Category learning from equivalence constraints

Hammer, Rubi; Hertz, Tomer; Hochstein, Shaul; Weinshall, Daphna

doi:10.1007/s10339-008-0243-x

Cited by 19 publications

(26 citation statements)

References 43 publications

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“…It is possible that the same basic mechanisms that underlie categorization are also used in playing the Set game. If this is the case, the bias that we found toward lower class sets may reflect the tendency for real-world categories to be organized around similarities, rather than around differences (Ashby & Maddox, 2005;Hammer et al, 2005Hammer et al, , 2007, in press; Medin et al, 1987; dimension); number of existing sets in the display, with RT acting according to the horse race model, implying independence of simultaneous searches; and the MAV and its group size, which was searched preferentially, also confirming the preference for similarity.…”

Section: Discussionmentioning

confidence: 99%

“…larity. It was found that the learning of categories is easier and more natural when one learns from exemplar pairs that belong to the same category than when one learns from pairs that belong to different categories (Hammer, Hertz, Hochstein, & Weinshall, 2005, 2007), even when the pairs are preselected to contain the same amount of information. In addition, children are even more biased toward learning from same-class pairs (Hammer, Diesendruck, Weinshall, & Hochstein, 2008).…”

Section: Most Abundant Valuementioning

confidence: 99%

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Set recognition as a window to perceptual and cognitive processes

Jacob

Hochstein

2008

Perception & Psychophysics

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Section: Discussionmentioning

confidence: 99%

Section: Most Abundant Valuementioning

confidence: 99%

Set recognition as a window to perceptual and cognitive processes

Jacob

Hochstein

2008

Perception & Psychophysics

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“…An exception is found in the recent work by Hammer et al (2009b), who compare the efficacy of what they call positive and negative equivalency constraints (PEC and NEC, respectively) during category learning. At least for binary valued features (ears present or absent, nose long or short, skin beige or purple) they show that PECs should be more useful because any given pair of (different) items from the same category rules out one or more features as irrelevant for classification.…”

Section: Comparison Context and Category Learningmentioning

confidence: 91%

Category learning in the context of co-presented items

2010

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A series of four studies explore how the presentation of multiple items on each trial of a categorization task affects the course of category learning. In a three-category supervised classification task involving multi-dimensionally varying artificial organism-like stimuli, learners are shown a target plus two context items on every trial, with the context items' category membership explicitly identified. These triads vary in whether one, two, or all three categories are represented. This presentation context can support within-category comparison and/or between-category contrast. The most successful learning occurs when all categories are represented in each trial. This pattern occurs across two different underlying category structures and across variations in learners' prior knowledge of the relationship between the target and context items. These results appear to contrast with some other recent findings and make clear the potential importance of context-based inter-item evaluation in human category learning, which has implications for psychological theory and for real-world learning environments.

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“…On the other hand, providing them with directions for the use of NECs dramatically improves performance, whereas the efficacy of using PECs is unchanged by the provision of similar directions. For further details concerning the experimental design and human findings reviewed here, see [9].…”

Section: Fig 1 Examples Of Positive Equivalence Constraints (Pecs -mentioning

confidence: 99%

“…We note that generally, labels indicate the relation between the training instances, telling the classifier whether different instances are from the same or different categories: Elements with the same label provide Positive Equivalence Constraints (PECs), and elements with different labels provide Negative Equivalence Constraints (NECs). Nevertheless, equivalence constraints can be provided without the use of labels [9,14]. In fact, it is not hard to think of many indirect contextual clues that may indicate the categorical relation between two or more exemplars.…”

Section: Introductionmentioning

confidence: 99%

Classification with Positive and Negative Equivalence Constraints: Theory, Computation and Human Experiments

Hammer

Hertz

Hochstein

et al.

Lecture Notes in Computer Science

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Abstract.We tested the efficiency of category learning when participants are provided only with pairs of objects, known to belong either to the same class (Positive Equivalence Constraints or PECs) or to different classes (Negative Equivalence Constraints or NECs). Our results in a series of cognitive experiments show dramatic differences in the usability of these two information building blocks, even when they are chosen to contain the same amount of information. Specifically, PECs seem to be used intuitively and quite efficiently, while people are rarely able to gain much information from NECs (unless they are specifically directed for the best way of using them). Tests with a constrained EM clustering algorithm under similar conditions also show superior performance with PECs. We conclude with a theoretical analysis, showing (by analogy to graph cut problems) that the satisfaction of NECs is computationally intractable, whereas the satisfaction of PECs is straightforward. Furthermore, we show that PECs convey more information than NECs by relating their information content to the number of different graph colorings. These inherent differences between PECs and NECs may explain why people readily use PECs, while many of them need specific directions to be able to use NECs effectively.

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Category learning from equivalence constraints

Cited by 19 publications

References 43 publications

Set recognition as a window to perceptual and cognitive processes

Set recognition as a window to perceptual and cognitive processes

Category learning in the context of co-presented items

Classification with Positive and Negative Equivalence Constraints: Theory, Computation and Human Experiments

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