Decision research in psychology has traditionally been influenced by the homo oeconomicus metaphor with its emphasis on normative models and deviations from the predictions of those models. In contrast, the principal metaphor of cognitive psychology conceptualizes humans as ‘information processors’, employing processes of perception, memory, categorization, problem solving and so on. Many of the processes described in cognitive theories are similar to those involved in decision making, and thus increasing cross-fertilization between the two areas is an important endeavour. A wide range of models and metaphors has been proposed to explain and describe ‘information processing’ and many models have been applied to decision making in ingenious ways. This special issue encourages cross-fertilization between cognitive psychology and decision research by providing an overview of current perspectives in one area that continues to highlight the benefits of the synergistic approach: cognitive modeling of multi-attribute decision making. In this introduction we discuss aspects of the cognitive system that need to be considered when modeling multi-attribute decision making (e.g., automatic versus controlled processing, learning and memory constraints, metacognition) and illustrate how such aspects are incorporated into the approaches proposed by contributors to the special issue. We end by discussing the challenges posed by the contrasting and sometimes incompatible assumptions of the models and metaphors.

The authors review their own empirical work inspired by the adaptive toolbox metaphor. The review examines factors influencing strategy selection and execution in multi-attribute inference tasks (e.g., information costs, time pressure, memory retrieval, dynamic environments, stimulus formats, intelligence). An emergent theme is the re-evaluation of contingency model claims about the elevated cognitive costs of compensatory in comparison with non-compensatory strategies. Contrary to common assertions about the impact of cognitive complexity, the empirical data suggest that manipulated variables exert their influence at the meta-level of deciding how to decide (i.e., which strategy to select) rather than at the level of strategy execution. An alternative conceptualisation of strategy selection, namely threshold adjustment in an evidence accumulation model, is also discussed and the difficulty in distinguishing empirically between these metaphors is acknowledged.

We claim that understanding human decisions requires that both automatic and deliberate processes be considered. First, we sketch the qualitative differences between two hypothetical processing systems, an automatic and a deliberate system. Second, we show the potential that connectionism offers for modeling processes of decision making and discuss some empirical evidence. Specifically, we posit that the integration of information and the application of a selection rule are governed by the automatic system. The deliberate system is assumed to be responsible for information search, inferences and the modification of the network that the automatic processes act on. Third, we critically evaluate the multiple-strategy approach to decision making. We introduce the basic assumption of an integrative approach stating that individuals apply an all-purpose rule for decisions but use different strategies for information search. Fourth, we develop a connectionist framework that explains the interaction between automatic and deliberate processes and is able to account for choices both at the option and at the strategy level.

Judgments and decisions under uncertainty are frequently linked to a prior sequential search for relevant information. In such cases, the subject has to decide when to stop the search for information. Evidence accumulation models from social and cognitive psychology assume an active and sequential information search until enough evidence has been accumulated to pass a decision threshold. In line with such theories, we conceptualize the evidence threshold as the “desired level of confidence” (DLC) of a person. This model is tested against a fixed stopping rule (one-reason decision making) and against the class of multi-attribute information integrating models. A series of experiments using an information board for horse race betting demonstrates an advantage of the proposed model by measuring the individual DLC of each subject and confirming its correctness in two separate stages. In addition to a better understanding of the stopping rule (within the narrow framework of simple heuristics), the results indicate that individual aspiration levels might be a relevant factor when modelling decision making by task analysis of statistical environments.

Several studies propose that exemplar retrieval contributes to multi-attribute decisions. The authors have proposed a process theory enabling a priori predictions of what cognitive representations people use as input to their judgment process (Sigma, for “summation”; P. Juslin, L. Karlsson, & H. Olsson, 2008). According to Sigma, exemplar retrieval is a back-up system when the task does not allow for additive and linear abstraction and integration of cue-criterion knowledge (e.g., when the task is non-additive). An important question is to what extent such shifts occur spontaneously as part of automatic procedures, such as error-minimization with the Delta rule, or if they are controlled strategy shifts contingent on the ability to identify a sufficiently successful judgment strategy. In this article data are reviewed that demonstrate a shift between exemplar memory and cue abstraction, as well as data where the expected shift does not occur. In contrast to a common assumption of previous models, these results suggest a controlled and contingent strategy shift.

The assumption that people possess a repertoire of strategies to solve the inference problems they face has been made repeatedly. The experimental findings of two previous studies on strategy selection are reexamined from a learning perspective, which argues that people learn to select strategies for making probabilistic inferences. This learning process is modeled with the strategy selection learning (SSL) theory, which assumes that people develop subjective expectancies for the strategies they have. They select strategies proportional to their expectancies, which are updated on the basis of experience. For the study by Newell, Weston, and Shanks (2003) it can be shown that people did not anticipate the success of a strategy from the beginning of the experiment. Instead, the behavior observed at the end of the experiment was the result of a learning process that can be described by the SSL theory. For the second study, by Bröder and Schiffer (2006), the SSL theory is able to provide an explanation for why participants only slowly adapted to new environments in a dynamic inference situation. The reanalysis of the previous studies illustrates the importance of learning for probabilistic inferences.

Contrary to the common belief that more information is always better, Gigerenzer et al. (1999) showed that simple decision strategies which rely on little information can be quite successful. The success of simple strategies depends both on bets about the structure of the environment and on the core capacities of the human mind, such as recognition memory (Gigerenzer, 2004). However, the interplay between the environment and the mind’s core capacities has rarely been precisely modeled. We illustrate how these environment-mind interactions could be formally modeled within the cognitive architecture ACT-R (J. R. Anderson et al., 2004). ACT-R is an integrated theory of mind that is tuned to the statistical structure of the environment, and it can account for a variety of phenomena such as learning, problem solving, and decision making. Here, we focus on studying decision strategies and show how the success of theses strategies in particular environments depends on characteristics of core cognitive capacities, such as recognition and short term memory.