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TOWARD A UNIFIED THEORY OF DECISION CRITERION LEARNING IN PERCEPTUAL CATEGORIZATION
 JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR
, 2002
"... 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 ..."
Abstract

Cited by 26 (12 self)
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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 flatmaxima hypothesis that assumes that the observer’s estimate of the rewardmaximizing 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 trialbytrial 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.
On the generality of optimal versus objective classifier feedback effects on decision criterion learning in perceptual categorization
 Memory & Cognition
"... Biased category payoff matrices engender separate reward and accuracymaximizing decision criteria. Although instructed to maximize reward, observers use suboptimal decision criteria that place greater emphasis on accuracy than is optimal. This study compared objective classifier feedback (the obj ..."
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Cited by 7 (1 self)
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Biased category payoff matrices engender separate reward and accuracymaximizing decision criteria. Although instructed to maximize reward, observers use suboptimal decision criteria that place greater emphasis on accuracy than is optimal. This study compared objective classifier feedback (the objectively correct response) with optimal classifier feedback (the optimal classifier’s response) at two levels of category discriminability when zero or negative costs accompanied incorrect responses for two payoff matrix multiplication factors. Performance was superior for optimal classifier feedback relative to objective classifier feedback for both zero and negative cost conditions, especially when category discriminability was low, but the magnitude of the optimal classifier advantage was approximately equal for zero and negative cost conditions. The optimal classifier feedback performance advantage did not interact with the payoff matrix multiplication factor. Modelbased analyses suggested that the weight placed on
Classification of Exemplars with Single and MultipleFeature Manifestations: The Effects of . . .
 JOURNAL OF EXPERIMENTAL PSYCHOLOGY: LEARNING, MEMORY, AND COGNITION
, 2003
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A Theoretical Framework for Understanding the Effects of Simultaneous BaseRate and Payoff . . .
, 2003
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Linear Transformations of the Payoff Matrix and Decision Criterion Learning in Perceptual Categorization
 J EXP PSYCHOL LEARN MEM COGN
, 2003
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Information search with situationspecific reward functions
 Judgment and Decision Making
, 2012
"... The goal of obtaining information to improve classification accuracy can strongly conflict with the goal of obtaining information for improving payoffs. Two environments with such a conflict were identified through computer optimization. Three subsequent experiments investigated people’s search beha ..."
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Cited by 2 (2 self)
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The goal of obtaining information to improve classification accuracy can strongly conflict with the goal of obtaining information for improving payoffs. Two environments with such a conflict were identified through computer optimization. Three subsequent experiments investigated people’s search behavior in these environments. Experiments 1 and 2 used a multiplecue probabilistic categorylearning task to convey environmental probabilities. In a subsequent search task subjects could query only a single feature before making a classification decision. The crucial manipulation concerned the searchtask reward structure. The payoffs corresponded either to accuracy, with equal rewards associated with the
A test of the optimal classifier's independence . . .
 PERCEPTION & PSYCHOPHYSICS
, 2003
"... this article are based on the decision boundmodel in Equation 5. Specifically, each model includes one "noise" parameter that represents the sum of perceptual and criterial noise (Ashby, 1992a; Maddox& Ashby, 1993). Each model assumes that the observer has accurate knowledge of the category structur ..."
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this article are based on the decision boundmodel in Equation 5. Specifically, each model includes one "noise" parameter that represents the sum of perceptual and criterial noise (Ashby, 1992a; Maddox& Ashby, 1993). Each model assumes that the observer has accurate knowledge of the category structures [i.e., l o (x pi )]. To ensure that this was a reasonable assumption, each observer completed a number of baseline trials and was required to meet a stringent performance criterion (see Method section). Finally,each model allows for suboptimal decision criterion placement where the decision criterion is determined from the flatmaxima hypothesis, the COBRA hypothesis, or both, following Equation 6. To determine whether the flatmaxima and COBRA hypothesesare important in accountingfor each observer's data, we developed four models. Each model makes different assumptions about the k r and w values used. The nested structure of the models is represented in Figure 5, with each arrow pointing to a more general model and Figure 4. Decision criterion [ln( b )] predicted from the flatmaxima hypothesisplotted against the decision criterion [ln( b )] predicted from the independence assumption of the optimal classifier for the six simultaneous baserate/payoff conditions. (A) 2:1B/2:1P condition. (B) 3:1B/3:1P condition
Matching, Maximizing and the Independence Assumption 1 Probability Matching, Accuracy Maximization, and a Test of the Optimal Classifier’s Independence Assumption in Perceptual Categorization
, 2003
"... Accepted for publication in Perception & Psychophysics Observers completed perceptual categorization tasks that included 25 baserate/payoff conditions constructed from the factorial combination of 5 baserate ratios (1:3, 1:2, 1:1, 2:1, and 3:1) with 5 payoff ratios (1:3, 1:2, 1:1, 2:1, and 3:1). T ..."
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Accepted for publication in Perception & Psychophysics Observers completed perceptual categorization tasks that included 25 baserate/payoff conditions constructed from the factorial combination of 5 baserate ratios (1:3, 1:2, 1:1, 2:1, and 3:1) with 5 payoff ratios (1:3, 1:2, 1:1, 2:1, and 3:1). This large database allowed an initial comparison of the competition between reward and accuracy maximization (COBRA) hypothesis with a competition between reward maximization and probability matching (COBRM) hypothesis, and an extensive and critical comparison of the flatmaxima hypothesis with the independence assumption of the optimal classifier. Modelbased instantiations of the COBRA and COBRM hypotheses provided good accounts of the data, but there was a consistent advantage for the COBRM instantiation early in learning, and the COBRA instantiation later in learning. This pattern held in the current study, and in a reanalysis of Bohil and Maddox (in press). Strong support was obtained for the flatmaxima hypothesis over the independence assumption, especially as the observers gained experience with the task. Model parameters indicated that observers ’ rewardmaximizing decision criterion rapidly approaches the optimal value, and that more weight is placed on accuracy maximization in separate baserate/payoff conditions than in simultaneous baserate/payoff conditions. The superiority of the flatmaxima hypothesis suggests that violations of the independence assumption are to be expected, and are well captured by the flatmaxima hypothesis without requiring any additional assumptions.