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SYSTEMS WITH RESPONSE‐DEPENDENT DEAD TIME
Author(s) -
Hoopen M. Ten,
Reuver H. A.
Publication year - 1972
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/j.2044-8317.1972.tb00478.x
Subject(s) - interval (graph theory) , function (biology) , measure (data warehouse) , dead time , probability density function , unit interval , sequence (biology) , response time , contrast (vision) , signal (programming language) , simple (philosophy) , operator (biology) , mathematics , computer science , statistics , algorithm , artificial intelligence , discrete mathematics , data mining , combinatorics , philosophy , computer graphics (images) , repressor , chemistry , genetics , biology , biochemistry , epistemology , evolutionary biology , transcription factor , programming language , gene
Conrad's (1951) data on the performance of a human operator who has to respond to a complex temporal sequence of stimuli (obtained by a parallel arrangement of two, three or four signal sources and each delivering periodic signals) are compared with the properties of three kinds of signal processing systems, each constituting special cases of a general concept. In model I a simple dead time is assumed, while in models II and III the dead time after each response is a linear function of the preceding inter‐response interval duration. It is argued that the mean number of errors per unit of time, the criterion used so far, is a somewhat crude measure and that more decisive conclusions can be drawn when a more detailed description of the timing of the responses is available; for instance, in the form of the probability density function, or the joint probability density function of the response interval durations. The models may also find application in neuronal spike train analysis, and for problems in the field of operations research.