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  1. Seeing Patterns in Randomness: A Computational Model of Surprise.Phil Maguire, Philippe Moser, Rebecca Maguire & Mark T. Keane - 2019 - Topics in Cognitive Science 11 (1):103-118.
    Much research has linked surprise to violation of expectations, but it has been less clear how one can be surprised when one has no particular expectation. This paper discusses a computational theory based on Algorithmic Information Theory, which can account for surprises in which one initially expects randomness but then notices a pattern in stimuli. The authors present evidence that a “randomness deficiency” heuristic leads to surprise in such cases.
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  • Perceptual Learning of Intonation Contour Categories in Adults and 9‐ to 11‐Year‐Old Children: Adults Are More Narrow‐Minded.Vsevolod Kapatsinski, Paul Olejarczuk & Melissa A. Redford - 2017 - Cognitive Science 41 (2):383-415.
    We report on rapid perceptual learning of intonation contour categories in adults and 9- to 11-year-old children. Intonation contours are temporally extended patterns, whose perception requires temporal integration and therefore poses significant working memory challenges. Both children and adults form relatively abstract representations of intonation contours: Previously encountered and novel exemplars are categorized together equally often, as long as distance from the prototype is controlled. However, age-related differences in categorization performance also exist. Given the same experience, adults form narrower categories (...)
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  • Learning words in space and time: Contrasting models of the suspicious coincidence effect.Gavin W. Jenkins, Larissa K. Samuelson, Will Penny & John P. Spencer - 2021 - Cognition 210 (C):104576.
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  • Learning Object Names at Different Hierarchical Levels Using Cross‐Situational Statistics.Chen Chi-Hsin, Zhang Yayun & Yu Chen - 2018 - Cognitive Science:591-605.
    Objects in the world usually have names at different hierarchical levels (e.g., beagle, dog, animal). This research investigates adults' ability to use cross‐situational statistics to simultaneously learn object labels at individual and category levels. The results revealed that adults were able to use co‐occurrence information to learn hierarchical labels in contexts where the labels for individual objects and labels for categories were presented in completely separated blocks, in interleaved blocks, or mixed in the same trial. Temporal presentation schedules significantly affected (...)
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  • Grounding Cognitive‐Level Processes in Behavior: The View From Dynamic Systems Theory.Larissa K. Samuelson, Gavin W. Jenkins & John P. Spencer - 2015 - Topics in Cognitive Science 7 (2):191-205.
    Marr's seminal work laid out a program of research by specifying key questions for cognitive science at different levels of analysis. Because dynamic systems theory focuses on time and interdependence of components, DST research programs come to very different conclusions regarding the nature of cognitive change. We review a specific DST approach to cognitive-level processes: dynamic field theory. We review research applying DFT to several cognitive-level processes: object permanence, naming hierarchical categories, and inferring intent, that demonstrate the difference in understanding (...)
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