Biomedical subjects

Jerome R Busemeyer

Publications and source records attributed to Jerome R Busemeyer.

13 recordsLinked to original sources

PubMed2006

Building bridges between neural models and complex decision making behaviour.

Diffusion processes, and their discrete time counterparts, random walk models, have demonstrated an ability to account for a wide range of findings from behavioural decision making for which the purely algebraic and deterministic models often used in economics and psychology cannot account. Recent studies that record neural activations in non-human primates during perceptual decision making tasks have revealed that neural firing rates closely mimic the accumulation of preference theorized by behaviourally-derived diffusion models of decision making. This article bridges the expanse between the neurophysiological and behavioural decision making literatures specifically, decision field theory [Busemeyer, J. R. & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100, 432-459], a dynamic and stochastic random walk theory of decision making, is presented as a model positioned between lower-level neural activation patterns and more complex notions of decision making found in psychology and economics. Potential neural correlates of this model are proposed, and relevant competing models are also addressed.

Action Potentials↗

PubMed2006

A formal cognitive model of the go/no-go discrimination task: evaluation and implications.

This article proposes and tests a formal cognitive model for the go/no-go discrimination task. In this task, the performer chooses whether to respond to stimuli and receives rewards for responding to certain stimuli and punishments for responding to others. Three cognitive models were evaluated on the basis of data from a longitudinal study involving 400 adolescents. The results show that a cue-dependent model presupposing that participants can differentiate between cues was the most accurate and parsimonious. This model has 3 parameters denoting the relative impact of rewards and punishments on evaluations, the rate that contingent payoffs are learned, and the consistency between learning and responding. Commission errors were associated with increased attention to rewards; omission errors were associated with increased attention to punishments. Both error types were associated with low choice consistency. The parameters were also shown to have external validity: Attention to rewards was associated with externalizing behavior problems on the Achenbach scale, and choice consistency was associated with low Welsh anxiety. The present model can thus potentially improve the sensitivity of the task to differences between clinical populations.

Adolescent↗

PubMed2006

Application of a computational decision model to examine acute drug effects on human risk taking.

In 3 previous experiments, high doses of alcohol, marijuana, and alprazolam acutely increased risky decision making by adult humans in a 2-choice (risky vs. nonrisky) laboratory task. In this study, a computational modeling analysis known as the expectancy valence model (J. R. Busemeyer & J. C. Stout, 2002) was applied to individual-participant data from these studies, for the highest administered dose of all 3 drugs and corresponding placebo doses, to determine changes in decision-making processes that may be uniquely engendered by each drug. The model includes 3 parameters: responsiveness to rewards and losses (valence or motivation); the rate of updating expectancies about the value of risky alternatives (learning/memory); and the consistency with which trial-by-trial choices match expected outcomes (sensitivity). Parameter estimates revealed 3 key outcomes: Alcohol increased responsiveness to risky rewards and decreased responsiveness to risky losses (motivation) but did not alter expectancy updating (learning/memory); both marijuana and alprazolam produced increases in risk taking that were related to learning/memory but not motivation; and alcohol and marijuana (but not alprazolam) produced more random response patterns that were less consistently related to expected outcomes on the 2 choices. No significant main effects of gender or dose by gender interactions were obtained, but 2 dose by gender interactions approached significance. These outcomes underscore the utility of using a computational modeling approach to deconstruct decision-making processes and thus better understand drug effects on risky decision making in humans.

Alcohol Drinking↗

PubMed2006

Modeling the effects of payoff on response bias in a perceptual discrimination task: bound-change, drift-rate-change, or two-stage-processing hypothesis.

Three hypotheses--the bound-change hypothesis, drift-rate-change hypothesis, and two-stage-processing hypothesis--are proposed to account for data from a perceptual discrimination task in which three different response deadlines were involved and three different payoffs were presented prior to each individual trial. The aim of the present research was to show (1) how the three different hypotheses incorporate response biases into a sequential sampling decision process, (2) how payoffs and deadlines affect choice probabilities, and (3) the hypotheses' predictions of response times and choice probabilities. The two-stage-processing hypothesis gave the best account, especially for the choice probabilities, whereas the drift-rate-change hypothesis had problems predicting choice probabilities as a function of deadlines.

Discrimination, Psychological↗

PubMed2005

Contrast effects or loss aversion? Comment on Usher and McClelland (2004).

M. Usher and J. L. McClelland (2004) recently proposed a new connectionist type of model to explain context effects on preferential choice including the similarity, attraction, and compromise effects. They compared their model with an earlier connectionist type model for these same effects proposed by R. Roe, J. R. Busemeyer, and J. T. Townsend (2001) and raised several new issues. The authors address these issues and point out the main theoretical differences between the 2 explanations for context effects.

Choice Behavior↗

PubMed2005

A dynamic, stochastic, computational model of preference reversal phenomena.

Preference orderings among a set of options may depend on the elicitation method (e.g., choice or pricing); these preference reversals challenge traditional decision theories. Previous attempts to explain these reversals have relied on allowing utility of the options to change across elicitation methods by changing the decision weights, the attribute values, or the combination of this information--still, no theory has successfully accounted for all the phenomena. In this article, the authors present a new computational model that accounts for the empirical trends without changing decision weights, values, or combination rules. Rather, the current model specifies a dynamic evaluation and response process that correctly predicts preference orderings across 6 elicitation methods, retains stable evaluations across methods, and makes novel predictions regarding response distributions and response times.

Humans↗

PubMed2005

Psychological processes underlying risky decisions in drug abusers.

Decision-making deficits are considered to be a significant contributing factor for drug abuse. Drug abusers performed poorly on a simulated gambling task (A. Bechara, H. Damasio, D. Tranel, & S. Anderson, 1994); however, the psychological processes that contribute to these deficits are unknown. The authors used cognitive decision models with a simulated gambling task (SGT) to examine underlying processes of decision making in 66 drug abusers and 58 control participants. As expected, male drug abusers performed more poorly than male controls, and model results showed that male drug abusers placed greater emphasis on wins. The findings for women were less clear because control women performed at chance level on the SGT. Additional studies of gender differences on the SGT are needed to clarify these findings of discrepant performance in the control women.

Adolescent↗

PubMed2005

Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits.

Findings from a complex decision-making task (the Iowa gambling task) show that individuals with neuropsychological disorders are characterized by decision-making deficits that lead to maladaptive risk-taking behavior. This article describes a cognitive model that distills performance in this task into three different underlying psychological components: the relative impact of rewards and punishments on evaluations of options, the rate that the contingent payoffs are learned, and the consistency between learning and responding. Findings from 10 studies are organized by distilling the observed decision deficits into the three basic components and locating the neuropsychological disorders in this component space. The results reveal a cluster of populations characterized by making risky choices despite high attention to losses, perhaps because of difficulties in creating emotive representations. These findings demonstrate the potential contribution of cognitive models in building bridges between neuroscience and behavior.

Cognition Disorders↗

PubMed2005

Comparison of basic assumptions embedded in learning models for experience-based decision making.

The present study examined basic assumptions embedded in learning models for predicting behavior in decisions based on experience. In such decisions, the probabilities and payoffs are initially unknown and are learned from repeated choice with payoff feedback. We examined combinations of two rules for updating past experience with new payoff feedback and of two choice rule assumptions for mapping experience onto choices. The combination of these assumptions produced four classes of models that were systematically compared. Two methods were employed to evaluate the success of learning models for approximating players' choices: One was based on estimating parameters from each person's data to maximize the prediction of choices one step ahead, conditioned by the observed past history of feedback. The second was based on making a priori predictions for the entire sequence of choices using parameters estimated from a separate experiment. The results indicated the advantage of a class of models incorporating decay of previous experience, whereas the ranking of choice rules depended on the evaluation method used.

Adult↗

PubMed2005

The conceptual basis of function learning and extrapolation: comparison of rule-based and associative-based models.

The purpose of this article is to provide a foundation for a more formal, systematic, and integrative approach to function learning that parallels the existing progress in category learning. First, we note limitations of existing formal theories. Next, we develop several potential formal models of function learning, which include expansion of classic rule-based approaches and associative-based models. We specify for the first time psychologically based learning mechanisms for the rule models. We then present new, rigorous tests of these competing models that take into account order of difficulty for learning different function forms and extrapolation performance. Critically, detailed learning performance was also used to conduct the model evaluations. The results favor a hybrid model that combines associative learning of trained input-prediction pairs with a rule-based output response for extrapolation (EXAM).

Animals↗

PubMed2004

Cognitive modeling analysis of decision-making processes in cocaine abusers.

This article examines the theoretical basis of decision-making deficits exhibited by cocaine abusers in a laboratory decision-making task first described by Bechara, Damasio, Damasio, and Anderson (1994). A total of 12 male cocaine abusers and 14 comparison subjects performed the task, and the cocaine group performed significantly worse than the comparison group. A cognitive modeling analysis (Busemeyer & Stout, 2002) was used to estimate three parameters that measure importance of the cognitive, motivational, and response processes for determining the observed performance deficit. The results of this analysis indicated, for the first time, that motivational and choice consistency factors, but not learning/memory were mainly responsible for the decision-making deficit of the cocaine abusers in this task.

Adult↗

PubMed2003

How do people learn to allocate resources? Comparing two learning theories.

How do people learn to allocate resources? To answer this question, 2 major learning models are compared, each incorporating different learning principles. One is a global search model, which assumes that allocations are made probabilistically on the basis of expectations formed through the entire history of past decisions. The 2nd is a local adaptation model, which assumes that allocations are made by comparing the present decision with the most successful decision up to that point, ignoring all other past decisions. In 2 studies, participants repeatedly allocated a capital resource to 3 financial assets. Substantial learning effects occurred, although the optimal allocation was often not found. From the calibrated models of Study 1, a priori predictions were derived and tested in Study 2. This generalization test shows that the local adaptation model provides a better account of learning in resource allocations than the global search model.

Adult↗

PubMed2002

A contribution of cognitive decision models to clinical assessment: decomposing performance on the Bechara gambling task.

The Bechara simulated gambling task is a popular method of examining decision-making deficits exhibited by people with brain damage, psychopathology, antisocial personality, or drug abuse problems. However, performance on this task is confounded by complex interdependencies between cognitive, motivational, and response processes, making it difficult to sort out and identify the specific processes responsible for the observed behavioral deficits. The authors compare 3 competing cognitive decision models of the Bechara task in terms of their ability to explain the performance deficits observed in Huntington's disease patients as compared with healthy populations and people with Parkinson's disease. The parameters of the best fitting model are used to decompose the observed performance deficit of the Huntington patients into cognitive, motivational, and response sources.

Brain Damage, Chronic↗