Probability matching in choice under uncertainty: Intuition versus deliberation

Gaissmaier and Schooler (2008) [Gaissmaier, W., & Schooler, L. J. (2008). The smart potential behind probability matching. Cognition, 109, 416-422] argue that probability matching, which has traditionally been viewed as a decision making error, may instead reflect an adaptive response to environments in which outcomes potentially follow predictable patterns. In choices involving monetary stakes, we find that probability matching persists even when it is not possible to identify or exploit outcome patterns and that many “probability matchers” rate an alternative strategy (maximizing) as superior when it is described to them. Probability matching appears to reflect a mistaken intuition that can be, but often is not, overridden by deliberate consideration of alternative choice strategies.     

SAwSu: An Integrated Model of Associative and Reinforcement Learning

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.     

A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision‐Making Models

Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia—the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision‐making tasks, which are brought to light by Parkinson's disease as well as genetic differences in dopamine receptors. This paper shows that a standard model of action selection in a cognitive architecture (ACT‐R) cannot replicate any of these findings, details an alternative solution that reconciles action selection in the architecture with the physiology of the basal ganglia, and extends the domain of application of cognitive architectures. The implication of this solution for other architectures and existing models are discussed.     

People accounting: Social category-based choice

This paper presents people accounting—a hypothesis that describes how a simple numerical imbalance in representation along nominal social category lines can affect people’s choice of candidates in highly competitive situations (e.g., awards, jobs, etc.). For example, two scholarship finalists from California and New York may be equally qualified, but the award-winning chance for the California candidate will drop precipitously if 8 of the past 10 winners were from California. Studies 1-3 illustrate this effect. Study 4 links people accounting to intergroup fairness concerns and suggests that people accounting is more likely to occur when the category dimension is meaningful (e.g., Stanford/Princeton) than when it is not (e.g., left/right-handedness). Study 5 shows that candidates from overrepresented categories (e.g., “Californians”) must achieve higher minimum standards in order to be selected. The implication is that highly competitive decisions are often influenced by headcounts along mundane social category lines.     

Rankings,standards, and competition: Task vs. scale comparisons

Research showing how upward social comparison breeds competitive behavior has so far conflated local comparisons in task performance (e.g. a test score) with comparisons on a more general scale (i.e. an underlying skill). Using a ranking methodology ( Garcia, Tor, & Gonzalez, 2006) to separate task and scale comparisons, Studies 1–2 reveal that an upward comparison on the scale (e.g. being surpassed in rank), rather than in the mere task (e.g., being outperformed), is necessary to generate competition among rivals proximate to a standard (e.g. ranked #3 vs. 4, near “the top”); rivals far from a standard (e.g. ranked #203 vs. 204), on the other hand, still tend to cooperate. Study 3 illustrates this finding with player trades in Major League Baseball. Study 4 further shows how an implicit scale comparison, instead of the commonly assumed explicit task comparison, may account for those classical competition findings in the literature. Study 5 then reveals how scale ranking becomes all important in the proximity of a standard, leading rivals to tolerate even an upward scale comparison to increase their proximity to the standard. Implications for the increasingly popular “forced ranking” management systems (e.g., at General Electric) are also discussed.     

Vocational choice: A decision making perspective

We propose a model of vocational choice that can be used for analyzing and guiding the decision processes underlying career and job choices. Our model is based on research in behavioral decision making (BDM), in particular the choice goals framework developed by Bettman, Luce, and Payne (1998). The basic model involves two major processes. First, the selection of a decision strategy according to four choice goals: maximizing decision accuracy, minimizing cognitive effort, minimizing negative emotion, and maximizing justifiability of the decision. Second, the construction of situation-specific preferences, which can reflect irrelevant task and context factors such as the evaluation mode. This basic model is extended to account for social influences and the long decision time typical of most career and job decisions. We review research on vocational choice in light of this model, discuss normative implications for counseling, and outline a research agenda for studying vocational choice from a behavioral decision making perspective.     

Cognitive models of choice: comparing decision field theory to the proportional difference model

People often face preferential decisions under risk. To further our understanding of the cognitive processes underlying these preferential choices, two prominent cognitive models, decision field theory (DFT; Busemeyer & Townsend, 1993 ) and the proportional difference model (PD; González-Vallejo, 2002 ), were rigorously tested against each other. In two consecutive experiments, the participants repeatedly had to choose between monetary gambles. The first experiment provided the reference to estimate the models' free parameters. From these estimations, new gamble pairs were generated for the second experiment such that the two models made maximally divergent predictions. In the first experiment, both models explained the data equally well. However, in the second generalization experiment, the participants' choices were much closer to the predictions of DFT. The results indicate that the stochastic process assumed by DFT, in which evidence in favor of or against each option accumulates over time, described people's choice behavior better than the trade-offs between proportional differences assumed by PD.     

Erroneous choice and foregone gains in hoarding gray jays (Perisoreus canadensis)

Under the conventional assumption that natural selection favors choice behavior that maximizes some fitness-related currency, a forager making repeated binary choices should consistently choose the more valuable option. Under the alternative assumption that natural selection favors choice behavior that minimizes costly errors, erroneous choice is not only expected but is expected to be common when the cost of errors is low. This cost depends on the potential rate of return: the higher this rate, the smaller the cost of choosing the less valuable option. When this rate is very high, a forager may err frequently and yet forego no appreciable fitness gain. Errors should thus be more common when interruptions to foraging are shorter. Our experimental results supported this prediction: gray jays chose the less valuable option more frequently when subjected to shorter interruptions (experimentally imposed delays to access to food rewards). This tendency is consistent with the idea that an adaptive decision-making process may routinely produce errors, not because errors are in some way adaptive but because their fitness cost is minimal, particularly when delays are short. From a proximate perspective, this tendency to commit errors more frequently following shorter delays may be due to constraints on the jays’ information-processing capacity. In general, choice behavior should be viewed as the joint byproduct of adaptive decision making and cognitive constraints. Received: 8 June 2000 / Accepted after revision: 21 October 2000     

Computational models for the combination of advice and individual learning

Decision making often takes place in social environments where other actors influence individuals' decisions. The present article examines how advice affects individual learning. Five social learning models combining advice and individual learning-four based on reinforcement learning and one on Bayesian learning-and one individual learning model are tested against each other. In two experiments, some participants received good or bad advice prior to a repeated multioption choice task. Receivers of advice adhered to the advice, so that good advice improved performance. The social learning models described the observed learning processes better than the individual learning model. Of the models tested, the best social learning model assumes that outcomes from recommended options are more positively evaluated than outcomes from nonrecommended options. This model correctly predicted that receivers first adhere to advice, then explore other options, and finally return to the recommended option. The model also predicted accurately that good advice has a stronger impact on learning than bad advice. One-time advice can have a long-lasting influence on learning by changing the subjective evaluation of outcomes of recommended options.     

How social and non-social information influence classification decisions: A computational modelling approach

Social information such as observing others can improve performance in decision making. In particular, social information has been shown to be useful when finding the best solution on one’s own is difficult, costly, or dangerous. However, past research suggests that when making decisions people do not always consider other people’s behaviour when it is at odds with their own experiences. Furthermore, the cognitive processes guiding the integration of social information with individual experiences are still under debate. Here, we conducted two experiments to test whether information about other persons’ behaviour influenced people’s decisions in a classification task. Furthermore, we examined how social information is integrated with individual learning experiences by testing different computational models. Our results show that social information had a small but reliable influence on people’s classifications. The best computational model suggests that in categorization people first make up their own mind based on the non-social information, which is then updated by the social information.     

Inference in the Wild: A Framework for Human Situation Assessment and a Case Study of Air Combat

Situation awareness is a key construct in human factors and arises from a process of situation assessment (SA). SA comprises the perception of information, its integration with existing knowledge, the search for new information, and the prediction of the future state of the world, including the consequences of planned actions. Causal models implemented as Bayesian networks (BNs) are attractive for modeling all of these processes within a single, unified framework. We elicited declarative knowledge from two Royal Australian Air Force (RAAF) fighter pilots about the information sources used in the identification (ID) of airborne entities and the causal relationships between these sources. This knowledge was represented in a BN (the declarative model) that was evaluated against the performance of 19 RAAF fighter pilots in a low‐fidelity simulation. Pilot behavior was well predicted by a simple associative model (the behavioral model) with only three attributes of ID. Search for information by pilots was largely compensatory and was near‐optimal with respect to the behavioral model. The average revision of beliefs in response to evidence was close to Bayesian, but there was substantial variability. Together, these results demonstrate the value of BNs for modeling human SA.     

Learning from experience in nonlinear environments: Evidence from a competition scenario

We test people’s ability to learn to estimate a criterion (probability of success in a competition scenario) that requires aggregating information in a nonlinear manner. The learning environments faced by experimental participants are kind in that they are characterized by immediate, accurate feedback involving either naturalistic outcomes (information on winning and/or ranking) or the normatively correct probabilities. We find no evidence of learning from the former and modest learning from the latter, except that a group of participants endowed with a memory aid performed substantially better. However, when the task is restructured such that information should be aggregated in a linear fashion, participants learn to make more accurate assessments. Our experiments highlight the important role played by prior beliefs in learning tasks, the default status of linear aggregation in many inferential judgments, and the difficulty of learning in nonlinear environments even in the presence of veridical feedback.     

Developmental trends in everyday conditional reasoning: the retrieval and inhibition interplay

Two experiments examined developmental patterns in children’s conditional reasoning with everyday causal conditionals. In Experiment 1, a group of pre-, early, young, and late adolescents generated counterexamples for a set of conditionals to validate developmental claims about the counterexample retrieval capacity. In Experiment 2, participants in the same age range were presented with a conditional reasoning task with similar conditionals. Experiment 1 established that counterexample retrieval increased from preadolescence to late adolescence. Experiment 2 showed that acceptance rates of the invalid affirmation of the consequent inference gradually decreased in the same age range. Acceptance rates of the valid modus ponens inference showed a U-shaped pattern. After an initial drop from preadolescence to early adolescence, modus ponens acceptance ratings increased again after the onset of adolescence. Findings support the claim that the development of everyday conditional reasoning can be characterized as an interplay between the development of a counterexample retrieval and inhibition process.     

Short-term gains, long-term pains: How cues about state aid learning in dynamic environments

Successful investors seeking returns, animals foraging for food, and pilots controlling aircraft all must take into account how their current decisions will impact their future standing. One challenge facing decision makers is that options that appear attractive in the short-term may not turn out best in the long run. In this paper, we explore human learning in a dynamic decision making task which places short- and long-term rewards in conflict. Our goal in these studies was to evaluate how people’s mental representation of a task affects their ability to discover an optimal decision strategy. We find that perceptual cues that readily align with the underlying state of the task environment help people overcome the impulsive appeal of short-term rewards. Our experimental manipulations, predictions, and analyses are motivated by current work in reinforcement learning which details how learners value delayed outcomes in sequential tasks and the importance that “state” identification plays in effective learning.     

Reappraisal and expected value modulate risk taking

Decision making is informed by appraisals of appetitive cues and their associated opportunities for rewards. Such appraisals can be modulated by cognitive regulation strategies in order to promote goal-directed choices. Little is known about how cognitive regulation strategies, especially reappraisal, alter risk taking during decision making. To characterise the effect of reappraisal on risk taking, we systematically varied both the goal of regulation and the value of the decision options. Participants engaged in two reappraisal strategies with opposite goals, to increase (“emphasise”) or decrease (“de-emphasise”) the importance of an upcoming decision, during the presentation of cues signalling monetary decisions. The expected value of taking a risk was systematically varied across decisions such that a risky choice could be beneficial or disadvantageous. Reappraisal strategies increased or decreased risk taking in accordance both with regulation goals and expected value information. These results suggest that reappraisal can be used to flexibly alter behaviour associated with appetitive cues while maintaining value information.     

Probability as a psychological distance: Construal and preferences

We argue that probability, like space and time, instantiates psychological distance. Unlikely outcomes may seem more remote than likely outcomes and may therefore be construed at a relatively high level. Specifically, when the probability of an outcome is low, ends-related primary features should be more salient than means-related secondary features, but as the probability of the outcome increases, means-related features may become no less and even more salient than ends-related features. Thus, increases in probability should increase the weight of means-related features relative to the weight of ends-related features in decisions, thereby decreasing (or even reversing) the preference for a more desirable/less feasible outcome over a less desirable/more feasible outcome. We observed this pattern in two experiments. Analyses of judgments, monetary decisions, and self-reported reasons for decisions showed that the weight of means-related features was more sensitive to changes in probability than the weight of ends-related features in decisions.     

Modeling the Stroop phenomenon: processes, processing flow, and development

A model of the Stroop phenomenon is proposed which postulates that the classic effect is an additive function of three parameters, that is, dimension selection (decision making about which dimension to respond to), dimension identification (encoding and identification of the relevant dimension), and interference control (filtering out of interference from non-relevant dimensions). The study used stimuli addressed to three symbol systems (verbal, numerical, and figural), two types of stimulus composition (compatible vs. incompatible), and two types of dimension selection (decision needed about the to-be-identified dimension vs. no decision needed). Participants were 9, 11, 13, and 15 years old and they were tested twice. The model was found to hold under all stimulus and presentation conditions. Moreover, it was found that the three parameters are differentially related to age. The implications of the model for general theories of cognition and cognitive development are discussed.     

Pricing risk and ambiguity: The effect of perspective taking

In the valuation of uncertain prospects, a difference is often observed between selling and buying perspectives. This paper distinguishes between risk (known probabilities) and ambiguity (unknown probabilities) in decisions under uncertainty and shows that the valuation disparity increases under ambiguity compared to risk. It is found that both the comparative versus noncomparative evaluation of risky and ambiguous prospects and the uniqueness of the valuation perspective (either seller or buyer) moderate this increase in the disparity under ambiguity. The finding is consistent with recent theoretical accounts of pricing under uncertainty. We discuss implications for market behaviour and for the ambiguity paradigm as a research tool.