Background noise is the irregular variation across repeated measurements of human performance. Background noise remains after task and treatment effects are minimized. Background noise refers to intrinsic sources of variability, the intrinsic dynamics of mind and body, and the internal workings of a living being. Two experiments demonstrate 1/f scaling (pink noise) in simple reaction times and speeded word naming times, which round out a catalog of laboratory task demonstrations that background noise is pink noise. Ubiquitous pink noise suggests processes of mind and body that change each other’s dynamics. Such interaction-dominant dynamics are found in systems that self-organize their behavior. Self-organization provides an unconventional perspective on cognition, but this perspective closely parallels a contemporary interdisciplinary view of living systems. Psychological science usually ignores the background noise in behavioral data. Background noise is what is left over when task demands, experimental manipulations, and other external sources of variability have been eliminated or minimized. What we call background noise is treated as random variability in most research, the nuisance factor in factorial experiments. We argue, to the

This paper presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision and semantic categorization, human readers behave as optimal Bayesian decision-makers. This leads to the development of a computational model of word recognition, the Bayesian Reader. The Bayesian Reader successfully simulates some of the most significant data on human reading. The model accounts for the nature of the function relating word-frequency to reaction time and identification threshold, the effects of neighborhood density and its interaction with frequency, and the variation in the pattern of neighborhood density effects seen in different experimental tasks. Both the general behavior of the model, and the way the model predicts different patterns of results in different tasks, follow entirely from the assumption that human readers approximate optimal Bayesian decision-makers.

Among the most valuable tools in behavioral science is statistically fitting mathematical models of cognition to data, response time distributions in particular. However, techniques for fitting distributions vary widely and little is known about the efficacy of different techniques. In this article, we assessed several fitting techniques by simulating six widely cited models of response time and using the fitting procedures to recover model parameters. The techniques include the maximization of likelihood and least-squares fits of the theoretical distributions to different empirical estimates of the simulated distributions. A running example was used to illustrate the different estimation and fitting procedures. The simulation studies revealed that empirical density estimates are biased even for very large sample sizes. Some fitting techniques yielded more accurate and less variable parameter estimates than others. Methods that involved least-squares fits to density estimates generally yielded very poor parameter estimates. How to Fit a Response Time Distribution The importance of considering the entire response time (RT) distribution in testing formal models of cognition is now widely appreciated. Fitting a model to mean RT alone can mask important details of the data that examination of the entire distribution would reveal, such as the behavior of fast and slow responses across the conditions of an experiment (e.g., Heathcote, Popiel & Mewhort, 1991), the extent of facilitation between perceptual channels (Miller, 1982), and the effects of practice on RT quantiles (Logan, 1992). Techniques for testing hypotheses based on the RT distribution have been developed (Townsend, 1990). In addition, the RT distribution provides an important meeting ground between theory and da...

We present a new model for lexical decision, REM-LD, that is based on REM theory (e.g., Shiffrin & Steyvers, 1997). REM-LD uses a principled (i.e., Bayes' rule) decision process that simultaneously considers the diagnosticity of the evidence for the #WORD# response and the #NONWORD# response. The model calculates the odds ratio that the presented stimulus is a word or a nonword by averaging likelihood ratios for lexical entries from a small neighborhood of similar words. We report two experiments that used a signal-to-respond paradigm to obtain information about the time course of lexical processing. Experiment 1 verified the prediction of the model that the frequency of the word stimuli affects performance for nonword

We present two experiments that measured lexical decision latencies and errors to words with few or many "orthographic neighbors" (i.e., Coltheart's N). The main goal of the study was to examine whether or not the neighborhood size effect in a lexical decision task could be affected by the exposure duration of the stimulus item (unlimited vs. limited time exposure, 150 msec plus a backward mask) and the type of decision involved in the task (yes/no vs. go/no-go lexical decision task). In the yes/no task, the results showed a facilitative neighborhood size effect for low-frequency which did not interact with exposure duration (Experiment 1). In contrast, in the go/no-go task (in this task, participants are instructed to respond as quickly as they can when a word is presented and not to respond if a nonword is presented), the neighborhood size effect for low-frequency words (and for nonwords) was greater under limited viewing time (Experiment 2). In addition, the word-frequency effect was...

Chater, 1998). The explanations provided by rational analysis have two properties: they emphasize the connection between behavior and the structure of the environment, and

We tested and confirmed the hypothesis that the prior presentation of nonwords in lexical decision is the net result of two opposing processes:(1) a relatively fast inhibitory process based on global familiarity, and (2) a relatively slow facilitatory process based on the retrieval of specific episodic information. In three studies, we manipulated speed-stress to influence the balance between the two processes. Experiment 1 showed item-specific improvement for repeated nonwords in a standard `respond-when-ready' lexical decision task. Experiment 2 used a 400 ms deadline procedure and showed performance for nonwords to be unaffected by up to four prior presentations. In Experiment 3 we used a signal-torespond procedure with variable time intervals and found negative repetition priming for repeated nonwords. These results can be accounted for by dual process models of lexical decision (e.g., Balota & Chumbley, 1984; Balota & Spieler, 1999). One of the most often used tasks in the field of visual word recognition is the lexical decision task. In lexical decision, participants have to decide as quickly and accurately as possible whether a presented letter string is a word (e.g., CHAIR) or a nonword (e.g., GREACH). The general assumption that underlies the use of the lexical decision task is that the speed and accuracy of responding to word stimuli indicate the efficiency with which word representations are activated or retrieved from lexical memory. Several variables are thought to reflect the speed of retrieval from lexical memory. For instance, Scarborough, Cortese, and Scarborough (1977) found that performance for high frequency words was better than performance for low frequency words. This phenomenon is known as the word frequency effect. Another extensively studied ph...

Using a continuous approximation, p(RT i | u ) < f (RT i , u )2L: (1) Note that the common factor 2L was absorbed into the arbitrary scale factor (not shown in Equation 1, which is expressed as a proportional relationship)because its value is unrelated to u . We will call estimates obtained by maximizing the right side of Equation 1 "continuousmaximum likelihood" (CML) estimates. Van Zandt's (2000) results on ML estimation were obtained using the CML method. Although Van Zandt (2000) found CML estimation to be the best method overall, her least squares cumulative distribution function (CDF) estimation method, which minimizes the sum of squared deviations between observed and theoretical cumulative probabilities at a set of data quantiles,was almost equally effective. Data quantiles are values below which a given proportionof the observed RT distributionlies, with the median beingthe most common example. Quantile-based methods may be superior CML in real data, because appropriately

We present a new model for lexical decision, REM-LD, that is based on REM theory (e.g., Shiffrin & Steyvers, 1997). REM-LD uses a principled (i.e., Bayes ’ rule) decision process that simultaneously considers the diagnosticity of the evidence for the ‘WORD ’ response and the ‘NONWORD ’ response. The model calculates the odds ratio that the presented stimulus is a word or a nonword by accumulating likelihood ratios for each lexical entry in a small neighborhood of similar words. We report two experiments that used the signal-to-respond paradigm to obtain information about the time course of lexical processing. Experiment 1 verified the prediction of the model that the frequency of the word stimuli affects performance for nonword stimuli. Experiment 2 was done to study the effects of nonword lexicality, word frequency, and repetition priming and to demonstrate how REM-LD can account for the observed results. We discuss how REM-LD can be extended to account for effects of phonology such as the pseudohomophone effect, and how REM-LD can predict response times in the popular ‘respond-when-ready ’ paradigm. Several other quantitative models of lexical decision are evaluated with respect to the findings reported here.

Dissociations of nouns and verbs following brain damage have been interpreted as evidence for distinct neural substrates underlying different aspects of the language system. Some neuroimaging studies have supported this claim by finding neural differentiation for nouns