Biomedical subjects

Joseph G Johnson

Publications and source records attributed to Joseph G Johnson.

4 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

Domain specificity in experimental measures and participant recruitment: an application to risk-taking behavior.

We challenge the prevailing notion that risk taking is a stable trait, such that individuals show consistent risk-taking/aversive behavior across domains. We subscribe to an alternative approach that appreciates the domain-specific nature of risk taking. More important, we recognize heterogeneity of risk profiles among experimental samples and introduce a new methodology that takes this heterogeneity into account. Rather than using a convenient subject pool (i.e., university students), as is typically done, we specifically targeted relevant subsamples to provide further validation of the domain-specific nature of risk taking. Our research shows that individuals who exhibit high levels of risk-taking behavior in one content area (e.g., bungee jumpers taking recreational risks) can exhibit moderate levels in other risky domains (e.g., financial). Furthermore, our results indicate that risk taking among targeted subsamples can be explained within a cost-benefit framework and is largely mediated by the perceived benefit of the activity, and to a lesser extent by the perceived risk.

Adult↗

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↗

PubMed2004

Individual differences of action orientation for risktaking in sports.

The goal of this article is to explain empirical risk-taking behavior in sports from an individual cognitive modeling perspective. A basketball task was used in which participants viewed four video options that varied in the degree of associated risk. The participants were independently classified by scores on the Questionnaire for Assessing Prospective Action Orientation and State Orientation in Success, Failure, and Planning Situations as action-oriented or state-oriented decision makers. The results of the experiment show that action-oriented players shoot faster and more often to the basket and that state-oriented players prefer to pass to a playmaker more often. Four versions of a computational model of decision making, Decision Field Theory, were compared to evaluate whether behavioral differences depend on the focus of attention, the initial preferences, threshold values, or an approach-avoidance interpretation of the task. Different starting preferences explained individual choices and decision times most accurately. Risk taking in basketball shooting behavior can be best explained by different preferences for starting values for risky and safe options caused by different levels of action orientation.

Adult↗