The classic problem of stimulus-response (S-R) compatibility (SRC) is addressed. A cognitive model is proposed that views the stimulus and response sets in S-R ensembles as categories with dimensions that may or may not overlap. If they do overlap, the task may be compatible or incompatible, depending on the assigned S-R mapping. If they do not overlap, the task is noncompatible regardless of the assigned mapping. The overlapping dimensions may be relevant or not. The model provides a systematic account of SRC effects, a taxonomy of simple performance tasks that were hitherto thought to be unrelated, and suggestive parallels between these tasks and the experimental paradigms that have traditionally been used to study attentional, controlled, and automatic processes.

Measurements of reaction time have played a major role in developing theories about the menial processes that underlie sensation, perception, memory, cognition, and action. The interpretation of reaction time data requires strong assumptions about how subjects trade accuracy for speed of performance and about whether there is a discrete or continuous transmission of information from one component process to the next. Conventional reaction time and speed-accuracy trade-off procedures are not, by themselves, sufficiently powerful to test these assumptions. However, the deficiency can be remedied in part through a new speed-accuracy decomposition technique. To apply the technique, one uses a hybrid mixture of (a) conventional reaction time trials in which subjects must process a given test stimulus with high accuracy and (b) peremptory response-signal trials in which subjects must make prompted guesses before stimulus processing has been finished. Data from this "titrated reaction time procedure" are then analyzed in terms of a parallel sophisticated-guessing model, under which normal mental processes and guessing processes are assumed to race against each other in producing overt responses. With the model, one may estimate the amount of partial information that subjects have accumulated about a test stimulus at each intermediate moment during a reaction time trial. Such estimates provide deeper insights into the rate at which partial information is accumulated over time and into discrete versus continuous modes of information processing. An application of speed-accuracy decomposition to studies of word recognition illustrates the potential power of the technique. People do not think or act instantaneously. The time required to take action depends systematically on mental and physical processes that precede an overt response. Thus, throughout many areas of psychology, conclusions about the nature of mind and body have been based on measurements of human reaction time.' Past uses of reaction time data extend from studies of elementary sensory mechanisms (e.g., Green & Luce, 1973) to studies of perception (e.g.

Results are reported from a new paradigm that uses movement-related brain potentials to detect response preparation based on partial information. The paradigm uses a hybrid choice-reaction go/nogo procedure in which decisions about response hand and whether to respond are based on separate stimulus attributes. A lateral asymmetry in the movement-related brain potential was found on nogo trials without overt movement. The direction of this asymmetry depended primarily on the signaled response hand rather than on properties of the stimulus. When the asymmetry first appeared was influenced by the time required to select the signaled hand, and when it began to differ on go and nogo trials was influenced by the time to decide whether to respond. These findings indicate that both stimulus attributes were processed in parallel and that the asymmetry reflected preparation of the response hand that began before the go/nogo decision was completed.

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A countermanding procedure and race model are used to assess separately the effects of experimental factors before and after the "point of no return" in response preparation. The results reveal details about processes that so closely precede the initiation of movement that they cannot be inhibited. These processes appear to be affected by the repetition of stimulus-response pairs, but not by the physical or semantic properties of the stimuli. A model of response preparation is supported in which response inhibition depends upon the outcome of a race between independent excitatory and inhibitory processes, and reaction time is the sum of the durations of at least two stages, separated by the point of no return.

Lateralized readiness potentials (LRPs) were used to determine the stage(s) of reaction time (RT) responsible for speed-accuracy trade-offs (SATs). Speeded decisions based on several types of information were examined in 3 experiments, involving, respectively, a line discrimination task, lexical decisions, and an Erikson flanker task. Three levels of SAT were obtained in each experiment by adjusting response deadlines with an adaptive tracking algorithm. Speed stress affected the duration of RT stages both before and after the start of the LRP in all experiments. The latter effect cannot be explained by guessing strategies, by variations in response force, or as an indirect consequence of the pre-LRP effect. Contrary to most models, it suggests that SAT can occur at a late postdecisional stage.

Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. In this article we demonstrate single-trial detection by linearly integrating information over multiple spatially distributed sensors within a predefined time window. We report an average, single-trial discrimination performance of A z Ϸ 0.80 and fraction correct between 0.70 and 0.80, across three distinct encephalographic data sets. We restrict our approach to linear integration, as it allows the computation of a spatial distribution of the discriminating component activity. In the present set of experiments the resulting component activity distributions are shown to correspond to the functional neuroanatomy consistent with the task (e.g., contralateral sensorymotor cortex and anterior cingulate). Our work demonstrates how a purely data-driven method for learning an optimal spatial weighting of encephalographic activity can be validated against the functional neuroanatomy.

Cognitive psychologists have characterized the temporal properties of human information processing in terms of discrete and continuous models. Discrete models postulate that component mental processes transmit a finite number of intermittent outputs (quanta) of information over time, whereas continuous models postulate that information is transmitted in a gradual fashion. These postulates may be tested by using an adaptive response-priming procedure and analysis of reaction-time mixture distributions. Three experiments based on this procedure and analysis are reported. The experiments involved varying the temporal interval between the onsets of a prime stimulus and a subsequent test stimulus to which a response had to be made. Reaction time was measured as a function of the duration of the priming interval and the type of prime stimulus. Discrete models predict that manipulations of the priming interval should yield a family of reaction-time mixture distributions formed from a finite number of underlying basis distributions, corresponding to distinct preparatory states. Continuous models make a different prediction. Goodness-of-fit tests between these predictions and the data supported either the discrete or the continuous models, depending on the nature of the stimuli and responses being used. When there were only two alternative responses and the stimulus-response mapping was a compatible one, discrete models with two or three states of preparation fit the results best. For larger response sets with an incompatible stimulus-response mapping, a continuous model fit some of the data better. These results are relevant to the interpretation of reaction-time data in a variety of contexts and to the analysis of speed-accuracy trade-offs in mental processes. 0 19x5 Academic PXSS, IK