When a person attempts to produce from memory a given spatial or temporal interval, there is inevitably some error associated with the estimate. The time course of this error was measured in a series of experiments where subjects repeatedly attempted to replicate given target intervals. Sequences of the errors in both spatial and temporal replications were found to fluctuate as 1/f noises. 1/f noise is encountered in a wide variety of physical systems and is theorized to be a characteristic signature of complexity.

A long-standing issue in the study of how people acquire visual information centers around the scheduling and deployment of attentional resources: Is the process serial, or is it parallel? A substantial empirical effort has been dedicated to resolving this issue (e.g., J. M. Wolfe, 1998a, 1998b). However, the results remain largely inconclusive because the methodologies that have historically been used cannot make the necessary distinctions (J. Palmer, 1995; J. T. Townsend, 1972, 1974, 1990). In this article, the authors develop a rigorous procedure for deciding the scheduling problem in visual search by making improvements in both search methodology and data interpretation. The search method, originally used by A. H. C. van der Heijden (1975), generalizes the traditional single-target methodology by permitting multiple targets. Reaction times and error rates from 29 representative search studies were analyzed using Monte Carlo simulation. Parallel and serial models of attention were defined by coupling the appropriate sequential sampling algorithms to realistic constraints on decision making. The authors found that although most searches are conducted by a parallel limited-capacity process, there is a distinguishable search class that is serial.

Two distinct families of statistical processes are considered in the production of psychophysical time series data (Gilden, 1997(Gilden, , 2001Gilden, Thornton, & Mallon, 1995). We inquire whether the spectral signatures of the underlying dynamics are better described in terms of short-range autoregressive movingaverage (ARMA) processes or long-range fractal processes. A thorough presentation of both families is given so as to clarify the scope and generalizability of the models as descriptions of choice reaction time data. Analyses of data are supplemented by the construction of a spectral likelihood classifier that discriminates between the two families of processes. The classifier has sufficient sensitivity to ensure that fractals are correctly identified and that ARMA processes will rarely be misconstrued as belonging to the fractal family. Spectral likelihood classification illustrates an extremely general framework for testing competing spectral hypotheses and is offered for use in measuring the specific character of fluctuations in designed experiments.

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The conditions for serial search are described. A multiple target search methodology (Thornton & Gilden, 2007) is used to home in on the simplest target/distractor contrast that effectively mandates a serial scheduling of attentional resources. It is found that serial search is required when (a) targets and distractors are mirror twins, and (b) when the search elements lack the Gestalt property of intrinsic orientation. The finding is put into the context of Feature Integration Theory (Treisman & Gelade, 1980) that first identified the occasions of serial search to be important to object perception and understanding.

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