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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
Fabian Tomaschek; Michael Ramscar; Jessie S. Nixon – Cognitive Science, 2024
Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences--and the relations between the elements they comprise--are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the…
Descriptors: Sequential Learning, Learning Processes, Serial Learning, Executive Function
Erdin Mujezinovic; Vsevolod Kapatsinski; Ruben van de Vijver – Cognitive Science, 2024
A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney,…
Descriptors: Morphology (Languages), Phonology, Morphemes, Cues
Engelmann, Felix; Jager, Lena A.; Vasishth, Shravan – Cognitive Science, 2019
We present a comprehensive empirical evaluation of the ACT-R-based model of sentence processing developed by Lewis and Vasishth (2005) (LV05). The predictions of the model are compared with the results of a recent meta-analysis of published reading studies on retrieval interference in reflexive-/reciprocal-antecedent and subject-verb dependencies…
Descriptors: Cues, Sentences, Language Processing, Memory
Johns, Brendan T.; Mewhort, Douglas J. K.; Jones, Michael N. – Cognitive Science, 2019
Distributional models of semantics learn word meanings from contextual co-occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co-occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co-occurrences…
Descriptors: Semantics, Learning Processes, Models, Prediction
Yazbec, Angele; Kaschak, Michael P.; Borovsky, Arielle – Cognitive Science, 2019
Children and adults use established global knowledge to generate real-time linguistic predictions, but less is known about how listeners generate predictions in circumstances that semantically conflict with long-standing event knowledge. We explore these issues in adults and 5- to 10-year-old children using an eye-tracked sentence comprehension…
Descriptors: Sentences, Language Processing, Prediction, Adults
Freudenthal, Daniel; Ramscar, Michael; Leonard, Laurence B.; Pine, Julian M. – Cognitive Science, 2021
Children with developmental language disorder (DLD) have significant deficits in language ability that cannot be attributed to neurological damage, hearing impairment, or intellectual disability. The symptoms displayed by children with DLD differ across languages. In English, DLD is often marked by severe difficulties acquiring verb inflection.…
Descriptors: Verbs, Language Impairments, Symptoms (Individual Disorders), Associative Learning
Çöltekin, Çagri – Cognitive Science, 2017
This study investigates a strategy based on predictability of consecutive sub-lexical units in learning to segment a continuous speech stream into lexical units using computational modeling and simulations. Lexical segmentation is one of the early challenges during language acquisition, and it has been studied extensively through psycholinguistic…
Descriptors: Speech Communication, Phonemes, Prediction, Computational Linguistics
de Kleijn, Roy; Kachergis, George; Hommel, Bernhard – Cognitive Science, 2018
Sequential action makes up the bulk of human daily activity, and yet much remains unknown about how people learn such actions. In one motor learning paradigm, the serial reaction time (SRT) task, people are taught a consistent sequence of button presses by cueing them with the next target response. However, the SRT task only records keypress…
Descriptors: Sequential Learning, Reinforcement, Psychomotor Skills, Reaction Time
Primativo, Silvia; Reilly, Jamie; Crutch, Sebastian J – Cognitive Science, 2017
The Abstract Conceptual Feature (ACF) framework predicts that word meaning is represented within a high-dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF…
Descriptors: Semantics, Prediction, Abstract Reasoning, Eye Movements
Waldmann, Michael R. – Cognitive Science, 2007
In everyday life, people typically observe fragments of causal networks. From this knowledge, people infer how novel combinations of causes they may never have observed together might behave. I report on 4 experiments that address the question of how people intuitively integrate multiple causes to predict a continuously varying effect. Most…
Descriptors: Cues, Context Effect, Prediction, Intuition