Toward a Unified Theory of Decision Criterion Learning in Perceptual Categorization.
Optimal decision criterion placement maximizes expected reward and requires sensitivity to the category base rates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When base rates are unequal, human decision criterion is nearly optimal, but when payoffs are...
| Publicado en: | Journal of the Experimental Analysis of Behavior Vol. 78; no. 3; pp. 567 - 596 |
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| Formato: | Artículo |
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Society for the Experimental Analysis of Behavior
November 2002
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=513156553&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513156553 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00225002 JOE jtl: Journal of the Experimental Analysis of Behavior issn: 00225002 maglogo: N pubinfo: dt: November 2002 vid: 78 iid: 3 pid: 508 pub: Society for the Experimental Analysis of Behavior artinfo: ui: 513156553 10.1901/jeab.2002.78-567 ppf: 567 ppct: 29 formats: tig: atl: Toward a Unified Theory of Decision Criterion Learning in Perceptual Categorization. aug: au: Maddox, W. Todd su: Signal detection (Psychology) Concept learning Categorization (Psychology) sug: subj: Signal detection (Psychology) Concept learning Categorization (Psychology) ab: Optimal decision criterion placement maximizes expected reward and requires sensitivity to the category base rates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When base rates are unequal, human decision criterion is nearly optimal, but when payoffs are unequal, suboptimal decision criterion placement is observed, even when the optimal decision criterion is identical in both cases. A series of studies are reviewed that examine the generality of this finding, and a unified theory of decision criterion learning is described (Maddox & Dodd, 2001). The theory assumes that two critical mechanisms operate in decision criterion learning. One mechanism involves competition between reward and accuracy maximization: The observer attempts to maximize reward, as instructed, but also places some importance on accuracy maximization. The second mechanism involves a flat-maxima hypothesis that assumes that the observer's estimate of the reward-maximizing decision criterion is determined from the steepness of the objective reward function that relates expected reward to decision criterion placement. Experiments used to develop and test the theory require each observer to complete a large number of trials and to participate in all conditions of the experiment. This provides maximal control over the reinforcement history of the observer and allows a focus on individual behavioral profiles. The theory is applied to decision criterion learning problems that examine category discriminability, payoff matrix multiplication and addition effects, the optimal classifier's independence assumption, and different types of trial-by-trial feedback. In every case the theory provides a good account of the data, and, most important, provides useful insights into the psychological processes involved in decision criterion learning. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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