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On the danger of averaging across observers when comparing decision bound and generalized context models of categorization
 Perception & Psychophysics
, 1999
"... Averaging across observers is common in psychological research. Often averaging reduces the measurement error, and thus does not affect the inference drawn about the behavior of individuals. However, in other situations, averaging alters the structure of the data qualitatively, leading to an incorre ..."
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Cited by 59 (40 self)
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Averaging across observers is common in psychological research. Often averaging reduces the measurement error, and thus does not affect the inference drawn about the behavior of individuals. However, in other situations, averaging alters the structure of the data qualitatively, leading to an incorrect inference about the behavior of individuals. This research investigated the influence of averaging across observers on the fits of decision bound models (F.G. Ashby, 1992a) and generalized context models (GCM; R.M. Nosofsky, 1986) through Monte Carlo simulation of a variety of categorization conditions, perceptual representations, and individual difference assumptions, and in an experiment. The results suggest that (a) averaging has little effect when the GCM is the correct model, (b) averaging often improves the fit of the GCM and worsens the fit of the decision bound model when the decision bound model is the correct model, (c) the GCM is quite flexible, and under many conditions can mimic the predictions of the decision bound model; the decision bound model, on the other hand, is generally unable to mimic the predictions of the GCM, (d) the validity of the decision bound model’s perceptual representation assumption can have a large effect on the inference drawn about the form of the decision bound, and (e) the experiment supported the claim that averaging improves the fit of the GCM. These results underscore the importance of performing single observer analysis if one is interested in understanding the categorization performance of individuals. The ability to categorize quickly and accurately is fundamental to survival. Everyday, we make hundreds of categorization judgments. Several detailed theories and quantitative models have been proposed to account for the perceptual and cognitive processes involved in categorization; the goal being to understand the categorization performance of individual behaving organisms.
Striatal Contributions to Category Learning: Quantitative modeling of simple linear and complex nonlinear rule learning in patients with Parkinson's disease
, 2001
"... The contribution of the striatum to category learning was examined by having patients with Parkinson's disease (PD) and matched controls solve categorization problems in which the optimal rule was linear or nonlinear using the perceptual categorization task. Traditional accuracybased analyses, as ..."
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Cited by 56 (39 self)
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The contribution of the striatum to category learning was examined by having patients with Parkinson's disease (PD) and matched controls solve categorization problems in which the optimal rule was linear or nonlinear using the perceptual categorization task. Traditional accuracybased analyses, as well as quantitative modelbased analyses were performed. Unlike accuracybased analyses, the modelbased analyses allow one to quantify and separate the effects of categorization rule learning from variability in the trialbytrial application of the participant's rule. When the categorization rule was linear, PD patients showed no accuracy, categorization rule learning, or rule application variability deficits. Categorization accuracy for the PD patients was associated with their performance on a test believed to be sensitive to frontal lobe functioning. In contrast, when the categorization rule was nonlinear, the PD patients showed accuracy, categorization rule learning, and rule application variability deficits. Furthermore, categorization accuracy was not associated with performance on the test of frontal lobe functioning. Implications for neuropsychological theories of categorization learning are discussed. (JINS, 2001, 7, 710 727.) Keywords: Categorization, Parkinson's disease, Striatum, Memory, Learning
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
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.
Costs and benefits in perceptual categorization
 Memory & Cognition
"... conditions, and costs were either zero or nonzero. The costbenefit structures were selected so that performance across conditions was equivalent with respect to the optimal classifier. Each observer completed several blocks of trials in each of the experimental conditions, and a series of nested m ..."
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Cited by 13 (11 self)
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conditions, and costs were either zero or nonzero. The costbenefit structures were selected so that performance across conditions was equivalent with respect to the optimal classifier. Each observer completed several blocks of trials in each of the experimental conditions, and a series of nested models were applied to the individual observer data from all conditions. In general, performance became more nearly optimal as observers gained experience with the costbenefit structures, but performance reached asymptote at a suboptimal level. Observers behaved differently in the zero and nonzero cost conditions, performing consistently worse when costs were nonzero. A test of the hypothesis that observers weight costs more heavily than benefits was inconclusive. Some aspects of the data supported this differential weighting hypothesis, but others did not. Implications for current theories of costbenefit learning are discussed. Everyday we make important decisions based on uncertain information. For example, we might decide to “bring ” or “not bring ” an umbrella to work based solely on uncertain predictors of rain, like the degree of overcast. This is a categorization problem because there are many degrees of overcast that one might observe, but
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