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Models of paradoxical lesion effects and rules of inference for imputing function to structure in the brain

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Models of paradoxical lesion effects and rules of inference for imputing function to structure in the brain. / Young, Malcolm P; Hilgetag, Claus; Scannell, Jack W.

In: NEUROCOMPUTING, Vol. 26-27, 01.06.1999, p. 933-938.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Young, MP, Hilgetag, C & Scannell, JW 1999, 'Models of paradoxical lesion effects and rules of inference for imputing function to structure in the brain', NEUROCOMPUTING, vol. 26-27, pp. 933-938. https://doi.org/10.1016/s0925-2312(99)00012-0

APA

Young, M. P., Hilgetag, C., & Scannell, J. W. (1999). Models of paradoxical lesion effects and rules of inference for imputing function to structure in the brain. NEUROCOMPUTING, 26-27, 933-938. https://doi.org/10.1016/s0925-2312(99)00012-0

Vancouver

Young MP, Hilgetag C, Scannell JW. Models of paradoxical lesion effects and rules of inference for imputing function to structure in the brain. NEUROCOMPUTING. 1999 Jun 1;26-27:933-938. https://doi.org/10.1016/s0925-2312(99)00012-0

Bibtex

@article{50ab69fe33be4fae82b3a2e2e5f4ec19,
title = "Models of paradoxical lesion effects and rules of inference for imputing function to structure in the brain",
abstract = "Studies of the effects of brain lesions on behaviour have informed brain sciences for more than 100 years. Paradoxical results from some experiments, however, contradict widely accepted logic for imputing function to structure, including the `gold standard'; double dissociation. Orienting systems have produced clear examples of paradoxical lesion effects and provide the opportunity to identify reliable inference for imputing function. Our models reproduced the experimental effects. Analysis of the models demonstrated why single and double dissociation studies do not yield reliable conclusions, but suggested more reliable methods for understanding the delegation of functions to neuroanatomical structures in large-scale neural networks.",
author = "Young, {Malcolm P} and Claus Hilgetag and Scannell, {Jack W}",
year = "1999",
month = jun,
day = "1",
doi = "10.1016/s0925-2312(99)00012-0",
language = "English",
volume = "26-27",
pages = "933--938",
journal = "NEUROCOMPUTING",
issn = "0925-2312",
publisher = "ELSEVIER SCIENCE BV",

}

RIS

TY - JOUR

T1 - Models of paradoxical lesion effects and rules of inference for imputing function to structure in the brain

AU - Young, Malcolm P

AU - Hilgetag, Claus

AU - Scannell, Jack W

PY - 1999/6/1

Y1 - 1999/6/1

N2 - Studies of the effects of brain lesions on behaviour have informed brain sciences for more than 100 years. Paradoxical results from some experiments, however, contradict widely accepted logic for imputing function to structure, including the `gold standard'; double dissociation. Orienting systems have produced clear examples of paradoxical lesion effects and provide the opportunity to identify reliable inference for imputing function. Our models reproduced the experimental effects. Analysis of the models demonstrated why single and double dissociation studies do not yield reliable conclusions, but suggested more reliable methods for understanding the delegation of functions to neuroanatomical structures in large-scale neural networks.

AB - Studies of the effects of brain lesions on behaviour have informed brain sciences for more than 100 years. Paradoxical results from some experiments, however, contradict widely accepted logic for imputing function to structure, including the `gold standard'; double dissociation. Orienting systems have produced clear examples of paradoxical lesion effects and provide the opportunity to identify reliable inference for imputing function. Our models reproduced the experimental effects. Analysis of the models demonstrated why single and double dissociation studies do not yield reliable conclusions, but suggested more reliable methods for understanding the delegation of functions to neuroanatomical structures in large-scale neural networks.

U2 - 10.1016/s0925-2312(99)00012-0

DO - 10.1016/s0925-2312(99)00012-0

M3 - SCORING: Journal article

VL - 26-27

SP - 933

EP - 938

JO - NEUROCOMPUTING

JF - NEUROCOMPUTING

SN - 0925-2312

ER -