Manual choice reaction times in the rate-domain

Harris, Christopher M. and Waddington, Jonathan and Biscione, Valerio and Manzi, Sean (2014) Manual choice reaction times in the rate-domain. Frontiers in Human Neuroscience, 8 (June). ISSN 1662-5161

Full content URL: http://journal.frontiersin.org/Journal/10.3389/fnh...

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Item Type:Article
Item Status:Live Archive

Abstract

Over the last 150 years, human manual reaction times (RTs) have been recorded countless times. Yet, our understanding of them remains remarkably poor. RTs are highly variable with positively skewed frequency distributions, often modeled as an inverse Gaussian distribution reflecting a stochastic rise to threshold (diffusion process). However, latency distributions of saccades are very close to the reciprocal Normal, suggesting that "rate" (reciprocal RT) may be the more fundamental variable. We explored whether this phenomenon extends to choice manual RTs. We recorded two-alternative choice RTs from 24 subjects, each with 4 blocks of 200 trials with two task difficulties (easy vs. difficult discrimination) and two instruction sets (urgent vs. accurate). We found that rate distributions were, indeed, very close to Normal, shifting to lower rates with increasing difficulty and accuracy, and for some blocks they appeared to become left-truncated, but still close to Normal. Using autoregressive techniques, we found temporal sequential dependencies for lags of at least 3. We identified a transient and steady-state component in each block. Because rates were Normal, we were able to estimate autoregressive weights using the Box-Jenkins technique, and convert to a moving average model using z-transforms to show explicit dependence on stimulus input. We also found a spatial sequential dependence for the previous 3 lags depending on whether the laterality of previous trials was repeated or alternated. This was partially dissociated from temporal dependency as it only occurred in the easy tasks. We conclude that 2-alternative choice manual RT distributions are close to reciprocal Normal and not the inverse Gaussian. This is not consistent with stochastic rise to threshold models, and we propose a simple optimality model in which reward is maximized to yield to an optimal rate, and hence an optimal time to respond. We discuss how it might be implemented. © 2014 Harris, Waddington, Biscione and Manzi.

Additional Information:This article is part of the Research Topic Advances in Modern Mental Chronometry
Keywords:reaction times, Latency, Reciprocal Normal, autoregressive integrated moving average (ARIMA), speed-accuracy trade-off, Pieron's law, optimality, JCOpen
Subjects:C Biological Sciences > C800 Psychology
B Subjects allied to Medicine > B140 Neuroscience
Divisions:College of Social Science > School of Psychology
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ID Code:14431
Deposited On:01 Jul 2014 07:59

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