Results 1 
6 of
6
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)
 Add to MetaCart
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 ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
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
AND
"... This review considers experimental research that has used probability theory and statistics as a framework within which to study human statistical inference. The experiments have investigated estimates of proportions, means, variances, and correlations, both of samples and of populations. In some ex ..."
Abstract
 Add to MetaCart
This review considers experimental research that has used probability theory and statistics as a framework within which to study human statistical inference. The experiments have investigated estimates of proportions, means, variances, and correlations, both of samples and of populations. In some experiments, parameters of populations were stationary; in others, the parameters changed over time. The experiments also investigated the determination of sample size and trialbytrial predictions of events to be sampled from a population. In general, the results indicate that probability theory and statistics can be used as the basis for psychological models that integrate and account for human performance in a wide range of inferential tasks. "Given... an intelligence which could comprehend all the forces of which nature is animated and the respective situation of the beings who compose it—an intelligence sufficiently vast to submit these data to analysis... nothing would be uncertain and the future, as the past, would be present to its eyes [Laplace, 1814]. " In lieu of such omniscience, man must cope with an environment about which he has only fallible information, "while God may not gamble, animals and humans do,... they cannot help but to gamble in an ecology that is of essence only partly accessible to their foresight [Brunswik, 1955]. " And man gambles well. He survives and prospers while using the fallible information to infer the states of his uncertain environment and to predict future events. Man's problems with his uncertain environment are similar 'to those faced by social enterprises such as science, industry, and agriculture. Satisfactory decisions require sound inferences about prevailing and future states of the environments in which these enter
Decision Criterion Learning 1 Toward a Unified Theory of Decision Criterion Learning in Perceptual Categorization
, 2002
"... Optimal decision criterion placement that maximizes expected reward requires sensitivity to the category baserates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When baserates are unequal, human decision criterion is nearly optimal, but when payoffs ar ..."
Abstract
 Add to MetaCart
Optimal decision criterion placement that maximizes expected reward requires sensitivity to the category baserates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When baserates 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 developed and tested (Maddox & Dodd, 2001). The theory assumes that two critical mechanisms are operative in decision criterion learning. One mechanism involves competition between reward and accuracy maximization: The observer attempts to maximize reward, as instructed, but also place 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 importantly provides useful insights into the psychological processes involved in decision criterion learning.
Institute of Mental Health, National Institutes of Health. We thank
"... On the generality of optimal versus objective classifier feedback effects on decision criterion learning in perceptual categorization ..."
Abstract
 Add to MetaCart
On the generality of optimal versus objective classifier feedback effects on decision criterion learning in perceptual categorization