stage analysis of the reaction process using brain-evoked potentials and reaction time

Psychol Res (1984) 46:15-32 Psychological Research ~' Springer-Verlag 1984

Stage analysis of the reaction process using brain-evoked potentials and reaction time

G. Mulder, A.B.M. Gloerich, K.A. Brookhuis, H.J. van DeUen, and L.J.M. Mulder

Institute of Experimental Psychology, University of Groningen, Haren, The Netherlands

Summary. Motor processes partly determine reaction time (RT) in both choice reaction time and in binary classification tasks. These latter tasks are popular in cognitive psychology because the experimenter believes that he has kept the motor component simple and constant and therefore can attri- bute changes in RT to perceptual or cognitive processes. In this paper we used the P3 component of the event-related potential (ERP) as a time marker indicating the duration of perceptual and cognitive processes. The latency of this component is believed to reflect stimulus evaluation time independent of response selection and organization time.

Two types of tasks were used: a choice-reaction time task and a binary classification task. Signal similarity and S-R compatibility additively affected RT, but only signal similarity affected P3 latency. The number of items in the positive set and response type affected both P3 latency and RT. Relative response frequency changed the bias in the cognitive evaluation of the stimulus, reflected in the latency of the P3 component, and affected RT only if the subjects preset their motor system (indexed by the late CNV). A model was presented which proposes that motor processes may partially overlap with the perceptual and cognitive evaluation of the stimulus. Both ERPs and RT are necessary tools in the study of the relative timing of these processes.

Reaction time (RT) is a favorite dependent variable of cognitive psychologists. The RT-method is in particular often used in fast process research. Fast process research deals with tasks usually performed in the order of ms or, at most, a few seconds. In general, fast process research aims at the discovery of elementary information processes such as encoding, verification, short-term memory scanning, mental rotation,

Offprint requests to: G. Mulder, Institute of Experimental Psychology, University of Groningen, Kerklaan 30, 9751 NN Haren, The Netherlands

16 G. Mulder et al.

semantic memory search, quantification, etc. (see Chase, 1978, for a review). The additive factor method (Sternberg, 1969) is widely used in this area of human information processing. This method requires finding task variables that affect one stage and not the others, and consequently the appropriate test is to look at interactions in the analysis of variance. Experimental separation of stages is successful if there are main effects and no interactions. In his 1980 article "Stage Analysis of the Reaction Process," Sanders argued that the additive factor method has revealed the existence of at least six independent serial stages in choice reaction time (CRT) tasks: preprocessing (typical task-variable signal contrast); feature extraction (typical task- variable signal quality); identification (typical task-variable signal discriminability); response cboice (typical taskwariable S-R compatibility); response programming (typical task-variable response specificity); motor adjustment (typical task-variable instructed muscle tension or motor presetting). A year later Frowein (1981) added an extra stage, the motor initiation stage (typical task variable accessory) between the motor programming and motor adjustment stage.

From these models it is clear that motor processes are highly involved in choice reaction time (four stages in Frowein's model are related to motor processes!). It would therefore be highly desirable to know how much time of the total CRT is explained by nonmotor stages (preprocessing, feature extraction, identification) and how much by motor stages (response choice, motor programming, motor initiation, motor adjustment).

The fact that motor processes may considerably affect RT has also been realized by cognitive psychologists. For that reason they often use binary classification tasks. A binary classification task is one in which a decision rule partitions a set of stimuli into two exhaustive and mutually exclusive classes or categories. Usually the subject is asked to determine as quickly as possible in which of the two specified classes a stimulus belongs.

A well-known example of a binary classification task is Sternberg's short-term memory-scanning task (Sternberg, 1966). The basis of the popularity of this type of task is the belief that it is possible to manipulate the perceptual or cognitive demands of the situation, while keeping the motor components simple and constant (Nicker- son, 1973). In his review article Nickerson (1973) states that the differences in RT the researcher obtains are indeed attributable to the cognitive and not to the motor aspects of the task. In order to achieve this goal he must keep the latter constant. In addition, the cognitive psychologist wants to be sure that the motor components of the task represent a sufficiently small fraction of the total processing time to ensure that the variability associated with motor processes will not obscure what he is looking f o r - which means keeping the motor component simple. In binary classification tasks, the subject is only required to make a simple "Yes-No" or "Same-Different" response, contingent upon a decision rule of arbitrary complexity.

Using a binary classification task and the additive-factor method, Sternberg was able to identify four stages in memory-scanning tasks: stimulus encoding (typical task-variable stimulus quality);serial comparison (typical task-variable size of positive set); binary decision (typical task-variable response type, positive or negative) and translation and response organization (typical task-variable relative frequency of response type; Sternberg, 1969).

Evoked potentials and reaction time 17

However, one specific problem with the additive-factor method is that if no interactions are found, one is not allowed to conclude that an additive stage model is correct. A model may be used stating that stages are overlapping or nonindependent (Taylor, 1976; McClelland, 1979; Miller, 1982). Such a view can predict nonsignifi- cant interactions while, in fact, task variables affect a common stage. In terms of the model of Sternberg (1969), it is conceivable that stimulus quality, positive set size, and response type affect in addition the response translation and organization stage, if these latter stages partially overlap the encoding and serial comparison stage. In this paper we shall explore how far the P3 component in the event-related potential (ERP) can help in separating nonmotor versus motor stages in both choice-reaction time tasks and binary classification tasks.

Event-related potentials are believed to be the far-field reflections of patterned neural activities associated with information transactions in the brain (see Hillyard & Kutas, 1983, for a review). ERPs are elicited in conjunction with sensory, cogni-

tive, and motor events and can be easily detected by means of noninvasive electrical recordings from the scalp. In these scalp-recorded ERPs, a series of components can be identified, characterized by positive or negative peaks. The very early components (5:100 ms) vary as a function of physical stimulus parameters and are relatively insensitive to changes in information-processing demands. In other words, they are believed to reflect the registration of the physical attributes of the stimuli (Hil!yard & Kutas, 1983; Ritter, Vaughan, & Simson, 1983). In contrast, some of the longer latency components (> 100 ms) only appear in conjunction with specific perceptual and cognitive processes. One of them is the P3 complex (see Fig. 1).

In Fig. 1 we have depicted an ERP obtained during a binary classification task (a combined visual and memory search task, Brookhuis et al., 1981). Around 300 ms the P3 complex is clearly visible. This complex may consist of multiple positive components (Friedman, Vaughan, & Erlenmeyer-Kimling, 1981), which sometimes partially overlap and which can be dissociated by varying the processing demands of the task. In the present case it is clear that a subcomponent, indicated as the P550 component, increases in latency with the number of positive items in memory. The latency of the P3 complex, or of subcomponents, is believed to reflect primarily the duration of processes concerned with stimulus evaluation and is relatively unaf- fected by processes concerned with response selection and execution (e.g., Donchin, 1978; Duncan-Johnson, 1981; MacCarthy & Donchin, 1981; Pritchard, 1981; McCarthy & Donehin, 1983). In terms of Sanders' and Frowein's models, the P3 latency should only be affected by task variables affecting preprocessing, feature extraction and identification, and in terms of Sternberg's model, P3 latency should only be affected by task variables affecting encoding (i.e., preprocessing, feature extraction and identification), serial comparison, and binary decision.

In this paper we shall discuss two experiments investigating this suggestion. In Experiment 1 a choice-reaction time task was used. In this experiment we studied the effects of signal discriminability (a task variable believed to affect stimulus encoding) and stimulus response compatibility (a task variable believed to affect the response selection stage). Only the first task variable should affect P3 latency.

In the second experiment we used a binary classification task. In this task the subject had to combine visual and memory search (Schneider & Shiffrin, 1977). In

18 G. Mulder e t a l .

- P1 P2N,

",S

375 9550

I I I I J f I f I 100 200 300 ~00 500 600 ?00 900 900 1000

TIME IN MSEC

Fig. 1. An event-related brain potential (ERP) obtained in a visual search task with a display load (D) of four items and a memory load (M) of either 1 (solid line) or 2 (dotted line). The positive (P) and negative (N) deflections refer to different components of the ERP. The 93 complex contains several positive components, one at about 375 ms (P3a or 9375) and one at about 550ms (P3b or P550 ). Note that the late positive component (9550) is delayed in case of an increased memory load. The 93 complex is largest at the parietal (Pz) derivation

this exper iment we studied the effects o f the number of positive items (a task varia-

ble believed to affect the serial comparison stage), response type (a task variable

bel ieved to affect the binary decision stage), and relative frequency of response type (a task variable believed to affect the response translat ion and organizat ion stage).

In addit ion, the subjects were instructed to motor preset (an instruct ion variable

bel ieved to affect the motor -ad jus tment stage). Only the first two task variables

should affect P3 latency. In bo th exper iments the t ime elapsing be tween the emit- tance of P3 and the m o m e n t at which the subject presses a bu t ton (i.e., RT-P3) was

believed to reflect response selection, motor -programming t ime, motor- in i t ia t ion

t ime, and motor -ad jus tment t ime.


Experiment 1 : Spatial signal response compatibility and signal similarity in a choice reaction time task

Introduction

To date two experiments concerning the effects of S-R compatibility on P3 and RT have been carried out. The first experiment (McCarthy & Donchin, 1981, 1983) showed no effect of S-R compatibility on P3 latency; the second experiment did show an effect (Ragot & Renault, 1981).

In the McCarthy-Donchin experiment, subjects indicated by button press which of two target words (either "Right" or "Left") had been presented on a given trial. There were two experimental factors. Stimulus discriminability was manipulated by presenting the target word in a matrix consisting of four rows and six columns. The targets were always written horizontally, but could appear in any row. The remaining matrix positions were filled with symbols ((4) in the high discriminability (no noise) condition, or with randomly chosen, alphabetic characters in the low discriminability (noise) condition. S-R compatibility was manipulated by varying the relationship between the responding hand and the target word. In the compatible condition, the target word "Right" required a right-hand response, and the target word "Left" required a left-hand response. In the incompatible condition, the target word "Right" required a left-hand response and the target word "Left" a right-hand response.

As the target word had to be identified before the responding hand could be determined (ie., response selection), manipulation of compatibility would affect only RT, while discriminability would affect both P3 and RT. These predictions were con- firmed. However, the effect of discriminability on RT was larger than on P3 latency. There was also an interaction between row and discriminability: targets presented in the outer rows (top and bottom) were responded to more slowly than in the middle row. This effect was only obtained for the noise trials. P3 latency was affected by this variable in a similar way. It could be argued that McCarthy and Donchin in fact manipulated visual search instead of discriminability: in the noise condition the subject has to search for the presence of the target word; in the no-noise condition the subject can find the target word almost automatically, since letters have different features (Treisman & Gelade, 1980), leading to automatic detection.

Ragot and Renault (1981) also manipulated S-R compatibility. In contrast to the data of McCarthy and Donchin, P3 latency was shorter for the compatible S-R pairs. However, in their study there was confusion between stimulus properties (color and position) and S-R compatibility.

In the present experiment we used a S-R mapping like that in the consistent task (Task C-2) of Duncan's Experiment 3 (see Fig. 2; Duncan, 1978). The largest difference (380 ms) was obtained between the conditions in which the stimuli and responses were opposite, as compared to the condition in which the stimuli and responses corresponded. Therefore, we decided to use these two different compatibility conditions. Probably this lane difference in RT is due to two spatial decisions the subject had to make: (1) is the stimulus at the left or at the right side and (2) is it the inner or outer stimulus?

In addition, we varied signal similarity, a task variable which is believed to affect the identification stage. We hypothesized that signal similarity affects the stimulus

20

$

D S

C

I I 0 0 0 0 13

IC

I 0 0 0 0

T,T T o T

G. Mulder et al.

Fig. 2. S-R mapping in Experiment 1. In the Similar condition (S) the subject had to identify the longest vertical bar and press the appropriate button (O), indicated by an arrow. In the Dissimilar condition (DS), only one bar is presented, the possible positions of the other bars are indicated by two dots. In the Compatible condition (C) the stimulus location and response location corresponded; in the imcompatible condition (1C) stimulus location and response location were opposite. Inner and outer positions are indicated by two arrows at the bottom of the figure. B indicates buttons

identification stage and consequently should affect both P3 latency and RT, while S-R compatibility affects response selection and should only affect RT. Both task variables should have additive effects on RT.

M e t h o d

Subjects . There were eight subjects, aged 18-31 . They had normal or corrected-to- normal vision. They were paid for their participation.

Task. In the Simi lar conditions (S) the subjects were presented stimuli consisting of single vertical lines, arranged in a horizontal row on an Hewlett-Packard 1310A visual display at a viewing distance of 70 cm. The entire array subtended horizontally a visual angle of 1 ° 4'. The stimuli were presented in a darkened room. Four vertical

bars were presented, one slightly longer than the other three (see Fig. 2). The subject had to respond to the longest vertical bar. In the Dissimilar condition (DS), there was one vertical bar on one of four possible positions; the other positions were marked with two dots. The subject had to respond to the longest vertical bar. The responses were key depressions, made with the middle and forefingers of both hands, and on each trial a single stimulus was presented and a single response required. Two S-R mappings were employed, as shown in Fig. 2.

Procedure. Each subject served in one experimental session. This session consisted of four sections, each involving a different S-R mapping and similarity condition. The order of these sections was counterbalanced across subjects. After being instructed the subject bad 30 practice trials, followed by a second run of 100 trials. Each run was of the following structure. A fixation spot (the warning signal) was presented at the center of the display and after a variable foreperiod (500-2000 ms), the imperative signal was presented for 1000 ms. During the 3340 ms after the expiration


of the display, responses were still recorded. Instructions were to perform as rapidly and accurately as possible. The experiment was run on a PDP-8E computer.

Recording and data analysis. The EEG was recorded with Ag-AgC1 electrodes from the locations 0 z, Pz' Cz' and F z, referenced to the left ear lobe with a time constant of 1 s (passband from 0 .03-35 Hz). The vertical EOG was recorded from electrodes above and below the right eye. EEG and EOG were sampled every 10 ms for a period of 3840 ms, starting 500 ms before the imperative signal. Trials with eye blinks and eye movements exceeding 300 ~tV were rejected. This relatively high value was chosen because the experiment was repeated with elderly subjects who blinked more often than the younger subjects in the present study. Less than 0.5% of the trials were rejected. Data were subjected to a digital filter with a high-frequency limit of 5.5 Hz and then latency adjusted using an adaptive averaging technique (Mulder et al., 1980; Brookhuis et al., 1981). The template (the initial P3 waveform obtained after averaging) was moved at 10 ms increments across the 350-510 ms segment of each trial. In fact, only the descending slope of the P3a component was used as template. This was done in order to avoid optimalization on earlier components (P2, N2) on P3a on the ascending slope of the P3a component (see Fig. 2). P3 latency was estimated by computing the distance at which the covariance between the template

and the corresponding part of the ERP on each single trial was at its maximum. The wbole single ERP was then shifted accordingly. After adjusting the latency of P3 for each single trial, a new average was created and a second iteration, using the new average as template, was started. The peak latencies of P3a were computed after 4 iterations for each of the four conditions and the two stimulus positions (inner, outer; see Fig. 2). Since this paper is restricted to P3, we shall only discuss P3 latencies obtained at the parietal (Pz) derivation, the location at which P3 is at its maximum.

R esults

Performance. Figure 3 shows mean correct RTs as a function of the type of task. The mean correct RT was longer in the condition Similar (1255 ms) than in the condition Dissimilar (756 ms) I F ( l , 7 ) = 69.72, p < 0.0001]. The mean correct RT was also longer in the condit ion Incompatible (1171 ms) than in the condition Compatible (840 ms) [F(1,7) = 37.30, p < 0.0005]. There was no significant interaction between Discriminability and Compatibility.

The conditions Similar and Dissimilar differed in number of errors, 8.06 and 0.43, respectively. In the conditions Compatible and Incompatible, these values were 2.93 and 5.56, respectively. To give a more complete picture of the data, responses to stimuli 1 and 4 (outer) have been separated from stimuli 2 and 3 (inner). There was a significant interaction I F ( l , 7 ) = 63.91, p < 0.0001] between discriminability and stimulus position: subjects reacted faster to the inner stimuli under similar conditions (1091 ms) than to the outer stimuli (1491 ms). Under the dissimilar condition these effects were reversed: now the subjects reacted faster to the outer stimuli (720 ms) than to the inner stimuli (792 ms).

22 G. Mulder et al.

RESPONSE LATENC IES 17'3o

is00

t m SIMILAR

o DISSIMILAR

. 0

COMP INCOMP CONDITION

P3A LBTENCIES 5~

Fig. 3. Effects of similarity and S-R compatibility on mean correct RT

5~

~50

riO0

35O

[] SIMILAR

0 DISSIMILAR

[] []

0 . . . . . . . . . . . . . . . . . . . . .

I I COMP INCOMP

CONDITION

Fig. 4. Effects of similarity and S-R compatibility on the latency of the P375 (P3a) component for correct responses

Evoked potentials. Inspection of the ERPs learned that there was only one positive component in the P3 complex, the P3a component. The mean latency of the P3a component was smaller in the Dissimilar condition (334 ms) as compared to the condition Similar (351 ms). This difference is significant [F(1 ,7)= 8.06, p < 0.025] (see Fig. 4).

Neither stimulus-response compatibility nor stimulus position significantly affected the latency of the P3a component.

Discussion

The task variables Discriminability and S-R compatibility produced strong and additive effects on CRT. The first task variable is believed to affect the identification stage, the second the response selection stage• Additivity between these task variables was shown earlier (e.g., Pachella & Fischer, 1969; Shwartz et al., 1977). Only the task variable Discriminability affected P3 latency. These results support the hypothe-


sis that P3 latency indexes stimulus evaluation time independent of motor selection processes. Our results also support the findings of McCarthy and Donchin (1981, 1983). However, in the condition Similar the subjects reacted faster to inner stimuli, and in the condition Dissimilar they reacted faster to the outer stimuli. These effects cannot be found in P3 latencies. It may be that our measurement procedures of P3 latency at the single trial level are still not sensitive enough to show these effects.

In addition, the effects of Discriminability on P3 latency are much smaller (+- 17 ms) than on RT (+ 500 ms). Discrepancies between RT data and P3 latency have been reported earlier (e.g., Brookhuis et al., 1981 ; Duncan-Johnson & Donchin, 1982; McCarthy & Donchin, 1983). In memory-scanning experiments, for example, it has been a repeated finding that the slope of the function relating P3 latency to the number of positive items in the memory set is smaller than the slope of the function relating RT to the number of items in memory (see Chapman, 1981; Mulder et al., in press). Also, it has been reported that behavioral data often indicate self-terminating search, while P3 latencies on the other hand suggest exhaustive search (Brookhuis et al., 1981; Ford et al., 1980). Ford et al. (1980) also showed that stimulus quality not only affected P3 latency, but also the RT-P3 interval. If we assume that this interval reflects motor processes, it is tempting to conclude that a degraded stimulus slows down the response-preparation process and, therefore, affects RT to a larger extent than P3 latency. The absence of a stimulus position effect on P3 latency may thus indicate that all stimulus positions are evaluated exhaustively and that the stimulus position effect on RT arises from a post identification process. The transla- t ion of stimulus position to finger movements is a response-related process and consequently not reflected in the latency of the P3 component.

Experiment 2: Relative response frequency, motor presetting, memory load and response type in a binary classification task

Introduction

In this experiment we used a binary classification task. In the present task the subject is first presented with a memory set containing one or more letters, followed by a visual display also containing one or more letters. The subject's task is to indicate as soon as possible the presence or absence of a target (a memory-set item) on the visual display by pressing one of two buttons (Schneider & Shiffrin, 1977; Mulder & Mulder, 1981; Brookhuis et al., 1981). The probabil i ty that the display contains a target is in these experiments always 0.50. The behavioral data in these experiments invariably indicate self-terminating search. The latency of P3 increases systematically with the number of items in memory or in the visual display or their product, with negative responses being later than positive responses. In addition, the slope of both types of responses is about equal, suggesting exhaustive search (Brookhuis et al., 1981; Hoffman et al., in press). In other words, the latencies of the P3 component appear to reflect serial comparison and binary decision. In his 1969 article Sternberg argued that relative response frequency affects the translation and response organiza-

24 G. Mulder et al.

t ion stage. In his experiment he varied relative response frequency by manipulation of the proport ion of trials on which the test stimulus was contained in the positive set. However, in his experiment the positive set was fixed throughout a series of trials, and consequently he confounded response probabil i ty with stimulus frequency. Increased frequency of presentation of a certain memory item may decrease stimulus-identification time (see Sanders, 1980) and explain the additive effects Sternberg obtained. Also, Theios (1975) argued that relative response frequency affects the "response program selection" stage, but in his studies (Theios et al., 1973, Theios & Walters, 1974) as well, confusion of stimulus probabil i ty and relative response frequency could not be avoided. In the present experiment we used a varied set procedure and consequently avoided this poblem. In addition, we instructed the subjects to preset the most probable response.

If relative response frequency and motor presetting only affect the motor stages, they should affect the RT-P3 interval. On the other hand, the number of positive items and response type should affect both P3 latency and RT.

Metbod

Subjects. Fourteen subjects participated in this experiment. Their age ranged from 19 to 31 years. All subjects were volunteers and were paid for their participation. They

had normal or corrected-to-normal vision.

Task. All stimuli were presented on a Hewlett-Packard 13 IOA display at a distance of 70 cm from the subject. Only consonants were employed as stimuli; on each new trial targets and distractors were randomly chosen from the set of all possible consonants (except the Q). A trial started with a presentation of one or two consonants to be memorized during one trial, randomly chosen from the same set (varied mapping procedure, Shiffrin & Schneider, 1977) and presented for 2 s. The probabil i ty

of trials with memory size of either 1 or 2 was equal (p = 0.50), with the trials being randomly intermixed. The imperative signal consisted of four consonants which were displayed in a square around a central fixation dot, subtending 1.3 °. The display set was exposed for 1000 ms and was followed by a response period of 2 s. A complete trial lasted 6750 ms. A fixed interval of 1750 ms elapsed before the imperative signal was presented. In this interval a warning signal consisting of a fixation dot in the center of the display was presented for 5 0 0 - 1 1 0 0 ms before the imperative signal, creating a variable foreperiod.

Procedure. In the control condit ion there was a probabil i ty of p = 0.50 that the display contained a memory-set item; in the experimental condition this probabil i ty was p = 0.25. The position of the memory-set item was randomized but balanced across the corners of the square. The control condit ion consisted of 120 trials, 60 trials with a memory set of 1 item (M = 1) and 60 trials with a memory set of 2 items (M = 2), each subdivided into 30 positive (target present) and 30 negative (target absent) trials. The experimental condit ion consisted of 240 trials of which 60 were positive and 180 were negative, again half of the trials with M = 1 and half with M = 2.


Subjects were trained in both conditions before participating in the experiment. The

instructions were to react as quickly as possible, while maintaining a high level of accuracy. Positive responses were to be given with the preferred finger of the dominant hand, negative ones with the preferred finger of the nondominant hand. The response consisted of a mere lifting of the finger. Subjects were informed about the response probabilities. In the experimental condition they were explicitly instructed to pre- pare for the frequent, i.e., the negative response, while remaining as accurate as possible.

Recording and data analysis. The EEG was recorded with Ag-AgC1 electrodes from P , C z, F z, with reference to the right ear lobe. All signals were digitized at a rate of 100 Hz, starting at the offset of the memory set and continuing until 2000 ms after the onset of the imperative stimulus. Trials with an EOG voltage of more than 100 pV were removed. Less than 3% of the trials were rejected. Thus, all subjects were mostly successful in avoiding eye movements during the critical time interval. P3 latency was determined by using an adaptive averaging technique (see Brookhuis et al., 1981).

Results

It had already turned out in the pilot phase of this study that not all subjects were able to adopt a response strategy which resulted in faster negative and slower positive responses in the experimental condition, as compared with the control condition. On the basis of the RT for negative responses obtained in each trial and in each condition, we formed two groups of subjects: those showing a significant (p < 0.05) difference (group "Well") between the control and the experimental condition and those who did not show a significant difference (group "Poor"). The behavioral and physiological data of these two groups will also be reported.

Performance. Analysis of variance on the mean correct RT of all subjects revealed an overall main effect of the number of items in the positive set [F (1,13) = 77.08, p < 0.0001] and of response type [F (1,13) = 45.48,p < 0.0001]. In other words, RT increased as the memory set size increased (M = 1 to M = 2), whereas negative responses were emitted more slowly than positive responses. Memory load and response type interacted significantly [F(1,13) = 13.84, p < 0.0030]. The difference between positive and negative responses was largest in trials with the highest processing load, suggesting self-terminating search (see Fig. 5). There was also a significant interaction between response probabil i ty and response type IF (1,13) = 43.99, p < 0.0001]. This interaction was caused by a strong decrease of the RT for negative responses and an increase of the RT for positive responses in the experimental condition (see Fig. 6).

To summarize, the RT data indicate that response probabil i ty does not affect the search rate, but does affect the binary decision stage (the t ime needed to decide for a negative or positive response). As expected, the two groups labeled "Well" and "Poor" were not equally sensitive to the effects of response probabili ty. The "Well"

26 G. Mulder et al.

RT {CONTROL CONDITION) {9 YES

i ! f ONO . . - " .... e

1'~4 2'k4 MEMORY SET 51ZE

FIT {EXPEFIIMENTRL COND,} TES

1~4 2 ~ 4 MEMOFIY SET SIZE

Fig. 5. Effects of memory-set size and type of decision on the mean correct RT in the control condition (probability of a positive response = probability of a negative response, left panel), and the experimental condition (probability of a positive response is 0.25 and of a negative response 0.75)

FIT (YES RESPONSES}

mM=I

® M=2

[3-------- I I

SO% 25% CONTROL EXPEFII MENTRL

. . . . . . . . . . 4 . 0

50% 75% CONTROL EXPEFII MENTRL Fig. 6. Effects on the mean correct RT of relative response frequency on positive (left panel) and negative (right panel) responses for each memory load condition

Table 1. Percentage of errors under different conditions

Probability Load Response Well Poor

Yes 5.8 4.6 M = I

No 1,3 4.6 Control (50--50%)

Yes 7.1 5,8 M = 2

No 3.1 4.2

Yes 9.2 7.1 M = I

No 3.8 11.7 Experimental (25%--75%)

Yes 19.6 11.3 M = 2

No 2.1 13.3


group showed the expected bias for negative responses, evidenced by a highly significant interaction [F(1,7) = 81.22, p < 0.0001] between response probabil i ty and response mode (Yes-No). The "Poor" group was not much affected by the probabil i ty manipulation, and consequently there was no statistical significant interaction between response mode and response probabili ty I F ( l , 5 ) = 1.60, p < 0.2617]. The number of errors for both groups in all conditions is presented in Table 1. From this table it is evident that the subjects in the "Well" group committed more errors in the positive responses: apparently these subjects had a bias for negative responses and consequently also tended to emit a negative response if a target was present.

Evoked potentials. Inspection of the ERP data indicated that the P3 complex consisted of two positive components, the P3a and P3b components. Only the latency of the latter was affected by number of positive set items and response mode. Analy- sis of variance on the P3b latencies of all subjects showed an overall main effect of number of positive items [F(1,13) = 19.75, p < 0.0007]. The latency of P3b increased as the number of positive items increased under both experimental and control conditions and equally for positive and negative responses, which indicated an exhaustive search process. There was neither a significant effect of response type nor a significant interaction between number of items and response type. On the other hand, there was a very significant interaction between response probabil i ty and response type [F (1,13) = 33.78, p < 0.0001]. The latency of P3b on negative trials was smaller than on positive trials in the experimental condition. In the control condition the latency of P3b was longer on negative trials than on positive trials (see Figs. 7 and 8). To summarize, the P3b latencies indicate that relative response frequency does not affect the search rate, but does affect the time needed to decide for a positive or negative cognitive response. In contrast to the performance data, analysis of variance on the P3b latencies of the two groups separately did not show differential effects of response probabil i ty. In both the "Well" and the "Poor" groups, response probabil i ty interacted significantly with response mode [F(1,7) = 9.27, p < 0.0188 and

~_r

E l

P3B (CONTROL CONDITIONI TES

o NO

I L _ _ 1 ~ 4 2 ~ 4

HEHORT SET SIZE

P3B (EXPERIMENTI=IL CON.I El YES o NO

J J 1~4 2~4

HEHORT SET SIZE

Fig. 7. Effects of memory-set size and type of decision on the latency of the P3b component in the control (left panel) and experimental (right panel) condition (see also Fig. 5)

28 G. Mulde r et al.

~r P3B [YES RESPONSESI

D M:I 0 M:2

I I 50% 25% CONTROL EXPERIMENTFIL

3[ P3B (NO RESPONSES)

~M=!

~L

50% 75% CONTROL EXPERIMENTRL

Fig. 8. Effects on the latency of the P3b component of relative response frequency on positive (left panel) and negative (right panel) responses for each memory load condition

GRAND AVERAGES

~ ~ELL

POOR -

' t "'"%-, ', j

o

i t I I I L I I $ "'"/J I I 2000 I ~ 0 12~ ~ 0 ~ 0 ~ ~ 0 12~ I ~ 0 20~

TIME IN MSEC

Fig. 9. Grand average across subjects sensitive (Well) and insensitive (Poor) to the relative response-frequency manipulation. Note that in the Well group there is a negative EEG after the expiration of the memory set (at 2000 ms). The imperative signal is presented at 0 ms. After the imperative signal the N1, P375' and P550 are clearly visible

F ( 1 , 5 ) = 31.55, p < 0 .0025 , respect ively] . These da ta suggest t h a t the bias of t h e

"Wel l " group, as ev idenced in t he i r R T data , depends o n a response- re la ted process.

The p e r f o r m a n c e da ta suggested t h a t t he subjects in t he "Wel l " g roup had a bias for

negat ive responses . It is possible t h a t t he subjects in t he "Wel l" group, in con t r a s t


with the subjects in the "Poor" group, selectively preset their motor system. In order to check this possibility, we computed the late CNV (Contingent Negative Variation), an evoked brain-potential component believed to index motor presetting

(Galliard, 1978; see Fig. 9). In Fig. 9 the grand averages across all conditions are shown for the "Well" and

"Poor" groups separately. It is clear that in contrast to the "Well" group, the "Poor" group did not show a late CNV. An analysis of variance was carried out on four points, 900, 600, 300 and 0 ms before the imperative signal. In the "Well" group a significant downward linear trend (p < 0.011) over these four CNV points was found. In the "Poor" group this trend was not significant (p < 0.365).

General Discussion

The results of Experiment 2 show that the P3b component is very sensitive to the effects of number of items in memory, response type, and relative frequency of response type. The first two effects confirm earlier studies (e.g., Brookhuis et al., 1981 ; Ford et al., 1980; Hoffman et al., in press). In contrast to what Sternberg suggests, relative response frequency does affect binary decision. Murdock (1982) considers the decision process as a statistical problem of inferring the origin of a single observation drawn from two possible distributions: nontargets and targets. The subject uses a criterion: if the observation falls below the criterion, the subject reacts with a negative response; if the observation falls above the criterion, the subject reacts with a positive response. By changing the relative response frequency, one apparently mani- pulates this criterion, and this is visible in the latency of the P3 component. In other words, if there is a bias towards negative responses, the latency of the P3b component is faster for negative responses than for positive responses. Normally, the latency for negative responses is longer than for positive responses (e.g., Brookhuis et al., 1981; Ford et al., 1982).

It was very remarkable to see that all subjects did apparently change their decision criterion, while this was not always apparent in their behavior. In this respect, the latency of the P3b component is far more sensitive than RT. Only the subjects who preset their motor system, indexed by the late CNV, showed marked effects of relative response frequency in their RTs.

The RT-P3b difference increases with memory-set size from about 78 ms (1 positive item) to 212 ms (2 positive items). If we assume that the RT-P3b difference indexes the time needed in response selection, programming, initiation, and adjustment, we must conclude that the task variable number of positive items also affected some of the motor stages in addition to the serial comparison stage. Such a result was also found in the first study and indicates that one does not keep the motor component constant as Nickerson (1973) suggested. Similar findings have been reported earlier and suggested a model to Ford et al. (1980) that assumes that stimulus evaluation (encoding, serial comparison, and binary decision) proceed in parallel with motor preparation. A degraded stimulus or an increased memory-set size slow down the response preparation process (the subject waits with response preparation until more evidence is available) and therefore affects RT to a larger extent than

30 G. Mulder et al.

the latency of P3. Such a view is also compatible with data obtained with speed accuracy experiments (Kutas et al., 1977; McCarthy & Donchin, 1983). McCarthy and Donchin (1983) reported data that strongly suggest that subjects in speeded responses are responding before the completion of the evaluation process (indexed by P3 latency), with the evaluation process going on to completion even when an erroneous response has been made earlier! Kutas et al. (1977) showed that the median correlation between single trial P3 latencies and RT became higher if subjects work under accuracy regimes.

Altogether, the evidence suggests that the P3 complex separates the time needed for perceptual and cognitive evaluation of the stimulus from the time needed in motor stages. The latency of the P3a component was affected by stimulus discriminability, and the latency of the P3b component by number of items in the positive set, by response type, and by task variables believed to affect perceptual and cognitive stages. On the other hand, the latency of this component was not affected by stimulus-response compatibility and motor presetting, which are variables believed to affect the response selection and motor adjustment stages respectively.

Three hypotheses have been presented in the literature as to the neural generator of the P3 component: hippocampus, parietal cortex, and frontal cortex/mesencephalic reticular formation (see Squires et al., 1983 for review). Most probably, the P3 component has a limbic source, the hippocampus, a structure which is believed to have working memory properties.

In summary ERP studies suggest that motor processes may partially overlap with perceptual and cognitive processes and that task variables believed to affect only perceptual or cognitive stages may affect motor stages in addition. Combined ERP and RT studies will be needed to obtain a more complete picture of human information processing, allowing us to study the effects of task variables and strategies of processing on the relative timing of perceptual, cognitive, and motor processes.

Acknowledgements. The authors wish to thank Dr. Herbert Heuer and two anonymous reviewers for their constructive and helpful comments on an earlier version of this manuscript. This work was supported by the Dutch Organization for Pure Research Z.W.O., grant no.:15-26-13.

References

Brookhuis, K.A., Mulder, G., Mulder, L.J.M., Gloerich, A.B.M., Dellen, H.J. van, Meere, J.J. van der, & Ellerman, H.H. (1981). Late positive components and stimulus evaluation time. Biological Psychology, 13 ,107 -123 .

Chapman, R.M., McCrary, J.W., Chapman, J.A. (1981). Memory processes and evoked potentials. Canadian Journal of Psychology, 35, 201-212.

Chase, W.G. (1978). Elementary information processes: In: W.K. Estes (ed.), Hand- book of Learning and Cognitive Processes. Lawrence Erlbaum.

Donchin, E., Ritter, W., McCallum, W.C. (1978). Cognitive psychophysiology: The endogenous components of the ERP. In: E. Callaway, P. Tueting and S.H. Koslow (eds.), Event Related Brain Potentials in Man. New York, Academic Press.


Duncan, J. (1978). Response selection in spatial choice reaction: Further evidence against associative models. Quarterly Journal of Experimental Psychology, 30, 429 -44O.

Duncan-Johnson, C.C. The P30o latency: A new metric of information processing. Psychophysiology, 18,207-215.

Duncan-Johnson, C.C. & Donchin, E. (1982). The P30O component of the event- related brain potential as an index of information processing. Biological Psycho- logy, 14, 1--52.

Ford, J.M., Mohs, R.C., Pfefferbaum, A. & Kopell, B.S. (1980). On the utility of P3 latency and RT fo r studying cognitive processes. In: Kornhuber, H.H. & Deecke, L. (eds.) Motivation, Motor and Sensory Processes of the Brain: Electrical Potentials, Behavior and Clinical Use. Amsterdam, North Holland Publ. Comp.

Friedman, D., Vaughan, H.G. Jr. and Erlenmeyer-Kimling, L. (1981). Multiple late positive potentials in two visual discrimination tasks. Psycbopbysiology, 18, 6 3 5 - 649.

Frowein, H.W. (1981). Selective drug effects on information processing. Thesis, University of Tilburg, The Netherlands.

Gaillard, A.W.K. (1978). Slow brain potentials preceding task performance. Thesis, Amsterdam, Academic Press B.V.

Hillyard, S.A. & Kutas, M. (1983). Electrophysiology of cognitive processing. Annual Review of Psychology, 34, 33-61.

Hoffman, J.E., Simons, R.F., Houck, M.R. (in press). Event-related potentials during controlled and automatic target detection. Psycbopbysiology.

Kutas, M., McCarthy, G. & Donchin, E. (1977). Augmenting mental chronometry: The P30o as a measure of stimulus evaluation time. Science, 197,792-795.

McCarthy, G. & Donchin, E. (1983). Chronometric analysis of human information processing.. In: A.W.K. Gaillard & W. Ritter (eds.), Tutorials in Event-Related Potential Research: Endogenous Components. Amsterdam, North Holland Publ. Comp.

McCarthy, G. & Donchin, E. (1981). A metric for thought: A comparison of P3o0 latency and reaction time. Science, 211, 77-79.

McClelland, J. (1979). On the time relations of mental processes. A framework for analyzing processes in cascade. Psychological Review, 86, 287--330.

Miller, J. (1982). Discrete versus continuous stage models of human information processing: In search of partial output. Journal of Experimental Psychology: Human Perception and Performance 8, 273-296.

Mulder, L.J.M., Brookhuis, K.A., Arkel, A. van & Mulder, G. (1980). Detection of variable latency components in EEG-evoked potentials. In: Proceedings of tbe Digital Equipment Computer Users Society, vol. 7, no. 1. Amsterdam.

Mulder, G., Brookhuis, K.A., Dellen, H.A. van, Goerich, A.B.M. & Mulder, L.J.M. (in press). Memory search and practice effects. In: C. McCallum (ed.), Proceedings of EPIC VII.

Murdock, B.B. (1982). Recognition memory. In: C.R. Puff fed.), Handbook of Re- search Metbods in Memory and Cognition. New York, Academic Press.

Nickerson, R.S. (1973). The use of binary classification tasks in the study of human information processing: A tutorial review, in S. Kornblum fed.), Attention and Performance IV. New York, Academic Press.

32 G. Mulder etal.

Pachella, R.G. & Fisher, D.F. (1969). Effect of stimulus degradation and stimulus similarity on the trade-off between speed and accuracy in absolute judgments. Journal of Experimental Psychology, 81, 7-9.

Pritchard, W.S. (1981). Psychophysiology of P3oo' Psychological Bulletin, 89, 506-540.

Ragot, R. & Renault, B. (1981). P3oo as a function of S-R compatibility and motor programming. Biological Psychology, 13,289-294.

Ritter, W., Vaughan, H.G. Jr. and Simson, R. (1983). On relating event-related potentials components to stages of information processing. In: A.W.H. Gaillard & W. Ritter (eds.), Tutorial in Event-Related Potential Reserach: Endogenous Com- ponents. Amsterdam, North Holland Publ. Comp.

Sanders A.F. (1980). Stage analysis of reaction processes. In: Stelmach, G.E. & Re- quin, J. (eds.), Tutorials in Motor Behavior. Amsterdam, North Holland Publ. Comp,

Schneider , W. & Shiffrin, R.M. (1977). Controlled and automatic information processing. Detection, search and attention. Psychological Review, 84, 1-66.

Shiffr!n, R.M. & Schneider, W. (1977). Controlled and automatic human information processing lh Perceptual learning, automatic attending and a general theory. Psychological Review, 84, 127-190.

Shwartz, S.P., Pomerantz, J.R. & Egeth, H.H. (1977). State and process limita- tions in information processing: An additive factor analysis. Journal of Experi- mental Psychology: Human Perception and Performance, 3,402-410.

Sternberg, S. (1966). High speed scanning in human memory. Science, 153, 652- 654.

Sternberg, S. (1969). The discovery of processing stages: Extensions of Donders' method. In: Koster, W.G. (ed.), Attention and Performance II (= Acta Psycholo- gica, 30, 276--315).

Squires, N.K., Halgrem, E, Wilson, Ch. and Crandall, P. (1983). Human endogenous limbic potentials. In: A.W.K. Gaillard and W. Ritter (eds.), Tutorials in Event- Related Potential Research: Endogenous Components. Amsterdam: North- Holland Publishing Co.

Taylor, D.A. (1976). Stage analysis of reaction processes. Psychological Bulletin, 83, 161-191.

Theios, J., Smith, P.G., Haviland, S.E., Traupmann, J. and Moy, M.C. (1973). Mem- ory scanning as a serial, self-terminating process. Journal of Experimental Psy- chology, 97, 323-336.

Theios, J. and Walter, D.G. (1979). Stimulus and response frequency and sequential effects in memory scanning reaction times. Journal of Experimental Psychology, 102, 1092-1099.

Theios, J. (1975). The components of response latency in simple human information processing tasks. In: P.M.A. Rabbitt and S. Dornic (eds.), Attention and Per- formance V. London, Academic Press.

stage analysis of the reaction process using brain-evoked potentials and reaction time

Documents