Automatic optimism: Biased use of base rate information for positive and negative events

People generally judge that positive events will occur in their lives and negative events will not, even when both events have the same objective likelihood to occur. In four studies, we examined the possibility that this optimistic bias is the result of people’s automatic affective reactions to future events. Studies 1 and 2 demonstrate, in two different contexts, that people are consistently optimistic in their predictions, despite identical base rates for positive and negative events. In Study 2, optimistic bias was not influenced by incentives for motivated reasoning or rewards for accuracy, suggesting that bias was the result of automatic processes. Studies 3 and 4 showed that optimistic bias was more pronounced when predictions were speeded and when participants made predictions after exposure to affectively valenced words. Together, these findings suggest that people optimistically interpret base rates and that this optimism is due to an effortless affective process.     

Model structure adequacy analysis: selecting models on the basis of their ability to answer scientific questions

Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as a means to select models based on their ability to answer specific scientific questions given the current understanding of the relevant aspects of the real world.     

A new formulation of the Principle of Indifference

The idea of a probabilistic logic of inductive inference based on some form of the principle of indifference has always retained a powerful appeal. However, up to now all modifications of the principle failed. In this paper, a new formulation of such a principle is provided that avoids generating paradoxes and inconsistencies. Because of these results, the thesis that probabilities cannot be logical quantities, determined in an objective way through some form of the principle of indifference, is no longer supportable. Later, the paper investigates some implications of the new principle of indifference. To conclude, a re-examination of the foundations of the so-called objective Bayesian inference is called for.     

Quantified coalition logic

We add a limited but useful form of quantification to Coalition Logic, a popular formalism for reasoning about cooperation in game-like multi-agent systems. The basic constructs of Quantified Coalition Logic (QCL) allow us to express such properties as “every coalition satisfying property P can achieve φ” and “there exists a coalition C satisfying property P such that C can achieve φ”. We give an axiomatisation of QCL, and show that while it is no more expressive than Coalition Logic, it is nevertheless exponentially more succinct. The complexity of QCL model checking for symbolic and explicit state representations is shown to be no worse than that of Coalition Logic, and satisfiability for QCL is shown to be no worse than satisfiability for Coalition Logic. We illustrate the formalism by showing how to succinctly specify such social choice mechanisms as majority voting, which in Coalition Logic require specifications that are exponentially long in the number of agents.     

Are some negotiators better than others? Individual differences in bargaining outcomes

The authors address the long-standing mystery of stable individual differences in negotiation performance, on which intuition and conventional wisdom have clashed with inconsistent empirical findings. The present study used the Social Relations Model to examine individual differences directly via consistency in performance across multiple negotiations and to disentangle the roles of both parties within these inherently dyadic interactions. Individual differences explained a substantial 46% of objective performance and 19% of subjective performance in a mixed-motive bargaining exercise. Previous work may have understated the influence of individual differences because conventional research designs require specific traits to be identified and measured. Exploratory analyses of a battery of traits revealed few reliable associations with consistent individual differences in objective performance-except for positive beliefs about negotiation, positive affect, and concern for one's outcome, each of which predicted better performance. Findings suggest that the field has large untapped potential to explain substantial individual differences. Limitations, areas for future research, and practical implications are discussed.     

The prediction of Honesty–Humility-related criteria by the HEXACO and Five-Factor Models of personality

We examined two questions involving the relative validity of the HEXACO and Five-Factor Models of personality structure. First, would the HEXACO model outpredict the Five-Factor Model (FFM) with regard to several diverse criteria that are conceptually relevant to the Honesty–Humility dimension of personality? If so, would the addition of a proxy Honesty–Humility scale—as computed from relevant facets of the FFM Agreeableness domain—allow the FFM to achieve predictive validities matching those of the HEXACO model? Results from self- and observer ratings in three samples (each N > 200) indicated that the HEXACO model showed considerable predictive validity advantages over the FFM. When a measure of Honesty–Humility derived from the FFM was added to the original five domains of that model, the predictive validity reached that of the HEXACO model for some criteria, but remained substantially below for others.     

Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

Using FMRI to test models of complex cognition

This article investigates the potential of fMRI to test assumptions about different components in models of complex cognitive tasks. If the components of a model can be associated with specific brain regions, one can make predictions for the temporal course of the BOLD response in these regions. An event-locked procedure is described for dealing with temporal variability and bringing model runs and individual data trials into alignment. Statistical methods for testing the model are described that deal with the scan-to-scan correlations in the errors of measurement of the BOLD signal. This approach is illustrated using a "sacrificial" ACT-R model that involves mapping 6 modules onto 6 brain regions in an experiment from Ravizza, Anderson, and Carter (in press) concerned with equation solving. The model's visual encoding predicted the BOLD response in the fusiform gyrus, its controlled retrieval predicted the BOLD response in the lateral inferior prefrontal cortex, and its subgoal setting predicted the BOLD response in the anterior cingulate cortex. On the other hand, its motor programming failed to predict anticipatory activation in the motor cortex, its representational changes failed to predicted the pattern of activity in the posterior parietal cortex, and its procedural component failed to predict an initial spike in caudate. The results illustrate the power of such data to direct the development of a theory of complex problem solving, both at the level of a specific task model as well as at the level of the cognitive architecture.