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Titolo:
DETECTING MULTIMODALITY IN SACCADIC REACTION-TIME DISTRIBUTIONS IN GAP AND OVERLAP TASKS
Autore:
GEZECK S; TIMMER J;
Indirizzi:
BEKSTR 15 D-75180 PFORZHEIM GERMANY UNIV FREIBERG,INST BIOPHYS,BRAIN RES UNIT FREIBERG GERMANY UNIV FREIBERG,CTR DATA ANAL & MODELLING FREIBERG GERMANY
Titolo Testata:
Biological cybernetics
fascicolo: 4, volume: 78, anno: 1998,
pagine: 293 - 305
SICI:
0340-1200(1998)78:4<293:DMISRD>2.0.ZU;2-7
Fonte:
ISI
Lingua:
ENG
Soggetto:
HUMAN EXPRESS SACCADES; VISUALLY GUIDED SACCADES; EYE-MOVEMENTS; WARNING SIGNALS; FIXATION POINT; STIMULUS ONSET; LATENCY; MODEL; INTEGRATION; OFFSET;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
43
Recensione:
Indirizzi per estratti:
Citazione:
S. Gezeck e J. Timmer, "DETECTING MULTIMODALITY IN SACCADIC REACTION-TIME DISTRIBUTIONS IN GAP AND OVERLAP TASKS", Biological cybernetics, 78(4), 1998, pp. 293-305

Abstract

in many cases the distribution of saccadic reaction times (SRT) deviates considerably from a unimodal distribution and may often exhibit several peaks. We present a statistical approach to determining the number and form of the individual peaks. The overall density of the reaction times f(i)(t), i = 1...M obtained in M different experiments with the same subject is described as the sum of K basis functions X-k(t), k= 1...K with different weights and an error term. A change in the experimental conditions is assumed to cause a change in the weights, not in the basis functions. We minimize the square of the difference (measured data minus approximation), divided by the error of the data. Incrementing K step by step we determine the necessary number of basis functions. This method is applied to data of six subjects tested in different saccade tasks. We detect five different modes: two in the range 80-140 ms (express modes), two in the range 145-190 ms (fast-regular mode) and one at about 230 ms (slow-regular mode). These modes are located at about the same positions for different subjects. The method presented here not only proves statistically the existence of several modes in SRT distributions but also allows the distributions to be described by a few characteristic numbers that go beyond the mean values and standard deviations.

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Documento generato il 11/07/20 alle ore 03:02:33