International Journal of Psychophysiology 61 (2006) 98 – 112
www.elsevier.com/locate/ijpsycho
Task modulation of the effects of brightness on reaction time and
response force
Piotr Jaśkowski a,*, Dariusz Waodarczyk b
a
Department of Cognitive Psychology, University of Finance and Management, Pawia 55, 01-030 Warsaw, Poland
b
Department of Biophysics, Karol Marcinkowski University of Medicine of Poznań, Poland
Received 15 April 2004; received in revised form 12 April 2005; accepted 10 July 2005
Available online 28 September 2005
Abstract
Van der Molen and Keuss [van der Molen, M.W., Keuss, P.J.G., 1979. The relationship between reaction time and intensity in discrete
auditory tasks. Quarterly Journal of Experimental Psychology 31, 95 – 102; van der Molen, M.W., Keuss, P.J.G., 1981. Response selection
and the processing of auditory intensity. Quarterly Journal of Experimental Psychology 33, 177 – 184] showed that paradoxically long
reaction times (RT) occur with extremely loud auditory stimuli when the task is difficult (e.g. needs a response choice). It was argued that this
paradoxical behavior of RT is due to active suppression of response prompting to prevent false responses. In the present experiments, we
demonstrated that such an effect can also occur for visual stimuli provided that they are large enough. Additionally, we showed that response
force exerted by participants on response keys monotonically grew with intensity for large stimuli but was independent of intensity for small
visual stimuli. Bearing in mind that only large stimuli are believed to be arousing this pattern of results supports the arousal interpretation of
the negative effect of loud stimuli on RT given by van der Molen and Keuss.
D 2005 Elsevier B.V. All rights reserved.
Keywords: Reaction time; Stimulus intensity; Response force
1. Introduction
1.1. Negative effect of loud stimuli on RT
Simple reaction time (RT) is known to decrease as a
function of stimulus intensity, approaching an asymptote for
the most intense stimuli. These changes of simple RT have
usually been assigned to early stages of sensory processing: it
is assumed that intensity influences the duration of early,
perceptual stages, and not later, motor-related stages (for
reviews see Jaśkowski, 1996, 1999; Miller et al., 1999b).
This opinion is questionable. To verify this view, the effect of
intensity on simple RT and the latency of early components of
event related potentials (ERP) could be compared. Using this
approach, Vaughan et al. (1966), Wilson and Lit (1981) and
Jaśkowski et al. (1990) found that visual intensity had
* Corresponding author.
E-mail address: jaskowski@vizja.pl (P. Jaśkowski).
0167-8760/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.ijpsycho.2005.07.010
identical effect on SRT and on ERP latency. However,
Jaśkowski et al. (1994a) found a larger effect of intensity on
simple RT than on ERP latency for auditory stimuli, and more
recently comparable results were reported for visual stimuli
by Kammer et al. (1999). This logic, however, implies that
latencies of ERP components and RT are directly comparable, which has been criticized by Meyer et al. (1988).
Another approach to locate stimulus intensity effects was
applied by van der Molen and Keuss’ experiments (Keuss
and van der Molen, 1982; van der Molen and Keuss, 1979,
1981; van der Molen and Orlebeke, 1980). They showed
that for auditory stimuli the relation between RT and
loudness depended on the participants’ task. While for the
simple and go/no-go task RT monotonically decreased with
intensity, the relation was flatter or even U-shaped when the
task was more difficult (e.g. choice or Simon task): for
extremely loud auditory stimuli (above 85 dB) paradoxically long RTs were found.
The results of these experiments suggest at least that
loudness affects more distal processes. The logic behind this
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
inference is straightforward. If stimulus intensity affects
only very early stages of processing, then it should affect RT
independently of the tasks participants perform. Any
changes across tasks in the effect of intensity on RT may
be interpreted as evidence in favor of the hypothesis that
intensity also affects later stages of processing.
In their early writing, van der Molen and Keuss (1979)
suggested that strong auditory signals elicit a startle reflex
which has to be suppressed in order to prevent a
degradation of performance when the participant’s task is
difficult. Later, they referred, after Sanders (1980), to the
concept of immediate arousal. Sanders (1983) claimed that
van der Molen and Keuss’ effect could be accounted for in
the framework of his model of stress which assumes that the
main chain of processing stages (computational stages) is
supplied by three energetic interdependent resources:
arousal, activation, and effort. Arousal is considered as a
transient response to the input, related to the stage of feature
extraction. Activation is readiness to respond and is related
to the state of response preparation. Arousal is assumed to
directly affect activation, with the degree of this influence
being regulated by effort. In particular, if the task is more
complex or needs precision (like choice reaction) effort is
able to reduce the influence of arousal to avoid unacceptable error rates (Sanders, 1983). Thus, the input signals
increase the arousal level leading to automatic increase of
activation and shortening of simple RTs. If, however, the
response needs a choice, the effect of immediate arousal on
activation evoked by input signals has to be suppressed,
which may be implemented by disconnecting arousal from
activation.
A shortcoming of this hypothesis is that it does not make
clear why RTs are longer for the loudest than for moderately
loud stimuli. Indeed, when intensity increases then RT
should still asymptotically decrease due to the shortening of
early computational stages, even when RT does not reap
profit from increased arousal anymore. To account for the
RT increase one may assume that in choice tasks the
suppression of arousal overshoots leading to a suboptimal
level of readiness. Such a mechanism was proposed by van
der Molen and Keuss (Keuss and van der Molen, 1982;
van der Molen and Keuss, 1981).
1.2. Effect of brightness on RT
With visual stimuli, the shape of the RT – intensity
relationship is usually independent of the complexity of
the tasks the participants have to perform (Azorin et al.,
1995; Egeth, 1977; Everett et al., 1985; Pins and Bonnet,
1996; Schwarz et al., 1977; Schweickert et al., 1988; Van
Duren and Sanders, 1988). For example, Pins and Bonnet
(1996) showed that although the asymptotic value of RT
depended on task complexity, the variable part of RT
changed with intensity identically, irrespective of whether
simple or choice reactions were required and whether
identification or categorization was needed.
99
The lack of the difference with visual stimuli as
compared to the effect with auditory stimuli as found by
van der Molen and Keuss has usually been ascribed to the
non-arousing properties of visual stimuli. This view is based
on experiments where the effect of foreperiod duration on
RT was measured for different intensities. With auditory
stimuli, the cost of lengthening the foreperiod from 1 to 5 s
was reduced for loud tones as compared to soft tones
(Bertelson, 1969; Niemi, 1979; Sanders and Wertheim,
1973). Sanders and Wertheim (1973) ascribed this finding to
the arousal characteristics of auditory stimuli: arousing
properties of imperative stimuli are less effective when
arousal is already high, i.e. just after warning stimuli. In
contrast, RTs to visual stimuli of different intensities suffer
equally from increasing the foreperiod (Bertelson, 1969;
Niemi, 1979; Sanders and Wertheim, 1973).
These results with visual stimuli seem to imply that
brightness affects only early computational stages that are
shared in both tasks. As a consequence, the effect of
brightness should be identical for these tasks.
Sanders (1975) and Niemi and Lehtonen (1982) showed
that visual intensity interacted with foreperiod duration
when visual stimuli were large and/or very bright similarly
as in case of auditory stimuli. These findings suggested that
such visual stimuli possess also arousal properties. Niemi
and Lehtonen (1982) used stimuli of size 29 32- and of
luminance 130 cd/m2. Maximal luminance of stimuli used
by Sanders was equal to 650 cd/m2. No information was
given about the size of stimuli. If visual stimuli indeed have
arousing properties, large and bright stimuli should produce
a pattern of results similar to that obtained by van der Molen
and Keuss, i.e. a U-shaped RT – intensity relationship.
As mentioned, it has usually been shown that relationship between RT and brightness is independent of task
difficulty (Azorin et al., 1995; Egeth, 1977; Everett et al.,
1985; Pins and Bonnet, 1996; Schwarz et al., 1977;
Schweickert et al., 1988; Van Duren and Sanders, 1988).
However, in such studies usually small stimuli of moderate
luminances were used. For example, in the extensive study
by Pins and Bonnet (1996) stimuli were as small as 30 7.5
min of arc and at most 2 3- (in their Experiment 4) and
covered a rather narrow intensity range (for simple RT
stimulus intensities ranged from 0.18 to 58.2 cd/m2 only,
and from 0.28 to 3.09 cd/m2 only in choice tasks).
Schweickert et al. (1988) used a wider range of luminance
but their stimuli were rather small (0.4 1.8-) and their
maximal luminance was as small as 30 cd/m2. There are,
however, at least two studies where some effect of task on
the RT –brightness relationship was found. Stanovitch and
Pachella (1977) reported that the effect of luminance on RT
was smaller when stimulus –response compatibility was low
than when it was high. Even more interesting is a study by
Kaswan and Young (1965) who found a U-shaped RT –
luminance curve for their most difficult tasks. Unfortunately,
their data were rather noisy and the authors provided no
statistical analysis to show that the effect was significant.
100
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
The main aim of the present study was to check if van der
Molen and Keuss-like effects can also be observed for
visual stimuli when they are large and cover a wide range
of luminance. In Experiment 1 we tested this hypothesis
with stimuli as large as 8.5 8.5- with a luminance over
300 cd/m2.
1.3. Response force
Besides RT, we measured response force (RF, i.e. the
maximal force that participants exert on the response key).
This was done for three reasons:
(1) According to one of the contemporary models RF is
predominately determined by arousal (for reviews see
Jaśkowski et al., 2000; Mattes et al., 2002; Miller et al.,
1999a). There is some evidence that general arousal or stress
affects RF. The higher the level of arousal, the larger is RF.
Thus, responses were stronger under more stressful conditions like time pressure, i.e. when the time for response
was limited (Jaśkowski et al., 1994b, 2000; Van der Lubbe
et al., 2001), knowledge of results, i.e. when RT results were
fed back after every trial (Jaśkowski and Waodarczyk, 1997)
and occasional delivery of task-irrelevant electrical shocks
(Jaśkowski et al., 1994c). Correspondingly, suboptimal
activation induced by sleep deficit leads to delay of RTs
and weakening of RF (Waodarczyk et al., 2002).
There are also some indications that stimulus-induced
transient arousal affects RF. It was shown several times that
in simple RT task participants responded not only faster to
loud than to soft tones but also more forcefully (Jaśkowski
et al., 1995; Miller et al., 1999a; Ulrich et al., 1998). As
mentioned, it is commonly believed that loud tones elicit
transient arousal. For example, Sanders (1983) argued that
arousal is transmitted to the motor stages via the activation
system, thus outside the information-processing system.
Similar bypass mechanism is assumed by Miller et al.
(1999a) to account for the relation between RF and
loudness. Some other arguments were provided recently
by Mattes et al. (2002) who showed that in a go/no-go task
stronger responses were elicited by less frequent stimuli.
One possibility to account for this finding discussed by
Mattes et al. is that rare stimuli evoke a transient increase of
arousal level. Some indirect support for this idea is,
according to Mattes et al., neuronal activity evoked by
infrequent stimuli like mismatch negativity (Näätänen,
1995) and P300 (Verleger, 1988, 1998).
(2) Arousal models predict that RF should increase with
loudness but much less with luminance, since auditory
signals are considered more arousing than visual stimuli as
discussed above. While the effect of loudness on RF was
quite robust (Jaśkowski et al., 1995; Miller et al., 1999a,b;
Ulrich et al., 1998), the relation for visual stimuli is not so
clear. Jaśkowski et al. (1995) and Miller et al. (1991) found
no effect of brightness on RF. However, in Angel’s (1973)
pioneering study as well as in a more recent study by Ulrich
et al. (1998) effects of both luminance and loudness on RF
were found. Reasons for these discrepancies are not clear.
One possibility is that the stimuli used by Jaśkowski et al.
(1995) and by Miller et al. (1991) were less arousing that
those used by Angel (1973) and by Ulrich et al. (1998). Our
results of Experiment 2, where the effect of stimulus
luminance on RF and RT was compared for large and small
stimuli, indicate that this is a very probable reason for the
discrepant results reported by different authors.
(3) Assuming that arousal is responsible for more
forceful responses we expected a gradual increase of RF
with intensity brightness for the simple-response task as
found by Ulrich et al. (1998) given that large visual stimuli
posses an arousing property. Of special interest are,
however, the changes of RF in the choice task. The
suppression hypothesis by van der Molen and Keuss
suggests an inverted U-shaped relation because for very
bright stimuli arousal has to be suppressed to avoid wrong
responses and, consequently, participants should respond
weaker in this task.
2. Experiment 1
In Experiment 1 we used relatively large (8.5-) and
bright (over 300 cd/m2) visual stimuli. A general prediction based on van der Molen and Keuss’ papers (Keuss
and van der Molen, 1982; van der Molen and Keuss, 1979;
van der Molen and Orlebeke, 1980; van der Molen and
Keuss, 1981) is that a monotonically decreasing RT –
intensity relation should be found for simple or go/no-go
tasks whereas a U-shaped relation should be found when a
choice is required. Furthermore, the higher the response
selection demands, the more curvilinear the RT –intensity
function. The last conclusion was based on the experiment
with Simon-like task (van der Molen and Keuss, 1981). A
high or low tone was presented monaurally to the left or
right ear. The participants’ task was to react with their left
or right hand according to pitch while ignoring the tone’s
presentation side. In the so-called correlated condition, a
cue was presented before the tone which indicated that the
stimulus content corresponds with the presentation side.
This way the selection demands were remarkably reduced
in respect to uncorrelated conditions where the cue was
uninformative. The curvilinearity of the RT – intensity
curve was higher under the uncorrelated condition.
Bearing this in mind, we used the Simon task to keep
response-selection demands as high as possible.
We additionally manipulated speed – accuracy trade-off to
even more enhance van der Molen and Keuss’ effect. This
manipulation was motivated by a study of van der Molen and
Orlebeke (1980). They showed that while the choice RT –
intensity curve for correct responses was U-shaped, the
relation turned out to be monotonous for incorrect responses.
They assumed that when a choice is required subjects have to
suppress the instantaneous increase of readiness evoked by
loud stimuli to avoid impulsive and chaotic responses.
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
Increasing error rate with increasing loudness proves that this
suppression becomes more and more difficult: apparently the
response choice stage is more and more frequently bypassed.
We reasoned that we can manipulate these two competitive response mechanisms by speed – accuracy trade-off.
It is conceivable that if speed is enhanced participants would
likely take the liberty of making more reflex-like responses
bypassing the choice stage. This should lead to more ‘‘lucky
guesses’’, i.e. correct responses delivered before the choice
process is completed. This, in turn, should make the RT –
intensity relation more monotonic. Conversely, with emphasis put on accuracy, the bypassing mechanism was expected
to be suppressed and a U-shaped relation should occur.
It should be noted that this prediction implicitly assumes
that accuracy/speed instruction can exert its effect early
enough. Indeed, van der Molen and Keuss (1979) suggested
that it is the response selection which is retarded at the
loudest tones because no re-increase of RT was found for a
go/no-go task. Some further support for this hypothesis
came from an observation that RT – intensity relation is more
markedly curvilinear for incompatible than for compatible
trials in a Simon-like task (van der Molen and Keuss, 1981).
Moreover, Rinkenauer et al. (2004) have convincingly
showed that SAT instruction affected not only that portion of
RT which starts after onset of lateralized readiness potential
(LRP) (as argued by Osman et al., 2000 and Van der Lubbe
et al., 2001) but also earlier processes.
2.1. Method
2.1.1. Participants
20 naive participants drawn from the student population of
University of Poznań (10 males and 10 females aged 19 – 24)
took part in the experiment. All were naive to the purposes of
the experiment and took part in a psychophysical experiment
for the first time. All had normal vision by self-report.
2.1.2. Stimuli and apparatus
Visual stimuli were generated by means of two LED
arrays. They consisted of 64 (8 8) red (660 nm) ultra-light
LEDs and their centers were located 5.1- to the left and to
the right of the fixation point (a red LED, 3.0 cd/m2). From
the observation distance of 50 cm, their surfaces covered
8.5- 8.5-.
The LEDs in an array were controlled by a microprocessor system which supplied every LED with a short
current pulse. Luminance was changed by varying both
duration and amplitude of the pulses. With this system
luminance could be varied in the range 1:104 identically for
every turned-on LED. Refresh rate was 110 Hz.
The stimuli were two patterns of LEDs arranged as letters
‘‘A’’ or ‘‘O’’. The letters were ‘‘drawn’’ black on red, i.e. the
LEDs consisting of a letter were switched off while the
remaining LEDs were on. 8 luminance levels (0.12, 0.39,
1.15, 3.83, 10.7, 35.1, 108, 327 cd/m2) were used. Stimuli
lasted 250 ms.
101
Each trial started with a warning signal. This was a flash
of two small squares (one on each array) formed by the 4
LEDs (9.0 cd/m2) located in the center of the arrays. The
duration of the foreperiods (interval between the warning
and imperative stimuli) was sampled from an exponential
distribution with a mean of 700 ms plus a constant period of
700 ms. The warning signal of the next trial was presented 3
s after participant’s response.
Participants sat in an experimental chamber with their
straight index fingers resting on the force-sensitive response
keys during the whole session.
A mechano-to-electrical converter was built into each
key. These electrical response signals were amplified,
sampled by an A/D converter, and fed to the computer.
Response signals were sampled at a rate of 500 Hz starting
20 ms before stimulus onset and continuing for 1500 ms.
To reduce learning effects, participants were given a short
practice session, before the experiment. The results obtained
in this session were excluded from further analysis.
2.1.3. Procedure
In the simple-response task participants made the same
response for each stimulus irrespective of letter identity and
presentation side. In the Simon task, the left/right response
key was assigned to ‘‘A’’/’’O’’ irrespective of stimulus side.
The total number of stimuli was 288 (18 replications 2
sides 8 intensities) in the simple-response task and 576
(= 288 compatible and 288 incompatible trials) in the Simon
task. Stimulus intensities, presentation sides, and intensities
were arranged in random order within each block. The
sequence of the two blocks corresponding to Simon task and
simple task alternated between participants (AB, BA).
Before each block, the participants were informed what
kind of task was to be performed. Participants were unaware
that the force of their responses was being measured. The
session lasted about 70 min.
2.2. Speed –accuracy trade-off
Participants were randomly assigned to one of two
groups. One group worked with a payoff equation that
emphasized speed, the other worked with an equation which
emphasized accuracy. Before the session each participant
was told about this emphasis and about the payoff system.
After each trial, the bonus was calculated according to the
following equations:
for the accuracy group :
0:1IpIð1150 RTÞ
2000
1:5Ið1 pÞ Polish zlotyð ¼ 0:25$Þ
for the speed group :
0:1IpIð600 RTÞ
1000
0:075Ið1 pÞ Polish zloty;
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
where p = 1 for a correct response and p = 0 for an incorrect
response, and RT denotes reaction time in ms.
At the end of a given block, participants were informed
about the resultant bonus (being the sum of the trial
bonuses). Depending on results participants could earn
about 10 –40 zloty (40 zloty = 1/30 of the average monthly
salary in Poland).
2.3. Data analysis
All parameters were derived from the force – time
functions. Responses were classified as correct when a
force of 2 N was exceeded between 100 ms and 1000 ms
after stimulus onset on the required side and not on the
other side. Three dependent measures were analyzed: the
percentage of correctly responded trials (PC); the RT
defined as the time from onset of stimulus to the moment
at which the force exceeded 2.0 N relative to the baseline
measured 20 ms before stimulus onset; and third, response
force (RF) defined as the maximum value of the exerted
force. RT and RF were determined in each trial and
averaged across all correctly responded trials. The three
parameters were determined separately for each stimulus
intensity in each task. To evaluate data statistically
analyses of variances were performed with two withinsubjects factors (intensity and task) and one betweensubjects factor (trade-off). All p-values obtained from
analysis of variance were adjusted using GreenhouseGeisser coefficients. As the Simon-compatible and Simonincompatible levels were mixed within blocks, a separate
analysis was performed to compare the effect of compatibility in the Simon task. Only effects of compatibility will
be reported from this separate analysis
2.4. Results
2.4.1. Premature response and misses
Trials in which RTs were shorter than 100 ms were
defined as premature responses. As there were few such
responses they were not further analyzed.
Trials in which RTs were longer than 1000 or responses
were not given at all were considered as misses. There were
only 1.49% of misses. The differences between percent of
misses among different conditions were very small and
insignificant.
2.4.2. Percentage of correct choices
A response made with the proper hand was considered as
a correct choice. Percentages of correct choices (PC) were
analyzed only for Simon task. They were higher in the
accuracy than in the speed group (99.4 vs. 94.6;
F(1,18) = 23.1, MSE = 8.0, p < 0.001).
Significant was also the effect of intensity ( F(7,126) =
4.4, MSE = 1.6, p = 0.003, see Fig. 1): PC decreased
monotonically when intensity increased. PC was higher in
compatible than incompatible trials (98 vs. 96, F(1,18) = 8.8,
100
98
Percent correct
102
96
94
92
90
0.1
10
1000
Luminance (cd/m2)
Fig. 1. Percent correct as a function of luminance for the simple-response
and the Simon task for both groups (Experiment 1). Empty symbols denote
the data of the speed-instruction group, filled symbols of the accuracyinstruction group.
MSE = 13.0, p = 0.002). However, this effect was mainly due
to smaller PC for incompatible trials in the speed group
(comp. 96, incomp. 93). For the accuracy group PCs for
compatible and incompatible trials were equal (99%). This
is supported by the significant interaction between compatibility and trade-off ( F(1,18) = 7.3, MSE = 0.8, p = 0.015).
2.4.3. Reaction time
RTs were longer for the Simon than for the simpleresponse task (585 vs. 286 ms, F(1,18) = 384.8, MSE =
16204, p < 0.001) and in the accuracy than the speed group
(507 vs. 479 ms, F(1,18) = 13.6, MSE = 23066, p = 0.002).
Moreover, RTs decreased with intensity ( F(7,126) = 49.5,
MSE = 625.6, p < 0.001). The significant interaction between
task and intensity indicated ( F(7,126) =18.0, MSE = 684,
p < 0.001) that this relation was different for both tasks (Fig.
2): while simple RTs decreased monotonically with intensity,
the curves were U-shaped for the Simon task. A separate
ANOVA performed only for the three highest intensities for
the Simon task showed that RT indeed re-increased in this
range ( F(2,36) = 4.2, MSE = 173, p = 0.033). The overall
effect of intensity (maximal RT minimal RT) was almost
twice as large for simple RTs than for the Simon task (66 vs.
35 ms). No other interaction was found to be significant, in
particular the effects of intensity did not interact with speed –
accuracy trade-off.
The separate ANOVA for the Simon task indicated that
RTs were shorter for compatible than for incompatible trials
(560 vs. 570 ms, F(1,18) = 9.5, MSE = 678, p = 0.006).
Moreover, the RT –intensity curves had different shapes
for both types of trials (interaction intensity compatibility:
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
360
660
simple
Simon
320
620
280
580
240
540
200
0.1
10
1000
0.1
10
Reaction time (ms)
Reaction time (ms)
103
500
1000
Luminance (cd/m2)
Fig. 2. Reaction times as function of luminance for the simple-response and the Simon task for both groups (Experiment 1). Empty symbols denote the data of
the speed-instruction group, filled symbols of the accuracy-instruction group.
F(7,126) = 2.49, MSE = 2670, p = 0.02): the U-shape of the
curve for compatible arrangement seems to be more
pronounced (see Fig. 3). No other interaction of compatibility was significant.
2.4.4. Response force
Participants responded more forcefully in the Simon than
in simple-response task (see Fig. 4, note different RF axes;
12.3 vs. 10.5 N; F(1,18) = 5.9, MSE = 43.3, p = 0.026).
Moreover, RFs increased with luminance ( F(7,126) = 4.8,
MSE = 1.9, p < 0.001). Fig. 4 suggests that this increase was
larger for the Simon than the simple-response task but the
interaction intensity task was insignificant ( F(7,126) =
1.35, MSE = 1.5, p = 0.246).
The effect of trade-off was only marginally significant in
spite of the large absolute differences between conditions
(see Fig. 4, 13.0 N for speed and 10.5 N for accuracy,
F(1,18) = 4.16, MSE = 598, p = 0.056). No other interaction
was found.
The separate ANOVA for the Simon task indicated no
effect of compatibility.
660
2.5. Discussion
Reaction time (ms)
620
580
540
500
0.1
10
1000
Luminance (cd/m2)
Fig. 3. Reaction times as function of luminance for compatible and
incompatible trials in the Simon task (Experiment 1), pooled across the two
groups. Empty symbols denote incompatible trials, filled symbols compatible trials.
The general pattern of results was very similar to that
obtained by van der Molen and Keuss (Keuss and van der
Molen, 1982; van der Molen and Keuss, 1979; van der
Molen and Orlebeke, 1980; van der Molen and Keuss,
1981) for auditory stimuli. First of all, intensity had different
effects on RTs in the simple-response task and choice task
(Simon paradigm). The RT –intensity curve was flatter for
the Simon than for the simple-response task. Moreover, a
significant increase of RT was found for the highest
intensities in case of the Simon task. This is at odds with
previous findings concerning the effect of brightness on RT:
no effect of task difficulty on the RT – intensity relation was
usually reported for visual stimuli (Azorin et al., 1995;
Egeth, 1977; Everett et al., 1985; Pins and Bonnet, 1996;
Schwarz et al., 1977; Schweickert et al., 1988; Van Duren
and Sanders, 1988). In contrast to these previous studies we
applied, however, conditions which were intended to
enhance possible arousing properties of visual stimuli, i.e.
we used relatively large and bright stimuli. For example, in
the extensive study by Pins and Bonnet (1996) stimuli were
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
104
14.0
11.0
simple
Simon
13.5
10.0
13.0
9.5
12.5
9.0
12.0
8.5
11.5
8.0
0.1
10
1000
0.1
10
Response force (N)
Response force (N)
10.5
11.0
1000
Luminance (cd/m2)
Fig. 4. Peak force as a function of intensity for the simple-response and the Simon task in the two groups (Experiment 1). Empty symbols denote the data of the
speed-instruction group, filled symbols of the accuracy-instruction group.
as small as 30 7.5 min of arc and at most 2 3- (in their
Experiment 4) and covered a rather narrow intensity range
compared to the present study (for simple RT stimulus
intensities ranged from 0.18 to 58.2 cd/m2 only, and even
less for choice tasks: from 0.28 to 3.09 cd/m2).
With regard to RF, we expected within the frameworks of
the arousal-suppression model to find a monotonic increase
of RF for the simple-response task whereas this tendency
should be stopped or even reversed for the more demanding
tasks. On the contrary, RFs continued to grow for the
brightest stimuli. The increase of RF with intensity was
independent of the task. Therefore, if we assume that RF
reflects immediate arousal, this finding contradicts the idea
of the suppression of arousal for more demanding tasks.
A gradual increase of RF with intensity was found by
Angel (1973) and Ulrich et al. (1998) for a simple task, but
Jaśkowski et al. (1995) reported no changes of RF with
brightness. Thus, the present results seem to support Angel
(1973) and Ulrich et al. (1998) findings rather than
Jaśkowski et al. (1995). However, the conditions used in
Experiment 1 and other studies differed remarkably. In
particular, different luminance ranges and stimulus sizes
were used. Namely, Jaśkowski et al. (1995) used rather
small targets (0.19-) with luminance ranging from 0.3 cd/m2
to 2000 cd/m2, whereas Ulrich et al. (1998) applied unusual
bright stimuli ranging from 220 cd/m2 to 22 000 cd/m2 (for
comparison, the maximal luminance of a typical computer
monitor is about 150 cd/m2). Furthermore, their targets were
also substantially larger than the ones used by Jaśkowski et
al. (5.4-). Thus, the reason for the different results obtained
by different authors is that RF depends on brightness only if
the target is sufficiently large and/or bright.
RF was larger for the Simon task than for the simple task,
which is at odds with some previous results. RF was found to
be independent of tasks in previous studies from our group
(Jaśkowski et al., 2003; Van der Lubbe et al., 2001) and in a
recent study by Miller et al. (1999a). Miller et al.’s subjects
performed simple, go/no-go and choice tasks for auditory
stimuli of different intensities.1 Even more surprising, they
found no effect of task on the RT– intensity relation, in
contrast to the present study and to previous findings by van
der Molen and Keuss (1979, 1981) and Keuss and van der
Molen (1982) as well as Waodarczyk et al. (2002). We are not
able to explain why these results are at odds.
To boost the effect of task on the RT– intensity relation,
we applied speed – accuracy trade-off. One can expect that,
with emphasis laid on accuracy, the mechanisms preventing
accidental responses should be elicited rendering the RT –
intensity relation more curvilinear than when speed was
emphasized. Participants from the speed group made more
errors and responded faster than participants from the other
group. Moreover, the more demanding the task, the more
effective was the trade-off as measured by the RT difference
between speed and accuracy groups. These findings prove
that the induction of speed –accuracy trade-off was successful. However, the instruction had no effect on the relation
between RT and stimulus intensity.
3. Experiment 2
Results of Experiment 1 suggest that a negative effect
of very strong stimuli on RT in demanding tasks is not
1
It should be noted that in no-go task RFs were found to be larger than
for other tasks (Ulrich et al., 1999). But this effect should probably be
assigned to the response- or stimulus-probability effect than to the taskcomplexity effect (Mattes et al., 2002).
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
3.1. Method
3.1.1. Participants
3 women and 7 men (mean age 22.5, S.D. 1.8) served as
participants. They were recruited from the student population of Adam Mickiewicz University of Poznań, were naive
to the purposes of the experiment and took part in a
psychophysical experiment for the first time. All had normal
vision by self-report.
3.1.2. Stimuli and apparatus
As in Experiment 1, visual stimuli were generated by
means of two LED arrays. The stimuli were two neighboring LEDs arranged vertically or horizontally. These ‘‘lines’’
(1.7- in length) were ‘‘drawn’’ either black on red (‘‘large
stimuli’’, i.e. the two LEDs forming a ‘‘line’’ were switched
off while the remaining 62 LEDs were on) or red on black
(‘‘small stimuli’’, i.e. the two LEDs forming a ‘‘line’’ were
switched on while the remaining 62 LEDs were off). In
other words, only 2 LEDs were flashed in case of small
targets and all but two were flashed in case of large targets.
Small and large stimuli were presented in a random
sequence.
Luminance levels and stimulus duration was identical as
in Experiment 1.
3.1.3. Procedure
In the simple-response task participants made the same
response for each stimulus irrespective of stimulus orientation and the presentation side. In the Simon task the left/
right response key was assigned to horizontal/vertical
orientation or vice versa irrespective of stimulus side. The
total number of stimuli in a session was 256 (8 replications 2 sides 8 intensities 2 target sizes) in the
simple-response task and 512 in the Simon task (= 256
compatible and 256 incompatible trials). Two sessions were
performed for every participant on two consecutive days.
Statistical analysis was the same as in Experiment 1.
3.2. Results
3.2.1. Premature response and misses
Trials in which RTs were shorter than 100 ms were
defined as premature responses. As there were few such
responses (< 0.2%) they were not further analyzed.
Trials in which RTs were longer than 1000 or responses
were not given at all were considered as misses. There were
only 2.0% of misses. The only effect on percent of misses
found was the interaction between task and stimulus size
( F(1,9) = 6.3, MSE = 0.08, p = 0.033): for simple reactions,
percent of misses was smaller for large than for small
stimuli (2.42% vs. 1.25%); no such an effect of size was
found for the Simon task (1.56% vs. 1.64%).
3.2.2. Percent correct
Percent correct choices in the Simon task (see Fig. 7) was
higher for small than for large targets (93.0 vs. 86.6,
F(1,9) = 22.5, MSE = 14.6, p = 0.001). Moreover, responses
were more erroneous for incompatible than for compatible
trials (86.5. vs. 93.0; F(1,9) = 10.2, MSE = 3.3, p = 0.011) but
there was no interaction of compatibility with other factors
(Fig. 5).
Insignificant was also the interaction between task and
intensity which was found to be significant in Experiment 1.
3.2.3. Reaction time
RTs (see Fig. 6) were longer for the Simon than for the
simple-response task (260 vs. 502 ms, F(1,9) = 628,
MSE = 7449, p < 0.001) and for the small than large targets
(374 vs. 387 ms, F(1,9) = 25.9, MSE = 508, p = 0.001).
Target size had different effects in both tasks (see Fig. 6
100
98
Percent correct
restricted to auditory stimuli but can also be observed for
visual stimuli. Previous studies failed to find any effect
of task complexity on the RT – intensity relation in case
of visual stimuli. In Experiment 2 we examined a
possible reason for this discrepancy. We suggest, following Niemi and Lehtonen (1982), that the crucial factor is
the stimulus size. Niemi and Näätänen (1981) claimed
that, unlike small stimuli, large stimuli posses arousing
property like auditory stimuli. This suggestion was further
explored in Experiment 2 in which the effects of
brightness on RT and RF were compared for large and
small stimuli. First, we expected that the negative effect
of very bright stimuli should disappear for small stimuli
or at least be weaker than for large stimuli. Second, task
demands should exert no effect on RT for small stimuli.
Moreover, RF should increase with brightness for large
stimuli but not for small stimuli.
105
96
94
92
90
0.1
10
1000
Luminance (cd/m2)
Fig. 5. Percent correct as a function of luminance for the simple-response
and the Simon task and for the two target sizes (Experiment 2). Empty
symbols denote data of small targets, filled symbols of large targets.
Reaction time (ms)
350
550
300
500
simple
250
200
0.1
1000 0.1
10
Simon
450
10
400
1000
Reaction time (ms)
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
106
Luminance (cd/m2)
Fig. 6. Reaction time as a function of luminance for the simple-response and the Simon task for the two target sizes (Experiment 2). Empty symbols denote data
of small targets, filled symbols of large targets.
compatible than incompatible trials, the three-way interaction was insignificant. Also insignificant turned out to be
the interaction intensity compatibility.
3.2.4. Response force
Participants responded more forcefully in the Simon than
the simple-response task (Fig. 8—note different RF axes,
11.9 vs. 9.6 N; F(1,9) = 19.0, MSE = 22, p = 0.002), to large
than small targets (11.0 vs. 10.6, F(1,9) = 12.3, MSE = 1.3,
560
540
Reaction time (ms)
interaction task size, F(1,9) = 317, MSE = 345, p < 0.001).
RTs were longer to large stimuli than to small stimuli in
the Simon task (526 vs. 477 ms; separate ANOVA: F(1,9) =
157.3; p < 0.001) and shorter in the simple-response task
(247 vs. 272 ms; separate ANOVA: F(1,9) = 104.8;
p < 0.001).
Moreover, RTs depended on intensity ( F(7,63) = 106.7,
MSE = 234, p < 0.001). The significant interaction between
task and intensity ( F(7,126) = 4.8, MSE = 264, p = 0.007)
indicated, however, that these relations were different for
both tasks. As Fig. 8 shows, simple RTs decreased
monotonically with intensity, nearly parallel for both target
sizes. The overall change of simple RT was about 60 ms.
The situation was quite different for the Simon task. For the
small target, the curve was monotonic, with the overall
change of RT between lowest and highest luminance being
approximately equal to the change for simple RT. For the
large target, the curve was U-shaped with an overall change
of 17 ms and a maximal difference of 27 ms. These
observations are supported by the significant interaction
size intensity (larger effect of intensity for the small than
large target; F(7,63) = 8.7, MSE = 411, p = 0.001) and the
three-way interaction (task size intensity, F(7,63) = 3.2,
MSE = 204, p = 0.029). However, neither a Tukey test nor an
ANOVA performed only for the four highest luminances
showed any significant re-increase of RTs.
The separate ANOVA for the Simon task (see Fig. 7)
indicated that RTs were shorter for compatible than incompatible trials (490 vs. 514 ms, F(1,9) = 13.8, MSE = 3188, p =
0.005). The overall compatibility effect was slightly larger for
the small than for large target (29 vs. 17 ms, F(1,9) = 6.8,
MSE = 378, p = 0.028).
Although, similarly to Experiment 1, the re-increase of
RTs for the highest intensities looked more pronounced for
520
500
Simon
480
460
440
420
0.1
10
1000
Luminance (cd/m2)
Fig. 7. Reaction times as function of luminance for compatible and
incompatible trials in the Simon task and for small and large targets, pooled
across the two groups. (Experiment 2). Empty symbols denote incompatible
trials, filled symbols compatible trials.
12.0
14.0
11.5
13.5
11.0
13.0
Simon
10.5
10.0
12.0
simple
9.5
9.0
0.1
12.5
107
Response force (N)
Response force (N)
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
11.5
10
1000
0.1
11.0
1000
10
Luminance (cd/m2)
Fig. 8. Peak force as a function of luminance for the simple-response and the Simon task for the two target sizes (Experiment 2). Empty symbols denote data of
small targets, filled symbols of large targets.
The separate ANOVA for the Simon task (see Fig. 9)
indicated no effect of compatibility.
p = 0.007) and to brighter stimuli ( F(7,63) = 5.3, MSE = 1.5,
p = 0.016). Moreover, the effect of target size was slightly
greater for the Simon than the simple-response task
(interaction size task F(1,9) = 1.9, MSE = 1.4, p = 0.054).
Target size differentially affected the relation RF –
intensity, which was steeper for large than small targets
(interaction size intensity F(7,63) = 2.5, MSE = 0.3, p =
0.039). ANOVAs performed for each size separately showed
no effect of intensity for the small target ( p = 0.12) and a
significant effect for the large target ( F(7,63) = 5.77,
MSE = 0.44, p = 0.011).
The interaction of task intensity and the threefold
interaction were insignificant.
3.3. Discussion
In Experiment 1, we found a clear effect of stimulus
intensity on RF and a modulating effect of task difficulty on
the shape of the RT – intensity relation. Both these effects
might be elicited by the arousal properties of strong and
large visual targets. Experiment 2 was designed to check
this possibility by comparing the effect of brightness on RT
and RF for large and small visual stimuli. This experiment
yielded two main findings. First, task demands clearly
14.0
Response force (N)
13.5
13.0
large
12.5
small
12.0
11.5
11.0
0.1
10
1000
0.1
10
1000
Luminance (cd/m2)
Fig. 9. Peak force as a function of luminance for compatible and incompatible trials in the Simon task for small and large targets (Experiment 2). Empty
symbols denote incompatible trials, filled symbols compatible trials.
108
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
affected shapes of the RT – intensity curves for large targets
like in Experiment 1, whereas these curves were almost
identical between tasks for small targets. Second, the effect
of intensity on RF was likewise found for large targets only,
whereas RF –intensity relations were virtually flat for small
targets. Both these findings can be accounted for by
assuming different arousing properties of large and small
targets. This is exactly what we expected.
Although we found clear modulating effect of task on
RT – intensity relation, re-increase of RTs for the brightest
stimuli and Simon task was found in contrast to results of
Experiment 1. It should be noted that under some special
conditions van der Molen and Keuss reported also
monotonic rather than U-shaped curves even for choice
tasks. Monotonic relations were found when a preknowledge about the stimulus intensity was given before each
trial and for very long foreperiod (30 s). Such an
‘‘instability’’ of the RT – intensity relation might indicate
that the shape of the relation for difficult tasks is prone to
the influences of some other factors like motivation and
general stress induced by the experimental situation. A
factor, which could preclude re-increase of RTs, might be
mixing of large and small stimuli (e.g. by lowering the
overall arousal). Alternatively, the luck of a clear reincrease might be due to low statistical power of Experiment 2. Indeed, to keep the session length reasonable we
had to lower the number of trial for a given condition (16 in
case of simple RT and 32 in case of Simon task). Bearing in
mind that RTs in Simon task were relatively variable, it is
not surprising that the re-increase we found (Fig. 7) did not
reach significance level.
Of considerable interest is that RTs were longer for the
large than small target in case of the Simon task, whereas
the reversed relation was noted in the simple task. This
effect could also be accounted for by assuming arousing
properties of large stimuli. In a difficult task, arousing
properties of the stimulus should result in delayed RT
because inhibitory processes have to be triggered to
prevent premature and inaccurate responses. In contrast,
arousing properties are welcome when no choice is
necessary and when the response can be initiated without
complete stimulus recognition. Alternatively, shortening of
RTs for the simple task could partially be due to shorter
perceptual latency for central than peripheral stimulation
(Payne, 1966, 1967; Rains, 1963). Indeed, the inner LEDs
of the large target were located more centrally than the two
LEDs switched on in case of the small target. This might
facilitate reactions, as detection of the light emitted by the
border LEDs was sufficient to respond in case of the
simple task.
So far we used the term brightness and stimulus intensity
interchangeably. This usage could be somewhat misleading
especially for readers representing more psychophysiological tradition. Indeed, Barry and James (1981), for example,
manipulated stimulus intensity by changing the target size.
In psychophysical tradition, however, brightness reflects
subjective feeling of how bright a target is. It is measurable
by using the magnitude-estimation method in which
participants assign different ranks to stimuli of different
perceived brightness. Systematic measurements of this kind
were made by Mansfield (1973). He showed that once the
target area exceeded a critical value perceived brightness did
not depend on the size anymore being a function of
luminance only. According to his results, the critical target
area is as small as 0.17- (in diameter). Only for very small
targets perceived brightness was found to be a function of
the product of target area and luminance (luminous flux).
It is, however, clear from our data that RF depends on
combined effect of luminance and size. Of interest would be
to see if RF changes as a function of luminous flux.
Although we did not vary target area systematically, we
could plot RF as a function of luminous flux bearing in
mind that the large stimuli had 31 times larger lighting area
than the small stimuli. The curves for large and small targets
should overlap. We found that they did not (not shown). It
means that the relationship between RF, target luminance
and target size must be more complicated.
4. General discussion
4.1. Van der Molen and Keuss’ effect for visual stimuli
In two experiments we explored the intensity effect on
reaction time and response force for visual stimuli. The
point of departure for this study was van der Molen and
Keuss’ finding (Keuss and van der Molen, 1982; van der
Molen and Keuss, 1979; van der Molen and Orlebeke,
1980; van der Molen and Keuss, 1981) that task difficulty
may modify the RT –intensity relation in case of auditory
stimulation: while for a simple task RTs gradually
decreased as a function of intensity, a re-increase of RTs
was observed for the loudest tones when a choice task was
to be accomplished. This effect was accounted for by
arousing properties of loud auditory stimuli. It was
assumed that in case of simple tasks immediate arousal
evoked by strong auditory signals has a beneficial effect
on reaction times. If, however, the task is more complex
(needs a choice) and stimuli are very loud subjects try ‘‘to
suppress the impulsive prompting of a motor response’’
(van der Molen and Orlebeke, 1980, p. 475) to avoid too
many premature and/or wrong responses.
Our main goal was to check if a similar pattern of results
occurs for visual stimuli provided that they are arousing.
Based on Niemi and Lehtonen (1982) findings we expected
a modulatory effect of task on the RT –intensity relation
when visual targets are large enough. Indeed, the shape of
RT –intensity relation depended on task demands when the
stimuli were relatively large. For small targets the relation
was monotonic and almost identical with that for the simple
task. Furthermore, stimulus size did not affect the relation
when a simple task was to be done. The relation changed
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
dramatically once participants have to respond to large
targets and the task was demanding. In such a case, besides
flattening of RT –intensity curve, we observed a re-increase
of RTs for the brightest stimuli, similar to that observed by
van der Molen and Keuss for auditory stimuli. This is the
first demonstration of the van der Molen and Keuss’ effect
for visual stimuli.
4.2. The effect of arousal
The U-shaped intensity –RT relation for large and not for
small targets provides an important support for van der
Molen and Keuss’ claim that arousal is responsible for the
RT re-increase for very intense stimuli. Indeed, visual
stimuli commonly used in experimental psychology evoke
no arousal and, therefore, no effect of task on RT – intensity
relation was found so far. Arousing properties were,
however, reported for large (Niemi and Lehtonen, 1982)
or extremely bright stimuli (Sanders, 1975). In accordance
with this claim we found the effect of task on the RT –
intensity relation only for large but not for small stimuli.
Taken together, van der Molen and Keuss’ results as well as
the present findings provided converging evidence that in
case of demanding tasks arousal can modify the RT –
intensity curve.
How does arousal exert its effect on RT? Following
Sanders (1977), van der Molen and Orlebeke (1980) seem
to accept that arousal affects readiness to respond. In
Sanders’ later writing this idea is expressed as follows
(Sanders, 1983, pp. 89– 90) ‘‘the rise in arousal triggers a
signal to the activation system, enhancing response readiness’’. Also Niemi and Näätänen (1981) and Näätänen
(1971) considered immediate arousal as a factor influencing preparation/readiness. Usually, response preparation is
modeled as the distance between motor readiness and a
threshold called action limit (e.g. Niemi and Näätänen,
1981). If readiness increases, the distance to action limit
decreases. The overt response is executed if motor readiness crosses a threshold level. The more advanced is
motor preparation, the smaller becomes the distance
between the current value of readiness and the motoraction limit. Therefore, better motor preparation means
smaller distance between motor readiness and the action
limit. Therefore, reaction time is short for well-prepared
responses. Accordingly, one can assume that readiness
increases instantaneously whenever an arousing stimulus
(e.g. a loud tone) is presented. Such an instantaneous
increase of readiness must somehow be suppressed if a
choice is required. This, in turn, means a remarkable
increase of the distance between motor activation and
action limit, leading to RT lengthening. This model might
successfully account for the U-shaped RT – intensity
relationship reported for arousing stimuli by van der
Molen and Keuss and in the present study. But as we
will show in the following, this proposal seems to be
inconsistent with force data.
109
4.3. Response force
4.3.1. Motor readiness and response force
Näätänen’s (1971) model of motor readiness was recently
extended by Mattes et al. (1997) to account for RF results.
They assumed that RF is directly related to the maximal
overshoot of motor activation over the action limit.
Furthermore, they assumed that the larger the distance from
the current motor activity at the moment of response
initiation to the action limit, the larger the overshoot. In
other words, in case of a badly prepared response the
distance between readiness prior to stimulus onset is large
and activation overshoots the action limit by a large amount,
resulting in a forceful response. In contrast, if a response is
well prepared the distance to the action limit is small and
‘‘the required increment of activation can be calibrated well,
producing only a small activation overshoot and consequently a less forceful response’’ (Mattes et al., 2002, p.
479). This nicely fits their results on the effect of response
probability on RT as they found that participants responded
faster and weaker when the response probability was high.
4.3.2. Arousal and response force
However, as mentioned in the Introduction, some evidence indicates that both general and immediate arousal can
affect RF. Unfortunately, Mattes et al. (1997; see also Mattes
et al., 2002) provided no conception of how to model the
problem of the arousal effects on RF. If we assume, as it was
done in the preceding section, that arousal affects readiness
by decreasing the distance between motor activation and
action limit, the arousing stimuli should be associated with
weaker responses. This is obviously not the case.2
In the following we draft another model in which we
supplemented an elaborated version of Mattes et al.’s
overshoot idea by a possible mechanism of arousal effects.
The assumptions of the model are the following.
(i) Arousing factors are assumed to increase general
internal noise which entails larger variability of all motor
processes. For simplification we will assume that arousing
factors affect only variability of the action-limit level. As
Jaśkowski et al. (2000) pointed out, a majority of RF results
could be accounted for by an assumption that immediate and
general arousal lead to an increase of overall variability of
neural events. Such a mechanism was originally proposed
by Van Galen and de Jong (1995; see also Van Galen and
van Huygevoort, 2000; Van Gemmert and Van Galen, 1997)
to account for changes of axial pen pressure during aiming
movements in response to increased mental load, physical
factors or task demands (Van den Heuvel et al., 1998; Van
Galen and de Jong, 1995; Van Galen and van Huygevoort,
2000; Van Gemmert and Van Galen, 1997). Van Galen and
2
It should be mentioned that Mattes et al. (2002) recently reported data
which were inconsistent with the original formulation of the model. They
showed, in contrary to the predictions of the model, that also stimulus
probability affects RT. This forced a post-hoc reformulation of the model.
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P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
de Jong (1995) postulated that arousing/stressing factors
increase the general neuromotor noise which leads to an
unacceptable rate of premature and/or wrong participants’
responses. To prevent such a situation, limb stiffness is
increased by contraction of both agonist and antagonist
muscles, resulting in an increase of axial pen pressure.
(ii) To account for our data we also have to assume that
increase of internal noise is very fast. Many models of
arousal had to assume fast response of the system for the
abrupt changes in the environment. Sanders (1983) introduced a term ‘‘immediate arousal’’ to describe dynamical
properties of some arousing factors like stimulus intensity
which can affect response speed in a trial-by-trial fashion.
Also Van Gammert and van Galen assumed that very load
stimuli might evoke an immediate stiffness response,
possibly like startle response in animal conditioning.
(iii) Response force is directly related to motor
activation.
(iv) After stimulus onset, motor activation increases,
crossing eventually the action limit. We will assume that
amount of motor activation over the action limit must be
proportional to its variability. In other words, the signal-tonoise ratio (i.e. maximal motor activation over action limit
related to the variability of action limit) has to be constant.
Hence, we can write
ð M AÞ=rð AÞ ¼ a or
RF ¼ f ð M Þ ¼ f ðaIrð AÞ þ AÞ;
where r(A) denotes variability of action limit, A is the
action limit, M is maximal motor activation, f is a
monotonically increasing function relating to motor activation to RF, and a is a constant.
(v) We will also relate response preparation to the
lowering of the action limit rather than to the level of
motor readiness: better preparation means, as before, smaller
distance between motor readiness and the action limit.
The model provides therefore the straightforward
account for two basic empirical facts concerning RF. (1)
An increase of arousal level results in more forceful
responses. This is because of increase of r(A). (2) Since
preparation leads to a reduction of action limit, A, better
prepared responses are weaker.
In accordance with our previous considerations, we will
assume that when a choice is needed the distance between
motor readiness and action limit has to be high enough to
avoid incorrect responses. If an arousing stimulus is
presented, the increase of arousal level results in the related
increase of action-limit variability. Accordingly, action limit
must be elevated because of a risk of incorrect responses.
This leads to two consequences. First, RF increases as both
r(A) and A increases. Second, RT may increase because of
the elevation of action limit.
Note also that RF should be smaller for easy than for
complex tasks only if stimuli are arousing. This is because
for a non-arousing situation there is no need to elevate the
action limit. Conversely, under arousing conditions, action
limit has to be elevated if percent of correct responses must
be kept on a reasonable level. In such a situation, RF should
be larger for choice tasks than for a simple task. In the
present experiments, we found larger RFs for the Simon
than detection task and this difference was larger for the big
target (more arousing stimulus) than for the small target
(less arousing stimulus). Failure to find task effect on RF by
other authors (Miller et al., 1999a; Van der Lubbe et al.,
2001) could be due to non-arousing stimuli they used.
4.3.3. Speed – accuracy trade-off
Our main prediction concerning manipulation of speed –
accuracy trade-off was that the van der Molen and Keuss’
effect should vanish or be at least less pronounced under
speed condition. This prediction relied on the results
reported by van der Molen and Orlebeke (1980) who
showed that the re-increase of RT for very loud stimuli does
not occur for incorrect responses which were assumed to
bypass the choice mechanisms. We expected that this
mechanism is more frequently bypassed under speed
condition than under accuracy condition leading to higher
error rate and lucky guesses whose contribution to the mean
should alleviate the curvilinearity of the RT – intensity
relations. Instead, we found virtually parallel RT –intensity
relations for the both conditions. There are some possibilities why SA instruction failed to affect RT – intensity
relationship.
The most trivial possibility is that the number of lucky
guesses and, consequently, their contribution to the RT
mean was too subtle to be detected in our experiment. This
possibility seems to be supported by the fact that our speed –
accuracy manipulation was relatively ineffective. RT was
only by 15% shorter in speed- than accuracy group for the
Simon task.
Another possibility is that the action limits are more or
less equal under both conditions. Although under accuracy
condition r(A)/A has to be lower because of the risk of too
many errors, speed condition could be by itself more
arousing. It is, for example, a common finding that RF is
larger under time pressure (Jaśkowski et al., 1994a,b,c,
2000; Van der Lubbe et al., 2001). Also in the present study,
participants responded stronger under speed than under
accuracy condition. This extra arousal under speed condition might force some additional elevation of action limit
which can luckily reach the same level as for accuracy
condition. Whether this leads to parallel RT – intensity
functions for the both conditions or not depends heavily
on the relation between arousal and stimulus strength.
4.4. Summary
The main results of the present study can be summarized
as follows. (1) RT – intensity relation depends on task
difficulty under arousing conditions: for a more demanding
P. Jaśkowski, D. Wlodarczyk / International Journal of Psychophysiology 61 (2006) 98 – 112
task like Simon paradigm RT – intensity curve becomes Ushaped rather than monotonic. This finding, originally
demonstrated by Van der Molen and Keuss for auditory
stimulation, was extended here to visual stimuli. Additionally, it was shown, in accordance to previous findings, that
only large stimuli have such arousing properties. (2) With
large visual targets, RF increases monotonously with
intensity and this relation is independent of task. (3) In
more demanding tasks participants responded more forcefully. (4) Speed –accuracy instruction does not affect the RT/
RF – intensity relation. However, the relation is less markedly U-shaped for fast than for slow responses. All these
findings can be accounted for by the outlined model.
Acknowledgements
The authors would like to thank Rolf Verleger and Rob
van der Lubbe for their helpful comments and suggestions.
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