The authors assume that individuals adapt rationally to a utility function given constraints imposed by their cognitive architecture and the local task environment. This assumption underlies a new approach to modeling and understanding cognition-cognitively bounded rational analysis-that sharpens the predictive acuity of general, integrated theories of cognition and action. Such theories provide the necessary computational means to explain the flexible nature of human behavior but in doing so introduce extreme degrees of freedom in accounting for data. The new approach narrows the space of predicted behaviors through analysis of the payoff achieved by alternative strategies, rather than through fitting strategies and theoretical parameters to data. It extends and complements established approaches, including computational cognitive architectures, rational analysis, optimal motor control, bounded rationality, and signal detection theory. The authors illustrate the approach with a reanalysis of an existing account of psychological refractory period (PRP) dual-task performance and the development and analysis of a new theory of ordered dual-task responses. These analyses yield several novel results, including a new understanding of the role of strategic variation in existing accounts of PRP and the first predictive, quantitative account showing how the details of ordered dual-task phenomena emerge from the rational control of a cognitive system subject to the combined constraints of internal variance, motor interference, and a response selection bottleneck.Keywords: rational adaptation, bounded optimality, cognitive architecture, theory comparison, response ordering, dual taskThe extraordinarily flexible and adaptive nature of human behavior presents both unique opportunities and unique challenges for developing a science of the mind and brain. On the one hand, treating the mind as an adaptive system opens up possibilities for deep explanations of behavior that are grounded primarily in the observable structure and contingencies of the task environment, along with an assumption of rationality or optimal adaptation. This insight is the point of departure for a range of approaches to understanding cognition and perception, including rational analysis and related Bayesian approaches (Anderson, 1990;Berthier, Rosenstein, & Barto, 2005;Bogacz, Brown, Moehlis, Holmes, & Cohen, 2006;Chater & Oaksford, 1999;Geisler, 2003;Tenenbaum, Griffiths, & Kemp, 2006), optimal motor control approaches (Maloney, Trommershäuser, & Landy, 2007;Meyer, Abrams, Kornblum, Wright, & Smith, 1988;Trommershäuser, Maloney, & Landy, 2003a, 2003bReichle & Laurent, 2006), as well as signal detection theory and ideal observer analysis (Green & Swets, 1966;Swets, Tanner, & Birdsall, 1961;Tanner & Swets, 1954). For example, in the arena of perception, ideal observer models demonstrate that human performance on some very simple discrimination tasks is limited only by external photon noise (Geisler, 2003). In the arena of memory, the decay over time of i...

We propose a framework for including information-processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of rational action. Theories are specified as optimal program problems, defined by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing "levels" approaches to explanation, and to other optimality-based modeling approaches.

This paper presents a novel mathematical model for visual search and selection time in linear menus. Assuming two visual search strategies, serial and directed, and a pointing sub-task, it captures the change of performance with five factors: 1) menu length, 2) menu organization, 3) target position, 4) absence/presence of target, and 5) practice. The novel aspect is that the model is expressed as probability density distribution of gaze, which allows for deriving total selection time. We present novel data that replicates and extends the Nielsen menu selection paradigm and uses eye-tracking and mouse tracking to confirm model predictions. The same parametrization yielded a high fit to both menu selection time and gaze distributions. The model has the potential to improve menu designs by helping designers identify more effective solutions without conducting empirical studies.

Contextual preference reversals occur when a preference for one option over another is reversed by the addition of further options. It has been argued that the occurrence of preference reversals in human behavior shows that people violate the axioms of rational choice and that people are not, therefore, expected value maximizers. In contrast, we demonstrate that if a person is only able to make noisy calculations of expected value and noisy observations of the ordinal relations among option features, then the expected value maximizing choice is influenced by the addition of new options and does give rise to apparent preference reversals. We explore the implications of expected value maximizing choice, conditioned on noisy observations, for a range of contextual preference reversal types—including attraction, compromise, similarity, and phantom effects. These preference reversal types have played a key role in the development of models of human choice. We conclude that experiments demonstrating contextual preference reversals are not evidence for irrationality. They are, however, a consequence of expected value maximization given noisy observations.

We report the results of a dual-task study in which participants performed a tracking and typing task under various experimental conditions. An objective payoff function was used to provide explicit feedback on how participants should trade off performance between the tasks. Results show that participants' dual-task interleaving strategy was sensitive to changes in the difficulty of the tracking task and resulted in differences in overall task performance. To test the hypothesis that people select strategies that maximize payoff, a Cognitively Bounded Rational Analysis model was developed. This analysis evaluated a variety of dual-task interleaving strategies to identify the optimal strategy for maximizing payoff in each condition. The model predicts that the region of optimum performance is different between experimental conditions. The correspondence between human data and the prediction of the optimal strategy is found to be remarkably high across a number of performance measures. This suggests that participants were honing their behavior to maximize payoff. Limitations are discussed.