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Johnston, Angie M.; Johnson, Samuel G. B.; Koven, Marissa L.; Keil, Frank C. – Developmental Science, 2017
Like scientists, children seek ways to explain causal systems in the world. But are children scientists in the strict Bayesian tradition of maximizing posterior probability? Or do they attend to other explanatory considerations, as laypeople and scientists--such as Einstein--do? Four experiments support the latter possibility. In particular, we…
Descriptors: Young Children, Thinking Skills, Inferences, Bayesian Statistics
Cummins, Denise Dellarosa – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2014
People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive…
Descriptors: Inferences, Prediction, Experiments, Experimental Psychology
Hahn, Ulrike; Warren, Paul A. – Psychological Review, 2010
We (Hahn & Warren, 2009) recently proposed a new account of the systematic errors and biases that appear to be present in people's perception of randomly generated events. In a comment on that article, Sun, Tweney, and Wang (2010) critiqued our treatment of the gambler's fallacy. We had argued that this fallacy was less gross an error than it…
Descriptors: Probability, Incidence, Prediction, Misconceptions
Altmann, Erik M. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2011
This study takes inventory of available evidence on response repetition (RR) effects in task switching, in particular the evidence for RR cost when the task switches. The review reveals that relatively few task-switching studies in which RR effects were addressed have shown statistical support for RR cost, and that almost all are affected by 1 of…
Descriptors: Task Analysis, Evidence, Cues, Cognitive Processes
Bowers, Jeffrey S.; Davis, Colin J. – Psychological Bulletin, 2012
According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak.…
Descriptors: Bayesian Statistics, Psychology, Brain, Theories
Simen, Patrick; Contreras, David; Buck, Cara; Hu, Peter; Holmes, Philip; Cohen, Jonathan D. – Journal of Experimental Psychology: Human Perception and Performance, 2009
The drift-diffusion model (DDM) implements an optimal decision procedure for stationary, 2-alternative forced-choice tasks. The height of a decision threshold applied to accumulating information on each trial determines a speed-accuracy tradeoff (SAT) for the DDM, thereby accounting for a ubiquitous feature of human performance in speeded response…
Descriptors: Decision Making, Models, Reaction Time, Rewards
Lench, Heather C. – Journal of Experimental Psychology: General, 2009
People generally judge that the future will be consistent with their desires, but the reason for this desirability bias is unclear. This investigation examined whether affective reactions associated with future events are the mechanism through which desires influence likelihood judgments. In 4 studies, affective reactions were manipulated for…
Descriptors: Bias, Risk, Relationship, Evaluative Thinking
Nilsson, Hakan; Winman, Anders; Juslin, Peter; Hansson, Goran – Journal of Experimental Psychology: General, 2009
This article explores the configural weighted average (CWA) hypothesis suggesting that extension biases, like conjunction and disjunction errors, occur because people estimate compound probabilities by taking a CWA of the constituent probabilities. The hypothesis suggests a process consistent with well-known cognitive constraints, which…
Descriptors: Experimental Psychology, Prediction, Probability, Bias
Haberman, Shelby J. – Psychometrika, 2006
When a simple random sample of size n is employed to establish a classification rule for prediction of a polytomous variable by an independent variable, the best achievable rate of misclassification is higher than the corresponding best achievable rate if the conditional probability distribution is known for the predicted variable given the…
Descriptors: Bias, Computation, Sample Size, Classification