Brain Struct Funct DOI 10.1007/s00429-014-0847-0
ORIGINAL ARTICLE
Parallel and serial processing in dual-tasking differentially involves mechanisms in the striatum and the lateral prefrontal cortex Ali Yildiz • Christian Beste
Received: 6 March 2014 / Accepted: 8 July 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract The lateral prefrontal cortex and the basal ganglia are known to be important for response selection processes, also in dual-task situations. However, response selection in dual-task situations can be achieved using different modes ranging from a parallel selection to a more serial selection of responses. Nothing is known whether differences in these processing modes during dual-tasking have distinct functional neuroanatomical correlates. In this fMRI study we analyzed performance in a psychological refractory period paradigm. In this paradigm we design a PRP task where we vary the frequency of short and long stimulus onset asynchronies between the two tasks. Using mathematical constraints we interpret the effects of this manipulation with respect to processing modes ranging from more serial to more parallel response selection. Contrastingly these blocks showed that response selection in dual-tasking under the constraint of more parallel processing is mediated by mechanisms operating at the striatal level, while response selection under the constraint of more serial processing is mediated via mechanisms operating in the lateral prefrontal cortex. The results suggest that lateral prefrontal and striatal regions are ‘optimized’ for a certain processing modes in dual tasking.
A. Yildiz C. Beste (&) Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU Dresden, Schubertstrasse 42, 01309 Dresden, Germany e-mail: [email protected] A. Yildiz C. Beste Department of Biopsychology, Institute for Cognitive Neuroscience, Ruhr-Universita¨t Bochum, Bochum, Germany
Keywords Basal ganglia Response selection fMRI Serial processing Parallel processing Dual-tasking Lateral prefrontal cortex
Introduction Dual-task situations impose increased demands on response selection mechanisms, which regulate the processing stream of different response options we are faced with. The question how many actions can be processed at virtually the same time has been debated in cognitive psychology for a long time. The ‘‘psychological refractory period (PRP)’’ paradigm has often been used to study constraints of dual-tasking (Wu and Liu 2008) in different contexts (Beste et al. 2013; Yildiz et al. 2013). Here, two tasks are presented in close succession and participants are asked to respond as quickly as possible to each task. The typical finding is that responses (RT2) on the stimulus of the second task (S2) are slower when this stimulus was presented shortly after another first stimulus (S1) signaling a different reaction (RT1) (= PRP effect). With increasing time (stimulus onset asynchrony, SOA) between the stimuli signaling different reactions, the RT2 becomes shorter (Pashler 1984; Welford 1952). That is, the sum of the RTs increase with short SOAs, compared to the long-SOA conditions. This index has also been used as a criterion for measuring the response selection processes/dual-task interferences or limitations. The nature of this processing limitation is still open to debate (e.g. Meyer and Kieras 1997; Sommer et al. 2001; Oberauer and Kliegl 2004; Miller et al. 2009; for review: Wu and Liu 2008). An early theory was proposed by Pashler (1984). According to this view simultaneously or quasi-simultaneously competing tasks lead to dual-task
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interferences due to a bottleneck on a central response selection level through which response options can only be processed in a step-by-step fashion. However, this structural bottleneck theory has been challenged by several results showing that manipulation of task difficulty does not have the same effect on short and long SOAs (Sommer et al. 2001; Schumacher et al. 1999). Moreover, the structural bottleneck model was not able to explain practice effects in dual-tasking (Oberauer and Kliegl 2004), as well as effects of task similarity or between task code overlap (e.g. Koch and Prinz 2002; Lien and Proctor 2002). This is why limited-capacity models have been put forward. These limited-capacity models (for review: Wu and Liu 2008) assume that the PRP effect occurs because processing capacity for both tasks are demanded to a different extent. These limited-capacity theories assume that processing capacity can be shared between tasks with e.g. 70 % of capacity allocated to one task and 30 % to the other task (Miller et al. 2009). This suggests that there is at least some overlap in the processing of two responses, which suggest that parallel processing of different responses should at least be possible to some extent (Mu¨ckschel et al. 2013). In this regard Miller et al. (2009) were able to show that manipulating the frequency of short SOAs in the PRP paradigm makes it possible to shift participants’ response selection between a more serial and a parallel mode. Miller et al. (2009) used a Bayesian optimization framework to examine the role of the probabilities of different SOAs for the mode of response selection. Using this optimization framework, Miller et al. showed that it is possible to induce a more serial and a more parallel processing mode in subjects to minimize the time spent to perform both tasks. They showed that more frequent long SOAs induce a more serial processing mode (which minimizes the total RT spent on each trial), while more frequent short SOAs induce a more parallel processing mode (increasing the total RT spent on each trial). However, it is unclear if there are differences in the neuronal implementation of a more serial and a more parallel processing mode. In fMRI studies, the classical PRP effect has repeatedly been shown to be mediated via areas in the superior and middle frontal gyrus (Dux et al. 2006; Marois et al. 2006; Szameitat et al. 2006; Stelzel et al. 2008; Marois and Ivanoff, 2005), as well as areas in the parietal cortex (Hesselmann et al. 2011). The lateral prefrontal cortex is well-known to play a major function in executive control (e.g. Miller and Cohen 2001), especially, when the temporal dimension of executive control is concerned (Koechlin and Jubault 2006; Koechlin et al. 2003) and temporally concomitant events have to be processed to allow a hierarchical organization of action plans (Koechlin and Jubault 2006; Koechlin et al. 2000). However, while these results show that the temporal order critical for the emergence of the PRP
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effect modulates processing in prefrontal areas, this does not imply that the mode with which a temporal order of events is processed is also modulating processes in the prefrontal cortex. Alternative models of action control also consider basal ganglia structures as important for response selection processes (e.g. Redgrave et al. 1999; Frank 2005; Humphries et al. 2006; Leblois et al. 2006) and recent results suggest that especially the basal ganglia play a role when response selection processes have to be performed in parallel manner (e.g. Beste and Saft 2013; Ness and Beste 2013; Beste et al. 2012). However, these previous studies focusing on the basal ganglia were not able to show in how far the network changes when response selection is manipulated in a way that it operates either in a more serial or a more parallel processing mode. What is lacking is a direct comparison of how the functional neuroanatomy differs between a more serial and a more parallel mode of response selection in dual-tasking in the same subjects.
Materials and methods Participants Twenty (N = 20) healthy, right-handed subjects (10 females) from 22 to 28 years of age took part in the experiment. Handedness, as assessed using the Edinburgh Handedness Inventory (Oldfield 1971) was 91 (SD = 4). The subjects received course credits or financial compensation. Written informed consent was obtained, before the study protocol was commenced. The study was approved by the ethics committee of the Ruhr-University of Bochum. Before testing in the scanner, the participants were familiarized with the paradigm and underwent training for 15 min. Experimental paradigm The experimental paradigm is an adapted psychological refractory period paradigm. The stimulus sequence used in this paradigm is shown in Fig. 1.
Fig. 1 Schematic illustration of the PRP paradigm. The tone task is always presented first and the letter task is always presented second in a defined stimulus onset asynchrony (SOA). Participants are required to respond first on the tone and second on the letter task
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Basically, this experiment comprised two tasks: a ‘‘tone task’’ (task 1) and a ‘‘letter task’’ (task 2). In the ‘‘letter task’’, white letters (‘‘H’’ or ‘‘O’’; 1.8° 9 2.3° visual angle) are presented on a dark blue screen and subjects had to indicate, whether an ‘‘H’’, or and ‘‘O’’ was presented on the screen (task 2). In the ‘‘tone task’’, sine wave tones were presented with a pitch of 300 or 900 Hz (task 1) and participants had to judge the pitch of the tone (i.e., high tone vs. low tone). Each stimulus lasted for 200 ms. Each trial consists of both of these tasks and begins with the presentation of a central fixation cross at the center of the screen. After one second the stimulus S1 (tone) was presented, followed by the presentation of the S2 stimulus (letter) in a predefined stimulus onset asynchrony (SOA) of either 16, 133, 500 or 1,000 ms. Participants were given written instructions describing the tasks and instructing participants to respond as quickly and accurately as possible to each stimulus. Subjects were clearly instructed to minimize the time spent on each trial, i.e., they were instructed to minimize the RTs on both of the tasks. By placing equal emphasis on both tasks, subjects were encouraged to minimize the total RT spent on each trial, which is the optimization criterion in this task (c.f. Miller et al. 2009). Participants were instructed to respond first on the tone stimulus (S1) and second on the letter stimulus (S2). The participants received no feedback after their response. The experiment lasted approximately 80 min. For the tone stimulus, a button had to be pressed using the left middle finger for low tones (300 Hz) and using the left index finger for the high tone (900 Hz). For the letters, subjects pressed with their right index finger on an ‘‘H’’ and with their right middle finger for an ‘‘O’’ (see also: Miller et al. 2009). The assignment of response buttons for tone and visual stimuli was counterbalanced across subjects. In the remainder of the text, reaction times (RTs) on the ‘‘tone task’’ are referred to as ‘‘RT1’’, RTs on the ‘‘letter task’’ are referred to as ‘‘RT2’’. The program required the subjects to respond to the second stimulus within 2,000 ms. Trials exceeding this deadline were defined as misses. In case of misses the next trial was started within 1,500 ms jittered between 500 and 2,500 ms. For trials, in which responses were given within 2,000 ms, the next trial was started after a response-stimulus interval (RSI) jittering between 1,000 and 4,000 ms. As for short SOAs two consecutive stimuli signaling responses may possibly be presented within the same TR, the jittering was constrained in a way that two consecutive trials cannot fall within the same TR. This was done to reduce correlation among trial-based regressors in the general linear model used for fMRI analysis. In the time between the trials, only the blank screen with the fixation cross was presented. The degree of serial versus parallel processing is experimentally manipulated by varying the probability of stimulus
onset asynchrony (SOA) in each block (for details see below). Induction of shifts between more serial and more parallel processing and data analysis To ensure shifts between a more serial and a more parallel response selection mode the optimization framework put forward by Miller et al. (2009) was applied. This framework is built around the question of how far subjects are able to minimize the total time spent on a given trial. The task of the subjects is hence to minimize the time spent on each trial, by minimizing the reaction times on task 1 and task 2. The model by Miller et al. (2009) shows on the basis of mathematical proofs and empirical data that the mode of response selection is dependent on the probability that the two tasks are separated by short or long SOAs; i.e., manipulating the frequency of trials with short and long SOAs will modulate the response selection to operate either more parallel, or more serial. This previous knowledge was used in the current study to define one SF block to induce more parallel processing and one LF block to induce more serial processing. The SF block included 336, 48, 48, 48 trials (480 trials in total), with SOAs 16, 133, 500 and 1,000 ms. The LF block to induce more serial processing included 48, 48, 48, 336 trials (480 trials in total), respectively, for these SOA values (see also: Miller et al. 2009). The trials within each block were randomly distributed. The participants were noticed about the completion of the first block and the beginning of the second block. The participants were informed about the probability manipulation. The experiment consisted of one SF block and one LF block. The order of the SF and the LF block was counterbalanced across subjects. To allow a quantification of the degree of serial and parallel processing on the basis of the obtained reaction time (RT) data the slope m = DRT/DSOA can be determined for each of the three segments of a SOA-RT2 function, which is constituted by the different SOAs (i.e., 16, 133, 500 and 1,000 ms) [i.e., m1 = (RT2133 - RT216)/ (133 - 16), m2 = (RT2500 - RT2133)/(500 - 133), and m3 = (RT21,000 - RT2500)/(1,000 - 500)]. This calculation is performed for each participant and block with a high probability of short SOAs (SF condition) and high probability of long SOAs (LF-condition). The overall slope of this SOA-RT2 function (mx = m1 ? m2 ? m3/3) for the SF and LF-condition gives an estimate as to whether a more serial, or a more parallel processing mode was applied. According to Miller et al. (2009) this function is steeper, when long SOAs are frequent than when short SOAs are frequent. In other words; the SOA function is steeper when a more serial processing mode is evident, since this processing mode is optimal when long SOAs are frequent.
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Using this framework it is, therefore, possible to estimate the degree of parallel or serial processing for each block in the PRP effect paradigm, which is simply the value of the slope mx. To calculate this slope trials were excluded in which ‘response grouping’ occurred. Response grouping refers to the phenomenon that reactions are not executed once the stimuli occur but are grouped together after the second stimulus occurred. This reflects a violation of the task instruction. Also, trials were excluded where reactions on the tone and/or the letter task were missed and where erroneous reactions on the tone and/or the letter task, or errors in task order occurred. fMRI data collection and analysis Presentation and timing of all stimuli, response events and fMRI synchronization were achieved using the ‘Presentation’ software (Neurobehavioral Systems Inc.). Functional MR imaging was carried out with a 3T Philips Scanner with a 32-channel bird-cage head coil. Visual stimuli were presented via MRI-compatible goggles. Functional MRI datasets were recorded using echo planar imaging sequences (EPI) with ‘time-to-repetition’ = 3,000 ms (TR), ‘time-to-echo’ = 35 ms (TE), flip angle 90°, FOV 256 9 256, and 40 oblique slices (oriented towards AC– PC line). Statistical analysis and image processing of the fMRI data were done using SPM5. Pre-processing steps included realignment, normalization and smoothing (8-mm isotropic Gaussian kernel). Data were filtered with a high-pass filter applying a cut-off period of 128 s. After pre-processing, ‘first level’ analyses of the data at single subject level were performed. Trials were modeled as events with 0 s duration. The first level design matrix contained two sessions; one for the SF block and another one for the LF block. Within each session (i.e., for the SF block and the LF block separately) the following regressors were defined: four regressors were introduced to model the different SOA conditions (i.e., SOA 16, 133, 500 and 1,000 ms), the other regressors (of no interest) included one regressor to model trials where ‘response grouping’ occurred, one to model trials where reactions on the tone and/or the letter task were missed and one regressor where erroneous reactions on the tone and/or the letter task occurred, this regressor also included errors in task order. Doing so, these trials did not distort the implicit baseline used for the analyses. At last, the six motion parameters from the realignment step were included in the model (also of no interest) to control for residual motion effects. For the modeling the canonical HRF function was used. BOLD contrast differences (t-contrasts) were calculated as a function of BOLD signal change compared to noise level (implicit baseline) for each single subject. Eight t-contrasts
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for each subject were calculated in total: i.e., 16SF [ implicit baseline, 133SF [ implicit baseline, 500SF [ implicit baseline, 1000SF [ implicit baseline and 16LF [ implicit baseline, 133LF [ implicit baseline, 500LF [ implicit baseline, 1000LF [ implicit baseline. These contrasts against the implicit baseline were entered into the ‘second-level’ analysis. In the ‘second level’ analyses, a full-factorial design was used with the constraint of dependence between the two introduced factors. This model contained the factor ‘‘block’’ (SF vs. LF) and the factor SOA interval (16, 133, 500 and 1,000 ms). Post hoc t test for directional effects were performed contrasting the SF block against the LF block (SF [ LF and SF \ LF) using paired samples t tests. Moreover, the classical PRP effect was analyzed in the fMRI data by contrasting the SOA 16 condition against the SOA 1,000 condition using paired samples t tests. Since the frequency of trials in these conditions varies depending on the SF and the LF block, the SOA 16 and SOA 1,000 condition were pooled across the SF and the LF block to obtain the effect of SOA independent of task block. It is important to note that the contrasts SF [ LF and SF \ LF are not similar to the contrast SOA 16 [ SOA 1,000, or SOA 16 \ SOA 1,000. The contrast 16 [ 1,000 yields the classical PRP effect. This effect is evident in the SF block and the LF block. Hence, the contrast 16 [ 1,000 compares the SOA 16 conditions in the SF and the LF block with the SOA 1,000 condition in the SF and the LF block. In this sense this contrast averages over the SF and the LF condition to estimate the PRP effect. The short SOA conditions do not generally induce parallel processing. This is only the case, when these are highly frequent (as it is the case in the SF block). In the LF block a more serial processing mode is evident. This is why a second contrast concerning the block irrespective of SOA is necessary. The contrasts used examine conceptually different aspects. Anatomical localizations of activated brain regions were obtained by reference to standard stereotaxic atlas by Talairach and Tournoux (1988). Random effects analyses (i.e., one-sample t tests comparing condition against the implicit baseline) were performed based on the individual subject’s contrast images to receive group contrast maps for all conditions. For all reported analyses, an individual voxel type I error of p \ 0.01 (FDR-corrected) and a cluster extent of ten contiguous resampled voxels was applied. Statistics Behavioral data were analyzed by conducting ANOVAs and post hoc t tests on response times, error rates and slopes of the SOA-RT2 function using Predictive Analytics Software (PASW) V 18.0. Greenhouse-Geisser correction
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was applied where necessary and post hoc tests were Bonferroni-corrected. Before testing, Kolmogorov–Smirnov tests were carried to test normal distribution. All variables included were normally distributed (p [ 0.4).
Results Behavioral data For the RT data analysis across SOAs the data were screened for trials in which the difference in RT between task 1 and task 2 was 100 ms or less, to account for possible effects of ‘response grouping’ (see also: Miller et al. 2009). Only 5 % of trials had to be discarded due to ‘response grouping’ and there was no difference between the SF and the LF block (p [ 0.4). Only trials, where no response grouping was evident and where all responses for task 1 and task 2 were correct, were included in the analysis of the SOA-RT2 function in the SF and the LF block. The behavioral data are plotted in Fig. 2. For descriptive values the mean and standard error of the mean (SEM) is given. Figure 2a shows the SOA-RT function for the tones and the letters. The letters were always presented second. As can be seen, the RT2 increased with decreasing SOA, while RT1 was less affected by the SOA (refer Fig. 2a). This is underlined in the statistical analysis. For the RT1s, there was no SOA effect [F(1, 19) = 1.23; p [ 0.3] and no interaction between SOA and ‘‘block’’. However, there was a main effect ‘‘block’’ [F(1, 19) = 13.53; p = 0.002; g2 = 0.42] showing that RTs were longer in the SF (572 ± 30), compared to the LF block (529 ± 22), which is in line with the optimization framework (c.f. Miller et al. 2009). Since block order (SF or LF block) was counterbalanced across subjects, the results are most likely unbiased by learning effects.1 For the RT2, the repeated measures ANOVA revealed a main effect SOA [F(3, 57) = 181.24; p \ 0.001; g2 = 0.90]. Bonferroni-corrected pair-wise comparisons revealed that RT2 at each SOA differed from each other (all p \ 0.001). There was no main effect of ‘‘block’’ (SF vs. LF) [F(1, 19) = 0.25; p [ 0.6], but importantly there was an interaction ‘‘SOA 9 block’’ for RT2s [F(3, 1
When calculating the mean RT (i.e., RT1 ? RT2/2) for all SOAs and the SF and LF block there was an interaction ‘‘SOA 9 block’’ [F(3, 57) = 2.88; p = 0.050; g2 = 0.126] showing that there was at long SOAs this mean RT was slower in the SF than in the LF block. A main effect ‘‘SOA’’ [F(3, 57) = 81.83; p \ 0.001; g2 = 0.126] revealed that the mean RT lowest in the SOA 1,000 condition and increased to the SOA 16 condition (all conditions differed from each other (all p \ 0.03). There was no main effect ‘‘block’’ [F(1, 19) = 0.41; p [ 0.5].
Fig. 2 a Mean reaction times (RTs) on task 1 and task 2 are plotted depending on SOA and task block (SF and LF) imposing either a more parallel, or a more serial response selection mode. b Average slope of the SOA-RT2 function across subjects is given for the SF and the LF block
57) = 4.14; p = 0.010; g2 = 0.25]. This interaction suggests that reaction times were differentially modulated across SOA depending on the block and hence the probability of short vs. long SOAs. This is what is predicted by Miller’s optimization framework. To test this, differences between the SF and LF condition are obtained when calculating statistics on the average slope of the SOA-RT2 function (i.e., slope for the SOA-RT function on RT2s). The slope of the SOA-RT2 function is the main parameter allowing an estimation of the degree of more serial, or more parallel processing according to the framework by Miller et al. (2009). The slope of the SOA-RT2 function was steeper in the LF (-0.45 ± 0.10), than in the SF block (-0.31 ± 0.10) [t(19) = 6.37; p \ 0.001] (refer Fig. 2b). Comparing the average RT2 across SOA between the SF and the LF block did not reveal a significant effect [t(19) = -0.52; p [ 0.3], which is in line with the findings by Miller et al. (2009).
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There was no difference in the rate of response errors between the SF (10.5 ± 4.1) and the LF (10.1 ± 3.1) block (p [ 0.3), or SOA (p [ 0.4). Additionally the misses did not differ between the SF (1.6 ± 0.9) and the LF block (1.2 ± 0.4) (p [ 0.8). Calculating an ANOVA using ‘‘block’’ and ‘‘SOA’’ as within-subject factors also revealed no evidence for an interaction ‘‘block 9 SOA’’ [F(3, 57) = 0.95; p [ 0.5], which shows that there was no speed-accuracy trade-off in the task. The above data suggest that the mode of response selection was modulated along a serial-parallel continuum by manipulating the frequency of SOA between the experimental blocks. Yet, it may be argued that the used SOA frequency manipulation not only varies first-order frequencies (i.e., frequencies of SOA) but also secondorder (and higher-order) frequencies, that is, the frequencies of SOA pairs across trials. This is critical because expectancy and preparation effects or effects of conditioning of critical time points (e.g. Los 2010; Los and Horoufchin 2011) may drive the effects. We, therefore, also analyzed the data as a function of whether the SOA in the previous trial was longer vs. shorter than ‘‘expected’’. For SF, this would be SOA = 16 ms vs. SOA [ 16 ms, and for LF it would be SOA = 1,000 ms vs. SOA \ 1,000 ms. Calculating the slope of the SOA-RT2 function for trials sorted whether the previous trial was SOA = 16 ms or whether SOA was greater than 16 ms revealed that the slopes of the SOA-RT2 function did not differ (slopeSOA=16 = -0.30 ± 0.09; slopeSOA [ 16 = -0.32 ± 0.10) (p [ 0.4). For the LF block the results were similar, i.e., there was no difference in the slope of the SOA-RT2 function for sorting whether the previous trial was SOA = 1,000 ms or sorting where the previous trial was SOA \ 1,000 ms (slopeSOA=1,000 = -0.48 ± 0.11; slopeSOA \ 1,000 = -0.43 ± 0.12) (p [ 0.3). Importantly, it is further shown that the slopes of the SOA-RT2 function still differed between the SF and the LF when these different sorting of trials depending on the nature of the previous trial were made (all p \ 0.009). The results are, therefore, unbiased with respect to possible second-order or higher-order frequencies effects of SOA manipulation. fMRI data Random effects analyses were carried out for each SOA condition (16, 133, 500, 1,000 ms), across the SF and the LF block. Furthermore, random effects analyses were carried out for each block separately (i.e., SF and LF) across SOA conditions. The results of the random effects analyses are shown in Table 1. The random effects analyses of the different SOA conditions revealed that areas including the caudate, the putamen, the inferior, middle (BA21, BA39, BA46, BA37)
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and superior temporal gyrus (BA22, BA39, BA41) are activated (see Table 1 for details). The random effect analyses on the SF and the LF block showed that in the SF block areas including the caudate and putamen, as well as the pulvinar and superior temporal gyrus (BA22, BA41) were activated. For the LF condition, several areas in the temporal cortex (BA19, BA37, BA41) as well as the middle and medial frontal gyrus (BA9, BA6) were activated. For the further fMRI data analysis we first examined the classical psychological refractory period effect by contrasting the SOA 16 ms condition with the SOA 1,000 ms condition across blocks; i.e., SOA 16 conditions from the SF and the LF block were compared against the SOA 1,000 condition from the SF and the LF block (SOA 16 [ SOA 1,000). The results are shown in Fig. 3. We obtained a significant activation cluster in the middle frontal gyrus (BA46) ranging into area 46d and 46v according to Petrides and Pandya (1994). This region showed higher activation intensity in the SOA 16, compared to the SOA 1,000 condition [t(13) = 4.48; p \ 0.001]. For the reverse contrast we did not find clusters exceeding threshold level. The activations found for the classical PRP effect in the middle frontal gyrus (BA46) are in line with previous results, where frontal areas including the superior and middle frontal gyrus (MFG) (Dux et al. 2006; Marois et al. 2006; Szameitat et al. 2006; Stelzel et al. 2008; Marois and Ivanoff 2005) have been reported. For the PRP effect we only used the SOA 16 and SOA 1,000 condition. Due to the skewness of the SOA distribution (refer methods section), there is a lower frequency of trials with an SOA of 133 ms and 500 ms, compared to the SOA 16 and SOA 1,000 condition when collapsed across the SF and the LF condition. Since this entails a different signal-to-noise ratio, only the SOA 16 and 1,000 condition were used for the examination of the PRP effect at the fMRI level. To examine the effect of more serial and more parallel processing, as indicated by the different slopes of the SOART2 function on a neuronal level, we contrasted the SF with the LF block. The results are shown in Fig. 4. There was no difference between both blocks (SF and LF) in terms of average regressor correlations, so both blocks were comparable in terms of GLM model structure. Figure 4 shows that striatal structures (i.e., head of the left caudate nucleus) showed BOLD response differences in the SF, compared to the LF block (contrast: SF [ LF). There was a positive BOLD response in the caudate head in the SF block, while there was a negative BOLD response in the caudate head in the LF block. The reverse contrast (SF \ LF) revealed significant activation clusters in the left middle frontal gyrus (BA9), for which it is shown that there was a negative BOLD response in the SF condition,
Brain Struct Funct Table 1 Activated regions in the different random effect analyses
Condition SOA 16
SOA 133
SOA 500
SOA 1,000
x
y
z
Area
Number of voxels
Parahippocampal gyrus (BA19)
215
35
-50
2
-38
34
14
Inferior frontal gyrus (BA46)
-30
-38
10
Temporal gyrus (BA41)
60
20
-21
22
Caudate body
29
60
-20
9
16
21
-20
-16
24
30
-18
-33
20
Caudate tail
12
-26
-17
54
Precentral gyrus (BA6)
45
-27
-18
8
Putamen
30
27
-2
5
19
-24
-8
13
16
-24
-50
65
43
-61
1
Middle temporal gyrus (BA37)
20
-58
-22
6
Superior temporal lobe (BA41)
14
-17
-38
12
-38
34
6
18
-11
-24
Postcentral gyrus (BA7)
Caudate tail
35
290
Inferior frontal gyrus (BA46)
40
26
Caudate body
32
3
-8
Putamen
21
28
1
10
-28
-43
7
Hippocampus Superior temporal gyrus (BA22)
61
-25
7
-62
-20
2
21 315 40 43
58
-4
-3
Middle temporal gyrus (BA21)
45
-61
7
Middle temporal gyrus (BA37)
25 26
-26
-7
58
Middle frontal gyrus (BA6)
31
35
-48
6
Superior temporal gyrus (BA39)
201
-60
-11
0
Superior temporal gyrus (BA22)
101
66
-21
6
-37
35
11
Inferior frontal gyrus (BA46) Caudate tail
21 35
16
-25
24
-18
-34
18
109
-19
-16
24
Caudate body
45
20
-52
33
Precunes (BA31)
43 12
86
-2
-54
31
Precunes (BA7)
-14
1
52
Medial frontal gyrus (BA6)
20
22
-21
-24
Parahippocampal gyrus (BA28)
20
Caudate tail
20
19
-36
16
-36
32
8
-16
-17
24
20
-22
21
28
-2
5
Putamen
51
-60
-5
0
Superior temporal gyrus (BA22)
30
Inferior frontal gyrus (BA46)
65
Caudate body
35 55
66
-18
7
-42
-34
13
Superior temporal gyrus (BA41)
22
19
50
-59
2
Middle temporal gyrus (BA37)
25
34
-46
0
Parahippocampal gyrus (BA19)
26
-15
-18
60
Precentral gyrus (BA6)
20
5
50
-12
Medial frontal gyrus (BA11)
20
-18
-47
14
Posterior cingulate (BA29)
22
32
-34
-2
Hippocampus
23
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Brain Struct Funct Table 1 continued
Condition SF
LF
The table denotes the different SOA conditions, as well as the SF and LF block. Activation clusters are given with their coordinate in the Talairach space including the cluster size volume (p \ 0.001; k [ 10 voxel)
x
y
z
Area
27
-2
8
-26
-6
10
-46
18
32
Middle frontal gyrus (BA9)
52
-38
35
22
Inferior frontal gyrus (BA46)
25
Caudate body
-22
-10
30
18
-10
26
-20
-27
22
18
-35
16
Putamen
Number of voxels 45 33
58 35
Caudate tail
16 12
52
-10
8
Superior temporal gyrus (BA22)
39
-54
-25
8
Superior temporal gyrus (BA41)
22
34
-48
3
Parahippocampal gyrus (BA19)
15
35
-56
14
Middle temporal gyrus (BA19)
189
Middle temporal gyrus (BA37)
13
48
-61
0
-44
17
31
Middle frontal gyrus (BA9)
34
-36
34
20
Inferior frontal gyrus (BA46)
44
6
-19
66
16
-13
-22
22
-20
21
12
-42
65
-58
-17
3
-21
5
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Medial frontal gyrus (BA6)
33
Parahippocampal gyrus (BA28)
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Caudate body
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Postcentral gyrus (BA5)
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Superior temporal gyrus (BA41)
25
Putamen
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Fig. 3 Comparison of the SOA 16 and SOA 1,000 condition (contrast SOA 16 [ SOA 1,000) collapsed over the SF and the LF block (p \ 0.01 FDR; k [ 10 voxel) including activation intensity for the activated region
compared to a positive BOLD response in the LF condition in BA9. For each of the brain regions (i.e., left BA9 and left caudate head) we calculated the absolute value of the difference in the beta-weight between the SF and the LF block. Similarly, we calculated the absolute value of the difference in the slope of the SOA-RT2 function between the SF and the LF block. As can be seen in Fig. 5, for both the absolute activation differences in BA9 and the absolute activation differences in the caudate head there were strong linear correlations of these measures with the absolute value of the difference in the slope of the SOA-RT2 function. For BA9 the correlation was r = 0.794; R2 = 0.63; p \ 0.001. For the caudate head the correlation was r = 0.815; R2 = 0.66; p \ 0.001. Activation differences in BA9 and the caudate head were also highly correlated (r = 0.596; R2 = 0.35; p = 0.003). This means that participants showing larger BOLD signal differences
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in both regions (BA9 and caudate head) also show larger differences in the slope.
Discussion In the current study we manipulated the mode of response selection in a psychological refractory period paradigm along a serial-parallel continuum. This was done to examine possible differences in underlying neuronal mechanisms between a more serial, and a more parallel processing in dual-tasking. The results reveal a robust PRP effect showing that reaction times on task two (RT2) were longer, when the SOA between stimuli of the two tasks was short (review: Wu and Liu 2008). Response times on the first task (RT1) were not affected by variations of the SOA. This classical PRP effect was reflected by activation differences in the middle frontal gyrus (BA46), which was
Brain Struct Funct Fig. 4 Comparison of the SF and the LF block on correct trials. The contrast LF [ SF is given in the upper row denoting BOLD signal differences in BA9, including activation intensities. The contrast LF \ SF is given in the bottom row denoting BOLD signal differences in the caudate head, including activation intensities (p \ 0.01 FDR; k [ 10 voxel)
more activated in the SOA 16 vs. SOA 1,000 condition. This result is in line with previous results, where frontal areas including the superior and middle frontal gyrus (MFG) (Dux et al. 2006; Marois et al. 2006; Szameitat et al. 2006; Stelzel et al. 2008; Marois and Ivanoff 2005) and the inferior frontal gyrus (Jiang et al. 2004) have been reported. However, focus of this study was on the analysis of the data of the PRP effect using the mathematical framework proposed by Miller et al. (2009) to gain insights into the neural mechanisms mediating different processing modes in dual-tasking. The results show that manipulating the frequency of short vs. long SOAs affects the slope of the SOA-RT2 function: Subjects showed a relatively more parallel mode of response selection (i.e., flatter SOA-RT2 function), when short SOAs were frequent, while they showed a relatively more serial mode of response selection when long SOAs were frequent. The results obtained are unlikely to reflect simple training effects, since SF and LF task order was counterbalanced across subjects. They are also unlikely to reflect simple effects of task difficulty. If task difficulty would have been critical, more errors would have been expected in the SF condition, compared to the LF condition. Yet, this was not the case. Instead, and comparable to the findings by Miller et al. (2009), there were also no differences between the SF and the LF block on mean RT2s, which shows that the lack of differences in error rates between the SF and the LF block is not due an adjustment towards longer reaction times to perform more accurate in a putatively ‘difficult’ condition. Lastly, the data analysis rules out that the effects of SOA frequency manipulation may be better interpreted in terms of expectancy and preparation effects or effects of conditioning of
critical time points (e.g. Los 2010; Los and Horoufchin 2011), since an analysis of the behavioral data as a function of whether the SOA in the previous trial was longer vs. shorter than ‘‘expected’’ did not change the slopes of the SOA-RT2 function and hence the critical parameter for the interpretation of task manipulation effects along a serialparallel continuum. The current results, therefore, underline Miller et al.’s interpretation that the effects are attributable to differences in the mode of response selection (i.e., more serial vs. more parallel) and not due to task difficulty or time on task (training) effects. As to the functional neuroanatomical network mediating differences between a more serial and a more parallel processing mode the fMRI data revealed higher activation of striatal structures in the block with frequent short SOAs entailing a more parallel processing of responses. Opposed to this, response selection in the block with frequent long SOAs, entailing more serial processing, was related to higher activations in the middle frontal gyrus (BA9). This result shows that different modes of processing in dualtasks are mediated via distinct functional neuroanatomical structures. It seems that an important constraint, modulating whether response selection is performed in striatal versus neocortical structures, is the mode of response selection: neocortical structures are recruited when there is a more serial response selection mode. Striatal structures are recruited when there is a stronger overlap between tasks, imposing parallel response selection processes. It has been proposed that the basal ganglia ‘chunk’ different actions (Graybiel 1998) for the sake of reducing the complexity of information to be processed at a striatal level (Bar-Gad et al. 2003). By means of chunking, the basal ganglia are supposed to construct performance units made
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Fig. 5 Correlations between the absolute activation differences in BA9 and the caudate head with the absolute value of the difference in the slope of the SOA-RT2 function. a Correlation for BA9 and b correlation for the caudate head
up of multiple acts that are implemented in a specific temporal order (Graybiel 1998). It is possible that the high frequent, temporal close succession of tasks inducing more parallel processing, promotes chunking of the two responses in the basal ganglia. As a more serial processing mode is induced by high, frequent, temporarily distant event signaling responses, this processing mode may possibly not fit with the processing mode dominating in the basal ganglia and is, therefore, processed in areas closely connected to the basal ganglia, i.e. the lateral prefrontal cortex. Yet, this does not imply that the serial processing mode modulating activation in the lateral prefrontal cortex is not mediated via action chunking, but the temporal properties of stimulus presentations are more suitable for processing in the lateral prefrontal cortex. This is what is
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suggested by other studies stressing the importance of the lateral prefrontal cortex for the processing of the temporal dimension of executive control when different events have to be processed to allow a hierarchical organization of action plans (Koechlin and Jubault 2006; Koechlin et al. 2000, 2003). It is important to note that the current data do not address the question what brain structures mediate the switching process between ‘more serial’ and ‘more parallel’ processing and hence the shift between more neocortical and more striatal processing. The data simply reflect differences in the neuronal architecture between ‘more serial’ and ‘more parallel’ processing. As the subjects knew in advance that the distribution of short frequent and long frequent SOAs was changed between the experimental blocks, the subjects were able to switch their processing strategy in advance. However, the correlation analyses show that the absolute activation differences in BA9 as well as in the caudate head were linearly correlated of these measures with the absolute value of the difference in the slope of the SOA-RT2 function and hence the individual magnitude of shifts along the serial-parallel continuum during response selection. This at least suggests that shifts from one mode to the other mode are mediated by similar functional brain structures. The finding that no primary visual areas as well as multisensory integration areas (e.g. in the parietal cortex) differentially activated across conditions suggest that the processing differences observed along the constraint of the mathematical model proposed by Miller et al. (2009) are not an effect of altered stimulus processing or multisensory integration. Rather, it seems that it is the response selection level that is modulated. This is well in line with other studies on the PRP task showing that this effect emerges as a consequence of a response selection bottleneck (e.g. Sigman and Dehaene 2008). The current study used the mathematical framework put forward by Miller et al. (2009) to examine the functional architecture underlying more serial or more parallel processing during dual-tasking. A limitation of this study is that only this framework was used to examine this question. As another behavioral indicator, compatibility effects during dual-tasking may provide additional insights into the processing mode during dual-tasking. Lehle and Hu¨bner (2009), for example showed that flanker interference was larger if subjects were forced to respond more quickly, suggesting more parallel processing in dual-task performance, which is, again, consistent with the idea that response selection is probably much less serial than assumed by the ‘‘hard core’’ structural RSB model (see Pashler 1994). It should be noted that effects of SF and LF blocks are not restricted to RT2, but there were also significant differences on RT1 and a general disadvantage in RTs at long SOA conditions in the SF block (refer footnote
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1 in the ‘‘Results’’ section), which may imply back crosstalk (e.g. Miller 2006). While the optimization framework by Miller et al. was designed on the basis to minimize total RTs to induce a more serial or a more parallel mode of processing, it seems that the framework has also some effect on RT1 in terms of backward crosstalk effects. Yet, when comparing the effect sizes in the interaction effects (SF/LF block 9 SOA) of the analyses on the mean RT data (i.e., RT1 ? RT2/2) and the RT2 data effect sizes are twice as high for the RT2 data (g2 = 0.25) than on the RT1 ? RT2/2 data (g2 = 0.12). This suggests that even though the optimization framework applied not only affects RT2s, effects of the optimization framework have stronger effects on this parameter of the response selection bottleneck. In summary, the results suggest that more serial and more parallel processing modes in dual-tasking differ in the functional organization of networks. Response selection in dual-tasking under the constraint of more parallel processing is mediated via striatal mechanisms, while response selection under the constraint of more serial processing is mediated via prefrontal cortical mechanisms. The results suggest that lateral prefrontal and striatal areas are ‘optimized’ for a certain processing mode (more parallel vs. more serial) in dual tasking. Acknowledgments This research was supported by a Grant from the Deutsche Forschungsgemeinschaft BE4045/10-1 and 10-2. We thank the reviewers, Dr. Iring Koch and an anonymous reviewer for their helpful criticisms on the manuscript.
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Humphries M, Stewart RD, Gurney KN (2006) A physiologically plausible model of action selection and oscillatory activity in the basal ganglia. J Neurosci 26:12921–12942 Jiang Y, Saxe R, Kanwisher N (2004) Functional magnetic resonance imaging provides new constraints on theories of the psychological refractory period. Psychol Sci 15:390–396 Koch I, Prinz W (2002) Process interference and code overlap in dualtask performance. J Exp Psychol Hum Percept Perform 28:192–201 Koechlin E, Jubault T (2006) Broca’s area and the hierarchical organization of human behavior. Neuron 50:963–974 Koechlin E, Corrado G, Pietrini P, Grafman J (2000) Dissociating the role of the medial and lateral anterior prefrontal cortex in human planning. Proc Natl Acad Sci USA 97:7651–7656 Koechlin E, Ody C, Kouneiher F (2003) The architecture of cognitive control in the human prefrontal cortex. Science 302:1181–1185 Leblois A, Boraud T, Meissner W, Bergman W, Hansel D (2006) Competition between feedback loops underlies normal and pathological dynamics in the basal ganglia. J Neurosci 26:3567–3583 Lehle C, Hu¨bner R (2009) Strategic capacity sharing between two tasks: evidence from tasks with the same and with different task sets. Psychol Res 73:707–726 Lien MC, Proctor RW (2002) Stimulus-response compatibility and psychological refractory period effects: implications for response selection. Psychonom Bull Rev 9:212–238 Los SA (2010) Foreperiod and the sequential effect. Theory and data. In: Coull J, Nobre AC (eds) Attention and time. Oxford University Press, Oxford, pp 289–302 Los SA, Horoufchin H (2011) Dissociative pattern of foreperiod effects in temporal discrimination and reaction time tasks. Q J Exp Psychol 64:1009–1020 Marois R, Ivanoff J (2005) Capacity limits of information processing in the brain. Trends Cogn Sci 9:296–305 Marois R, Larson JM, Chun MM, Shima D (2006) Response-specific sources of dual-task interference in human pre-motor cortex. Psychol Res 70:436–447 Meyer DE, Kieras DE (1997) A computational theory of executive cognitive processes and multiple-task performance: I. Basic mechanisms. Psychol Rev 104:3–65 Miller J (2006) Backward crosstalk effects in psychological refractory period paradigms: effects of second-task response types on firsttask response latencies. Psychol Res 70:484–493 Miller EK, Cohen J (2001) An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24:167–202 Miller J, Ulrich R, Rolke B (2009) On the optimality of serial and parallel processing in the psychological refractor period paradigm: effects of the distribution of stimulus onset asynchronies. Cogn Psychol 58:273–310 Mu¨ckschel M, Stock AK, Beste C (2013) Psychophysiological mechanisms of interindividual differences in goal activation modes during action cascading. Cereb Cortex. doi:10.1093/ cercor/bht066 Ness V, Beste C (2013) The role of the striatum in goal activation of cascaded actions. Neuropsychologia 51:2562–2571 Oberauer K, Kliegl R (2004) Simultaneous cognitive operations in working memory after dual-task practice. J Exp Psychol Hum Percept Perform 30:689–707 Pashler H (1984) Processing stages in overlapping tasks: evidence for a central bottleneck. J Exp Psychol Hum Percept Perform 10:358–377 Petrides M, Pandya DN (1994) Comparative architectonic analysis of the human and the macaque frontal cortex. In: Boller F, Grafman J (eds) Handbook of neuropsychology, vol 9. Elsevier, Amsterdam, pp 17–58
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Brain Struct Funct Redgrave P, Prescott TJ, Gurney K (1999) The basal ganglia: a vertebrate solution to the selection problem? Neuroscience 89:1009–1023 Schumacher EH, Lauber EJ, Glass JM, Zurbriggen EI, Gmeindl L, Kieras DE, Meyer DE (1999) Concurrent response-selection processes in dual-task performance: evidence for adaptive executive control of task scheduling. J Exp Psychol Hum Percept Perform 25:791–814 Sigman M, Dehaene S (2008) Brain mechanisms of serial and parallel processing during dual-task performance. J Neurosci 28:7585–7598 Sommer W, Leuthold H, Schubert T (2001) Multiple bottlenecks in information processing? An electrophysiological examination. Psychon Bull Rev 8:81–88 Stelzel C, Kraft A, Brandt SA, Schubert T (2008) Dissociable neural effects of task order control and task set maintenance during dual-task processing. J Cogn Neurosci 20:613–628
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Szameitat AJ, Lepsien J, von Cramon DY, Sterr A, Schubert T (2006) Task-order coordination in dual-task performance and the lateral prefrontal cortex: an event- related fMRI study. Psychol Res 70:541–552 Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. In: 3- Dimensional proportional system: an approach to cerebral imaging. Thieme, New York Welford AT (1952) The ‘‘psychological refractory period’’ and the timing of high-speed performance—a review and a theory. Br J Psychol 43:2–19 Wu C, Liu Y (2008) Queuing network modeling of the psychological refractory period (PRP). Psychol Rev 115:913–954 Yildiz A, Chmielewski W, Beste C (2013) Dual-task performance is differentially modulated by rewards and punishments. Behav Brain Res 250:304–307
ORIGINAL ARTICLE
Parallel and serial processing in dual-tasking differentially involves mechanisms in the striatum and the lateral prefrontal cortex Ali Yildiz • Christian Beste
Received: 6 March 2014 / Accepted: 8 July 2014 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract The lateral prefrontal cortex and the basal ganglia are known to be important for response selection processes, also in dual-task situations. However, response selection in dual-task situations can be achieved using different modes ranging from a parallel selection to a more serial selection of responses. Nothing is known whether differences in these processing modes during dual-tasking have distinct functional neuroanatomical correlates. In this fMRI study we analyzed performance in a psychological refractory period paradigm. In this paradigm we design a PRP task where we vary the frequency of short and long stimulus onset asynchronies between the two tasks. Using mathematical constraints we interpret the effects of this manipulation with respect to processing modes ranging from more serial to more parallel response selection. Contrastingly these blocks showed that response selection in dual-tasking under the constraint of more parallel processing is mediated by mechanisms operating at the striatal level, while response selection under the constraint of more serial processing is mediated via mechanisms operating in the lateral prefrontal cortex. The results suggest that lateral prefrontal and striatal regions are ‘optimized’ for a certain processing modes in dual tasking.
A. Yildiz C. Beste (&) Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU Dresden, Schubertstrasse 42, 01309 Dresden, Germany e-mail: [email protected] A. Yildiz C. Beste Department of Biopsychology, Institute for Cognitive Neuroscience, Ruhr-Universita¨t Bochum, Bochum, Germany
Keywords Basal ganglia Response selection fMRI Serial processing Parallel processing Dual-tasking Lateral prefrontal cortex
Introduction Dual-task situations impose increased demands on response selection mechanisms, which regulate the processing stream of different response options we are faced with. The question how many actions can be processed at virtually the same time has been debated in cognitive psychology for a long time. The ‘‘psychological refractory period (PRP)’’ paradigm has often been used to study constraints of dual-tasking (Wu and Liu 2008) in different contexts (Beste et al. 2013; Yildiz et al. 2013). Here, two tasks are presented in close succession and participants are asked to respond as quickly as possible to each task. The typical finding is that responses (RT2) on the stimulus of the second task (S2) are slower when this stimulus was presented shortly after another first stimulus (S1) signaling a different reaction (RT1) (= PRP effect). With increasing time (stimulus onset asynchrony, SOA) between the stimuli signaling different reactions, the RT2 becomes shorter (Pashler 1984; Welford 1952). That is, the sum of the RTs increase with short SOAs, compared to the long-SOA conditions. This index has also been used as a criterion for measuring the response selection processes/dual-task interferences or limitations. The nature of this processing limitation is still open to debate (e.g. Meyer and Kieras 1997; Sommer et al. 2001; Oberauer and Kliegl 2004; Miller et al. 2009; for review: Wu and Liu 2008). An early theory was proposed by Pashler (1984). According to this view simultaneously or quasi-simultaneously competing tasks lead to dual-task
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interferences due to a bottleneck on a central response selection level through which response options can only be processed in a step-by-step fashion. However, this structural bottleneck theory has been challenged by several results showing that manipulation of task difficulty does not have the same effect on short and long SOAs (Sommer et al. 2001; Schumacher et al. 1999). Moreover, the structural bottleneck model was not able to explain practice effects in dual-tasking (Oberauer and Kliegl 2004), as well as effects of task similarity or between task code overlap (e.g. Koch and Prinz 2002; Lien and Proctor 2002). This is why limited-capacity models have been put forward. These limited-capacity models (for review: Wu and Liu 2008) assume that the PRP effect occurs because processing capacity for both tasks are demanded to a different extent. These limited-capacity theories assume that processing capacity can be shared between tasks with e.g. 70 % of capacity allocated to one task and 30 % to the other task (Miller et al. 2009). This suggests that there is at least some overlap in the processing of two responses, which suggest that parallel processing of different responses should at least be possible to some extent (Mu¨ckschel et al. 2013). In this regard Miller et al. (2009) were able to show that manipulating the frequency of short SOAs in the PRP paradigm makes it possible to shift participants’ response selection between a more serial and a parallel mode. Miller et al. (2009) used a Bayesian optimization framework to examine the role of the probabilities of different SOAs for the mode of response selection. Using this optimization framework, Miller et al. showed that it is possible to induce a more serial and a more parallel processing mode in subjects to minimize the time spent to perform both tasks. They showed that more frequent long SOAs induce a more serial processing mode (which minimizes the total RT spent on each trial), while more frequent short SOAs induce a more parallel processing mode (increasing the total RT spent on each trial). However, it is unclear if there are differences in the neuronal implementation of a more serial and a more parallel processing mode. In fMRI studies, the classical PRP effect has repeatedly been shown to be mediated via areas in the superior and middle frontal gyrus (Dux et al. 2006; Marois et al. 2006; Szameitat et al. 2006; Stelzel et al. 2008; Marois and Ivanoff, 2005), as well as areas in the parietal cortex (Hesselmann et al. 2011). The lateral prefrontal cortex is well-known to play a major function in executive control (e.g. Miller and Cohen 2001), especially, when the temporal dimension of executive control is concerned (Koechlin and Jubault 2006; Koechlin et al. 2003) and temporally concomitant events have to be processed to allow a hierarchical organization of action plans (Koechlin and Jubault 2006; Koechlin et al. 2000). However, while these results show that the temporal order critical for the emergence of the PRP
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effect modulates processing in prefrontal areas, this does not imply that the mode with which a temporal order of events is processed is also modulating processes in the prefrontal cortex. Alternative models of action control also consider basal ganglia structures as important for response selection processes (e.g. Redgrave et al. 1999; Frank 2005; Humphries et al. 2006; Leblois et al. 2006) and recent results suggest that especially the basal ganglia play a role when response selection processes have to be performed in parallel manner (e.g. Beste and Saft 2013; Ness and Beste 2013; Beste et al. 2012). However, these previous studies focusing on the basal ganglia were not able to show in how far the network changes when response selection is manipulated in a way that it operates either in a more serial or a more parallel processing mode. What is lacking is a direct comparison of how the functional neuroanatomy differs between a more serial and a more parallel mode of response selection in dual-tasking in the same subjects.
Materials and methods Participants Twenty (N = 20) healthy, right-handed subjects (10 females) from 22 to 28 years of age took part in the experiment. Handedness, as assessed using the Edinburgh Handedness Inventory (Oldfield 1971) was 91 (SD = 4). The subjects received course credits or financial compensation. Written informed consent was obtained, before the study protocol was commenced. The study was approved by the ethics committee of the Ruhr-University of Bochum. Before testing in the scanner, the participants were familiarized with the paradigm and underwent training for 15 min. Experimental paradigm The experimental paradigm is an adapted psychological refractory period paradigm. The stimulus sequence used in this paradigm is shown in Fig. 1.
Fig. 1 Schematic illustration of the PRP paradigm. The tone task is always presented first and the letter task is always presented second in a defined stimulus onset asynchrony (SOA). Participants are required to respond first on the tone and second on the letter task
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Basically, this experiment comprised two tasks: a ‘‘tone task’’ (task 1) and a ‘‘letter task’’ (task 2). In the ‘‘letter task’’, white letters (‘‘H’’ or ‘‘O’’; 1.8° 9 2.3° visual angle) are presented on a dark blue screen and subjects had to indicate, whether an ‘‘H’’, or and ‘‘O’’ was presented on the screen (task 2). In the ‘‘tone task’’, sine wave tones were presented with a pitch of 300 or 900 Hz (task 1) and participants had to judge the pitch of the tone (i.e., high tone vs. low tone). Each stimulus lasted for 200 ms. Each trial consists of both of these tasks and begins with the presentation of a central fixation cross at the center of the screen. After one second the stimulus S1 (tone) was presented, followed by the presentation of the S2 stimulus (letter) in a predefined stimulus onset asynchrony (SOA) of either 16, 133, 500 or 1,000 ms. Participants were given written instructions describing the tasks and instructing participants to respond as quickly and accurately as possible to each stimulus. Subjects were clearly instructed to minimize the time spent on each trial, i.e., they were instructed to minimize the RTs on both of the tasks. By placing equal emphasis on both tasks, subjects were encouraged to minimize the total RT spent on each trial, which is the optimization criterion in this task (c.f. Miller et al. 2009). Participants were instructed to respond first on the tone stimulus (S1) and second on the letter stimulus (S2). The participants received no feedback after their response. The experiment lasted approximately 80 min. For the tone stimulus, a button had to be pressed using the left middle finger for low tones (300 Hz) and using the left index finger for the high tone (900 Hz). For the letters, subjects pressed with their right index finger on an ‘‘H’’ and with their right middle finger for an ‘‘O’’ (see also: Miller et al. 2009). The assignment of response buttons for tone and visual stimuli was counterbalanced across subjects. In the remainder of the text, reaction times (RTs) on the ‘‘tone task’’ are referred to as ‘‘RT1’’, RTs on the ‘‘letter task’’ are referred to as ‘‘RT2’’. The program required the subjects to respond to the second stimulus within 2,000 ms. Trials exceeding this deadline were defined as misses. In case of misses the next trial was started within 1,500 ms jittered between 500 and 2,500 ms. For trials, in which responses were given within 2,000 ms, the next trial was started after a response-stimulus interval (RSI) jittering between 1,000 and 4,000 ms. As for short SOAs two consecutive stimuli signaling responses may possibly be presented within the same TR, the jittering was constrained in a way that two consecutive trials cannot fall within the same TR. This was done to reduce correlation among trial-based regressors in the general linear model used for fMRI analysis. In the time between the trials, only the blank screen with the fixation cross was presented. The degree of serial versus parallel processing is experimentally manipulated by varying the probability of stimulus
onset asynchrony (SOA) in each block (for details see below). Induction of shifts between more serial and more parallel processing and data analysis To ensure shifts between a more serial and a more parallel response selection mode the optimization framework put forward by Miller et al. (2009) was applied. This framework is built around the question of how far subjects are able to minimize the total time spent on a given trial. The task of the subjects is hence to minimize the time spent on each trial, by minimizing the reaction times on task 1 and task 2. The model by Miller et al. (2009) shows on the basis of mathematical proofs and empirical data that the mode of response selection is dependent on the probability that the two tasks are separated by short or long SOAs; i.e., manipulating the frequency of trials with short and long SOAs will modulate the response selection to operate either more parallel, or more serial. This previous knowledge was used in the current study to define one SF block to induce more parallel processing and one LF block to induce more serial processing. The SF block included 336, 48, 48, 48 trials (480 trials in total), with SOAs 16, 133, 500 and 1,000 ms. The LF block to induce more serial processing included 48, 48, 48, 336 trials (480 trials in total), respectively, for these SOA values (see also: Miller et al. 2009). The trials within each block were randomly distributed. The participants were noticed about the completion of the first block and the beginning of the second block. The participants were informed about the probability manipulation. The experiment consisted of one SF block and one LF block. The order of the SF and the LF block was counterbalanced across subjects. To allow a quantification of the degree of serial and parallel processing on the basis of the obtained reaction time (RT) data the slope m = DRT/DSOA can be determined for each of the three segments of a SOA-RT2 function, which is constituted by the different SOAs (i.e., 16, 133, 500 and 1,000 ms) [i.e., m1 = (RT2133 - RT216)/ (133 - 16), m2 = (RT2500 - RT2133)/(500 - 133), and m3 = (RT21,000 - RT2500)/(1,000 - 500)]. This calculation is performed for each participant and block with a high probability of short SOAs (SF condition) and high probability of long SOAs (LF-condition). The overall slope of this SOA-RT2 function (mx = m1 ? m2 ? m3/3) for the SF and LF-condition gives an estimate as to whether a more serial, or a more parallel processing mode was applied. According to Miller et al. (2009) this function is steeper, when long SOAs are frequent than when short SOAs are frequent. In other words; the SOA function is steeper when a more serial processing mode is evident, since this processing mode is optimal when long SOAs are frequent.
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Using this framework it is, therefore, possible to estimate the degree of parallel or serial processing for each block in the PRP effect paradigm, which is simply the value of the slope mx. To calculate this slope trials were excluded in which ‘response grouping’ occurred. Response grouping refers to the phenomenon that reactions are not executed once the stimuli occur but are grouped together after the second stimulus occurred. This reflects a violation of the task instruction. Also, trials were excluded where reactions on the tone and/or the letter task were missed and where erroneous reactions on the tone and/or the letter task, or errors in task order occurred. fMRI data collection and analysis Presentation and timing of all stimuli, response events and fMRI synchronization were achieved using the ‘Presentation’ software (Neurobehavioral Systems Inc.). Functional MR imaging was carried out with a 3T Philips Scanner with a 32-channel bird-cage head coil. Visual stimuli were presented via MRI-compatible goggles. Functional MRI datasets were recorded using echo planar imaging sequences (EPI) with ‘time-to-repetition’ = 3,000 ms (TR), ‘time-to-echo’ = 35 ms (TE), flip angle 90°, FOV 256 9 256, and 40 oblique slices (oriented towards AC– PC line). Statistical analysis and image processing of the fMRI data were done using SPM5. Pre-processing steps included realignment, normalization and smoothing (8-mm isotropic Gaussian kernel). Data were filtered with a high-pass filter applying a cut-off period of 128 s. After pre-processing, ‘first level’ analyses of the data at single subject level were performed. Trials were modeled as events with 0 s duration. The first level design matrix contained two sessions; one for the SF block and another one for the LF block. Within each session (i.e., for the SF block and the LF block separately) the following regressors were defined: four regressors were introduced to model the different SOA conditions (i.e., SOA 16, 133, 500 and 1,000 ms), the other regressors (of no interest) included one regressor to model trials where ‘response grouping’ occurred, one to model trials where reactions on the tone and/or the letter task were missed and one regressor where erroneous reactions on the tone and/or the letter task occurred, this regressor also included errors in task order. Doing so, these trials did not distort the implicit baseline used for the analyses. At last, the six motion parameters from the realignment step were included in the model (also of no interest) to control for residual motion effects. For the modeling the canonical HRF function was used. BOLD contrast differences (t-contrasts) were calculated as a function of BOLD signal change compared to noise level (implicit baseline) for each single subject. Eight t-contrasts
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for each subject were calculated in total: i.e., 16SF [ implicit baseline, 133SF [ implicit baseline, 500SF [ implicit baseline, 1000SF [ implicit baseline and 16LF [ implicit baseline, 133LF [ implicit baseline, 500LF [ implicit baseline, 1000LF [ implicit baseline. These contrasts against the implicit baseline were entered into the ‘second-level’ analysis. In the ‘second level’ analyses, a full-factorial design was used with the constraint of dependence between the two introduced factors. This model contained the factor ‘‘block’’ (SF vs. LF) and the factor SOA interval (16, 133, 500 and 1,000 ms). Post hoc t test for directional effects were performed contrasting the SF block against the LF block (SF [ LF and SF \ LF) using paired samples t tests. Moreover, the classical PRP effect was analyzed in the fMRI data by contrasting the SOA 16 condition against the SOA 1,000 condition using paired samples t tests. Since the frequency of trials in these conditions varies depending on the SF and the LF block, the SOA 16 and SOA 1,000 condition were pooled across the SF and the LF block to obtain the effect of SOA independent of task block. It is important to note that the contrasts SF [ LF and SF \ LF are not similar to the contrast SOA 16 [ SOA 1,000, or SOA 16 \ SOA 1,000. The contrast 16 [ 1,000 yields the classical PRP effect. This effect is evident in the SF block and the LF block. Hence, the contrast 16 [ 1,000 compares the SOA 16 conditions in the SF and the LF block with the SOA 1,000 condition in the SF and the LF block. In this sense this contrast averages over the SF and the LF condition to estimate the PRP effect. The short SOA conditions do not generally induce parallel processing. This is only the case, when these are highly frequent (as it is the case in the SF block). In the LF block a more serial processing mode is evident. This is why a second contrast concerning the block irrespective of SOA is necessary. The contrasts used examine conceptually different aspects. Anatomical localizations of activated brain regions were obtained by reference to standard stereotaxic atlas by Talairach and Tournoux (1988). Random effects analyses (i.e., one-sample t tests comparing condition against the implicit baseline) were performed based on the individual subject’s contrast images to receive group contrast maps for all conditions. For all reported analyses, an individual voxel type I error of p \ 0.01 (FDR-corrected) and a cluster extent of ten contiguous resampled voxels was applied. Statistics Behavioral data were analyzed by conducting ANOVAs and post hoc t tests on response times, error rates and slopes of the SOA-RT2 function using Predictive Analytics Software (PASW) V 18.0. Greenhouse-Geisser correction
Brain Struct Funct
was applied where necessary and post hoc tests were Bonferroni-corrected. Before testing, Kolmogorov–Smirnov tests were carried to test normal distribution. All variables included were normally distributed (p [ 0.4).
Results Behavioral data For the RT data analysis across SOAs the data were screened for trials in which the difference in RT between task 1 and task 2 was 100 ms or less, to account for possible effects of ‘response grouping’ (see also: Miller et al. 2009). Only 5 % of trials had to be discarded due to ‘response grouping’ and there was no difference between the SF and the LF block (p [ 0.4). Only trials, where no response grouping was evident and where all responses for task 1 and task 2 were correct, were included in the analysis of the SOA-RT2 function in the SF and the LF block. The behavioral data are plotted in Fig. 2. For descriptive values the mean and standard error of the mean (SEM) is given. Figure 2a shows the SOA-RT function for the tones and the letters. The letters were always presented second. As can be seen, the RT2 increased with decreasing SOA, while RT1 was less affected by the SOA (refer Fig. 2a). This is underlined in the statistical analysis. For the RT1s, there was no SOA effect [F(1, 19) = 1.23; p [ 0.3] and no interaction between SOA and ‘‘block’’. However, there was a main effect ‘‘block’’ [F(1, 19) = 13.53; p = 0.002; g2 = 0.42] showing that RTs were longer in the SF (572 ± 30), compared to the LF block (529 ± 22), which is in line with the optimization framework (c.f. Miller et al. 2009). Since block order (SF or LF block) was counterbalanced across subjects, the results are most likely unbiased by learning effects.1 For the RT2, the repeated measures ANOVA revealed a main effect SOA [F(3, 57) = 181.24; p \ 0.001; g2 = 0.90]. Bonferroni-corrected pair-wise comparisons revealed that RT2 at each SOA differed from each other (all p \ 0.001). There was no main effect of ‘‘block’’ (SF vs. LF) [F(1, 19) = 0.25; p [ 0.6], but importantly there was an interaction ‘‘SOA 9 block’’ for RT2s [F(3, 1
When calculating the mean RT (i.e., RT1 ? RT2/2) for all SOAs and the SF and LF block there was an interaction ‘‘SOA 9 block’’ [F(3, 57) = 2.88; p = 0.050; g2 = 0.126] showing that there was at long SOAs this mean RT was slower in the SF than in the LF block. A main effect ‘‘SOA’’ [F(3, 57) = 81.83; p \ 0.001; g2 = 0.126] revealed that the mean RT lowest in the SOA 1,000 condition and increased to the SOA 16 condition (all conditions differed from each other (all p \ 0.03). There was no main effect ‘‘block’’ [F(1, 19) = 0.41; p [ 0.5].
Fig. 2 a Mean reaction times (RTs) on task 1 and task 2 are plotted depending on SOA and task block (SF and LF) imposing either a more parallel, or a more serial response selection mode. b Average slope of the SOA-RT2 function across subjects is given for the SF and the LF block
57) = 4.14; p = 0.010; g2 = 0.25]. This interaction suggests that reaction times were differentially modulated across SOA depending on the block and hence the probability of short vs. long SOAs. This is what is predicted by Miller’s optimization framework. To test this, differences between the SF and LF condition are obtained when calculating statistics on the average slope of the SOA-RT2 function (i.e., slope for the SOA-RT function on RT2s). The slope of the SOA-RT2 function is the main parameter allowing an estimation of the degree of more serial, or more parallel processing according to the framework by Miller et al. (2009). The slope of the SOA-RT2 function was steeper in the LF (-0.45 ± 0.10), than in the SF block (-0.31 ± 0.10) [t(19) = 6.37; p \ 0.001] (refer Fig. 2b). Comparing the average RT2 across SOA between the SF and the LF block did not reveal a significant effect [t(19) = -0.52; p [ 0.3], which is in line with the findings by Miller et al. (2009).
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There was no difference in the rate of response errors between the SF (10.5 ± 4.1) and the LF (10.1 ± 3.1) block (p [ 0.3), or SOA (p [ 0.4). Additionally the misses did not differ between the SF (1.6 ± 0.9) and the LF block (1.2 ± 0.4) (p [ 0.8). Calculating an ANOVA using ‘‘block’’ and ‘‘SOA’’ as within-subject factors also revealed no evidence for an interaction ‘‘block 9 SOA’’ [F(3, 57) = 0.95; p [ 0.5], which shows that there was no speed-accuracy trade-off in the task. The above data suggest that the mode of response selection was modulated along a serial-parallel continuum by manipulating the frequency of SOA between the experimental blocks. Yet, it may be argued that the used SOA frequency manipulation not only varies first-order frequencies (i.e., frequencies of SOA) but also secondorder (and higher-order) frequencies, that is, the frequencies of SOA pairs across trials. This is critical because expectancy and preparation effects or effects of conditioning of critical time points (e.g. Los 2010; Los and Horoufchin 2011) may drive the effects. We, therefore, also analyzed the data as a function of whether the SOA in the previous trial was longer vs. shorter than ‘‘expected’’. For SF, this would be SOA = 16 ms vs. SOA [ 16 ms, and for LF it would be SOA = 1,000 ms vs. SOA \ 1,000 ms. Calculating the slope of the SOA-RT2 function for trials sorted whether the previous trial was SOA = 16 ms or whether SOA was greater than 16 ms revealed that the slopes of the SOA-RT2 function did not differ (slopeSOA=16 = -0.30 ± 0.09; slopeSOA [ 16 = -0.32 ± 0.10) (p [ 0.4). For the LF block the results were similar, i.e., there was no difference in the slope of the SOA-RT2 function for sorting whether the previous trial was SOA = 1,000 ms or sorting where the previous trial was SOA \ 1,000 ms (slopeSOA=1,000 = -0.48 ± 0.11; slopeSOA \ 1,000 = -0.43 ± 0.12) (p [ 0.3). Importantly, it is further shown that the slopes of the SOA-RT2 function still differed between the SF and the LF when these different sorting of trials depending on the nature of the previous trial were made (all p \ 0.009). The results are, therefore, unbiased with respect to possible second-order or higher-order frequencies effects of SOA manipulation. fMRI data Random effects analyses were carried out for each SOA condition (16, 133, 500, 1,000 ms), across the SF and the LF block. Furthermore, random effects analyses were carried out for each block separately (i.e., SF and LF) across SOA conditions. The results of the random effects analyses are shown in Table 1. The random effects analyses of the different SOA conditions revealed that areas including the caudate, the putamen, the inferior, middle (BA21, BA39, BA46, BA37)
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and superior temporal gyrus (BA22, BA39, BA41) are activated (see Table 1 for details). The random effect analyses on the SF and the LF block showed that in the SF block areas including the caudate and putamen, as well as the pulvinar and superior temporal gyrus (BA22, BA41) were activated. For the LF condition, several areas in the temporal cortex (BA19, BA37, BA41) as well as the middle and medial frontal gyrus (BA9, BA6) were activated. For the further fMRI data analysis we first examined the classical psychological refractory period effect by contrasting the SOA 16 ms condition with the SOA 1,000 ms condition across blocks; i.e., SOA 16 conditions from the SF and the LF block were compared against the SOA 1,000 condition from the SF and the LF block (SOA 16 [ SOA 1,000). The results are shown in Fig. 3. We obtained a significant activation cluster in the middle frontal gyrus (BA46) ranging into area 46d and 46v according to Petrides and Pandya (1994). This region showed higher activation intensity in the SOA 16, compared to the SOA 1,000 condition [t(13) = 4.48; p \ 0.001]. For the reverse contrast we did not find clusters exceeding threshold level. The activations found for the classical PRP effect in the middle frontal gyrus (BA46) are in line with previous results, where frontal areas including the superior and middle frontal gyrus (MFG) (Dux et al. 2006; Marois et al. 2006; Szameitat et al. 2006; Stelzel et al. 2008; Marois and Ivanoff 2005) have been reported. For the PRP effect we only used the SOA 16 and SOA 1,000 condition. Due to the skewness of the SOA distribution (refer methods section), there is a lower frequency of trials with an SOA of 133 ms and 500 ms, compared to the SOA 16 and SOA 1,000 condition when collapsed across the SF and the LF condition. Since this entails a different signal-to-noise ratio, only the SOA 16 and 1,000 condition were used for the examination of the PRP effect at the fMRI level. To examine the effect of more serial and more parallel processing, as indicated by the different slopes of the SOART2 function on a neuronal level, we contrasted the SF with the LF block. The results are shown in Fig. 4. There was no difference between both blocks (SF and LF) in terms of average regressor correlations, so both blocks were comparable in terms of GLM model structure. Figure 4 shows that striatal structures (i.e., head of the left caudate nucleus) showed BOLD response differences in the SF, compared to the LF block (contrast: SF [ LF). There was a positive BOLD response in the caudate head in the SF block, while there was a negative BOLD response in the caudate head in the LF block. The reverse contrast (SF \ LF) revealed significant activation clusters in the left middle frontal gyrus (BA9), for which it is shown that there was a negative BOLD response in the SF condition,
Brain Struct Funct Table 1 Activated regions in the different random effect analyses
Condition SOA 16
SOA 133
SOA 500
SOA 1,000
x
y
z
Area
Number of voxels
Parahippocampal gyrus (BA19)
215
35
-50
2
-38
34
14
Inferior frontal gyrus (BA46)
-30
-38
10
Temporal gyrus (BA41)
60
20
-21
22
Caudate body
29
60
-20
9
16
21
-20
-16
24
30
-18
-33
20
Caudate tail
12
-26
-17
54
Precentral gyrus (BA6)
45
-27
-18
8
Putamen
30
27
-2
5
19
-24
-8
13
16
-24
-50
65
43
-61
1
Middle temporal gyrus (BA37)
20
-58
-22
6
Superior temporal lobe (BA41)
14
-17
-38
12
-38
34
6
18
-11
-24
Postcentral gyrus (BA7)
Caudate tail
35
290
Inferior frontal gyrus (BA46)
40
26
Caudate body
32
3
-8
Putamen
21
28
1
10
-28
-43
7
Hippocampus Superior temporal gyrus (BA22)
61
-25
7
-62
-20
2
21 315 40 43
58
-4
-3
Middle temporal gyrus (BA21)
45
-61
7
Middle temporal gyrus (BA37)
25 26
-26
-7
58
Middle frontal gyrus (BA6)
31
35
-48
6
Superior temporal gyrus (BA39)
201
-60
-11
0
Superior temporal gyrus (BA22)
101
66
-21
6
-37
35
11
Inferior frontal gyrus (BA46) Caudate tail
21 35
16
-25
24
-18
-34
18
109
-19
-16
24
Caudate body
45
20
-52
33
Precunes (BA31)
43 12
86
-2
-54
31
Precunes (BA7)
-14
1
52
Medial frontal gyrus (BA6)
20
22
-21
-24
Parahippocampal gyrus (BA28)
20
Caudate tail
20
19
-36
16
-36
32
8
-16
-17
24
20
-22
21
28
-2
5
Putamen
51
-60
-5
0
Superior temporal gyrus (BA22)
30
Inferior frontal gyrus (BA46)
65
Caudate body
35 55
66
-18
7
-42
-34
13
Superior temporal gyrus (BA41)
22
19
50
-59
2
Middle temporal gyrus (BA37)
25
34
-46
0
Parahippocampal gyrus (BA19)
26
-15
-18
60
Precentral gyrus (BA6)
20
5
50
-12
Medial frontal gyrus (BA11)
20
-18
-47
14
Posterior cingulate (BA29)
22
32
-34
-2
Hippocampus
23
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Brain Struct Funct Table 1 continued
Condition SF
LF
The table denotes the different SOA conditions, as well as the SF and LF block. Activation clusters are given with their coordinate in the Talairach space including the cluster size volume (p \ 0.001; k [ 10 voxel)
x
y
z
Area
27
-2
8
-26
-6
10
-46
18
32
Middle frontal gyrus (BA9)
52
-38
35
22
Inferior frontal gyrus (BA46)
25
Caudate body
-22
-10
30
18
-10
26
-20
-27
22
18
-35
16
Putamen
Number of voxels 45 33
58 35
Caudate tail
16 12
52
-10
8
Superior temporal gyrus (BA22)
39
-54
-25
8
Superior temporal gyrus (BA41)
22
34
-48
3
Parahippocampal gyrus (BA19)
15
35
-56
14
Middle temporal gyrus (BA19)
189
Middle temporal gyrus (BA37)
13
48
-61
0
-44
17
31
Middle frontal gyrus (BA9)
34
-36
34
20
Inferior frontal gyrus (BA46)
44
6
-19
66
16
-13
-22
22
-20
21
12
-42
65
-58
-17
3
-21
5
-8
Medial frontal gyrus (BA6)
33
Parahippocampal gyrus (BA28)
33
Caudate body
12
Postcentral gyrus (BA5)
38
Superior temporal gyrus (BA41)
25
Putamen
31
Fig. 3 Comparison of the SOA 16 and SOA 1,000 condition (contrast SOA 16 [ SOA 1,000) collapsed over the SF and the LF block (p \ 0.01 FDR; k [ 10 voxel) including activation intensity for the activated region
compared to a positive BOLD response in the LF condition in BA9. For each of the brain regions (i.e., left BA9 and left caudate head) we calculated the absolute value of the difference in the beta-weight between the SF and the LF block. Similarly, we calculated the absolute value of the difference in the slope of the SOA-RT2 function between the SF and the LF block. As can be seen in Fig. 5, for both the absolute activation differences in BA9 and the absolute activation differences in the caudate head there were strong linear correlations of these measures with the absolute value of the difference in the slope of the SOA-RT2 function. For BA9 the correlation was r = 0.794; R2 = 0.63; p \ 0.001. For the caudate head the correlation was r = 0.815; R2 = 0.66; p \ 0.001. Activation differences in BA9 and the caudate head were also highly correlated (r = 0.596; R2 = 0.35; p = 0.003). This means that participants showing larger BOLD signal differences
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in both regions (BA9 and caudate head) also show larger differences in the slope.
Discussion In the current study we manipulated the mode of response selection in a psychological refractory period paradigm along a serial-parallel continuum. This was done to examine possible differences in underlying neuronal mechanisms between a more serial, and a more parallel processing in dual-tasking. The results reveal a robust PRP effect showing that reaction times on task two (RT2) were longer, when the SOA between stimuli of the two tasks was short (review: Wu and Liu 2008). Response times on the first task (RT1) were not affected by variations of the SOA. This classical PRP effect was reflected by activation differences in the middle frontal gyrus (BA46), which was
Brain Struct Funct Fig. 4 Comparison of the SF and the LF block on correct trials. The contrast LF [ SF is given in the upper row denoting BOLD signal differences in BA9, including activation intensities. The contrast LF \ SF is given in the bottom row denoting BOLD signal differences in the caudate head, including activation intensities (p \ 0.01 FDR; k [ 10 voxel)
more activated in the SOA 16 vs. SOA 1,000 condition. This result is in line with previous results, where frontal areas including the superior and middle frontal gyrus (MFG) (Dux et al. 2006; Marois et al. 2006; Szameitat et al. 2006; Stelzel et al. 2008; Marois and Ivanoff 2005) and the inferior frontal gyrus (Jiang et al. 2004) have been reported. However, focus of this study was on the analysis of the data of the PRP effect using the mathematical framework proposed by Miller et al. (2009) to gain insights into the neural mechanisms mediating different processing modes in dual-tasking. The results show that manipulating the frequency of short vs. long SOAs affects the slope of the SOA-RT2 function: Subjects showed a relatively more parallel mode of response selection (i.e., flatter SOA-RT2 function), when short SOAs were frequent, while they showed a relatively more serial mode of response selection when long SOAs were frequent. The results obtained are unlikely to reflect simple training effects, since SF and LF task order was counterbalanced across subjects. They are also unlikely to reflect simple effects of task difficulty. If task difficulty would have been critical, more errors would have been expected in the SF condition, compared to the LF condition. Yet, this was not the case. Instead, and comparable to the findings by Miller et al. (2009), there were also no differences between the SF and the LF block on mean RT2s, which shows that the lack of differences in error rates between the SF and the LF block is not due an adjustment towards longer reaction times to perform more accurate in a putatively ‘difficult’ condition. Lastly, the data analysis rules out that the effects of SOA frequency manipulation may be better interpreted in terms of expectancy and preparation effects or effects of conditioning of
critical time points (e.g. Los 2010; Los and Horoufchin 2011), since an analysis of the behavioral data as a function of whether the SOA in the previous trial was longer vs. shorter than ‘‘expected’’ did not change the slopes of the SOA-RT2 function and hence the critical parameter for the interpretation of task manipulation effects along a serialparallel continuum. The current results, therefore, underline Miller et al.’s interpretation that the effects are attributable to differences in the mode of response selection (i.e., more serial vs. more parallel) and not due to task difficulty or time on task (training) effects. As to the functional neuroanatomical network mediating differences between a more serial and a more parallel processing mode the fMRI data revealed higher activation of striatal structures in the block with frequent short SOAs entailing a more parallel processing of responses. Opposed to this, response selection in the block with frequent long SOAs, entailing more serial processing, was related to higher activations in the middle frontal gyrus (BA9). This result shows that different modes of processing in dualtasks are mediated via distinct functional neuroanatomical structures. It seems that an important constraint, modulating whether response selection is performed in striatal versus neocortical structures, is the mode of response selection: neocortical structures are recruited when there is a more serial response selection mode. Striatal structures are recruited when there is a stronger overlap between tasks, imposing parallel response selection processes. It has been proposed that the basal ganglia ‘chunk’ different actions (Graybiel 1998) for the sake of reducing the complexity of information to be processed at a striatal level (Bar-Gad et al. 2003). By means of chunking, the basal ganglia are supposed to construct performance units made
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Brain Struct Funct
Fig. 5 Correlations between the absolute activation differences in BA9 and the caudate head with the absolute value of the difference in the slope of the SOA-RT2 function. a Correlation for BA9 and b correlation for the caudate head
up of multiple acts that are implemented in a specific temporal order (Graybiel 1998). It is possible that the high frequent, temporal close succession of tasks inducing more parallel processing, promotes chunking of the two responses in the basal ganglia. As a more serial processing mode is induced by high, frequent, temporarily distant event signaling responses, this processing mode may possibly not fit with the processing mode dominating in the basal ganglia and is, therefore, processed in areas closely connected to the basal ganglia, i.e. the lateral prefrontal cortex. Yet, this does not imply that the serial processing mode modulating activation in the lateral prefrontal cortex is not mediated via action chunking, but the temporal properties of stimulus presentations are more suitable for processing in the lateral prefrontal cortex. This is what is
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suggested by other studies stressing the importance of the lateral prefrontal cortex for the processing of the temporal dimension of executive control when different events have to be processed to allow a hierarchical organization of action plans (Koechlin and Jubault 2006; Koechlin et al. 2000, 2003). It is important to note that the current data do not address the question what brain structures mediate the switching process between ‘more serial’ and ‘more parallel’ processing and hence the shift between more neocortical and more striatal processing. The data simply reflect differences in the neuronal architecture between ‘more serial’ and ‘more parallel’ processing. As the subjects knew in advance that the distribution of short frequent and long frequent SOAs was changed between the experimental blocks, the subjects were able to switch their processing strategy in advance. However, the correlation analyses show that the absolute activation differences in BA9 as well as in the caudate head were linearly correlated of these measures with the absolute value of the difference in the slope of the SOA-RT2 function and hence the individual magnitude of shifts along the serial-parallel continuum during response selection. This at least suggests that shifts from one mode to the other mode are mediated by similar functional brain structures. The finding that no primary visual areas as well as multisensory integration areas (e.g. in the parietal cortex) differentially activated across conditions suggest that the processing differences observed along the constraint of the mathematical model proposed by Miller et al. (2009) are not an effect of altered stimulus processing or multisensory integration. Rather, it seems that it is the response selection level that is modulated. This is well in line with other studies on the PRP task showing that this effect emerges as a consequence of a response selection bottleneck (e.g. Sigman and Dehaene 2008). The current study used the mathematical framework put forward by Miller et al. (2009) to examine the functional architecture underlying more serial or more parallel processing during dual-tasking. A limitation of this study is that only this framework was used to examine this question. As another behavioral indicator, compatibility effects during dual-tasking may provide additional insights into the processing mode during dual-tasking. Lehle and Hu¨bner (2009), for example showed that flanker interference was larger if subjects were forced to respond more quickly, suggesting more parallel processing in dual-task performance, which is, again, consistent with the idea that response selection is probably much less serial than assumed by the ‘‘hard core’’ structural RSB model (see Pashler 1994). It should be noted that effects of SF and LF blocks are not restricted to RT2, but there were also significant differences on RT1 and a general disadvantage in RTs at long SOA conditions in the SF block (refer footnote
Brain Struct Funct
1 in the ‘‘Results’’ section), which may imply back crosstalk (e.g. Miller 2006). While the optimization framework by Miller et al. was designed on the basis to minimize total RTs to induce a more serial or a more parallel mode of processing, it seems that the framework has also some effect on RT1 in terms of backward crosstalk effects. Yet, when comparing the effect sizes in the interaction effects (SF/LF block 9 SOA) of the analyses on the mean RT data (i.e., RT1 ? RT2/2) and the RT2 data effect sizes are twice as high for the RT2 data (g2 = 0.25) than on the RT1 ? RT2/2 data (g2 = 0.12). This suggests that even though the optimization framework applied not only affects RT2s, effects of the optimization framework have stronger effects on this parameter of the response selection bottleneck. In summary, the results suggest that more serial and more parallel processing modes in dual-tasking differ in the functional organization of networks. Response selection in dual-tasking under the constraint of more parallel processing is mediated via striatal mechanisms, while response selection under the constraint of more serial processing is mediated via prefrontal cortical mechanisms. The results suggest that lateral prefrontal and striatal areas are ‘optimized’ for a certain processing mode (more parallel vs. more serial) in dual tasking. Acknowledgments This research was supported by a Grant from the Deutsche Forschungsgemeinschaft BE4045/10-1 and 10-2. We thank the reviewers, Dr. Iring Koch and an anonymous reviewer for their helpful criticisms on the manuscript.
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