Testing the race model in a difficult redundant signals task

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Testing the race model in a difficult redundant signals task. / Gondan, Matthias; Dupont, Dawa; Blurton, Steven.

In: Journal of Mathematical Psychology, Vol. 95, 102323, 2020.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Gondan, M, Dupont, D & Blurton, S 2020, 'Testing the race model in a difficult redundant signals task', Journal of Mathematical Psychology, vol. 95, 102323. https://doi.org/10.1016/j.jmp.2020.102323

APA

Gondan, M., Dupont, D., & Blurton, S. (2020). Testing the race model in a difficult redundant signals task. Journal of Mathematical Psychology, 95, [102323]. https://doi.org/10.1016/j.jmp.2020.102323

Vancouver

Gondan M, Dupont D, Blurton S. Testing the race model in a difficult redundant signals task. Journal of Mathematical Psychology. 2020;95. 102323. https://doi.org/10.1016/j.jmp.2020.102323

Author

Gondan, Matthias ; Dupont, Dawa ; Blurton, Steven. / Testing the race model in a difficult redundant signals task. In: Journal of Mathematical Psychology. 2020 ; Vol. 95.

Bibtex

@article{8f4f81a0924747f09665180a48a7d45a,
title = "Testing the race model in a difficult redundant signals task",
abstract = "In the redundant signals task, participants respond, in the same way, to stimuli of several sources, which are presented either alone or in combination (redundant signals). The responses to the redundant signals are typically much faster than to the single signals. Several models explain this effect, including race and coactivation models of information processing. Race models assume separate channels for the two components of a redundant signal, with the response time determined by the faster of the two channels. Because the slower processing times in one channel are cancelled out by faster processing in the other channel, responses to redundant signals are, on average, faster than to single signals. In contrast, coactivation models relate the redundancy gain to some kind of integrated processing of the redundant information. The two models can be distinguished using the race model inequality (Miller, 1982, Cognitive Psychology, 14, 247–279) on the response time distribution functions. Miller{\textquoteright}s prediction was derived for experiments with 100% accuracy, and despite corrections for guesses and omitted responses, it is limited to easy tasks with negligible error rates. In this article we generalize Miller{\textquoteright}s inequality to non-trivial experimental tasks in which incorrect responses may occur systematically. The method is illustrated using data from a difficult discrimination task with choice responses.",
author = "Matthias Gondan and Dawa Dupont and Steven Blurton",
year = "2020",
doi = "10.1016/j.jmp.2020.102323",
language = "English",
volume = "95",
journal = "Journal of Mathematical Psychology",
issn = "0022-2496",
publisher = "Academic Press",

}

RIS

TY - JOUR

T1 - Testing the race model in a difficult redundant signals task

AU - Gondan, Matthias

AU - Dupont, Dawa

AU - Blurton, Steven

PY - 2020

Y1 - 2020

N2 - In the redundant signals task, participants respond, in the same way, to stimuli of several sources, which are presented either alone or in combination (redundant signals). The responses to the redundant signals are typically much faster than to the single signals. Several models explain this effect, including race and coactivation models of information processing. Race models assume separate channels for the two components of a redundant signal, with the response time determined by the faster of the two channels. Because the slower processing times in one channel are cancelled out by faster processing in the other channel, responses to redundant signals are, on average, faster than to single signals. In contrast, coactivation models relate the redundancy gain to some kind of integrated processing of the redundant information. The two models can be distinguished using the race model inequality (Miller, 1982, Cognitive Psychology, 14, 247–279) on the response time distribution functions. Miller’s prediction was derived for experiments with 100% accuracy, and despite corrections for guesses and omitted responses, it is limited to easy tasks with negligible error rates. In this article we generalize Miller’s inequality to non-trivial experimental tasks in which incorrect responses may occur systematically. The method is illustrated using data from a difficult discrimination task with choice responses.

AB - In the redundant signals task, participants respond, in the same way, to stimuli of several sources, which are presented either alone or in combination (redundant signals). The responses to the redundant signals are typically much faster than to the single signals. Several models explain this effect, including race and coactivation models of information processing. Race models assume separate channels for the two components of a redundant signal, with the response time determined by the faster of the two channels. Because the slower processing times in one channel are cancelled out by faster processing in the other channel, responses to redundant signals are, on average, faster than to single signals. In contrast, coactivation models relate the redundancy gain to some kind of integrated processing of the redundant information. The two models can be distinguished using the race model inequality (Miller, 1982, Cognitive Psychology, 14, 247–279) on the response time distribution functions. Miller’s prediction was derived for experiments with 100% accuracy, and despite corrections for guesses and omitted responses, it is limited to easy tasks with negligible error rates. In this article we generalize Miller’s inequality to non-trivial experimental tasks in which incorrect responses may occur systematically. The method is illustrated using data from a difficult discrimination task with choice responses.

U2 - 10.1016/j.jmp.2020.102323

DO - 10.1016/j.jmp.2020.102323

M3 - Journal article

VL - 95

JO - Journal of Mathematical Psychology

JF - Journal of Mathematical Psychology

SN - 0022-2496

M1 - 102323

ER -

ID: 233800576