Закон Йеркса - Додсона: связь между стимулированием и успешностью научения

The Yerkes - Dodson law: the link between stimulation and learning success

Sarunas Raudys, Viktoras Justickis

    Research output: Contribution to journalArticle

    Abstract

    The paper looks into the phenomenon of non-linearity of the link between success of learning and its stimulation (the Yerkes-Dodson law), arguing that the existing explanations are mostly based on the idea of overloading and disruption of appropriate psychological mechanism when the task is too difficult or learning stimulation too strong. The authors put forward an alternative explanation for non-linearity of the link between stimulation and success of learning: the Yerkes-Dodson effects maybe caused by peculiarities of the learning algorithm employed by the brain. The authors argue that the above effects do not manifest themselves if learning is based such instruction algorithms (widely used in no declarative learning) as D. Hebb's reinforcement o link by repetition, and outcome approximation described by the Rescorla-Wagner formula. An imitation experiment has demonstrated that the Yerkes-Dodson effects display themselves regularly if a person employs a more general variety of the abovementioned algorithm, i.e. the algorithm based on the scheme of neuron grids realizing the idea of non-linearity of the link between stimulation and outcome.
    Original languageEnglish
    Pages (from-to)119-126
    JournalВопросы психологии: научный журнал.
    Issue number3
    Publication statusPublished - 2008

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    Learning
    Psychology
    Neurons
    Brain

    Keywords

    • Non-declarative learning
    • Stimulation
    • Psychic processes

    Cite this

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    title = "Закон Йеркса - Додсона: связь между стимулированием и успешностью научения: The Yerkes - Dodson law: the link between stimulation and learning success",
    abstract = "The paper looks into the phenomenon of non-linearity of the link between success of learning and its stimulation (the Yerkes-Dodson law), arguing that the existing explanations are mostly based on the idea of overloading and disruption of appropriate psychological mechanism when the task is too difficult or learning stimulation too strong. The authors put forward an alternative explanation for non-linearity of the link between stimulation and success of learning: the Yerkes-Dodson effects maybe caused by peculiarities of the learning algorithm employed by the brain. The authors argue that the above effects do not manifest themselves if learning is based such instruction algorithms (widely used in no declarative learning) as D. Hebb's reinforcement o link by repetition, and outcome approximation described by the Rescorla-Wagner formula. An imitation experiment has demonstrated that the Yerkes-Dodson effects display themselves regularly if a person employs a more general variety of the abovementioned algorithm, i.e. the algorithm based on the scheme of neuron grids realizing the idea of non-linearity of the link between stimulation and outcome.",
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    AB - The paper looks into the phenomenon of non-linearity of the link between success of learning and its stimulation (the Yerkes-Dodson law), arguing that the existing explanations are mostly based on the idea of overloading and disruption of appropriate psychological mechanism when the task is too difficult or learning stimulation too strong. The authors put forward an alternative explanation for non-linearity of the link between stimulation and success of learning: the Yerkes-Dodson effects maybe caused by peculiarities of the learning algorithm employed by the brain. The authors argue that the above effects do not manifest themselves if learning is based such instruction algorithms (widely used in no declarative learning) as D. Hebb's reinforcement o link by repetition, and outcome approximation described by the Rescorla-Wagner formula. An imitation experiment has demonstrated that the Yerkes-Dodson effects display themselves regularly if a person employs a more general variety of the abovementioned algorithm, i.e. the algorithm based on the scheme of neuron grids realizing the idea of non-linearity of the link between stimulation and outcome.

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