jml.2008.12.001

Cited by 22 publications

(47 citation statements)

References 40 publications

(93 reference statements)

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“…One cannot easily predict which properties of objects will need to be inferred, and this uncertainty would likely lead to attention to many of the features of a category (see, e.g., Rehder et al, 2009). So, while anticipatory learning cannot explain the performance differences here, it may contribute to some learning in the real world.…”

Section: Classification and Inference Outside The Laboratorymentioning

confidence: 93%

“…These findings suggest that the label-feature distinction is a critical difference between these tasks, resulting in differences in goals and strategies when learning categories. Second, the anticipatory-learning account posits that inference learners will spread their attention to various feature dimensions because they anticipate being asked about those dimensions on future trials (Rehder et al, 2009). As a result, while classification learners focus only on learning a sufficient number of features that predict the label (because the label is the only thing that could be asked about on a classification trial), inference learners focus not just on features helping to infer the missing feature, but also on other features that may be asked about later.…”

Section: Classification Versus Inference: Explanationsmentioning

confidence: 99%

“…If so, then categorylearning theories based only on classification learning (almost all current theories) may not provide a strong foundation for a general understanding of category learning. Rehder, Colner, and Hoffman (2009) differentiated three explanations for the classification-inference performance differences (which range from inherent task differences to differences due to particulars of the paradigms): the category-centered learning hypothesis (e.g., Markman & Ross, 2003;Yamauchi & Markman, 1998), anticipatory learning (Rehder et al, 2009), and the set-of-rules model (Johansen & Kruschke, 2005). We describe these three explanations below and then propose how they may be tested.…”

Section: Classification Versus Inference: Explanationsmentioning

confidence: 99%

“…A. L. Anderson et al (2002) found that inference learners who only inferred two out of four features still learned more about the nonqueried features than did classification learners. However, Rehder et al (2009) showed that when participants are explicitly told they will only be asked about some particular features, their looking time to nonqueried features decreases drastically (although the Rehder et al features were not part of the usual integrated figures, but were spatially separated) Third, the set-of-rules model proposes that, because of the peculiarities of the studies, inference learners simply learn associations between the category label and each feature (Johansen & Kruschke, 2005). According to this account, the inference-classification performance differences can be explained by two differences in how category structures were implemented in previous studies.…”

Section: Classification Versus Inference: Explanationsmentioning

confidence: 99%

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Classification versus inference learning contrasted with real-world categories

Jones

Ross

2010

Mem Cogn

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Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories-what each category is like-while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.Keywords Categorization . Category learning . Classification learning . Inference learning Categories underlie a variety of cognitive tasks, including problem solving, classification, prediction, explanations, and inferences. Knowing the category an object belongs to enables access to categorical knowledge acquired from past experiences. We use category knowledge when retrieving the formula for solving permutation problems, classifying an approaching person as a soldier, or deciding whether to flee from an approaching Labrador retriever. One can argue that the importance of categories is not in knowing that the object in front of you is a chair or a table, but rather in using the category knowledge associated with the object to make inferences about it, such as whether to sit on it or place a glass on it (J. R. Anderson, 1991;Murphy, 2002). We learn categories in many ways, and it is likely that differences in processing during learning lead to differences in category knowledge (Markman & Ross, 2003). The goal of the present study is to examine whether two principal ways of learning categories lead to critical differences in category knowledge.Almost all empirical studies and extant theories have focused on one type of learning, classification learning, in which a learner determines what category an item belongs to. However, category learning is more than just classification, and it will be critical to understand these other ways of learning as well as the degree to which learners can flexibly use categories for a variety of tasks beyond classification. Classification versus inference learning: Paradigms and resultsTo better understand how different ways of learning affect what knowledge is acquired, some recent studies have compared classification learning to another major means of category learning, inference, where a learner predicts a missing feature of a classified item (e.g., A. L. Mem Cogn (2011) 39:764-777 DOI 10.3758/s13421-010-0058-8 Ross, 2004Yamauchi & Markman, 1998). ...

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Section: Classification and Inference Outside The Laboratorymentioning

confidence: 93%

Section: Classification Versus Inference: Explanationsmentioning

confidence: 99%

Section: Classification Versus Inference: Explanationsmentioning

confidence: 99%

Section: Classification Versus Inference: Explanationsmentioning

confidence: 99%

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Classification versus inference learning contrasted with real-world categories

Jones

Ross

2010

Mem Cogn

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“…In cognitive research, eyetracking has been proven to be an effective tool to study on-line attention (e.g., Ferreira & Clifton, 1986;Haider & Frensch, 1999;Just & Carpenter, 1984;Lee & Anderson, 2001;Rayner, 1998). In recent years, it has been successfully applied to studying selective attention in category learning in the absence of knowledge (Blair et al, 2009a;Blair, Watson, Walshe, & Maj, 2009b;Rehder & Hoffman, 2005a, 2005bRehder, Colner, & Hoffman, 2009;Watson & Blair, 2008). We now use eyetracking to study how attention is affected by prior knowledge.…”

Section: How Knowledge Might Affect Attentionmentioning

confidence: 99%

How prior knowledge affects selective attention during category learning: An eyetracking study

Kim

Rehder

2010

Mem Cogn

Self Cite

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Research has shown that category learning is affected by (a) attention, which selects which aspects of stimuli are available for further processing, and (b) the existing semantic knowledge that learners bring to the task. However, little is known about how knowledge affects what is attended. Using eyetracking, we found that (a) knowledge indeed changes what features are attended, with knowledge-relevant features being fixated more often than irrelevant ones, (b) this effect was not due to an initial attentional bias toward relevant dimensions but rather emerged gradually as a result of observing category members, and (c) this effect grew even after a learning criterion was reached, that is, despite the absence of negative feedback. We argue that models of knowledgebased learning will remain incomplete until they specify mechanisms that dynamically select prior knowledge in response to observed category members and which then directs attention to knowledge-relevant dimensions and away from irrelevant ones.

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Time limits in testing: An analysis of eye movements and visual attention in spatial problem solving

Roach

Fraser

Kryklywy

et al. 2017

Anatomical Sciences Ed

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Individuals with an aptitude for interpreting spatial information (high mental rotation ability: HMRA) typically master anatomy with more ease, and more quickly, than those with low mental rotation ability (LMRA). This article explores how visual attention differs with time limits on spatial reasoning tests. Participants were assorted to two groups based on their mental rotation ability scores and their eye movements were collected during these tests. Analysis of salience during testing revealed similarities between MRA groups in untimed conditions but significant differences between the groups in the timed one. Question-by-question analyses demonstrate that HMRA individuals were more consistent across the two timing conditions (κ = 0.25), than the LMRA (κ = 0.013). It is clear that the groups respond to time limits differently and their apprehension of images during spatial problem solving differs significantly. Without time restrictions, salience analysis suggests LMRA individuals attended to similar aspects of the images as HMRA and their test scores rose concomitantly. Under timed conditions however, LMRA diverge from HMRA attention patterns, adopting inflexible approaches to visual search and attaining lower test scores. With this in mind, anatomical educators may wish to revisit some evaluations and teaching approaches in their own practice. Although examinations need to evaluate understanding of anatomical relationships, the addition of time limits may induce an unforeseen interaction of spatial reasoning and anatomical knowledge. Anat Sci Educ 10: 528-537. © 2017 American Association of Anatomists.

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Feature inference learning and eyetracking

Cited by 22 publications

References 40 publications

Classification versus inference learning contrasted with real-world categories

Classification versus inference learning contrasted with real-world categories

How prior knowledge affects selective attention during category learning: An eyetracking study

Time limits in testing: An analysis of eye movements and visual attention in spatial problem solving

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