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Hayes, Brett K.; Liew, Shi Xian; Desai, Saoirse Connor; Navarro, Danielle J.; Wen, Yuhang – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both…
Descriptors: Logical Thinking, Inferences, Bias, Individual Differences
Austerweil, Joseph L.; Sanborn, Sophia; Griffiths, Thomas L. – Cognitive Science, 2019
Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people "learn" the appropriate way to generalize for a new context. To understand this capability, we…
Descriptors: Generalization, Logical Thinking, Inferences, Bayesian Statistics
Hinterecker, Thomas; Knauff, Markus; Johnson-Laird, P. N. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
Individuals draw conclusions about possibilities from assertions that make no explicit reference to them. The model theory postulates that assertions such as disjunctions refer to possibilities. Hence, a disjunction of the sort, "A or B or both," where "A" and "B" are sensible clauses, yields mental models of an…
Descriptors: Logical Thinking, Abstract Reasoning, Inferences, Probability
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
Oh, Hanna; Beck, Jeffrey M.; Zhu, Pingping; Sommer, Marc A.; Ferrari, Silvia; Egner, Tobias – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2016
Much of our real-life decision making is bounded by uncertain information, limitations in cognitive resources, and a lack of time to allocate to the decision process. It is thought that humans overcome these limitations through "satisficing," fast but "good-enough" heuristic decision making that prioritizes some sources of…
Descriptors: Decision Making, Cues, Cognitive Processes, Time
Martori, Francesc; Cuadros, Jordi; González-Sabaté, Lucinio – International Educational Data Mining Society, 2015
Student modeling can help guide the behavior of a cognitive tutor system and provide insight to researchers on understanding how students learn. In this context, Bayesian Knowledge Tracing (BKT) is one of the most popular knowledge inference models due to its predictive accuracy, interpretability and ability to infer student knowledge. However,…
Descriptors: Bayesian Statistics, Inferences, Prediction, Accuracy
Wellwood, Alexis; Gagliardi, Annie; Lidz, Jeffrey – Language Learning and Development, 2016
Acquiring the correct meanings of words expressing quantities ("seven, most") and qualities ("red, spotty") present a challenge to learners. Understanding how children succeed at this requires understanding, not only of what kinds of data are available to them, but also the biases and expectations they bring to the learning…
Descriptors: Syntax, Computational Linguistics, Task Analysis, Prediction
Jenkins, Gavin W.; Samuelson, Larissa K.; Smith, Jodi R.; Spencer, John P. – Cognitive Science, 2015
It is unclear how children learn labels for multiple overlapping categories such as "Labrador," "dog," and "animal." Xu and Tenenbaum (2007a) suggested that learners infer correct meanings with the help of Bayesian inference. They instantiated these claims in a Bayesian model, which they tested with preschoolers and…
Descriptors: Generalization, Young Children, Inferences, Models
Khajah, Mohammad; Lindsey, Robert V.; Mozer, Michael C. – International Educational Data Mining Society, 2016
In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better.…
Descriptors: Bayesian Statistics, Data Analysis, Prediction, Intelligent Tutoring Systems
Wonnacott, Elizabeth – Journal of Memory and Language, 2011
Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Such learning has been considered problematic for theories of acquisition: if learners generalize abstract patterns to new words, how do they learn lexically-based exceptions? One approach claims that learners use…
Descriptors: Child Language, Artificial Languages, Generalization, Inferences
Murphy, Gregory L.; Ross, Brian H. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2010
Two experiments investigated how people perform category-based induction for items that have uncertain categorization. Whereas normative considerations suggest that people should consider multiple relevant categories, much past research has argued that people focus on only the most likely category. A new method is introduced in which responses on…
Descriptors: Logical Thinking, Classification, Inferences, Prediction
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
Holyoak, Keith J.; Lee, Hee Seung; Lu, Hongjing – Journal of Experimental Psychology: General, 2010
A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source…
Descriptors: Inferences, Logical Thinking, Bayesian Statistics, Causal Models
Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Reading Comprehension, Reading Achievement, Elementary School Students, Secondary School Students
Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Response to Intervention, Achievement Gains, High Stakes Tests, Prediction
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