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Decision research has experienced a shift from simple algebraic theories of choice to an appreciation of mental processes underlying choice. A variety of process-tracing methods has helped researchers test these process explanations. Here, we provide a survey of these methods, including specific examples for subject reports, movement-based measures, peripheral psychophysiology, and neural techniques. We show how these methods can inform phenomena as varied as attention, emotion, strategy use, and understanding neural correlates. Two important future developments are identified: broadening the number of explicit tests of proposed processes through formal modeling and determining standards and best practices for data collection.

Many decisions are made under suboptimal circumstances, such as time constraints. We examined how different experiences of time constraints affected decision strategies on a probabilistic inference task and whether individual differences in working memory accounted for complex strategy use across different levels of time. To examine information search and attentional processing, we used an interactive eye-tracking paradigm where task information was occluded and only revealed by an eye fixation to a given cell. Our results indicate that although participants change search strategies during the most restricted times, the occurrence of the shift in strategies depends both on how the constraints are applied as well as individual differences in working memory. This suggests that, in situations that require making decisions under time constraints, one can influence performance by being sensitive to working memory and, potentially, by acclimating people to the task time gradually.

Choice models in marketing and economics are generally derived without specifying the underlying cognitive process of decision making. This approach has been successfully used to predict choice behavior. However, it has not much to say about such aspects of decision making as deliberation, attention, conflict, and cognitive limitations and how these influence choices. In contrast, sequential sampling models developed in cognitive psychology explain observed choices based on assumptions about cognitive processes that return the observed choice as the terminal state. We illustrate three advantages of this perspective. First, making explicit assumptions about underlying cognitive processes results in measures of deliberation, attention, conflict, and cognitive limitation. Second, the mathematical representations of underlying cognitive processes imply well documented departures from Luce’s Choice Axiom such as the similarity, compromise, and attraction effects. Third, the process perspective predicts response time and thus allows for inference based on observed choices and response times. Finally, we briefly discuss the relationship between these cognitive models and rules for statistically optimal decisions in sequential designs.

The current work builds on option-generation research using experts of various skill levels in a realistic task. We extend previous findings that relate an athlete's performance strategy to generated options and subsequent choices in handball. In a 2-year longitudinal study, we present eye-tracking data to independently verify decision strategies previously inferred from patterns of generated options. A verbal protocol identified the option-generation process for each individual prior to an allocation decision. Although athletes of varying expertise generated the same number of options on average, these options differed in quality between expert, near-expert, and nonexpert athletes for both their initial and final choices. These and other key results are formalized to elaborate a model of option generation, deliberation, and selection.

Decision making is studied from a number of different theoretical approaches. Normative theories focus on how to make the best decisions by deriving algebraic representations of preference from idealized behavioral axioms. Descriptive theories adopt this algebraic representation, but incorporate known limitations of human behavior. Computational approaches start from a different set of assumptions altogether, focusing instead on the underlying cognitive and emotional processes that result in the selection of one option over the other. This review comprehensively but concisely describes and contrasts three approaches in terms of their theoretical assumptions and their ability to account for behavioral and neurophysiological evidence from experimental research. Although each approach contributes substantially to our understanding of human decision making, we argue that the computational approach is more fruitful and parsimonious for describing and predicting choices in both laboratory and applied settings and for understanding the neurophysiological substrates of decision making. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

Objectives The purpose of this article is to provide an introduction to the theoretical, practical, and methodological advantages of applying cognitive models to sports decisions. The use of sequential sampling models, in particular, is motivated by their correspondence with the dynamic, variable processes that characterize decision-making in sports. This article offers a brief yet detailed description of these process models, and encourages their use in research on decision-making in sports. In addition, Appendix A provides the sufficient detail to formulate, simulate, and compute predictions for one of these models. Although the formulation focuses primarily on deliberation among a set of options, incorporating other critical task components (e.g. option generation, learning) is contemplated. Conclusions. Empirical evidence is reviewed that supports the use of sequential sampling models over other approaches to decision-making. Finally, future directions for fine tuning these models to the sports domain are discussed.

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.

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.

Experimental decision-making research often uses a task in which participants are presented with alternatives from which they must choose. Although tasks of this type may be useful in determining measures (e.g., preference) related to explicitly stated alternatives, they neglect an important aspect of many real-world decision-making environments—namely, the option-generation process. The goal of the present research is to extend previous literature that fills this void by presenting a model that attempts to describe the link between the use of different strategies and the subsequent option-generation process, as well as the resulting choice characteristics. Specifically, we examine the relationship between strategy use, number and order of generated options, choice quality, and dynamic inconsistency. “Take The First” is presented as a heuristic that operates in ill-defined tasks, based on our model assumptions. An experiment involving a realistic (sports) situation was conducted on suitable participants (athletes) to test the predictions of the model. Initial results support the model’s key predictions: strategies producing fewer generated options result in better and more consistent decisions.

Many decisions involve multiple stages of choices and events, and these decisions can be represented graphically as decision trees. Optimal decision strategies for decision trees are commonly determined by a backward induction analysis that demands adherence to three fundamental consistency principles: dynamic, consequential, and strategic. Previous research (Busemeyer et al. 2000, J. Exp. Psychol. Gen. 129, 530) found that decision-makers tend to exhibit violations of dynamic and strategic consistency at rates significantly higher than choice inconsistency across various levels of potential reward. The current research extends these findings under new conditions; specifically, it explores the extent to which these principles are violated as a function of the planning horizon length of the decision tree. Results from two experiments suggest that dynamic inconsistency increases as tree length increases; these results are explained within a dynamic approach–avoidance framework.

The ubiquity of psychological process models requires an increased degree of sophistication in the methods and metrics that we use to evaluate them. We contribute to this venture by capitalizing on recent work in cognitive science analyzing response dynamics, which shows that the bearing information processing dynamics have on intended action is also revealed in the motor system. This decidedly “embodied” view suggests that researchers are missing out on potential dependent variables with which to evaluate their models—those associated with the motor response that produces a choice. The current work develops a method for collecting and analyzing such data in the domain of decision making. We first validate this method using widely normed stimuli from the International Affective Picture System (Experiment 1), and demonstrate that curvature in response trajectories provides a metric of the competition between choice options. We next extend the method to risky decision making (Experiment 2) and develop predictions for three popular classes of process model. The data provided by response dynamics demonstrate that choices contrary to the maxim of risk seeking in losses and risk aversion in gains may be the product of at least one “online” preference reversal, and can thus begin to discriminate amongst the candidate models. Finally, we incorporate attentional data collected via eye-tracking (Experiment 3) to develop a formal computational model of joint information sampling and preference accumulation. In sum, we validate response dynamics for use in preferential choice tasks and demonstrate the unique conclusions afforded by response dynamics over and above traditional methods.

The tri-reference point (TRP) theory takes into account minimum requirements (MR), the status quo (SQ), and goals (G) in decision making under risk. The 3 reference points demarcate risky outcomes and risk perception into 4 functional regions: success (expected value of x ≥ G), gain (SQ×G), loss (MR ≤ xSQ), and failure (xMR). The psychological impact of achieving or failing to achieve these reference points is rank ordered as MRGSQ. We present TRP assumptions and value functions and a mathematical formalization of the theory. We conducted empirical tests of crucial TRP predictions using both explicit and implicit reference points. We show that decision makers consider both G and MR and give greater weight to MR than G, indicating failure aversion (i.e., the disutility of a failure is greater than the utility of a success in the same task) in addition to loss aversion (i.e., the disutility of a loss is greater than the utility of the same amount of gain). Captured by a double-S shaped value function with 3 inflection points, risk preferences switched between risk seeking and risk aversion when the distribution of a gamble straddled a different reference point. The existence of MR (not G) significantly shifted choice preference toward risk aversion even when the outcome distribution of a gamble was well above the MR. Single reference point based models such as prospect theory cannot consistently account for these findings. The TRP theory provides simple guidelines for evaluating risky choices for individuals and organizational management.

It has become increasingly more important for researchers to better capture the complexities of making a decision. To better measure cognitive processes such as attention during decision making, we introduce a new methodology: the decision moving window, which capitalizes on both mouse-tracing and eye-tracking methods. We demonstrate the effectiveness of this methodology in a probabilistic inferential decision task where we reliably measure attentional processing during decision making while allowing the person to determine how information is acquired. We outline the advantages of this methodological paradigm and how it can advance both decision-making research and the development of new metrics to capture cognitive processes in complex tasks.

This paper focuses on a model comparison to explain choices based on gaze behavior via simulation procedures. We tested two classes of models, a parallel constraint satisfaction (PCS) artificial neuronal network model and an accumulator model in a handball decision-making task from a lab experiment. Both models predict action in an option-generation task in which options can be chosen from the perspective of a playmaker in handball (i.e., passing to another player or shooting at the goal). Model simulations are based on a dataset of generated options together with gaze behavior measurements from 74 expert handball players for 22 pieces of video footage. We implemented both classes of models as deterministic vs. probabilistic models including and excluding fitted parameters. Results indicated that both classes of models can fit and predict participants’ initially generated options based on gaze behavior data, and that overall, the classes of models performed about equally well. Early fixations were thereby particularly predictive for choices. We conclude that the analyses of complex environments via network approaches can be successfully applied to the field of experts’ decision making in sports and provide perspectives for further theoretical developments.

This chapter presents a cognitive computational view of decision making as the search for, and accumulation of, evidence for options under consideration. It is based on existing models that have been successful in traditional decision tasks involving preferential choice. The model assumes shifting attention over time that determines momentary inputs to an evolving preference state. In this chapter, the cognitive model is extended to illustrate how links from the motor system may be incorporated. These links can basically be categorized into one of three influences: modifying the subjective evaluation of choice options, restricting attention, and altering the options that are to be found in the choice set. The implications for the formal model are introduced and preliminary evidence is drawn from the extant literature.

This concluding chapter is written as an exercise in evaluative and formative outcome assessment for the workshop that produced the individual content chapters in this edited volume (entitled Mind and motion: the bidirectional link between thought and action; 29-31 May 2008; Bielefeld, Germany). We go beyond simply summarizing and reiterating the academic progress that was made in each group that is presented in the group reports. Rather, we focus on the obstacles that we encountered along the way in hopes of recognizing what may impede future progress in the study of motor-cognitive links. In particular, we identified three key challenges that arose during our workshop discussions: the limitations of a reductionist tendency, the myopia associated with disciplinary biases, and the threats to inferential validity when dealing with mental phenomena. We offer prescriptive advice about how to overcome these limitations and provide a template for future interaction by interdisciplinary researchers who want to continue scientific research in this domain.

Until recently, the constraints imposed on decision makers by the human physical condition - situated both as a physical agent and within physical space - have played only an incidental, if not entirely inconsequential, role in conceptualizations of human decision making. The act of deciding has been positioned as the quintessence of traditional decision theory, while actual enactment of the decided action within physical space by a corporal actor, with all that this entails, has been regarded as the obvious and, therefore, scientifically uninteresting result of having made up one's mind (cf. Bagozzi et al., 2003). However, recent discoveries made in the area of embodied cognition regarding the involvement of fundamentally motoric representations in long-presumed "cognitive" systems (Wilson, 2002) potentially turned conventional wisdom upside-down. In this chapter, we go beyond prominent theories of action selection and decision making to rethink the link between mind and body as it pertains to the relatively novel frontier of embodied decision making. In particular, we reconceptualize what it means to evaluate one's options in light of recent advancements in embodied cognition, motor control, and dynamic decision making. In the process, we provide a much needed account of the primary theoretical issues that any good account would seem to be impelled to address. Perhaps the greatest contribution provided by the present chapter is an organizing framework that we hope will guide future research to the eventual answer to what it means to be an embodied decision maker.

This concluding chapter is written as an exercise in evaluative and formative outcome assessment for the workshop that produced the individual content chapters in this edited volume (entitled Mind and motion: the bidirectional link between thought and action; 29–31 May 2008; Bielefeld, Germany). We go beyond simply summarizing and reiterating the academic progress that was made in each group that is presented in the group reports. Rather, we focus on the obstacles that we encountered along the way in hopes of recognizing what may impede future progress in the study of motor-cognitive links. In particular, we identified three key challenges that arose during our workshop discussions: the limitations of a reductionist tendency, the myopia associated with disciplinary biases, and the threats to inferential validity when dealing with mental phenomena. We offer prescriptive advice about how to overcome these limitations and provide a template for future interaction by interdisciplinary researchers who want to continue scientific research in this domain.

Until recently, the constraints imposed on decision makers by the human physical condition — situated both as a physical agent and within physical space — have played only an incidental, if not entirely inconsequential, role in conceptualizations of human decision making. The act of deciding has been positioned as the quintessence of traditional decision theory, while actual enactment of the decided action within physical space by a corporal actor, with all that this entails, has been regarded as the obvious and, therefore, scientifically uninteresting result of having made up one's mind (cf. Bagozzi et al., 2003). However, recent discoveries made in the area of embodied cognition regarding the involvement of fundamentally motoric representations in long-presumed “cognitive” systems (Wilson, 2002) potentially turned conventional wisdom upside-down. In this chapter, we go beyond prominent theories of action selection and decision making to rethink the link between mind and body as it pertains to the relatively novel frontier of embodied decision making. In particular, we reconceptualize what it means to evaluate one's options in light of recent advancements in embodied cognition, motor control, and dynamic decision making. In the process, we provide a much needed account of the primary theoretical issues that any good account would seem to be impelled to address. Perhaps the greatest contribution provided by the present chapter is an organizing framework that we hope will guide future research to the eventual answer to what it means to be an embodied decision maker.

Cognitive architectures are on the one hand echoes of the original goal of creating an intelligent machine faithful to human intelligence and on the other hand attempts at theoretical unification in the field of cognitive psychology. This chapter discusses the current state of cognitive architectures to characterize four prime examples: The States, Operators, And Reasoning (SOAR) architecture, the Adaptive Control of Thought, Rational (ACT-R) theory, Executive-Process Interactive Control (EPIC) architecture, and Connectionist Learning with Adaptive Rule Induction Online (CLARION) architecture. The chapter examines a number of topics that can serve as constraints on modeling and discusses how four architectures offer solutions to help modeling in that topic area. The viewpoint of cognitive constraint is different from the perspective of how much functionality an architecture can provide, as expressed by, for example, Anderson and Lebiere.

This paper will examine the amount of time typically spent on data analysis tasks within the ground operations area during both prelaunch and postlaunch time periods. A proposed data hierarchy for reviewing data and obtaining the maximum amount of information that is available for analyzing space launch vehicle data is discussed. A typical space launch vehicle system architecture is also reviewed with an emphasis placed upon the differences in analyzing each vehicle system's data. In addition, the use of expert system technology and how this software technology has been used in the development of an automated data analysis system called Data Analyst's Intelligent System (DAIS) is discussed.