6 found
Order:
  1.  3
    What's in a Name? The Multiple Meanings of “Chunk” and “Chunking”.Fernand Gobet, Martyn Lloyd-Kelly & Peter C. R. Lane - 2016 - Frontiers in Psychology 7.
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark   3 citations  
  2.  3
    Evolving Process-Based Models From Psychological Data Using Genetic Programming.Peter C. R. Lane, Peter D. Sozou, Mark Addison & Fernand Gobet - unknown
    The development of computational models to provide explanations of psychological data can be achieved using semi-automated search techniques, such as genetic programming. One challenge with these techniques is to control the type of model that is evolved to be cognitively plausible – a typical problem is that of “bloating”, where continued evolution generates models of increasing size without improving overall fitness. In this paper we describe a system for representing psychological data, a class of process-based models, and algorithms for evolving (...)
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark   2 citations  
  3.  6
    Chunks, Schemata, and Retrieval Structures: Past and Current Computational Models.Fernand Gobet, Peter C. R. Lane & Martyn Lloyd-Kelly - 2015 - Frontiers in Psychology 6.
    Direct download (7 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  4. Mechanisms in Human Learning.Fernand Gobet, Peter C. R. Lane, Steve Croker, Peter C.-H. Cheng, Gary Jones, Iain Oliver & Julian M. Pine - 2001 - Trends in Cognitive Sciences 5 (6):236-243.
    Pioneering work in the 1940s and 1950s suggested that the concept of chunking might be important in many processes of perception, learning and cognition in humans and animals. We summarize here the major sources of evidence for chunking mechanisms, and consider how such mechanisms have been implemented in computational models of the learning process. We distinguish two forms of chunking: the first deliberate, under strategic control, and goal-oriented; the second automatic, continuous, and linked to perceptual processes. Recent work with discrimination-network (...)
     
    Export citation  
     
    Bookmark   2 citations  
  5.  20
    What Forms the Chunks in a Subject's Performance? Lessons From the CHREST Computational Model of Learning.Peter C. R. Lane, Fernand Gobet & Peter C.-H. Cheng - 2001 - Behavioral and Brain Sciences 24 (1):128-129.
    Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk.
    Direct download (8 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  6.  12
    The CHREST Model of Active Perception and its Role in Problem Solving.Peter C. R. Lane, Peter C.-H. Cheng & Fernand Gobet - 2001 - Behavioral and Brain Sciences 24 (5):892-893.
    We discuss the relation of the Theory of Event Coding (TEC) to a computational model of expert perception, CHREST, based on the chunking theory. TEC's status as a verbal theory leaves several questions unanswerable, such as the precise nature of internal representations used, or the degree of learning required to obtain a particular level of competence: CHREST may help answer such questions.
    Direct download (6 more)  
     
    Export citation  
     
    Bookmark