Feature highlighting enhances learning of a complex natural-science category.

Miyatsu, Toshiya; Gouravajhala, Reshma; Nosofsky, Robert M.; McDaniel, Mark A.

doi:10.1037/xlm0000538

Cited by 24 publications

(57 citation statements)

References 52 publications

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“…Considering the most effective methods of organising the training of air traffic controllers, he concludes that in many cases, teaching using visual aids is more effective than using verbal messages (Holt, 1964). Modern research shares the importance of visual images in learning (Miyatsu et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Role of Sensual Image in Scientific Generalisation

Rahmatullin

Semenova

Stoletov

et al. 2019

SACI

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The method of generalisation widely used in scientific research is a variant of the similar logical method. Generalisation in different scientific areas has its own specifics, but in fundamental knowledge, it allows to create general concepts. We explore generalisation in scientific knowledge, focusing on the peculiarities of the formation of general concepts and the role of sensory image in their functioning in scientific theory and the retranslation of scientific knowledge. Scientific, philosophical, religious, literary texts were studied and compared to determine the essence and characteristic features of the generalisation method. Subsequently, the information extracted from these texts was systematised using the methods of similarity and difference. The resulting material based on analogy served as the basis for the conclusion. The method of concomitant variations turned out to be necessary for establishing the dependence of scientific results on changes in research conditions, which was done on the example of the evolution of generalising images in the field of atomic physics. During generalisation, it is necessary to search for a successful image of a general concept, which is important for understanding its meaning. The image obtained as a result of generalisation of scientific knowledge is an essential semantic unit of a scientific retranslation. General concepts contain generalised images of classes of objects and thus configure the scientific picture of the world. They perform not only a representative but a communicative function in the process of reproducing of scientific knowledge both within science and in society. While scientific knowledge becomes more and more abstract, a sensual image makes it possible to detect relationships that are not accessible to a rational level of knowledge. It also facilitates the transferring of knowledge in learning.

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Section: Resultsmentioning

confidence: 99%

Role of Sensual Image in Scientific Generalisation

Rahmatullin

Semenova

Stoletov

et al. 2019

SACI

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“…Much other research has also pursued techniques for effectively teaching categories at a single level of a hierarchy. Such techniques include ones that manipulate the presentation sequences of training instances from contrasting categories (e.g., Carvalho & Goldstone, 2014; Eglington & Kang, 2017; Kornell & Bjork, 2008; Mathy & Feldman, 2016); the order in which hard versus easy instances are presented (e.g., Pashler & Mozer, 2013); the size of the sets of training instances and which specific training instances to use (e.g., Nosofsky et al, 2018, 2019; Wahlheim et al, 2012); and the use of explicit coaching such as visual highlighting of diagnostic features (Miyatsu, Gouravajhala, Nosofsky, & McDaniel, 2019). Combining these techniques with variants of the presently proposed two-stage response procedure might yield even better learning of hierarchically organized science categories.…”

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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“…Yet another approach that is emerging in the basic cognitive research is specifying the types of explicit Bcoaching^that can enhance category learning. One type of coaching involves providing learners with explicit information about characteristic features or rules that are diagnostic for each category (e.g., Miyatsu, Gouravajhala, Nosofsky, & McDaniel, 2018;Pashler & Lovelett, 2017). For instance, Miyatsu et al (2018) found that highlighting characteristic features of particular rock categories during training (by circling and describing specific features on the image of each training token) produced more accurate generalization to new instances than when learners were not provided such highlighting.…”

Section: Number and Variability Of Training Instancesmentioning

confidence: 99%

“…One type of coaching involves providing learners with explicit information about characteristic features or rules that are diagnostic for each category (e.g., Miyatsu, Gouravajhala, Nosofsky, & McDaniel, 2018;Pashler & Lovelett, 2017). For instance, Miyatsu et al (2018) found that highlighting characteristic features of particular rock categories during training (by circling and describing specific features on the image of each training token) produced more accurate generalization to new instances than when learners were not provided such highlighting. Likewise, Eglington and Kang (2017) found that, in crossexperiment comparisons, explicit highlighting of diagnostic features improved learning and generalization in the domain of chemistry categories.…”

Section: Number and Variability Of Training Instancesmentioning

confidence: 99%

Model-guided search for optimal natural-science-category training exemplars: A work in progress

et al. 2018

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Under the guidance of a formal exemplar model of categorization, we conduct comparisons of natural-science classification learning across four conditions in which the nature of the training examples is manipulated. The specific domain of inquiry is rock classification in the geologic sciences; the goal is to use the model to search for optimal training examples for teaching the rock categories. On the positive side, the model makes a number of successful predictions: Most notably, compared with conditions involving focused training on small sets of training examples, generalization to novel transfer items is significantly enhanced in a condition in which learners experience a broad swath of training examples from each category. Nevertheless, systematic departures from the model predictions are also observed. Further analyses lead us to the hypothesis that the high-dimensional feature-space representation derived for the rock stimuli (to which the exemplar model makes reference) systematically underestimates within-category similarities. We suggest that this limitation is likely to arise in numerous situations in which investigators attempt to build detailed feature-space representations for naturalistic categories. A low-parameter extended version of the model that adjusts for this limitation provides dramatically improved accounts of performance across the four conditions. We outline future steps for enhancing the current feature-space representation and continuing our goal of using formal psychological models to guide the search for effective methods of teaching science categories.

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Feature highlighting enhances learning of a complex natural-science category.

Cited by 24 publications

References 52 publications

Role of Sensual Image in Scientific Generalisation

Role of Sensual Image in Scientific Generalisation

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

Model-guided search for optimal natural-science-category training exemplars: A work in progress

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