Organized simultaneous displays facilitate learning of complex natural science categories

Meagher, Brian J.; Carvalho, Paulo F.; Goldstone, Robert L.; Nosofsky, Robert M.

doi:10.3758/s13423-017-1251-6

Cited by 14 publications

(12 citation statements)

References 20 publications

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“…Research suggests that the efficacy of a category-learning technique depends on which component of category similarity drives task difficulty (Carvalho & Goldstone, 2014a, b, 2015aEglington & Kang, 2017;Goldstone, Steyvers, & Rogosky, 2003;Hammer et al, 2008;Higgins & Ross, 2011;Higgins, 2017;Meagher, Carvalho, Goldstone, & Nosofsky, 2017;Zulkiply & Burt, 2013a; see Fig. 2).…”

Section: Moderator 1: Category Similaritymentioning

confidence: 99%

Visual category learning: Navigating the intersection of rules and similarity

Hughes

Thomas

2021

Psychon Bull Rev

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Visual categorization is fundamental to expertise in a wide variety of disparate domains, such as radiology, art history, and quality control. The pervasive need to master visual categories has served as the impetus for a vast body of research dedicated to exploring how to enhance the learning process. The literature is clear on one point: no category learning technique is always superior to another. In the present review, we discuss how two factors moderate the efficacy of learning techniques. The first, category similarity, refers to the degree of featural overlap of exemplars. The second moderator, category type, concerns whether the features that define category membership can be mastered through learning processes that are implicit/non-verbal (information-integration categories) or explicit/verbal (rule-based categories). The literature on each moderator has been conducted almost entirely in isolation, such that their potential interaction remains underexplored. We address this gap in the literature by reviewing empirical and theoretical evidence that these two moderators jointly influence the efficacy of learning techniques.

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Section: Moderator 1: Category Similaritymentioning

confidence: 99%

Visual category learning: Navigating the intersection of rules and similarity

Hughes

Thomas

2021

Psychon Bull Rev

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“…For this reason, trying to understand how people solve these problems has been one of the core programs of cognitive science for decades. Despite considerable theoretical progress, experimental validation has been limited largely to laboratory settings with artificial stimuli with simple representations (e.g., strings of binary digits, colored shapes; although for a recent exception see Meagher, Carvalho, Goldstone, & Nosofsky, 2017). Natural stimuli such as large sets of realistic images of animals will require a complex representation that may be difficult to easily interpret or manipulate in the lab.…”

Section: Introductionmentioning

confidence: 99%

Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations

2018

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Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying objects in natural images. These networks learn representations of real-world stimuli that can potentially be leveraged to capture psychological representations. We find that state-of-the-art object classification networks provide surprisingly accurate predictions of human similarity judgments for natural images, but they fail to capture some of the structure represented by people. We show that a simple transformation that corrects these discrepancies can be obtained through convex optimization. We use the resulting representations to predict the difficulty of learning novel categories of natural images. Our results extend the scope of psychological experiments and computational modeling by enabling tractable use of large natural stimulus sets.

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“…Much work remains for future investigations of the training techniques that might be most effective in teaching students to classify objects into hierarchically organized natural science categories. For example, Meagher, Carvalho, Goldstone, and Nosofsky (2017) found evidence that the use of organized, simultaneous visual displays provides a promising technique for conveying the hierarchical structure of rock categories. Much other research has also pursued techniques for effectively teaching categories at a single level of a hierarchy.…”

Section: Discussionmentioning

confidence: 99%

Learning hierarchically organized science categories: simultaneous instruction at the high and subtype levels

2019

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BackgroundMost science categories are hierarchically organized, with various high-level divisions comprising numerous subtypes. If we suppose that one’s goal is to teach students to classify at the high level, past research has provided mixed evidence about whether an effective strategy is to require simultaneous classification learning of the subtypes. This past research was limited, however, either because authentic science categories were not tested, or because the procedures did not allow participants to form strong associations between subtype-level and high-level category names. Here we investigate a two-stage response-training procedure in which participants provide both a high-level and subtype-level response on most trials, with feedback provided at both levels. The procedure is tested in experiments in which participants learn to classify large sets of rocks that are representative of those taught in geoscience classes.ResultsThe two-stage procedure yielded high-level classification performance that was as good as the performance of comparison groups who were trained solely at the high level. In addition, the two-stage group achieved far greater knowledge of the hierarchical structure of the categories than did the comparison controls.ConclusionIn settings in which students are tasked with learning high-level names for rock types that are commonly taught in geoscience classes, it is best for students to learn simultaneously at the high and subtype levels (using training techniques similar to the presently investigated one). Beyond providing insights into the nature of category learning and representation, these findings have practical significance for improving science education.

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Organized simultaneous displays facilitate learning of complex natural science categories

Cited by 14 publications

References 20 publications

Visual category learning: Navigating the intersection of rules and similarity

Visual category learning: Navigating the intersection of rules and similarity

Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations

Learning hierarchically organized science categories: simultaneous instruction at the high and subtype levels

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