A dual-task paradigm for behavioral and neurobiological studies in nonhuman primates.
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[Background]The dual-task paradigm is a procedure in which subjects are asked to perform two behavioral tasks concurrently, each of which involves a distinct goal with a unique stimulus–response association. Due to the heavy demand on subject's cognitive abilities, human studies using this paradigm have provided detailed insights regarding how the components of cognitive systems are functionally organized and implemented. Althoughdual-task paradigms are widely used in human studies, they are seldom used in nonhuman animal studies. [New method]We propose a novel dual-task paradigm for monkeys that requires the simultaneous performance of two cognitively demanding component tasks, each of which uses an independent effector for behavioral responses (hand and eyes). We provide a detailed description of an optimal training protocol for this paradigm, which has been lacking in the existing literature. [Results]An analysis of behavioral performance showed that the proposed dual-task paradigm (1) was quickly learned by monkeys (less than 40 sessions) with step-by-step training protocols, (2) produced specific behavioral effects, known as dual-task interference in human studies, and (3) achieved rigid and independent control of the effectors for behavioral responses throughout the trial. [Comparison with existing methods]The proposed dual-task paradigm has a scalable task structure, in that each of the two component tasks can be easily replaced by other tasks, while preserving the overall structure of the paradigm. [Conclusions]This paradigm should be useful for investigating executive control that underlies dual-task performance at both the behavioral and neuronal levels.
収録刊行物
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- Journal of neuroscience methods
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Journal of neuroscience methods 246 1-12, 2015-03-10
Elsevier B.V.
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詳細情報 詳細情報について
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- CRID
- 1050564285766945152
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- NII論文ID
- 120005625618
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- NII書誌ID
- AA00703367
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- ISSN
- 01650270
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- HANDLE
- 2433/198807
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- 本文言語コード
- en
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- 資料種別
- journal article
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- データソース種別
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- IRDB
- CiNii Articles