Rational models as theories - not standards - of behavior

People as Intuitive Scientists: Reconsidering Statistical Explanations of Decision Making

A persistent metaphor in decision-making research casts people as intuitive statisticians. Popular explanations based on this metaphor assume that the way in which people represent the environment is specified and fixed a priori. A major flaw in this account is that it is not clear how people know what aspects of an environment are important, how to interpret those aspects, and how to make decisions based on them. We suggest a theoretical reorientation away from assuming people's representations towards a focus on explaining how people themselves specify what is important to represent. This perspective casts decision makers as intuitive scientists able to flexibly construct, modify, and replace the representations of the decision problems they face.

Rationality, perception, and the all-seeing eye

Seeing-perception and vision-is implicitly the fundamental building block of the literature on rationality and cognition. Herbert Simon and Daniel Kahneman's arguments against the omniscience of economic agents-and the concept of bounded rationality-depend critically on a particular view of the nature of perception and vision. We propose that this framework of rationality merely replaces economic omniscience with perceptual omniscience. We show how the cognitive and social sciences feature a pervasive but problematic meta-assumption that is characterized by an "all-seeing eye." We raise concerns about this assumption and discuss different ways in which the all-seeing eye manifests itself in existing research on (bounded) rationality. We first consider the centrality of vision and perception in Simon's pioneering work. We then point to Kahneman's work-particularly his article "Maps of Bounded Rationality"-to illustrate the pervasiveness of an all-seeing view of perception, as manifested in the extensive use of visual examples and illusions. Similar assumptions about perception can be found across a large literature in the cognitive sciences. The central problem is the present emphasis on inverse optics-the objective nature of objects and environments, e.g., size, contrast, and color. This framework ignores the nature of the organism and perceiver. We argue instead that reality is constructed and expressed, and we discuss the species-specificity of perception, as well as perception as a user interface. We draw on vision science as well as the arts to develop an alternative understanding of rationality in the cognitive and social sciences. We conclude with a discussion of the implications of our arguments for the rationality and decision-making literature in cognitive psychology and behavioral economics, along with suggesting some ways forward.

Anomalies in the detection of change: When changes in sample size are mistaken for changes in proportions

Detecting changes, in performance, sales, markets, risks, social relations, or public opinions, constitutes an important adaptive function. In a sequential paradigm devised to investigate detection of change, every trial provides a sample of binary outcomes (e.g., correct vs. incorrect student responses). Participants have to decide whether the proportion of a focal feature (e.g., correct responses) in the population from which the sample is drawn has decreased, remained constant, or increased. Strong and persistent anomalies in change detection arise when changes in proportional quantities vary orthogonally to changes in absolute sample size. Proportional increases are readily detected and nonchanges are erroneously perceived as increases when absolute sample size increases. Conversely, decreasing sample size facilitates the correct detection of proportional decreases and the erroneous perception of nonchanges as decreases. These anomalies are however confined to experienced samples of elementary raw events from which proportions have to be inferred inductively. They disappear when sample proportions are described as percentages in a normalized probability format. To explain these challenging findings, it is essential to understand the inductive-learning constraints imposed on decisions from experience.

Accounting for occurrences: an explanation for some novel tendencies in causal judgment from contingency information

Contingency information is information about empirical associations between possible causes and outcomes. In the present research, it is shown that, under some circumstances, there is a tendency for negative contingencies to lead to positive causal judgments and for positive contingencies to lead to negative causal judgments. If there is a high proportion of instances in which a candidate cause (CC) being judged is present, these tendencies are predicted by weighted averaging models of causal judgment. If the proportion of such instances is low, the predictions of weighted averaging models break down. It is argued that one of the main aims of causal judgment is to account for occurrences of the outcome. Thus, a CC is not given a high causal judgment if there are few or no occurrences of it, regardless of the objective contingency. This argument predicts that, if there is a low proportion of instances in which a CC is present, causal judgments are determined mainly by the number of Cell A instances (i.e., CC present, outcome occurs), and that this explains why weighted averaging models fail to predict judgmental tendencies under these circumstances. Experimental results support this argument.

Naïve optimality: Subjects' heuristics can be better motivated than experimenters' optimal models

Is human cognition best described by optimal models, or by adaptive but suboptimal heuristic strategies? It is frequently hard to identify which theoretical model is normatively best justified. In the context of information search, naïve subjects' heuristic strategies are better motivated than some “optimal” models.

Optimal predictions in everyday cognition

Human perception and memory are often explained as optimal statistical inferences that are informed by accurate prior probabilities. In contrast, cognitive judgments are usually viewed as following error-prone heuristics that are insensitive to priors. We examined the optimality of human cognition in a more realistic context than typical laboratory studies, asking people to make predictions about the duration or extent of everyday phenomena such as human life spans and the box-office take of movies. Our results suggest that everyday cognitive judgments follow the same optimal statistical principles as perception and memory, and reveal a close correspondence between people's implicit probabilistic models and the statistics of the world.

Increased sensitivity to differentially diagnostic answers using familiar materials: implications for confirmation bias

Researchers have recently pointed out that neither biased testing nor biased evaluation of hypotheses necessitates confirmation bias--defined here as systematic overconfidence in a focal hypothesis--but certain testing/evaluation combinations do. One such combination is (1) a tendency to ask about features that are either very likely or very unlikely under the focal hypothesis (extremity bias) and (2) a tendency to treat confirming and disconfirming answers as more similar in terms of their diagnosticity (or informativeness) than they really are. However, in previous research showing the second tendency, materials that are highly abstract and unfamiliar have been used. Two experiments demonstrated that using familiar materials led participants to distinguish much better between the differential diagnosticity of confirming and disconfirming answers. The conditions under which confirmation bias is a serious concern might be quite limited.

A Bayesian view of covariation assessment

When participants assess the relationship between two variables, each with levels of presence and absence, the two most robust phenomena are that: (a) observing the joint presence of the variables has the largest impact on judgment and observing joint absence has the smallest impact, and (b) participants' prior beliefs about the variables' relationship influence judgment. Both phenomena represent departures from the traditional normative model (the phi coefficient or related measures) and have therefore been interpreted as systematic errors. However, both phenomena are consistent with a Bayesian approach to the task. From a Bayesian perspective: (a) joint presence is normatively more informative than joint absence if the presence of variables is rarer than their absence, and (b) failing to incorporate prior beliefs is a normative error. Empirical evidence is reported showing that joint absence is seen as more informative than joint presence when it is clear that absence of the variables, rather than their presence, is rare.

Finding useful questions: on Bayesian diagnosticity, probability, impact, and information gain

Several norms for how people should assess a question's usefulness have been proposed, notably Bayesian diagnosticity, information gain (mutual information), Kullback-Liebler distance, probability gain (error minimization), and impact (absolute change). Several probabilistic models of previous experiments on categorization, covariation assessment, medical diagnosis, and the selection task are shown to not discriminate among these norms as descriptive models of human intuitions and behavior. Computational optimization found situations in which information gain, probability gain, and impact strongly contradict Bayesian diagnosticity. In these situations, diagnosticity's claims are normatively inferior. Results of a new experiment strongly contradict the predictions of Bayesian diagnosticity. Normative theoretical concerns also argue against use of diagnosticity. It is concluded that Bayesian diagnosticity is normatively flawed and empirically unjustified.

A neuroscientific approach to normative judgment in law and justice

Developments in cognitive neuroscience are providing new insights into the nature of normative judgment. Traditional views in such disciplines as philosophy, religion, law, psychology and economics have differed over the role and usefulness of intuition and emotion in judging blameworthiness. Cognitive psychology and neurobiology provide new tools and methods for studying questions of normative judgment. Recently, a consensus view has emerged, which recognizes important roles for emotion and intuition and which suggests that normative judgment is a distributed process in the brain. Testing this approach through lesion and scanning studies has linked a set of brain regions to such judgment, including the ventromedial prefrontal cortex, orbitofrontal cortex, posterior cingulate cortex and posterior superior temporal sulcus. Better models of emotion and intuition will help provide further clarification of the processes involved. The study of law and justice is less well developed. We advance a model of law in the brain which suggests that law can recruit a wider variety of sources of information and paths of processing than do the intuitive moral responses that have been studied so far. We propose specific hypotheses and lines of further research that could help test this approach.