Factoring out nondecision time in choice reaction time data: Theory and implications.

In order to model within-person (WP) variance in a reaction time task, we applied a mixed location scale model using 335 participants from the second wave of the Zurich Longitudinal Study on Cognitive Aging. The age of the respondents and the performance in another reaction time task were used to explain individual differences in the WP variance. To account for larger variances due to slower reaction times, we also used the average of the predicted individual reaction time (RT) as a predictor for the WP variability. Here, the WP variability was a function of the mean. At the same time, older participants were more variable and those with better performance in another RT task were more consistent in their responses.

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Absolute Pitch as a Learned Phenomenon: Evidence Consistent with the Hick-Hyman Law

Music Perception An Interdisciplinary Journal ◽

10.2307/40285656 ◽

1994 ◽

Vol 12 (2) ◽

pp. 267-270 ◽

Cited By ~ 13

Author(s):

Jasba Simpson ◽

David Huron

Keyword(s):

Reaction Time ◽

Absolute Pitch ◽

Reaction Time Data ◽

Western Music ◽

Frequency Of Occurrence ◽

Time Data ◽

Expected Frequency ◽

Computer Based ◽

Additional Support

An analysis of reaction time data collected by Miyazaki (1989) provides additional support for absolute pitch as a learned phenomenon. Specifically, the data are shown to be consistent with the Hick- Hyman law, which relates the reaction time for a given stimulus to its expected frequency of occurrence. The frequencies of occurrence are estimated by analyzing a computer-based sample of Western music. The results are consistent with the view that absolute pitch is acquired through ordinary exposure to the pitches of Western music.

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A moving average model for sequenced reaction-time data

Journal of Mathematical Psychology ◽

10.1016/0022-2496(81)90053-5 ◽

1981 ◽

Vol 23 (2) ◽

pp. 115-133 ◽

Cited By ~ 2

Author(s):

Joseph B Kadane ◽

Jill H Larkin ◽

Richard H Mayer

Keyword(s):

Reaction Time ◽

Moving Average ◽

Reaction Time Data ◽

Time Data ◽

Average Model ◽

Moving Average Model

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Constraints imposed on taste physiology by human taste reaction time data

Neuroscience & Biobehavioral Reviews ◽

10.1016/0149-7634(86)90024-2 ◽

1986 ◽

Vol 10 (2) ◽

pp. 135-151 ◽

Cited By ~ 33

Author(s):

Bruce P. Halpern

Keyword(s):

Reaction Time ◽

Reaction Time Data ◽

Time Data

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Reaction times and burning rates for wind tunnel headfires

International Journal of Wildland Fire ◽

10.1071/wf02041 ◽

2003 ◽

Vol 12 (2) ◽

pp. 195 ◽

Cited By ~ 20

Author(s):

Ralph M. Nelson, Jr.

Keyword(s):

Reaction Time ◽

Wind Tunnel ◽

Wildland Fire ◽

Mass Loss Rate ◽

Fire Behavior ◽

Reaction Times ◽

Reaction Time Data ◽

Combustion Regime ◽

Fuel Particle ◽

Time Data

Catchpole et al. (1998) reported rates of spread for 357 heading and no-wind fires burned in the wind tunnel facility of the USDA Forest Service's Fire Sciences Laboratory in Missoula, Montana for the purpose of developing models of wildland fire behavior. The fires were burned in horizontal fuel beds with differing characteristics due to various combinations of fuel type, particle size, packing ratio, bed depth, moisture content, and wind speed. In the present paper, fuel particle and fuel bed data for 260 heading fires from that study (plus as-yet unreported combustion efficiency and reaction time data) are used to develop models for predicting fuel bed reaction time and mass loss rate. Reaction time is computed from the flameout time of a single particle and fuel bed structural properties. It is assumed that the beds burn in a combustion regime controlled by the rate at which air mixes with volatiles produced during pyrolysis, and that not all air entering the fuel bed reaction zone participates in combustion. Comparison of reaction time and burning rate predictions with experimental values is encouraging in view of the simplified modeling approach and uncertainties associated with the experimental measurements.

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