On measuring the minimum detection time: A simple reaction time study in the time estimation paradigm

2005 ◽  
Vol 58 (2) ◽  
pp. 259-284 ◽  
Author(s):  
Yung-Fong Hsu
1965 ◽  
Vol 20 (1) ◽  
pp. 175-180 ◽  
Author(s):  
Robert J. Resnick

Previous studies have raised the question of the validity of using only hospitalized Ss as controls when the results are generalized to a non-hospitalized population. Twelve male hospitalized Ss and 12 male non-hospitalized Ss participated in a reaction time study using the visual and auditory modalities under constant 6-, 9-, and 15-sec. foreperiod conditions. The data showed significant differences between groups in all foreperiods with the auditory stimuli and the 15-sec. foreperiod with the visual stimulus. Results are discussed in terms of the use of hospitalized patients as controls in research and the differential effect of foreperiod and modality on the two groups studied.


1973 ◽  
Vol 25 (3) ◽  
pp. 344-353 ◽  
Author(s):  
Jean Requin ◽  
Marilyn Granjon ◽  
Henri Durup ◽  
Guy Reynard

It was hypothesized that the time course of preparation during a variable interstimulus interval (ISI) of a simple reaction time (RT) experiment was partly determined by the subjective distribution of conditional probabilities of the executive signal (ES). Sixty subjects performed a simple auditory RT task with various ranges of six ISI durations organized in rectangular frequency distributions. In order to give the subjects information about elapsed time during ISI, a recurring time-marking click, the periodicity of which was varied, was introduced during the ISI in one of the three series of trials each subject performed. A strong decreasing RT–-ISI relationship was observed supporting the main hypothesis. However, a clear increase of mean RT over all ISIs combined, was also found. Because these two mixed effects were greatest when the click intervened at the possible times of ES occurrence only, three functions of time-information given by the click are discussed: (a) a reduction of the usual increase of time estimation error with increased ISI; (b) an increase of the subjects knowledge of the ISI range resulting from the discontinuity of the time-marking click which makes easier a discrete time-intervals numbering process; (c) a change of the simple-RT task into a discrimination task.


1977 ◽  
Vol 41 (1) ◽  
pp. 47-59 ◽  
Author(s):  
Suchoon S. Mo ◽  
Edward J. George

2007 ◽  
Vol 34 (S 2) ◽  
Author(s):  
TD Hälbig ◽  
S Assuras ◽  
J Barry ◽  
JC Borod ◽  
JM Gracies ◽  
...  

2021 ◽  
Vol 11 (5) ◽  
pp. 669
Author(s):  
Paweł Krukow ◽  
Małgorzata Plechawska-Wójcik ◽  
Arkadiusz Podkowiński

Aggrandized fluctuations in the series of reaction times (RTs) are a very sensitive marker of neurocognitive disorders present in neuropsychiatric populations, pathological ageing and in patients with acquired brain injury. Even though it was documented that processing inconsistency founds a background of higher-order cognitive functions disturbances, there is a vast heterogeneity regarding types of task used to compute RT-related variability, which impedes determining the relationship between elementary and more complex cognitive processes. Considering the above, our goal was to develop a relatively new assessment method based on a simple reaction time paradigm, conducive to eliciting a controlled range of intra-individual variability. It was hypothesized that performance variability might be induced by manipulation of response-stimulus interval’s length and regularity. In order to verify this hypothesis, a group of 107 healthy students was tested using a series of digitalized tasks and their results were analyzed using parametric and ex-Gaussian statistics of RTs distributional markers. In general, these analyses proved that intra-individual variability might be evoked by a given type of response-stimulus interval manipulation even when it is applied to the simple reaction time task. Collected outcomes were discussed with reference to neuroscientific concepts of attentional resources and functional neural networks.


1974 ◽  
Vol 38 (6) ◽  
pp. 461-470 ◽  
Author(s):  
R. Näätänen ◽  
V. Muranen ◽  
A. Merisalo

1982 ◽  
Vol 20 (2) ◽  
pp. 171-179 ◽  
Author(s):  
A.David Milner ◽  
Christopher R. Lines

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