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This article introduces a new technique designed to study the flow of information through processing stages in choice reaction time tasks. The technique was designed to determine whether response preparation can begin before stimulus identification is complete ("continuous" models), or if a stimulus must be fully identified prior to any response activation ("discrete" models). To control the information available at various times during stimulus identification, some relevant stimulus characteristics were made easy to discriminate and some were made hard to discriminate. The experimental strategy was to look for effects of partial output based on information conveyed by characteristics that were easy to discriminate. The technique capitalized on the fact, demonstrated in Experiment 1, that preparation of two response fingers on the same hand is more effective than preparation of two response fingers on different hands. The usefulness of partial output was varied by manipulating the assignments of stimuli to responses. For some mappings partial information could contribute to effective response preparation because the responses consistent with partial information were assigned to fingers on the same hand. For other mappings partial information could not contribute to effective response preparation because the responses consistent with partial information were assigned to fingers of different hands. Performance differences between these mappings were considered evidence that partial information about a stimulus was transmitted to response activation processes before the stimulus was uniquely identified, and thus were considered evidence against discrete transmission of information about the stimulus as a whole. A variety of stimulus sets were studied; the results suggest that information is transmitted discretely with respect to stimulus codes, although distinct codes activated by a single stimulus may be transmitted at different times.

Reaction time distributions were obtained from practiced subjects in a go/no-go detection task with attention divided across the visual and auditory modalities. Redundant signals were sometimes presented asynchronously on the two modalities, with the time between signals varying from 0 to 167 msec. An extension of the inequality derived by Miller (1982) was used to test between separate-decisions models, in which the response is initiated solely by whichever signal is detected first, and coactivation models, in which both signals contribute to the activation of a single response. As in previous studies with bimodal detection tasks, the results contradicted separate-decisions models and favored coactivation models. The largest violations of separatedecisions models were observed when the visual signal was presented 67-100 msec before the auditory signal. A new inequality was also derived to discriminate between two classes of coactivation models that differ about whether responses are generated by processes combining activation across time as well as across signals. Violations of this inequality ruled out exponential coactivation models, in which activation processes are sensitive only to the instantaneous properties of the signalfs), Instead, the results require an accumulation model of coactivation, in which both signals provide input to a process that accumulates activation over a considerable period oftime, even if signal conditions change during that time.In divided-attention tasks, people are often asked to monitor two different information channels and make a speeded response as soon as a signal is presented on either channel. To understand the division of attention, it is particularly important to determine what happens when subjects must process two signals presented simultaneously on different channels. Intuitively, it seems that the consequences of dividing attention should bemost pronounced when both channels require action at the same time, so the characteristics of the underlying division should be particularly apparent under such circumstances.Empirically, it is almost always the case that a response is made faster when it is indicated by two simultaneously presented signals, one on each channel, than when a single signal is presented on either channel alone (e.g., Raab, 1962). This paper is concerned with the explanation of this advantage, often called the redundant signals effect (RSE), in bimodal detection tasks with visual and auditory signals. The RSE has consistently been obtained in such tasks (e.g.;Miller, 1982), but explanations of the effect vary widely.The simplest explanation of the RSE is embodied in a class of models commonly called separate-decisions or

The model of a single central bottleneck for human information processing is critically examined. Most evidence cited in support of the model has been observed within the overlapping tasks paradigm. It is shown here that most findings obtained within that paradigm and that were used to support the model are also consistent with a simple resource model. The most prominent findings are the millisecondfor-millisecond slope at the left of the RT2-SOA curve, the high RT1-RT2 correlation, the additivity of the effects on RT2 of SOA and of the difficulty of selecting R2, and the washout of the effect of S2 discriminability on RT2 in a dual-task condition. In addition, the asymmetry of the effects of the dual-task requirement on RT1 and RT2 can be accounted for by the resource model provided that it assumes uneven allocation of resources, which is quite reasonable in view of the task asymmetry inherent in the demand characteristics of the paradigm. The same is true for two other findings that appear to support the single-bottleneck model-that in the dual-task condition, the demand of the first task affects equally RT1 and RT2 and that its effect on RT1 is the same as the corresponding effect in the singletask condition. Furthermore, the single-bottleneck model is hard to reconcile with a negative slope at the left of the RT1-SOA curve or a positive slope at the left of the IRI-SOA curve, unless augmented by ancillary assumptions that are yet to be substantiated. Representative data were fit by each of the models using its optimal set of parameters. Both models achieved quite good degrees of fit. It is further argued that since the overlapping tasks paradigm is heavily biased in favor of a speedy reaction to the stimulus that appears first, it is nonoptimal for testing the central bottleneck model. Finally, the bottleneck model is examined in terms of other scientific criteria.

We used computer simulations to evaluate different procedures for measuring changes in the onset latency of a representative range of event-related components (the auditory and visual N1, P3, N2pc, and the frequency-related P3 difference wave). These procedures included several techniques to determine onset latencies combined with approaches using both single-participant average waveforms and jackknife-subsample average waveforms. In general, the jackknife-based approach combined with the relative criterion technique or combined with the fractional area technique (J.C. Hansen & S.A. Hillyard, 1980; S.J. Luck, 2005) provided the most accurate method and the greatest statistical power, with no inflation of Type I error rate.Descriptors: ERP latency, Jackknife, N1, P3, N2pc, Frequency-related P3Research using event-related potentials (ERPs) often focuses on differences in amplitudes as well as differences in latencies of ERP components. However, for both measurements there are several scoring methods, and quite often researchers have to decide rather arbitrarily which method might be most appropriate. In this article we compare several methods for determining ERP latency differences. To evaluate the methods we ran computer simulations based on data of five ERP components: the visual N1, the auditory N1, the P3 (hereafter used to mean the P3b), the N2pc, and the frequency-related P3 component (infrequent minus frequent difference wave).These five components were chosen because they are very different and representative of a broad range of components, as is apparent from the following brief review of these components. The N1 components (visual, auditory) are relatively early components, strongly influenced by physical properties of the stimulus. They are characterized by a clear onset and a sharply increasing amplitude, but clearly reflect different underlying neural generators. In contrast, the P3 is a late component that is relatively insensitive to the physical properties of the stimulus (with the exception of tone intensity; see Covington & Polich, 1996), but is influenced by probabilities, expectations, and resource allocation (see Johnson, 1986). Quite often the P3 does not show a clear onset, and its peak latency is difficult to determine because the component has a wide temporal extension without a sharp peak. The N2pc and frequency-related P3, for their part, are measured from difference waves that are obtained by subtracting ERP waveforms computed at different electrode sites or at same electrode sites but in different experimental conditions. The N2pc is an index of the allocation of visualspatial attention and is isolated by subtracting the ERP at posterior electrode sites ipsilateral to an attended item from the ERP at the corresponding contralateral electrode site, whereas the frequency-related P3 is isolated by subtracting the ERP elicited by frequent targets from the ERP elicited by infrequent targets.For all components, researchers are often interested in estimating the latency differences a...