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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 ..."
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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.
Learning and Attention in Multidimensional Identification, and Categorization: Separating LowLevel Perceptual Processes and High Level Decisional Processes
, 2002
"... this article should be addressed to W. Todd Maddox, Department of Psychology, Mezes Hall 330 Mail Code B3800, University of Texas, Austin, Texas, 78712. Email: maddox@psy.utexas.edu ..."
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Cited by 21 (16 self)
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this article should be addressed to W. Todd Maddox, Department of Psychology, Mezes Hall 330 Mail Code B3800, University of Texas, Austin, Texas, 78712. Email: maddox@psy.utexas.edu
Informationintegration category learning in patients with striatal dysfunction
 Neuropsychology
, 2005
"... Informationintegration category learning was examined in patients with Parkinson’s disease (PD) and in healthy control participants in 2 different conditions. In the linear condition, optimal categorization required a nonverbalizable linear integration of information from the 2 stimulus dimensions, ..."
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Cited by 20 (8 self)
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Informationintegration category learning was examined in patients with Parkinson’s disease (PD) and in healthy control participants in 2 different conditions. In the linear condition, optimal categorization required a nonverbalizable linear integration of information from the 2 stimulus dimensions, whereas in the nonlinear condition, a nonlinear integration of information was required. Each participant completed 600 trials in each condition and was given corrective feedback following each trial. Results indicated that PD patients were not impaired in the linear condition across all trials, whereas the same patients were impaired in the nonlinear condition, but only later in training. The authors conducted modelbased analyses to identify participants who used an informationintegration approach, and a comparison of the accuracy rates of those individuals further revealed a specific deficit in informationintegration category learning in patients with PD. These findings suggest that the striatum may be particularly involved in informationintegration category learning when the rule is highly complex.
On the Relation Between Baserate and CostBenefit Learning in Simulated Medical Diagnosis
, 2001
"... Observers completed a series of simulated medical diagnosis tasks that differed in category discriminability and baserate/costbenefit ratio. Point, accuracy, and decision criterion estimates were closer to optimal (a) for category d' = 2.2 than for category d' = 1.0 or 3.2, (b) when baserates, as ..."
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Cited by 17 (13 self)
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Observers completed a series of simulated medical diagnosis tasks that differed in category discriminability and baserate/costbenefit ratio. Point, accuracy, and decision criterion estimates were closer to optimal (a) for category d' = 2.2 than for category d' = 1.0 or 3.2, (b) when baserates, as opposed to costbenefits were manipulated, and (c) when the cost of an incorrect response resulted in no point loss (nonnegative cost) as opposed to a point loss (negative cost). These results support the "flatmaxima" (von Winterfeldt & Edwards, 1982) and COmpetition Between Reward and Accuracy (COBRA; Maddox & Bohil, 1998a) hypotheses. A hybrid model that instantiated simultaneously both hypotheses was applied to the data. The model parameters indicated that (a) the rewardmaximizing decision criterion quickly approached the optimal criterion, (b) the importance placed on accuracy maximization early in learning was larger when the cost of an incorrect response was negative as opposed to nonnegative, and (c) by the end of training the importance placed on accuracy was equal for negative and nonnegative costs.
The effects of aging on the speed–accuracy compromise: Boundary optimality in the diffusion model. Psychology and Aging
, 2010
"... We evaluated agerelated differences in the optimality of decision boundary settings in a diffusion model analysis. In the model, the width of the decision boundary represents the amount of evidence that must accumulate in favor of a response alternative before a decision is made. Wide boundaries le ..."
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Cited by 13 (4 self)
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We evaluated agerelated differences in the optimality of decision boundary settings in a diffusion model analysis. In the model, the width of the decision boundary represents the amount of evidence that must accumulate in favor of a response alternative before a decision is made. Wide boundaries lead to slow but accurate responding, and narrow boundaries lead to fast but inaccurate responding. There is a single value of boundary separation that produces the most correct answers in a given period of time, and we refer to this value as the reward rate optimal boundary (RROB). We consistently found across a variety of decision tasks that older adults used boundaries that were much wider than the RROB value. Young adults used boundaries that were closer to the RROB value, although age differences in optimality were smaller with instructions emphasizing speed than with instructions emphasizing accuracy. Young adults adjusted their boundary settings to more closely approach the RROB value when they were provided with accuracy feedback and extensive practice. Older participants showed no evidence of making boundary adjustments in response to feedback or task practice, and they consistently used boundary separation values that produced accuracy levels that were near asymptote. Our results suggest that young adults attempt to balance speed and accuracy to achieve the most correct answers per unit time, whereas older adultts attempt to minimize errors even if they must respond quite slowly to do so.
Category discriminability, baserate, and payoff effects in perceptual organization
 Perception & Psychophysics
, 2001
"... (i.e., d ¢ level), base rates, and payoffs was examined. Baserate and payoff manipulations across two category discriminabilities allowed a test of the hypothesis that the steepness of the objective reward function affects performance (i.e., the flatmaxima hypothesis), as well as the hypothesis th ..."
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Cited by 10 (7 self)
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(i.e., d ¢ level), base rates, and payoffs was examined. Baserate and payoff manipulations across two category discriminabilities allowed a test of the hypothesis that the steepness of the objective reward function affects performance (i.e., the flatmaxima hypothesis), as well as the hypothesis that observers combine baserate and payoff information independently. Performance was (1) closer to optimal for the steeper objective reward function, in line with the flatmaxima hypothesis, (2) closer to optimal in baserate conditions than in payoff conditions, and (3) in partial support of the hypothesis that baserate and payoff knowledge is combined independently. Implications for current theories of baserate and payoff learning are discussed.
Feedback effects on cost–benefit learning in perceptual categorization
 Memory & Cognition
, 2001
"... Two experiments were conducted in which the effects of different feedback displays on decision criterion learning were examined in a perceptual categorization task with unequal cost–benefits. In Experiment 1, immediate versus delayed feedback was combined factorially with objective versus optimal cl ..."
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Cited by 8 (5 self)
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Two experiments were conducted in which the effects of different feedback displays on decision criterion learning were examined in a perceptual categorization task with unequal cost–benefits. In Experiment 1, immediate versus delayed feedback was combined factorially with objective versus optimal classifier feedback. Immediate versus delayed feedback had no effect. Performance improved significantly over blocks with optimal classifier feedback and remained relatively stable with objective feedback. Experiment 2 used a withinsubjects design that allowed a test of modelbased instantiations of the flatmaxima (von Winterfeldt & Edwards, 1982) and competition between reward and accuracy (Maddox & Bohil, 1998a) hypotheses in isolation and of a hybrid model that incorporated assumptions from both hypotheses. The modelbased analyses indicated that the flatmaxima model provided a good description of early learning but that the assumptions of the hybrid model were necessary to account for later learning. An examination of the hybrid model parameters indicated that the emphasis placed on accuracy maximization generally declined with experience for optimal classifier feedback but remained high, and fairly constant for objective classifier feedback. Implications for cost–benefit training are discussed.
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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