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Alicia M. Chen; Andrew Palacci; Natalia Vélez; Robert D. Hawkins; Samuel J. Gershman – Cognitive Science, 2024
How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N…
Descriptors: Bayesian Statistics, Models, Teaching Methods, Evaluation
Li, Michael Y.; Callaway, Fred; Thompson, William D.; Adams, Ryan P.; Griffiths, Thomas L. – Cognitive Science, 2023
Humans can learn complex functional relationships between variables from small amounts of data. In doing so, they draw on prior expectations about the form of these relationships. In three experiments, we show that people learn to adjust these expectations through experience, learning about the likely forms of the functions they will encounter.…
Descriptors: Learning Processes, Expectation, Experience, Relationship
Valentina Gliozzi – Cognitive Science, 2024
We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying…
Descriptors: Infants, Computation, Models, Cognitive Development
John Hollander; Andrew Olney – Cognitive Science, 2024
Recent investigations on how people derive meaning from language have focused on task-dependent shifts between two cognitive systems. The symbolic (amodal) system represents meaning as the statistical relationships between words. The embodied (modal) system represents meaning through neurocognitive simulation of perceptual or sensorimotor systems…
Descriptors: Verbs, Symbolic Language, Language Processing, Semantics
Ramírez-Ruiz, Jorge; Moreno-Bote, Rubén – Cognitive Science, 2022
When facing many options, we narrow down our focus to very few of them. Although behaviors like this can be a sign of heuristics, they can actually be optimal under limited cognitive resources. Here, we study the problem of how to optimally allocate limited sampling time to multiple options, modeled as accumulators of noisy evidence, to determine…
Descriptors: Decision Making, Heuristics, Cognitive Processes, Models
Barak, Libby; Harmon, Zara; Feldman, Naomi H.; Edwards, Jan; Shafto, Patrick – Cognitive Science, 2023
As children gradually master grammatical rules, they often go through a period of producing form-meaning associations that were not observed in the input. For example, 2- to 3-year-old English-learning children use the bare form of verbs in settings that require obligatory past tense meaning while already starting to produce the grammatical…
Descriptors: English Language Learners, Morphemes, Preschool Children, English (Second Language)
Chunking versus Transitional Probabilities: Differentiating between Theories of Statistical Learning
Emerson, Samantha N.; Conway, Christopher M. – Cognitive Science, 2023
There are two main approaches to how statistical patterns are extracted from sequences: The transitional probability approach proposes that statistical learning occurs through the computation of probabilities between items in a sequence. The chunking approach, including models such as PARSER and TRACX, proposes that units are extracted as chunks.…
Descriptors: Statistics Education, Learning Processes, Learning Theories, Pattern Recognition
Marchant, Nicolás; Quillien, Tadeg; Chaigneau, Sergio E. – Cognitive Science, 2023
The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with…
Descriptors: Classification, Bayesian Statistics, Causal Models, Evaluation Methods
Henne, Paul; O'Neill, Kevin – Cognitive Science, 2022
Mike accidentally knocked against a bottle. Seeing that the bottle was about to fall, Jack was just about to catch it when Peter accidentally knocked against him, making Jack unable to catch it. Jack did not grab the bottle, and it fell to the ground and spilled. In double-prevention cases like these, philosophers and nonphilosophers alike tend to…
Descriptors: Evaluative Thinking, Prevention, Logical Thinking, Individual Differences
Falandays, J. Benjamin; Smaldino, Paul E. – Cognitive Science, 2022
When a population exhibits collective cognitive alignment, such that group members tend to perceive, remember, and reproduce information in similar ways, the features of socially transmitted variants (i.e., artifacts, behaviors) may converge over time towards culture-specific equilibria points, often called cultural attractors. Because cognition…
Descriptors: Thinking Skills, Cultural Influences, Group Unity, Models
Eva Portelance; Michael C. Frank; Dan Jurafsky – Cognitive Science, 2024
Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question…
Descriptors: Vocabulary, Grammar, Visual Aids, Language Acquisition
Rey, Arnaud; Fagot, Joël; Mathy, Fabien; Lazartigues, Laura; Tosatto, Laure; Bonafos, Guillem; Freyermuth, Jean-Marc; Lavigne, Frédéric – Cognitive Science, 2022
The extraction of cooccurrences between two events, A and B, is a central learning mechanism shared by all species capable of associative learning. Formally, the cooccurrence of events A and B appearing in a sequence is measured by the transitional probability (TP) between these events, and it corresponds to the probability of the second stimulus…
Descriptors: Animals, Learning Processes, Associative Learning, Serial Learning
Ramotowska, Sonia; Steinert-Threlkeld, Shane; Maanen, Leendert; Szymanik, Jakub – Cognitive Science, 2023
According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth-conditional representations of vague…
Descriptors: Computation, Models, Semantics, Decision Making
Cheng, Patricia W.; Sandhofer, Catherine M.; Liljeholm, Mimi – Cognitive Science, 2022
The present paper examines a type of abstract domain-general knowledge required for the process of constructing useable domain-specific causal knowledge, the evident goal of causal learning. It tests the hypothesis that analytic knowledge of "causal-invariance decomposition functions" is essential for this process. Such knowledge…
Descriptors: Preschool Children, Learning Processes, Generalization, Heuristics
Abu-Zhaya, Rana; Arnon, Inbal; Borovsky, Arielle – Cognitive Science, 2022
Meaning in language emerges from multiple words, and children are sensitive to multi-word frequency from infancy. While children successfully use cues from single words to generate linguistic predictions, it is less clear whether and how they use multi-word sequences to guide real-time language processing and whether they form predictions on the…
Descriptors: Sentences, Language Processing, Semantics, Prediction