Dynamics of rule induction by making queries: Transition between strategies

Ginzburg, Iris; Sejnowski, Terry

doi:10.1037/e722892011-029

Cited by 4 publications

(4 citation statements)

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“…In the 2-4-6 task, PTS leads participants to ignore alternative hypotheses about the rule, such as increasing numbers and hence positive testing is often treated as an obstacle to learning (Nickerson, 1998). Note, however, that there are conditions under which it can be a successful strategy (Ginzburg & Sejnowski, 1996;Klayman & Ha, 1989;Navarro & Perfors, 2011;Nelson, Tenenbaum, & Movellan, 2001;Oaksford & Chater, 1994).…”

Section: Confirmatory: Positive Test Strategymentioning

confidence: 94%

Strategies to intervene on causal systems are adaptively selected

Coenen

Rehder

Gureckis

2015

Cognitive Psychology

133

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Section: Confirmatory: Positive Test Strategymentioning

confidence: 94%

Strategies to intervene on causal systems are adaptively selected

Coenen

Rehder

Gureckis

2015

Cognitive Psychology

133

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“…The question of how humans make intuitive judgments about the "usefulness" of new observations has been the focus of extensive study in the hypothesis-testing literature (Ginzberg & Sejnowski, 1996;Klayman & Ha, 1987;Oaksford & Chater, 1994;Skov & Sherman, 1986;Wason, 1960). Early findings raised concerns about the human ability to identify the value of future information (e.g., the pervasive evidence of confirmation bias; Wason, 1960; see also Nickerson, 1998, for a review).…”

Section: The Source Of Hypothesis-dependent Sampling Biasesmentioning

confidence: 99%

Is it better to select or to receive? Learning via active and passive hypothesis testing.

Markant¹,

Gureckis²

2014

Journal of Experimental Psychology: General

134

182

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People can test hypotheses through either selection or reception. In a selection task, the learner actively chooses observations to test his or her beliefs, whereas in reception tasks data are passively encountered. People routinely use both forms of testing in everyday life, but the critical psychological differences between selection and reception learning remain poorly understood. One hypothesis is that selection learning improves learning performance by enhancing generic cognitive processes related to motivation, attention, and engagement. Alternatively, we suggest that differences between these 2 learning modes derives from a hypothesis-dependent sampling bias that is introduced when a person collects data to test his or her own individual hypothesis. Drawing on influential models of sequential hypothesis-testing behavior, we show that such a bias (a) can lead to the collection of data that facilitates learning compared with reception learning and (b) can be more effective than observing the selections of another person. We then report a novel experiment based on a popular category learning paradigm that compares reception and selection learning. We additionally compare selection learners to a set of "yoked" participants who viewed the exact same sequence of observations under reception conditions. The results revealed systematic differences in performance that depended on the learner's role in collecting information and the abstract structure of the problem.

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“…37–38). Since about 1980, however, several authors have suggested that OED principles provide a good account of human information acquisition (McKenzie, 2004; Nelson, 2005, 2008; Trope & Bassok, 1982), even on Wason’s original tasks (Ginzburg & Sejnowski, 1996; McKenzie, 2004; Nelson, Tenenbaum, & Movellan, 2001; Oaksford & Chater, 1994). OED principles have been used to design experiments on human memory (Cavagnaro, Myung, Pitt, & Kujala, 2010), to explain eye movements as perceptual experiments (Butko & Movellan, 2008; Nelson & Cottrell, 2007; Rehder & Hoffman, 2005), to control eye movements in oculomotor robots (Denzler & Brown, 2002), and to predict individual neurons’ responses (Nakamura, 2006).…”

Section: Statistical Models and Human Information Acquisitionmentioning

confidence: 99%

Experience Matters

et al. 2010

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Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information—information gain, Kullback-Liebler distance, probability gain (error minimization), and impact—are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning to convey environmental probabilities, found that probability gain explained subjects’ information search better than the other statistical theories or the probability-of-certainty heuristic. Experiments 1 and 2 found that subjects behaved differently when the standard method of verbally presented summary statistics (rather than experience-based learning) was used to convey environmental probabilities. Experiment 3 found that subjects’ preference for probability gain is robust, suggesting that the other models contribute little to subjects’ search behavior.

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Dynamics of rule induction by making queries: Transition between strategies

Cited by 4 publications

References 0 publications

Strategies to intervene on causal systems are adaptively selected

Strategies to intervene on causal systems are adaptively selected

Is it better to select or to receive? Learning via active and passive hypothesis testing.

Experience Matters

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