Transmission of information concerning concepts through positive and negative instances.

Hovland, Carl I.; Weiss, Walter

doi:10.1037/h0062351

Cited by 281 publications

(168 citation statements)

References 7 publications

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“…Apart from tautological and self-contradictory concepts, all concepts divide the universe of possibilities into instances and non-instances of the concept. Parity is the notion that concepts should be easier to learn when the instances comprise the smaller set -an idea which originates from the finding that it is easier to learn concepts from their instances than from their non-instances (Hovland & Weiss, 1953;Hunt, 1962). Conjunction and disjunction have minimal descriptions of the same length given that they are both primitives in the given vocabulary, but parity predicts that conjunction (one instance) should be easier than disjunction (three instances).…”

Section: Concept Feldman's Heuristicmentioning

confidence: 99%

Mental models of Boolean concepts

Goodwin

Johnson‐Laird

2011

Cognitive Psychology

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a b s t r a c tNegation, conjunction, and disjunction are major building blocks in the formation of concepts. This article presents a new model-based theory of these Boolean components. It predicts that individuals simplify the models of instances of concepts. Evidence corroborates the theory and challenges alternative accounts, such as those based on minimal descriptions, algebraic complexity, or structural invariance. A computer program implementing the theory yields more accurate predictions than these rival accounts. Two experiments showed that the numbers of models of a Boolean concept predict the difficulty of formulating a description of it. As mental models may also underlie deductive reasoning, the present theory integrates two hitherto separate areas of investigation.

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Section: Concept Feldman's Heuristicmentioning

confidence: 99%

Mental models of Boolean concepts

Goodwin

Johnson‐Laird

2011

Cognitive Psychology

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“…Human learners are also susceptible to ordering effects (Bruner, Goodnow, & Austin, 1956;Garner & Whitman, 1965;Hovland & Weiss, 1953;Goldstone, 1996;Medin & Bettger, 1994). Ordering effects primarily arise from SUSTAIN's other principles (e.g., different item orderings lead to different pattern of feedback which affects the inferred category structure).…”

Section: Principles Of Sustainmentioning

confidence: 99%

SUSTAIN: A Network Model of Category Learning.

Love

Medin²,

Gureckis

2004

Psychological Review

696

880

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SUSTAIN (Supervised and Unsupervised STratified Adaptive IncrementalNetwork) is a model of how humans learn categories from examples. SUS-TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. Importantly, SUSTAIN's discovery of category substructure is affected not only by the structure of the world, but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts where identification learning is faster than classification learning.

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“…The only empirical investigation that we managed to find which directly and specifically tested the feature-positive effect in hypothesis evaluation is Christensen-Szalansky and Bushyhead's 1981 study on medical diagnosis in a real clinical setting: "This study also examined the physicians' ability to estimate the predictive value of an "absent symptom", since the absence of a symptom also can be helpful in assigning a diagnosis. Past psychological research has suggested that people do not efficiently process the "absence of cues" (Bourne & Guy, 1968;Hovland & Weiss, 1953;Nahinsky & Slaymaker, 1970)." (Christensen-Szalansky & Bushyhead, 1981, p. 931; the studies that the authors mentioned concern feature-positive effects in rule and concept learning, but not in hypothesis evaluation).…”

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confidence: 99%

Missing the dog that failed to bark in the nighttime: on the overestimation of occurrences over non-occurrences in hypothesis testing

Cherubini

Rusconi

Russo

et al. 2012

Psychological Research

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In three studies, we investigated whether and to what extent the evaluation of two mutually exclusive hypotheses is affected by a feature-positive effect, wherein present clues are weighted more than absent clues. Participants (N = 126) were presented with abstract problems concerning the most likely provenance of a card that was drawn from one of two decks. We factored the correct response (the hypothesis favored by the consideration of all clues) and the ratio of present-to-absent features in each set of observations. Furthermore, across the studies, we manipulated the presentation format of the features' probabilities by providing the probability distributions of occurrences (Study 1), non-occurrences (Study 3) or both (Study 2). In all studies, both participant preference and accuracy were mostly determined by an over-reliance on present features. Moreover, across participants, both confidence in the responses and the informativeness of the present clues correlated positively with the number of responses given in line with an exclusive consideration of present features. These results were mostly independent of both the rarity of the absent clues and the presentation format. We concluded that the feature-positive effect influences hypothesis evaluation, and we discussed the implications for confirmation bias.

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Transmission of information concerning concepts through positive and negative instances.

Cited by 281 publications

References 7 publications

Mental models of Boolean concepts

Mental models of Boolean concepts

SUSTAIN: A Network Model of Category Learning.

Missing the dog that failed to bark in the nighttime: on the overestimation of occurrences over non-occurrences in hypothesis testing

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