The motor system in simple reaction time experiments

Meijers, L.M.M.; Eijkman, E. G. J.

doi:10.1016/0001-6918(74)90041-9

Cited by 31 publications

(23 citation statements)

References 13 publications

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“…In our discussion in Section 5 we marshal some evidence (Evarts, 1966;Meijers and Eijkman, 1974;Kohfeld et al, 1981;Ratcliff et al, 2001) indicating that the variance of the base time is typically much smaller than the variance of the channel process variables A and V. In terms of our model parameters this means that we are most interested in the parameter regime in which β is greater than α(= ν). The deviations shown in Figure 11 for the regime β/α > 1 are not large; and when β/α > 2 (i.e when the standard deviation of the base time is less than twice the standard deviation of the process time) then the deviations are negligible.…”

Section: Suppose Then That a A′~ Exp(α) V V′~ Exp(ν) And B ~ Exp(βmentioning

confidence: 91%

“…5 If base time comprises mostly motor response time then we have reason to believe this (Meijers and Eijkman, 1974;Luce, 1986). Ratcliff and Tuerlinckx (2002) make a similar assumption when they calculate the effects of base time on the estimation of the parameters of diffusion models-see their Figure 11.…”

Section: Base Time As a Filtermentioning

confidence: 99%

“…Meijers and Eijkman (1974) pointed out that, based on the same single-cell recording data, "the ratio between the variance of premotor-time and the variance of the motor-time varied from 5-10". These estimates of base time are in accord with the data of Kohfeld et al (1981) who report a mean of 136msec and a standard deviation of 9.1msec for the base time.…”

Section: Varying Views Of Base Timementioning

confidence: 99%

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Consequences of base time for redundant signals experiments

Townsend

Honey

2007

Journal of Mathematical Psychology

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We report analytical and computational investigations into the effects of base time on the diagnosticity of two popular theoretical tools in the redundant signals literature: (1) the race model inequality and (2) the capacity coefficient. We show analytically and without distributional assumptions that the presence of base time decreases the sensitivity of both of these measures to model violations. We further use simulations to investigate the statistical power model selection tools based on the race model inequality, both with and without base time. Base time decreases statistical power, and biases the race model test toward conservatism. The magnitude of this biasing effect increases as we increase the proportion of total reaction time variance contributed by base time. We marshal empirical evidence to suggest that the proportion of reaction time variance contributed by base time is relatively small, and that the effects of base time on the diagnosticity of our modelselection tools are therefore likely to be minor. However, uncertainty remains concerning the magnitude and even the definition of base time. Experimentalists should continue to be alert to situations in which base time may contribute a large proportion of the total reaction time variance.

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Section: Suppose Then That a A′~ Exp(α) V V′~ Exp(ν) And B ~ Exp(βmentioning

confidence: 91%

Section: Base Time As a Filtermentioning

confidence: 99%

Section: Varying Views Of Base Timementioning

confidence: 99%

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Consequences of base time for redundant signals experiments

Townsend

Honey

2007

Journal of Mathematical Psychology

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“…More specifically, they assume that an overt response is produced as soon as the first k parallel channels have finished, from a total of n activated channels (cf. Meijers & Eijkman, 1974;Meijers, Teulings, & Eijkman, 1976;Miller & Ulrich, 2003). More formally, then, RT depends on the latency of the kth fastest channel, out of a total of n activated channels.…”

Section: Mixture Models and Their Delta Plotsmentioning

confidence: 99%

Response time models of delta plots with negative-going slopes

Schwarz

Miller

2012

Psychon Bull Rev

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Delta plots (DPs) graphically compare reaction time (RT) quantiles obtained under two experimental conditions. In some research areas (e.g., Simon effects), decreasing delta plots (nDPs) have consistently been found, indicating that the experimental effect is largest at low quantiles and decreases for higher quantiles. nDPs are unusual and intriguing: They imply that RT in the faster condition is more variable, a pattern predicted by few standard RT models. We describe and analyze five classes of well-established latency mechanisms that are consistent with nDPs-exhaustive processing models, correlated stage models, mixture models, cascade models, and parallel channels models-and discuss the implications of our analyses for the interpretation of DPs. DPs generally do not imply any specific processing model; therefore, it is more fruitful to start from a specific quantitative model and to compare the DP it predicts with empirical data.Keywords Delta plot . RT models . Simon effect . Activation suppression model Chronometric research in cognitive psychology often seeks to infer something about the number, nature, and temporal organization of basic information-processing components by looking at how reaction time (RT) varies across two or more experimental conditions. Classic examples of this approach using mean RT are Sternberg's (1969) additive factor method in memory scanning and Treisman and Gelade's (1980) analysis of the slope of RT versus set size functions in visual search.Increasingly, researchers have augmented the study of mean RT with studies of RT distributions. First, in some experimental paradigms, it is possible to distinguish among different classes of models on the basis of their specific predictions concerning between-condition effects on RT distributions as well as means, allowing model classes to be tested via distributional comparisons (e.g

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“…McGill suggested that the exponential distribution reflects the residual component of RT (the sum motor and perceptual and other nondecision stages), with the Gaussian distribution due to decision latency. However, Luce (1986) points out that a review of physiological mechanisms by Meijers and Eijkman (1974) found that the motor component of the residual contributes very little variability. Since the exponential component is typically the main contributor of variability to the ex-Gaussian, Luce suggests that it should not be identified with the low variance residual component ofRT.…”

Section: Estimating Rt Distribution Skewmentioning

confidence: 99%

RTSYS: A DOS application for the analysis of reaction time data

Heathcote

1996

Behavior Research Methods, Instruments, & Computers

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RTSYS is a menu-driven DOS application for the manipulation, analysis, and graphical display of reaction time data. It can be used either in a single-task environment under DOS,with access to a set operating system commands, or as an application under Windows. All functions have contextsensitive help. RTSYS fits the ex-Gaussian distribution to reaction time data without the difficulties usually associated with numerical parameter estimation. Distribution fitting and flexible censoring and rescaling options allow RTSYS to address the problems of reaction time distribution skew and outlying responses with reasonable sample sizes. RTSYS can automatically process multiple input files from experiments with arbitrary designs and produce formatted output of statistics for further processing by graphical and inferential statistical packages. The present article reviews and explains techniques used by RTSYS and provides an overview of the operation of the program.RTSYS is a DOS application, written in Turbo Pascal 6.0, that calculates distribution statistics for reaction time (RT) data. It also provides facilities for censoring and rescaling data. Statistics calculated include the number of RTs, percent of RTs censored, the median, mean, variance, and a nonparametric measure of skew [i.e., (mean -median)/ standard deviation)]. Because RT data are often scored as correct or incorrect, where incorrect RTs are relatively infrequent, percent error and mean error RT are also calculated. RTSYS fits the ex-Gaussian distribution and reports distribution parameters u, a, and !'.Fitting the ex-Gaussian also produces chi-squared and likelihood goodness-of-fit statistics. Goodness offit can be inspected visually by plotting RT histograms and superimposed fitted ex-Gaussian distributions.RTSYS will typically be used to convert raw data files collected from experiments into files of statistical parameters that are used as input to inferential and graphical applications. One ofRTSYS's most useful features is its ability to automatically process arbitrary factorial and nonfactorial between-and within-subject designs. Design cells can be collapsed both by mixing data from different factor levels or by Vincent averaging. Vincent averaging (usually performed over a subjects factor) approximately preserves distribution shape and is useful for obtaining ex-Gaussian fits where design cells contain too fewobservations for sufficiently precise parameter estimates. VinThe development of RTSYS began during the author's doctoral research, which was supported by a Canadian Commonwealth Fellowship. Thanks to Doug Mewhort and Richard Heath for comments on the first draft of this manuscript, to Trish Van Zandt, Steve Link, and an anonymous reviewer for their comments, and to the numerous people who have tested versions of RTSYS. Thanks also to Sarah Ogilvie for proofreading the manuscript and providing a quiet place in Melbourne to finish this project. Correspondence should be addressed to A. Heath-

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The motor system in simple reaction time experiments

Cited by 31 publications

References 13 publications

Consequences of base time for redundant signals experiments

Consequences of base time for redundant signals experiments

Response time models of delta plots with negative-going slopes

RTSYS: A DOS application for the analysis of reaction time data

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