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Showing 1 to 15 of 23 results Save | Export
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Hsu, Anne S.; Horng, Andy; Griffiths, Thomas L.; Chater, Nick – Cognitive Science, 2017
Identifying patterns in the world requires noticing not only unusual occurrences, but also unusual absences. We examined how people learn from absences, manipulating the extent to which an absence is expected. People can make two types of inferences from the absence of an event: either the event is possible but has not yet occurred, or the event…
Descriptors: Statistical Inference, Bayesian Statistics, Evidence, Prediction
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Douven, Igor; Mirabile, Patricia – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2018
There is a wealth of evidence that people's reasoning is influenced by explanatory considerations. Little is known, however, about the exact form this influence takes, for instance about whether the influence is unsystematic or because of people's following some rule. Three experiments investigate the descriptive adequacy of a precise proposal to…
Descriptors: Probability, Bayesian Statistics, Hypothesis Testing, Thinking Skills
Gagnon-Bartsch, J. A.; Sales, A. C.; Wu, E.; Botelho, A. F.; Erickson, J. A.; Miratrix, L. W.; Heffernan, N. T. – Grantee Submission, 2019
Randomized controlled trials (RCTs) admit unconfounded design-based inference--randomization largely justifies the assumptions underlying statistical effect estimates--but often have limited sample sizes. However, researchers may have access to big observational data on covariates and outcomes from RCT non-participants. For example, data from A/B…
Descriptors: Randomized Controlled Trials, Educational Research, Prediction, Algorithms
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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
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Gershman, Samuel J.; Pouncy, Hillard Thomas; Gweon, Hyowon – Cognitive Science, 2017
We routinely observe others' choices and use them to guide our own. Whose choices influence us more, and why? Prior work has focused on the effect of perceived similarity between two individuals (self and others), such as the degree of overlap in past choices or explicitly recognizable group affiliations. In the real world, however, any dyadic…
Descriptors: Social Influences, Social Cognition, Inferences, Models
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Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2016
Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…
Descriptors: Experiments, Inferences, Bayesian Statistics, Probability
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Skewes, Joshua C.; Gebauer, Line – Journal of Autism and Developmental Disorders, 2016
Convergent research suggests that people with ASD have difficulties localizing sounds in space. These difficulties have implications for communication, the development of social behavior, and quality of life. Recently, a theory has emerged which treats perceptual symptoms in ASD as the product of impairments in implicit Bayesian inference; as…
Descriptors: Autism, Pervasive Developmental Disorders, Auditory Perception, Bayesian Statistics
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Denison, Stephanie; Reed, Christie; Xu, Fei – Developmental Psychology, 2013
How do people make rich inferences from such sparse data? Recent research has explored this inferential ability by investigating probabilistic reasoning in infancy. For example, 8- and 11-month-old infants can make inferences from samples to populations and vice versa (Denison & Xu, 2010a; Xu & Denison, 2009; Xu & Garcia, 2008a). The…
Descriptors: Probability, Infants, Inferences, Young Children
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Yildirim, Ilker; Jacobs, Robert A. – Cognition, 2013
We study people's abilities to transfer object category knowledge across visual and haptic domains. If a person learns to categorize objects based on inputs from one sensory modality, can the person categorize these same objects when the objects are perceived through another modality? Can the person categorize novel objects from the same…
Descriptors: Novelty (Stimulus Dimension), Stimuli, Infants, Visual Stimuli
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Sobel, David M.; Munro, Sarah E. – Developmental Psychology, 2009
In 5 experiments the authors examined children's understanding of causal mechanisms and their reasoning about base rates across domains of knowledge. Experiment 1 showed that 3-year-olds interpret objects activating a machine differently from a novel agent liking each object; children are more likely to treat the latter as indicating the objects…
Descriptors: Statistical Inference, Inferences, Influences, Young Children
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Gershman, Samuel J.; Blei, David M.; Niv, Yael – Psychological Review, 2010
A. Redish et al. (2007) proposed a reinforcement learning model of context-dependent learning and extinction in conditioning experiments, using the idea of "state classification" to categorize new observations into states. In the current article, the authors propose an interpretation of this idea in terms of normative statistical inference. They…
Descriptors: Conditioning, Statistical Inference, Inferences, Bayesian Statistics
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Austerweil, Joseph L.; Griffiths, Thomas L. – Cognitive Psychology, 2011
Most psychological theories treat the features of objects as being fixed and immediately available to observers. However, novel objects have an infinite array of properties that could potentially be encoded as features, raising the question of how people learn which features to use in representing those objects. We focus on the effects of…
Descriptors: Visual Stimuli, Novelty (Stimulus Dimension), Bayesian Statistics, Learning
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Hilbig, Benjamin E.; Erdfelder, Edgar; Pohl, Rudiger F. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2011
A new process model of the interplay between memory and judgment processes was recently suggested, assuming that retrieval fluency--that is, the speed with which objects are recognized--will determine inferences concerning such objects in a single-cue fashion. This aspect of the fluency heuristic, an extension of the recognition heuristic, has…
Descriptors: Stimuli, Heuristics, Memory, Goodness of Fit
Kaplan, David – Society for Research on Educational Effectiveness, 2010
In recent years, attention in the education community has focused on the need for evidenced-based research, particularly educational policies and interventions that rest on "scientifically based research". The emphasis on scientifically based research in education has led to a corresponding increase in studies designed to provide strong warrants…
Descriptors: Evidence, Educational Research, Educational Policy, Models
Zhao, Yuan – ProQuest LLC, 2010
Learning a phonetic category (or any linguistic category) requires integrating different sources of information. A crucial unsolved problem for phonetic learning is how this integration occurs: how can we update our previous knowledge about a phonetic category as we hear new exemplars of the category? One model of learning is Bayesian Inference,…
Descriptors: Evidence, Cues, Phonetics, Prior Learning
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