Research Articles: Behavioral/Cognitive Motor Interference, but not Sensory Interference, Increases Midfrontal Theta Activity and Brain Synchronization During Reactive Control https://doi.org/10.1523/JNEUROSCI.1682-20.2020

Cite as: J. Neurosci 2021; 10.1523/JNEUROSCI.1682-20.2020 Received: 30 June 2020 Revised: 17 November 2020 Accepted: 17 December 2020

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Copyright © 2021 the authors Neural Dynamics during Attentional and Motor Conflicts 1

1 Running head: Neural Dynamics during Sensory and Motor Conflicts

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7 Motor Interference, but not Sensory Interference, Increases Midfrontal Theta Activity and

8 Brain Synchronization During Reactive Control

9 Jakob Kaiser1 & Simone Schütz-Bosbach1

10 1Ludwig-Maximilian-University, General and Experimental Psychology, D-80802 Munich,

11 Germany

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13 Corresponding author:

14 Jakob Kaiser, [email protected], Leopoldstr. 13, D-80802 Munich, Germany

15 Number of Pages: 41

16 Number of Figures: 10

17 Word Count Abstract: 227

18 Word Count Introduction: 643

19 Word Count Discussion: 1303

20 Acknowledgments: This work was supported by an LMUexcellent Grant to SSB. For help

21 with data collection we are grateful to Bettina Schmieder, Claudia Werner, Konstantin

22 Steinmassl, and Clara Dominke.

23 Competing Interests: The authors declare no conflict of interest.

24 Data Accessibility: Data and materials of this study are archived online at

25 https://osf.io/spwm9/?view_only=d9c0fd02768e4142bbcb110eaa2afc14

1 Neural Dynamics during Attentional and Motor Conflicts 2

26 Abstract

27 Cognitive control helps us to overcome task interference in challenging situations. Resolving

28 conflicts due to interfering influences is believed to rely on midfrontal theta oscillations.

29 However, different sources of interference necessitate different types of control. Attentional

30 control is needed to suppress salient distractors. Motor control is needed to suppress goal-

31 incompatible action impulses. While previous studies mostly studied the additive effects of

32 attentional and motor conflicts, we independently manipulated the need for attentional control

33 (via visual distractors) and motor control (via unexpected response deviations) in an EEG

34 study with male and female humans. We sought to find out whether these different types of

35 control rely on the same midfrontal oscillatory mechanisms. Motor conflicts, but not

36 attentional conflicts, elicited increases in midfrontal theta power during conflict resolution.

37 Independent of the type of conflict, theta power was predictive of motor slowing.

38 Connectivity analysis via phase-based synchronization indicated a wide-spread increase inter-

39 brain connectivity for motor conflicts, but a midfrontal-to-posterior decrease in connectivity

40 for attentional conflicts. For each condition, we found stronger midfrontal connectivity with

41 the parietal region contralateral than ipsilateral to the acting hand. Parietal lateralization in

42 connectivity was strongest for motor conflicts. Previous studies suggested that midfrontal

43 theta oscillations might represent a general control mechanism, which aids conflict resolution

44 independent of the conflict domain. In contrast, our results show that oscillatory theta

45 dynamics during reactive control mostly reflect motor-related adjustments.

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2 Neural Dynamics during Attentional and Motor Conflicts 3

47 Significance Statement

48 Humans need to exercise self-control over both their attention (to avoid distraction) and their

49 motor activity (to suppress inappropriate action impulses). Midfrontal theta oscillations have

50 been assumed to indicate a general control mechanism, which help to exert top-down control

51 during both motor and sensory interference. We are using a novel approach for the

52 independent manipulation of attentional and motor control to show that increases in

53 midfrontal theta power and brain-wide connectivity are linked to the top-down adjustments of

54 motor responses, not sensory interference. These findings clarify the function of midfrontal

55 theta dynamics as a key aspect of neural top-down control and help to dissociate domain-

56 general from motor-specific aspects of self-control.

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3 Neural Dynamics during Attentional and Motor Conflicts 4

58

59 Introduction

60 Cognitive control helps to maintain goal-directed behaviour when we are confronted

61 with internal or external interference. Processing conflicts due to task interference can occur

62 in different domains. Attentional conflicts typically arise due to the simultaneous presence of

63 task-relevant stimuli and salient distractors, such as when drivers need to focus on the traffic

64 despite colourful advertisements on the road side (Chelazzi et al., 2019; Gaspar & McDonald,

65 2014; Kerzel et al., 2018; Sawaki & Luck, 2010). Motor conflicts occur, when we must

66 deviate from a prepotent response pattern, such as when drivers need to hit the brakes due to

67 an unexpected stop sign (Aron, 2011; Botvinick et al., 2004; Cohen, 2014a; Egner, 2008).

68 Both attentional and motor conflicts lead to increased error rates and slower reaction times

69 (Criaud & Boulinguez, 2013; Gaspelin & Luck, 2018a; Liesefeld et al., 2019; Wessel, 2017).

70 To understand how the brain enables top-down control of our behaviour, it is crucial to find

71 out if different types of conflicts are resolved by separate, domain-specific or shared, domain-

72 general top-down control mechanisms in the brain (Cocchi et al., 2013; Gratton et al., 2017;

73 Hampshire & Sharp, 2015; Mackie et al., 2013).

74 Motor conflicts lead to an increase in oscillatory power in the theta range (4 – 7 Hz)

75 over the midfrontal cortex (Kaiser & Schütz-Bosbach, 2019; Kaiser et al., 2019; Mückschel

76 et al., 2017; Nigbur et al., 2011; Vissers et al., 2018). Higher theta power has been found to

77 predict motor slowing, which is believed to prevent a premature elicitation of prepotent but

78 goal-incompatible impulses (Chang et al., 2017; Töllner et al., 2017, Wessel & Aron, 2013,

79 2014). Conflict-related increases in midfrontal theta power have been observed in a wide

80 range of different experimental paradigms (Cohen & Cavanagh, 2011; Cooper et al., 2019;

81 Derosiere et al., 2018; Vissers et al., 2018). Therefore, it has been suggested that midfrontal

82 theta oscillations might be indicative of a general control mechanism (Cavanagh & Frank,

4 Neural Dynamics during Attentional and Motor Conflicts 5

83 2014; Cohen, 2014a). It has been speculated that theta synchronization between midfrontal

84 and other domain-specific brain areas could facilitate the adjustments necessary to overcome

85 different types of conflicts (Cooper et al., 2015; Duprez et al., 2019; van Driel et al., 2012).

86 Cognitive conflicts also often lead to decreases in alpha power (8 – 14 Hz; e.g., Popov et al.,

87 2018). Lower alpha power has been found to indicate increased attention towards task-

88 relevant stimuli (Clayton et al., 2017; Van Diepen et al., 2019).

89 For the simultaneous experimental manipulation of attentional and motor conflicts,

90 most previous studies used a version of the flanker paradigm (Nigbur et al., 2012; Nigbur et

91 al., 2011; Pastötter & Frings, 2018; Soutschek et al., 2013; Verbruggen et al., 2006). Here,

92 different types of target symbols (e.g., letters or numbers) necessitate different motor

93 responses. Each target is accompanied by a congruent or incongruent flanker. Congruent

94 flankers are identical to the target, which poses no interference. Incongruent flankers are

95 either different symbols associated with the target response, which primarily evokes sensory

96 interference, or different symbols associated with a target-incompatible response, evoking

97 both sensory and motor interference. Thus, attentional and motor interference in these studies

98 are necessarily additive. However, for a clear dissociation of attentional and motor control, it

99 is necessary to ensure an independent manipulation of distractor and motor conflicts within

100 the same task. Therefore, the current study combined the singleton distractor task, where a

101 distractor captures attention due to its unique colour (Gaspar & McDonald, 2014), with a

102 Go/Change-Go task, where participants have to change prepotent response patterns (Kaiser &

103 Schütz-Bosbach, 2019). We used this paradigm to compare the effect of attentional and

104 motor conflicts on midfrontal theta power as well as theta synchronization as a measure of

105 inter-brain connectivity. This allowed us to test if theta oscillations represent a domain-

106 general or task-specific mechanism of top-down control, and in how far the pattern of theta-

107 connectivity differs between attentional and motor conflicts.

5 Neural Dynamics during Attentional and Motor Conflicts 6

108

109 Methods

110 Participants

111 Thirty-seven participants (11 male) took part in the experiment for course credits or

112 financial reimbursement of 9 Euros per hour. Mean age of participants was 25.0 years (SD =

113 4.1). Three data sets were excluded from the analysis, either because they retained less than

114 50 trials for EEG analysis in at least one experimental condition after data preprocessing (n =

115 1), or because of extreme values in their behavioural performance (n = 2, see below),

116 resulting in a final sample of 34 participants.

117

118 Measurement Setup

119 For recording EEG, we used 65 active electrodes (BrainProducts ActiSnap) and one

120 additional ground electrode. Electrodes positions followed the international 10-20 system.

121 The FCz functioned as the online reference. EEG was recorded with a BrainVision

122 QuickAmp amplifier, using a 500 Hz sampling rate and a 0.016 Hz – 250 Hz online bandpass

123 filter.

124

125 Experimental Design

126 Figure 1 shows a schematic overview of the experimental design. On each trial, a

127 search display was shown which consisted of four items arranged around a fixation cross

128 (size: 0.8° x 0.8° visual angle) in the middle of the screen. Participants were instructed to

129 keep their eyes on the fixation cross at all times. Each of the items (1° x 1°) had a distance of

130 2° visual angle to the fixation cross. The four stimuli were either three circles and one square

131 or three squares and one circle. The item with the unique shape on each trial was

6 Neural Dynamics during Attentional and Motor Conflicts 7

132 defined as the target. All stimuli contained small black dots (0.1° x 0.1°) which were

133 positioned either on the left or right side of each shape. Participants were instructed to locate

134 the target as quickly as possible and press the left or right arrow button on the keyboard

135 depending on the position of the black dot within the target. All responses had to be

136 performed with the right index finger. The search display remained on the screen for 1.2 s. If

137 participants did not answer within this time period or if they pressed the wrong button, a

138 clock symbol or an X was shown as negative feedback for 0.3 seconds. The intertrial interval

139 randomly varied between 1.5 – 1.8 s.

140 To manipulate the degree of attentional conflict, we varied the colours of the search

141 items. On half of the trials, all items had the same colour (either green or red, no-distractor

142 trials). On the other half of the trials, one of the non-target items had a different colour than

143 all other items (singleton-distractor trials). Thus, a singleton-distractor trial could either

144 consist of three green items and one red colour singleton, or three red items with one green

145 colour singleton. We hypothesized that singleton distractor trials would lead to involuntary

146 attentional capture for the non-target with the unique colour, and therefore necessitate

147 increased attentional control. Note that the colours of the items did not have any direct

148 relevance for participants’ task. Participants were instructed to always ignore colours, but

149 only look for the target with the unique shape. The assignment of the two colours red/green

150 between singleton distractor and non-singleton items varied randomly between trials.

151 To manipulate the degree of motor conflict, we varied the relative frequency of the

152 two target actions. For each participant, either the left or right button press was prompted in

153 75 % of all trials (standard action), while the opposite button press was needed in 25 % of the

154 trials (conflict action). It was assumed that prompting the conflict action would increase the

155 need for motor control, since this action deviated from the more frequent standard response.

7 Neural Dynamics during Attentional and Motor Conflicts 8

156 The assignment between left/right and standard/conflict action was counterbalanced between

157 participants.

158 To ensure that our findings were not unduly influenced by systematic differences

159 between the search displays, we counterbalanced and randomized several factors between

160 trials. Target and singleton distractor appeared equally often in each of the four possible

161 positions around the fixation cross. The positions of the dots in the four items was

162 randomized so that each display contained two left-facing and two right-facing dots. For half

163 of the singleton-distractor trials, the distractor shape contained a dot which was target-

164 compatible, meaning it pointed in the same direction as the dot in the target shape (cf. Figure

165 1). For the other half of the trials, the dot in the singleton distractor was target-incompatible,

166 meaning it pointed in the opposite direction of the target (e.g., distractor with left-facing dot,

167 but target with right-facing dot). We included both target-compatible and target-incompatible

168 distractor trials, because in this way we ensured that participants could not infer the target

169 action from merely looking at the distractor, meaning that the salient distractor had no

170 information value for choosing the correct response.

171 The colour of singleton distractor and non-singleton items was counterbalanced, so

172 that in each condition half of the trial employed the same target/distractor colours as in the

173 directly preceding trial (e.g., standard items = green, distractor items = red), while the other

174 half of the trials used the opposite colour assignment (e.g., standard items = red, distractor

175 items = green). The shape of targets and non-targets (either circle or square) was randomized

176 across trials.

177 The experiment consisted of 1920 trials, subdivided into 16 blocks. The order of all

178 trials was randomized. After each block participants received feedback about their average

179 error rates and reaction times. Breaks between blocks were self-paced. Overall, the

180 experimental procedure took about 90 minutes per participant.

8 Neural Dynamics during Attentional and Motor Conflicts 9

181

182 Behavioural analysis

183 For both behavioural and EEG analysis, we only retained trials with correct responses,

184 leading to the mean removal of 4.93 % (SD = 2.52 %) of all trials. For trials with salient

185 distractors, we included in the main analysis only target-compatible distractors, where the

186 distractor dot was located at the same side as the target dot (cf. Figure 1). This was done

187 because distractors associated with target-incompatible actions are known to elicit motor

188 conflicts, and therefore would have confounded attentional and motor interference

189 (Verbruggen et al., 2006).

190 Error rates and reaction times for correct responses were averaged for each condition.

191 Box plots were employed for the identification of outliers. More specifically, we excluded all

192 participants whose error rates or reaction times deviated more than 1.5 times of the

193 interquartile range from the overall sample (Schwertman, Owens, & Adnan, 2004). This led

194 to the exclusion of two participants. Both behavioural measures were analysed with repeated-

195 measures ANOVAs with the factors ATTENTION (no attentional conflict/attentional

2 196 conflict) and MOTOR (no motor conflict/motor conflict). As effect size we report  p and

197 Cohen’s d. Additionally, we report Bayes Factors (BF), which indicate the evidence for the

198 alternative hypothesis (BF > 1) relative to evidence for the null hypothesis (BF < 1). It has

199 been suggested that BF > 10 represents strong evidence for the alternative hypothesis, while

200 BF < .10 indicates strong evidence for the null hypothesis (Jarosz & Wiley, 2014; Van de

201 Schoot et al., 2014; Wagenmakers et al., 2018). Tests were calculated with the R packages ez,

202 effsize, and BayesFactor.

203

9 Neural Dynamics during Attentional and Motor Conflicts 10

204 EEG Preprocessing

205 EEG preprocessing and data analysis was performed with the Matlab Toolbox

206 Fieldtrip (Oostenveld, Fries, Maris, & Schoffelen, 2011). For five participants, 1-4

207 exceedingly noisy electrodes were excluded and later replaced via interpolation using the

208 function ft_channelrepair. EEG data was referenced to an average of all electrodes and

209 filtered (low pass: 100 Hz, high pass: 0.75 Hz). To facilitate further processing, data was

210 downsampled to 250 Hz. We then extracted for each trial the EEG activity between -1.5 and

211 +2.2 seconds around trial onset. This time interval was chosen to be considerably longer than

212 the overall trial length, in order to avoid edge effects during the time-frequency calculation.

213 We used independent component analysis to identify components which represented eye

214 blinks or other non-brain-related artefacts. This led to the removal of 1-3 components per

215 participant (mean = 1.53). Furthermore, we removed all trials where any EEG channel

216 showed deflections higher than 90 µV, resulting in an average exclusion rate of 6.56 % (SD =

217 5.25) of all trials. High voltage deflections are most likely to stem from noise artefacts, such

218 as strong body movements, and are therefore commonly removed from EEG data sets (e.g.,

219 Cooper et al., 2019; Gaspelin & Luck, 2018; Yamanaka & Yamamoto, 2010). To ensure a

220 sufficient number of trials in each condition, we only retained participant with at least 50

221 trials per condition, leading to one exclusion. For the final sample, we retained on average for

222 each participant 638.0 trials (minimum: 546 – maximum: 717) without conflict, 325.0 trials

223 (265 – 357) with only attentional conflicts, 210.5 trials (171 – 236) with only motor conflicts,

224 and 100.3 trials (77 – 115) with both attentional and motor conflicts. Resulting data sets were

225 filtered with the function ft_scalpcurrentdensity employing a Laplacian (spatial) filter with

226 the spline method and a polynomial degree of 10. This increases the spatial specificity for the

227 results of time-frequency and connectivity analysis (Cohen, 2015).

228

10 Neural Dynamics during Attentional and Motor Conflicts 11

229 Statistical Analysis

230 Time-frequency data was calculated using Morlet Wavelets, as implemented in the

231 function ft_freqanalysis. For each condition, we estimated the oscillatory power from 1-20

232 Hz in 1-Hz steps, while increasing the number of Wavelet cycles from 3 to 8 cycles in

233 linearly spaced steps. These processing parameters are in line with general recommendations

234 and previous studies concerning neural theta oscillations (Cohen, 2014b; Cooper, Darriba,

235 Karayanidis, & Barceló, 2016; Harper, Malone, & Iacono, 2017).

236 The main goal of our analysis was to compare the neural impact of attentional

237 conflicts evoked by distractor displays with motor conflicts evoked by deviations from the

238 prepotent motor response. If attentional control was associated with midfrontal theta

239 oscillations, we would expect that the onset of distractor displays elicits a change in

240 midfrontal theta activity. Thus, a stimulus-locked analysis relative to the distractor onset

241 should identify neural processes elicited by sensory distraction. Concerning motor control,

242 we would expect that motor conflicts lead to an increase in midfrontal theta power prior to

243 action execution. However, since both attentional and motor conflicts have been shown to

244 slow down reaction times, stimulus-locked differences in activity between conditions around

245 the time of the motor response could simply reflect slower reactions during conflict trials

246 (Gaspelin & Luck, 2018a; Wessel, 2017). Thus, a response-locked analysis relative to the

247 motor response is more appropriate for comparing processes related to response preparation

248 across different conditions. Accordingly, we extracted time-frequency data in two different

249 ways: stimulus-centred, defined as 0 to 1.2 seconds relative to the onset of the search display,

250 and response-centred, defined as -0.8 to 0.4 seconds around the motor response based on

251 reaction times on each trial. Where appropriate, we present the results of both stimulus-

252 centred and response-centred analysis to aid the distinction between distractor-evoked and

253 response-locked neural processes. For each condition, we calculated average power values.

11 Neural Dynamics during Attentional and Motor Conflicts 12

254 Condition-wise averages were baseline-corrected via decibel conversion, using the mean

255 power of all trials between 0.3 to 0.1 seconds prior to trial onset as baseline value. Employing

256 a trial average as baseline for the calculation of time-frequency maps increases the signal-to-

257 noise ratio (Cohen, 2014b).

258 Statistical Analysis of time-frequency maps was based on the average of midfrontal

259 electrodes FCz, FC1, and FC2. These electrodes were chosen, because previous studies of

260 midfrontal theta power overwhelmingly found the peak of theta power around this site

261 (Chang et al., 2017; Cohen & Cavanagh, 2011; Kaiser & Schütz-Bosbach, 2019; Vissers et

262 al., 2018). Topographical plots of our data confirmed that this location represented the peak

263 of neural theta increases (Figure 2). We used cluster-based permutation analysis to identify

264 significant effects of the main factors ATTENTION, MOTOR, and the ATTENTION *

265 MOTOR interaction in the time-frequency data (Derosiere et al., 2018). Permutation analysis

266 allows for statistical tests over whole time-frequency maps, while still controlling for

267 multiple comparisons (Maris & Oostenveld, 2007). More specifically, for each main factor

268 and the interaction separately, statistical contrasts were first determined via F-tests for each

269 individual time-frequency point. Adjacent data points below a cut-off of p < .05 were

270 combined into clusters. Each cluster’s weight was calculated as the sum of the F-values of all

271 the tests for its individual time points. Statistical significance of each cluster was calculated

272 as the probability that a cluster with equal weight could appear due to chance, as determined

273 via 10,000 random permutations over the original data. We report all clusters which were

274 significant with p < .05. We also calculated post-hoc contrasts between individual conflict

275 conditions, using the same permutation approach, but instead of F-tests, employed t-tests,

276 which were corrected for two-sided comparisons.

277

12 Neural Dynamics during Attentional and Motor Conflicts 13

278 Brain-Behaviour Correlations

279 To investigate the relationship between neural activity and behavioural outcomes

280 during conflict resolution, we computed correlation maps between midfrontal oscillatory

281 activity and reaction time on the same trial in each condition (for similar approaches, cf.

282 Adelhöfer & Beste, 2020; Cohen & Cavanagh, 2011; Cooper et al., 2019). More specifically,

283 for each participant individually we calculated a separate time-frequency map with the same

284 parameters as in the main analysis for each trial. For each participant, we then correlated

285 every time-frequency point with the reaction time of the same trial separately for each

286 condition. Both oscillatory activity and reaction times were rank-transformed prior to the

287 calculations of correlations, which lowers the potential influence of extreme values on

288 correlation estimates (Cohen, 2014b; Conover, 2012). This analysis produced one time-

289 frequency map of correlation coefficients per condition and participant. Thus, the average

290 time-frequency maps of all participants represent the average correlation strength between

291 oscillatory power and reaction times. We used cluster-based permutation with the same

292 parameters as for the main power analysis, to test the correlation maps of all participants in

293 each condition for clusters which differed from 0. Correlation clusters which significantly

294 deviate from 0 indicate significant correlations between oscillatory power and reaction times.

295 We used cluster-based permutation with the same parameters as for the main power analysis,

296 to test the correlation maps of all participants in each condition for clusters which differed

297 from 0. Correlation clusters which significantly deviate from 0 indicate significant

298 correlations between oscillatory power and reaction times.

299

300 Connectivity Analysis

301 We calculated phase synchronization between the midfrontal electrodes selected for

302 the main analysis (FCz/FC1/FC2) and all other electrodes as a measure of interconnectivity

13 Neural Dynamics during Attentional and Motor Conflicts 14

303 between the midfrontal and other brain regions. For estimating synchronicity, we used the

304 debiased weighted phase lag index (WPLI; Vinck, Oostenveld, Van Wingerden, Battaglia, &

305 Pennartz, 2011). Compared to other measures of synchronization, the weighted phase lag

306 index is less susceptible to the influence of volume conduction, which can artificially inflate

307 connectivity estimates (Bastos & Schoffelen, 2016). WPLI between the midfrontal seed

308 electrodes and all other sensors was calculated separately for each condition. WPLI values for

309 each seed electrode itself were set to 0. Resulting synchronization values were baseline-

310 corrected by subtracting the mean WPLI between 0.30 and 0.10 seconds prior to trial onset.

311 Thus, higher WPLI values at any electrode indicate an increase in synchronization between

312 that sensor and the midfrontal areas. Synchronization values were averaged in 0.2 seconds

313 intervals. Since we wanted to explore potential changes of connectivity between the

314 midfrontal side and all other cortical regions, we used cluster-based permutation that included

315 all electrodes and time points to identify spatiotemporal clusters of condition differences in

316 synchronization in the theta range (4 – 7 Hz). To identify the electrodes with most reliable

317 effects, and to avoid false positives in this whole-head analysis, we chose a more

318 conservative cutoff value for individual time-electrode points of p < .01. The remaining

319 analysis parameters were identical to the ones employed for the main time-frequency

320 analysis.

321 The whole-head analysis revealed a significant interaction between attentional and

322 motor conflicts in the synchronization between midfrontal and parietal electrodes (Figure 7).

323 To explore this interaction further, we calculated additional heat maps of the average

324 midfrontal synchronicity for the parietal region, based on a representative array of the

325 centroparieteal area (P1/Pz/P2/PO3/POz/PO4). Permutation analysis with the same

326 parameters as in the main analysis was employed to test for effects of attentional and motor

327 conflicts in this region.

14 Neural Dynamics during Attentional and Motor Conflicts 15

328 Our whole-head analysis also revealed a pattern of lateralization in midfrontal-to-

329 parietal synchronicity (cf. Figure 7). Therefore, we chose to investigate the difference in

330 contralateral and ipsilateral phase synchronization relative to the acting hand. For this

331 purpose, we calculated the average synchronization values separately for the parietal

332 contralateral side (electrodes CP5/CP3/CP1/P5/P3/P1) and the parietal ipsilateral side

333 (electrodes CP6/CP4/CP2/P6/P4/P2). Cluster-based permutation was employed to compare

334 the time-frequency maps of phase synchronization values between contra- and ipsilateral side

335 within each condition, as well as for comparing the difference in lateralization effect

336 (contralateral - ipsilateral) between the different types of conflict.

337

338 Results

339 Behavioural results

340 Figure 3 shows box plots for error rates and reaction times. For error rates, we found a main

341 effect of ATTENTION, F(1,33) = 85.65, p < .001,  2 = 0.72, BF > 106, a main effect of p

342 MOTOR, F(1,33) = 64.60, p < .001, = 0.66, BF > 106, as well as an ATTENTION *

343 MOTOR interaction, F(1,33) = 26.86, p < .001, = 0.45, BF = 9.91. On average, the

344 presence of a singleton distractor compared to search displays without singleton distractor

345 increased the error rate by 5.81 % (SD = 4.48). Conflict actions compared to standard actions

346 increased the error rate by 5.76 % (SD = 4.91). The increase in errors for conflict actions

347 compared to standard actions was significantly higher when a singleton distractor was present

348 compared to when no singleton distractor was shown, t(33) = 5.18, p < .001, d = 0.89, BF =

349 1936.56.

350 For reaction times, we found a main effect of ATTENTION, F(1,33) = 244.74, p <

351 .001, = 0.88, BF > 106, a main effect of MOTOR, F(1,33) = 325.42, p < .001, = 0.91,

15 Neural Dynamics during Attentional and Motor Conflicts 16

352 BF > 106, but no ATTENTION * MOTOR interaction, F(1,33) < 0.01, p = .98, BF = 0.26.

353 Trials with singleton distractors compared to trials without singletons increased reaction

354 times by 60.1 ms (SD = 23.5). The need to perform conflict actions compared to standard

355 actions increased reaction times by 80.8 ms (SD = 27.0). Overall, both the presence of

356 singleton distractors and conflict actions interfered with participants’ performance, as

357 indicated by increases in both error rates and reaction times.

358

359 Midfrontal Oscillatory Power

360 Figure 2 shows time-frequency maps for midfrontal oscillatory power. Figure 4 shows

361 the results of the cluster-based permutation for the ANOVA with the factors ATTENTION

362 (no attentional conflict/attentional conflict) and MOTOR (no motor conflict/motor conflict).

363 Tests of main effect and interactions were performed for both stimulus-centred data (relative

364 to search display onset) and response-centred data (relative to motor response) to aid the

365 distinction between oscillatory effects elicited by the sensory distractors and motor conflicts.

366 For the factor ATTENTION, we found a significant cluster within the theta range,

367 stimulus-centred: 0.64 s – 1.2 s, p = .001, response-centred: -0.05 s – 0.4 s, p = .035. Trials

368 with salient distractors showed higher theta power compared to trials without distractors, but

369 only in the last half of the trials and only after the motor response. Thus, during task

370 execution, the mere presence of a salient distractor did not lead to a significant increase in

371 theta power. Additionally, the factor ATTENTION revealed a significant cluster in the alpha-

372 to-beta range prior to motor response, response-centred: -0.74 s – -0.3 s, p = .043. Compared

373 to trials without distractors, trials with salient distractors showed lower alpha/beta power

374 during response preparation.

375 For the factor MOTOR, we found a significant cluster, which indicated that trials with motor

376 conflicts compared to trials without motor conflicts lead to higher power with a peak in the

16 Neural Dynamics during Attentional and Motor Conflicts 17

377 theta range, stimulus-centred: 0.27 s – 1.2 s, p < .001, response-centred: -0.8 s – 0.08 s, p <

378 .001. Increases in theta power for motor conflicts emerged around 0.80 s prior to and peaked

379 shortly before the motor reaction. Additionally, for the factor MOTOR we found a significant

380 clusters in the alpha/beta range, indicating that motor conflicts compared to trials without

381 motor conflicts led to significantly lower alpha/beta activity prior to the motor reaction,

382 response-centred -0.8 s – -0.27 s, p = .001, as well as after the motor action, stimulus-centred:

383 0.73 s – 1.2 s, p < .001, response-centred: 0.09 s – 0.4 s, p = .005. Thus, motor conflicts led to

384 increases in theta power, which were more most prominent directly prior to the motor

385 response. Additionally, motor conflicts led to decreases in alpha/beta power.

386 Testing the ATTENTION * ACTION interaction revealed a significant cluster in the theta to

387 alpha range in the stimulus-centered data: 0.44 s – 0.77 s, p = .045. There was no significant

388 ATTENTION * MOTOR interaction in the response-centred data. As the interaction effect

389 occurred during the latter part of the trial and was not significant in the response-centred

390 analysis, the interaction in the stimulus-centred data could reflect differences in reaction

391 times between the conditions. Thus, we found no consistent evidence for a midfrontal

392 ATTENTION * MOTOR interaction.

393 In addition to the permutation ANOVA, we also present the direct comparisons of different

394 types of conflicts via permutation t-tests in Figure 5. These tests confirmed the main findings

395 of the ANOVA: Compared to trials with only attentional conflicts, trials which contained

396 either only a motor conflict or both motor and attentional conflicts simultaneously, showed

397 higher theta power in the later part of the trial prior to the motor response (all p’s < .002).

398 Trials with attentional conflicts and motor conflicts did not differ in theta activity in the post-

399 response interval. Additionally, trials which contained only a motor conflict or both types of

400 conflicts simultaneously compared to trials with only attentional conflicts showed lower

401 alpha/beta power at the end of the trial (all p’s < .02). The presences of both conflicts

17 Neural Dynamics during Attentional and Motor Conflicts 18

402 compared to only an attentional conflict also lead to lower power in the alpha/beta range prior

403 to the response, p = .002.

404 The simultaneous presence of distractor and motor conflicts compared to the presence

405 of only a motor conflict lead showed significantly higher power theta range at the end of the

406 trial in the stimulus-centred data, 0.75 s – 1.2 s, p = .021, as well as significantly lower power

407 in the alpha range in the first half of the trial, 0.17 s – 0.36 s, p = .046. However, these effects

408 did not occur in the response centred data. Here, both conflicts simultaneously compared to

409 trials with only motor conflicts showed a cluster of lower alpha/beta power during and after

410 the motor response, -0.08 s – 0.26 s, p = .034. This suggest that the significant clusters in the

411 stimulus-centred comparison occurred mostly due to differences in the timing of neural

412 activity. Thus, when controlled for differences in timing, both conflicts simultaneously

413 compared to only motor conflicts did not lead to an increase in theta power, but only a

414 decrease in alpha/beta power around and after the motor response.

415 To conclude, stimulus-locked analysis indicated increases in theta power for both attentional

416 and motor conflicts, which were mostly confined to the second half of the trial. Importantly,

417 for attentional conflicts, we found no evidence for early conflict-related increases in theta

418 power directly evoked by the onset of the distractor. Theta increases for attentional conflicts

419 only occurred after the response, meaning after the conflict resolution had already taken

420 place. Conversely, response-locked data showed early and prolonged increases in theta power

421 for motor conflicts. Thus, motor conflicts, but not attentional conflicts, were marked by

422 increases in theta power during task execution. Both types of conflicts evoked decreases in

423 alpha/beta power.

424

18 Neural Dynamics during Attentional and Motor Conflicts 19

425 Brain-Behaviour Correlations

426 Figure 6 shows average correlations between trial-wise reaction times and midfrontal

427 oscillatory power. Analysis of stimulus-centred data revealed clusters of positive correlations

428 between theta power and response times in the later part of the trial for all conditions: no

429 conflict: 0.32 s – 1.2 s, p < .001; attentional conflict: 0.34 s – 1.2 s, p < .001; motor conflict:

430 0.47 s – 1.2 s, p < .001; both conflicts: 0.56 s – 1.2 s, p < .001. For trials without conflict, as

431 well as for trials with attentional conflicts, we also found clusters of negative correlations in

432 the alpha/beta frequency range at the end of the trial period: no conflict: 0.6 s – 1.2 s, p =

433 .003; attentional conflict: 0.9 s – 1.2 s, p = .013.

434 Note that correlations of stimulus-centred data with reaction times could also occur

435 due to temporal differences in the on- and offset of task-related neural activity, rather than

436 due to a systematic relationship between the strength of neural activity and response times.

437 Importantly, analysis of response-centred data confirmed the presence of positive correlations

438 between theta power and reaction times. In every condition, positive correlation clusters

439 could be observed as early as 0.8 s prior to the action execution up until the end of the time

440 region of interest; no conflict: -0.8 s – 0.32 s, p < .001; attentional conflict/motor

441 conflict/both conflicts: -0.8 s – 0.4 s, p < .001. Additionally, in every condition we found a

442 cluster of negative correlations between alpha/beta power and reaction times, which peaked

443 around 0.50 s before response execution; no conflict: -0.8 s – -0.26 s, p = .002; attentional

444 conflict: -0.78 s – -0.33 s, p = .003; motor conflict: -0.72 s – -0.41 s, p = .005; both conflicts:

445 -0.77 s – -0.45 s, p = .005.

446 To conclude, for all conditions and independent of the type of conflict present, higher

447 midfrontal theta power and lower alpha/beta power was predictive of motor slowing.

448

19 Neural Dynamics during Attentional and Motor Conflicts 20

449 Midfrontal Theta Synchronization

450 Figure 7 shows average synchronization between the midfrontal electrodes

451 FCz/FC1/FC2 and all other electrodes. We again conducted permutation tests for the factors

452 ATTENTION, MOTOR, and the ATTENTION * MOTOR interaction. For the factor

453 ATTENTION, we found no significant clusters in the stimulus-centred data, indicating that

454 no significant changes in midfrontal connectivity could be found relative to the distractor

455 onset. For response-centred data, we found a parietal to posterior cluster with a decrease in

456 synchronization, -0.6 s – -0.2 s, p < .001. This suggests desynchronization between central

457 and parietal electrodes during attentional conflicts.

458 For the factor MOTOR, stimulus-centred analysis indicated two significant clusters of

459 synchronicity increases. These clusters comprised a wide array of electrodes, most of which

460 were located at central and left-parietal region, 0.4 s – 1 s, p < .001, as well as the frontal

461 region, 0.4 s – 1 s, p = .001. Analysis of response-centred data confirmed that the majority of

462 significant increases occurred directly prior to the motor response: -0.6 s – 0.2 s, p < .001.

463 Additionally, response-centred analysis indicated an early increase in synchronization with

464 mostly left-parietal electrodes: -0.8 s – -0.6 s, p = .001. Thus, motor conflicts led to an early

465 increase in synchronization with parietal electrodes, as well as a later increase in frontocentral

466 connectivity around the time of response execution, including a noticeable peak in

467 synchronization with left-parietal electrodes. Since all motor actions in this experiment had to

468 performed with the right hand, this could indicate synchronization between midfrontal

469 electrodes and parts of the parietal motor cortex contralateral to the acting hand.

470 We found evidence for an ATTENTION * MOTOR interaction in centroparietal

471 electrodes, stimulus-centred: 0.4 s – 0.6 s, p = .030; response-centred: -0.6 s – -0.4 s, p =

472 .044. This indicates that frontoparietal synchronization differed between trials where both

20 Neural Dynamics during Attentional and Motor Conflicts 21

473 conflicts were present compared to trials with either only attentional or motor conflicts. We

474 explored this interaction effect in a separate analysis (cf. next section).

475 We also tested for differences in theta connectivity between the different types of

476 conflicts via permutation t-tests. Figure 8 presents the results of these contrasts for response-

477 centred data, since the main ANOVA analysis showed that most significant changes in

478 connectivity were locked to the motor response. The analogous analysis of the same contrasts

479 for stimulus-centred data did not reveal any additional findings. As can be seen from Figure

480 8, contrasts between conflicts largely confirm the findings from the main analysis. Conditions

481 which contained a motor conflict (motor conflict only or both attentional and motor conflict

482 simultaneously) compared to trials with only attentional conflicts showed increases in theta

483 synchronization over a wide array of electrodes, including a prominent increase on frontal

484 and parietal electrodes (all p’s < .01). Conversely, conditions which contained an attentional

485 conflict compared to conditions which only contained motor conflicts showed significant

486 decreases in synchronization mostly confined to parietal and posterior electrodes (all p’s <

487 .03).

488 To conclude, motor conflicts, but not attentional conflicts, led to an increase in theta

489 synchronization with midfrontal electrodes across the cortex, including frontal, and

490 contralateral-parietal regions. In contrasts attentional conflicts showed a decrease in

491 midfrontal synchronization with centroparietal to posterior electrodes.

492

493 Interaction Effects in Frontoparietal Theta Synchronization

494 The whole-head connectivity analysis indicated an interaction effect between

495 attentional and motor conflicts on centroparietal electrodes. To explore this interaction, we

496 calculated average time-frequency maps of synchronicity between midfrontal and

497 centroparietal electrodes, and again performed ATTENTION * MOTOR ANOVA on the

21 Neural Dynamics during Attentional and Motor Conflicts 22

498 resulting values (Figure 9). For the factor ATTENTION, centroparietal electrodes showed a

499 significant decrease in synchronization in the theta to alpha range, -0.48 s – -0.04 s, p < .001.

500 For the factor MOTOR, we also found a cluster of lower synchronization, -0.53 s – -0.3 s, p <

501 .001. Additionally, the factor MOTOR showed two clusters of increases in theta

502 synchronization, -0.8 s – -0.42 s, p = .001; -0.23 s – 0.08 s, p = .006.

503 As in the whole-head analysis, we found a significant ATTENTION * MOTOR

504 interaction in the theta range, -0.62 s – -0.39 s, p = .031. We explored this interaction, by

505 testing the effect of motor conflicts separately for trials with and without attentional conflicts.

506 For motor conflicts in the absence of salient distractors, we again found significant increases

507 in theta synchronization, -0.8 s – -0.44 s, p < .001; -0.25 s – 0.05 s, p = .006. For motor

508 conflicts on trials with salient distractors, however, there was no significant increase in theta

509 synchronicity. Instead, we found a decrease in synchronization in the theta and alpha/beta

510 range, -0.53 s – -0.32 s, p < .001. To conclude, as indicated by the whole-head analysis,

511 motor conflicts and attentional conflicts differed in their synchronization between midfrontal

512 and centroparietal electrodes. While motor conflicts led to an increase in theta

513 synchronization, attentional conflicts were marked by a decrease in synchronization in the

514 theta and alpha range. As indicated by the interaction effect, the presence of sensory

515 distractors led to lower theta synchronization during motor conflicts.

516

517 Lateralization of Theta Synchronicity

518 Based on the findings of the whole-head connectivity analysis, we further investigated the

519 pattern of increased parietal synchronization contralateral to the acting hand in action

520 conflicts. Figure 10 shows the comparison of contralateral-parietal and ipsilateral-parietal

521 connectivity with the midfrontal region. For every condition, midfrontal phase

522 synchronization in the delta/theta frequency range was stronger for the contralateral

22 Neural Dynamics during Attentional and Motor Conflicts 23

523 compared to the ipsilateral side around the time of the motor response; no conflict: -0.43 s –

524 0.4 s, p = .004; attentional conflict: -0.25 s – 0.4 s, p = .005; motor conflict: -0.34 s – 0.32 s, p

525 < .001; both conflicts: -0.36 s – 0.09 s, p = .007. We tested if the lateralized difference in

526 phase synchronicity (contralateral – ipsilateral) depended on the type of conflict using again

527 an ATTENTION * MOTOR ANOVA. There was no main effect of ATTENTION, but a

528 main effect of MOTOR showing an increase in lateralization, -0.34 s – 0.04 s, p = .001. There

529 was no significant ATTENTION * MOTOR interaction. Thus, motor conflicts, but not

530 attentional conflicts, increased the degree of lateralization. Individual condition contrasts

531 confirmed that lateralization in synchronicity was significantly higher for motor conflict trials

532 compared to trials with no conflict, -0.3 s – 0.11 s, p = .002, as well as compared to trials

533 with attentional conflicts: -0.31 s – 0.11 s, p = .001. There were no significant differences

534 between trials with both attentional and motor conflicts compared to trials with only motor

535 conflicts. To conclude, synchronization between the midfrontal and parietal region was

536 stronger contralateral than ipsilateral to the acting hand in every condition. This lateralization

537 of midfrontal synchronicity was most pronounced during motor conflicts.

538

539 Discussion

540 The current study compared midfrontal oscillatory dynamics between attentional conflicts,

541 evoked via salient distractors, and motor conflicts, evoked via deviations from prepotent

542 actions. While previous studies of cognitive control mostly tested the additive effects of

543 sensory and motor interference, our design allowed for an independent manipulation of

544 attentional and motor conflicts. Motor conflicts led to a clear spike in theta activity directly

545 prior to the motor action when motor adjustments were most likely to take place. Conversely,

546 for attentional conflicts there was no increase in theta power during conflict resolution.

547 Moreover, while motor conflicts led to increased theta synchronization with the midfrontal

23 Neural Dynamics during Attentional and Motor Conflicts 24

548 region across the cortex, attentional conflicts led to a decrease in synchronization between

549 midfrontal and parietal electrodes.

550 If the resolution of sensory interference was related to midfrontal theta oscillations, stimulus-

551 locked analysis should have shown increases in theta activity directly after the distractor

552 onset. Instead, as demonstrated by the response-locked analysis, for attentional conflicts we

553 only found evidence for theta power increases after the conflict response had already been

554 performed. Post response theta activity did not significantly differ between attentional and

555 motor conflicts. Theta power increases after trials have been associated with proactive control

556 processes, such as anticipatory adjustments for upcoming trials (Kaiser et al., 2020; Luft et

557 al., 2013, van Driel et al., 2012). Thus, our results would be compatible with the assumption

558 that proactive control adjustments elicit theta power increases independent of the type of

559 conflict (Cooper et al., 2015; Derosiere et al., 2018). Future studies could test this assumption

560 directly, by comparing theta power during pre-trial intervals, in which participants anticipate

561 either sensory or motor interference. Importantly, our study shows, that theta activity during

562 the ongoing resolution of control conflicts is specific to motor conflicts and does not occur

563 for sensory interference.

564 Decreases in alpha power are often associated with increase in selective attention (Foster &

565 Awh, 2019; van Diepen, et al. 2019). In the current study, both attentional and motor

566 conflicts led to decreases in alpha/beta activity. This finding is consistent with previous

567 studies which found that both motor and sensory interference evoke alpha/beta suppression

568 (McDermott et al., 2017; Popov et al., 2018). Thus, our results show that while increase in

569 midfrontal theta power oscillations during behavioural conflicts are specific to motor

570 interference, decreases in the alpha/beta range occur independent of the source of

571 interference.

24 Neural Dynamics during Attentional and Motor Conflicts 25

572 In line with previous studies, we found that increases in theta power, as well as decreases in

573 alpha power, were associated with slower responses in all conditions (Töllner et al., 2017).

574 This finding suggests that midfrontal oscillatory activity during behavioural tasks partly

575 reflects a speed-accuracy trade-off. Delaying immediate responses is assumed to be necessary

576 to allow for the preparation of alternative behaviour during demanding situations (Elchlepp et

577 al., 2016; Logan et al., 2014; Schall et al., 2017; Wessel & Aron, 2013). Importantly, our

578 results show that neither sensory or motor interference fundamentally alter the functional

579 relationship between midfrontal oscillations and motor responses.

580 Oscillatory synchronization is assumed to aid information exchange between task-relevant

581 brain regions, which is an important aspect of neural conflict resolution (Cooper et al., 2015;

582 Duprez et al., 2019; Fries, 2005; van de Vijver et al., 2018). One important question is, in

583 how far changes in synchronization are domain-specific regarding the type of conflict

584 affordance (van Driel et al., 2012; Vissers et al., 2018). This study provides important new

585 evidence for the conflict-specific modulation of inter-brain connectivity. Motor conflicts, but

586 not attentional conflicts, led to markedly stronger theta synchronization around the time of

587 response execution in a wide array of electrodes. This included significant lateralization in

588 synchronicity in the left-parietal region contralateral to the acting hand. This finding could

589 indicate increased intercommunication between midfrontal regions and parts of the motor

590 cortex related to the currently action-relevant effector. Thus, increases in theta

591 synchronization were specific to the motor conflict which needed to be resolved.

592 In contrast to motor conflicts, attentional conflicts led to a decrease in synchronization

593 between midfrontal and centroparietal to posterior electrodes, which occurred both in the

594 theta and alpha range. Frontoparietal intercommunication has previously been associated with

595 attentional processes (Corbetta & Shulman, 2002; Dixon et al., 2018; Marshall et al., 2015;

596 Ptak, 2012). Additionally, cortical activity in posterior brain region is commonly associated

25 Neural Dynamics during Attentional and Motor Conflicts 26

597 with the processing of visual stimuli (Derosiere et al., 2018; Pastötter & Frings, 2018). Thus,

598 the current findings suggest that visual distractors temporarily lower the intercommunication

599 between the midfrontal regions and areas related to visual and attentional processing. Note

600 that measures of phase synchronization are non-directional, and thus do not distinguish

601 between the upstream (posterior-to-frontal) and downstream (frontal-to-posterior) flow of

602 information (Bastos & Schoffelen, 2016). On the one hand, this finding could be seen as

603 evidence for neural distractor interference, which appears to lead to a temporary disruption in

604 neural intercommunication. On the other hand, one could speculate that lowering the

605 synchronization with parietal and posterior areas during salient distraction might be adaptive,

606 since it could effectively lower the upstream propagation of distracting, task-irrelevant

607 information. Independent of the functional relevance of distractor-related desynchronization,

608 this finding clearly demonstrates that motor conflicts, but not attentional conflicts, increase

609 brain-wide theta synchronization.

610 For interpreting the current findings, it is important to consider the feasibility of dissociating

611 attentional and motor processes during conflict processing. In almost all behavioural tasks,

612 including the current one, sensory and motor interference are, to a certain extent, intertwined.

613 Since sensory distractors attract attention, suppressing distractors can entail to suppress

614 automatic eye movements towards the distractor location. Thus, attentional conflicts can

615 increase the need for adaptive motor control (Chelazzi et al., 2019; Gaspelin & Luck, 2018b).

616 Conversely, motor conflicts most likely necessitate a speedy redirection of attentional

617 resources to ensure the implementation of the correct motor response. Thus, motor conflicts

618 can likewise increase the need for attentional control (Elchlepp et al., 2016; Mackie et al.,

619 2013). The finding that both attentional and motor conflicts lead to decreases in alpha power,

620 is consistent with the view that both attentional and motor conflicts evoked increased

621 attentional control. Accordingly, our experimental manipulation should not be viewed as a

26 Neural Dynamics during Attentional and Motor Conflicts 27

622 complete separation of attentional and motor interference. However, given the inherent

623 interdependence of attentional and motor adjustments, it seems striking that our results do

624 show clear differences between attentional and motor conflicts with respect to midfrontal

625 theta power and connectivity. This suggests that the current approach allows for a at least

626 partial dissociation of motor-specific and attention-specific midfrontal control processes,

627 beyond what was previously described in the literature.

628 The present study focussed on reactive control during sensory and motor interference.

629 Accordingly, our results cannot rule out that increases in midfrontal theta power can also

630 occur in other behavioural task contexts (e.g., Hsieh & Ranganath, 2014; Kaiser et al., 2020;

631 Riddle et al., 2020). Recent studies have found evidence that different neural generators

632 project theta activity on the midfrontal cortex (Töllner et al., 2017; Zuure et al., 2020). Thus,

633 rather than a unitary neural mechanism, midfrontal theta activity could reflect different

634 underlying neural processes depending on the task context.

635 With respect to the implementation of cognitive control, theta oscillations represent one of

636 the most frequent discussed neural phenomena. Previous studies tended to conceptualize

637 midfrontal theta oscillations as a ‘lingua franca’ of cognitive control, meaning part of a

638 general, domain-independent mechanism for conflict resolution (Cavanagh et al., 2012;

639 Cavanagh & Frank, 2014; Duprez et al., 2019; Kaiser et al., 2019). In contrast, our results

640 show that midfrontal theta oscillations during reactive control are not independent of the

641 source of interference, but more specific to motor-related adjustments. This represents a

642 novel interpretation of midfrontal theta oscillations during reactive control, by demonstrating

643 domain-specific effects of theta power and connectivity. The further dissociation between

644 general and domain-specific neural effects during different types of behavioural interferences

645 is an important step in understanding the neural implementation of cognitive control.

646

27 Neural Dynamics during Attentional and Motor Conflicts 28

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889

37 Neural Dynamics during Attentional and Motor Conflicts 38

890 Figure 1

891 Schematic Representation of Study Design

892

893 Note. Figure shows examples for stimulus displays, either with or without salient singleton

894 distractor (here: red square). Participants had to locate the unique shape (here: circle) and

895 respond according to the position of the dot within the target shape.

896

897 Figure 2

898 Condition-Wise Topographical Plots and Time-Frequency Heat Maps

899

900 Note. Topographical Plots show average baseline-corrected power in the theta range (4 – 7

901 Hz) 0.4 – 0.8 s after stimulus onset. Heatmaps show average baseline-corrected oscillatory

902 power over the whole frequency spectrum analysed for the full trial interval at the electrodes

903 FCz/FC1/FC2 (marked with a star in the topographical plots) relative to stimulus onset.

904

905 Figure 3

906 Average Error Rates and Reaction Times

907

908 Note. Grey lines show changes in average performance between conditions for individual

909 participants.

910

911

912

913

914

38 Neural Dynamics during Attentional and Motor Conflicts 39

915 Figure 4

916 Main and Interaction Effects of Attentional and Motor Conflicts on Midfrontal Power 917

918 Note. Heatmaps show main and interaction effects on midfrontal power (averaged over

919 electrodes FCz/FC1/FC2). Marked areas show clusters of significant differences based on

920 permutation analysis (p < .05). Vertical dashed line for response-centred data indicates time

921 of motor response.

922

923 Figure 5

924 Condition-Wise Contrasts in Oscillatory Power between different Conflict Types 925

926 Note. Heatmaps show differences in midfrontal power (averaged over electrodes

927 FCz/FC1/FC2). Marked areas show clusters of significant differences based on permutation

928 analysis (p < .05). Vertical dashed line for response-centred data indicates time of motor

929 response.

930

39 Neural Dynamics during Attentional and Motor Conflicts 40

931 Figure 6

932 Correlations between Midfrontal Oscillatory Power and Reaction Times 933

934 Note. Heatmaps show correlations between reaction times and midfrontal oscillatory power

935 (averaged over electrodes FCz/FC1/FC2). Marked areas show clusters of significant

936 correlations based on permutation analysis (p < .05). Vertical dashed line for response-

937 centred data indicates time of motor response.

938

939 Figure 7

940 Conflict Effects on Theta Synchronization with Midfrontal Electrodes

941

942 Note. Topographical plots show average theta phase synchronization (4-7 Hz) between

943 FCz/FC1/FC2 (marked with stars) and all other electrodes. Values for FCz are set to 0. Black

944 dots indicate electrodes belonging to significant clusters (p < .05).

945

946

40 Neural Dynamics during Attentional and Motor Conflicts 41

947 Figure 8

948 Condition-Wise Contrasts in Theta Syncronization between different Conflict Types 949

950 Note. Topographical plots show average theta phase synchronization (4-7 Hz) between

951 FCz/FC1/FC2 (marked with stars) and all other electrodes for response centred data (with 0 s

952 = motor response). Values for FCz/FC1/FC2 are set to 0. Black dots indicate electrodes

953 belonging to significant clusters (p < .05).

954

955 Figure 9

956 Syncronization Between Midfrontal and Centroparietal Electrodes 957

958 Note. Heatmaps show synchronization between midfrontal electrodes (highlighted with stars

959 in the topographical plot) and centroparietal electrodes (marked with dots). Marked areas in

960 heat maps show clusters of significant effects based on permutation analysis (p < .05).

961 Vertical dashed line indicates time of motor response.

962

963 Figure 10

964 Lateralization of Midfrontal Theta Synchronization

965

966 Note. Blue dots in topographical plots show selected electrodes for heatmaps. Marked areas

967 show clusters of significant differences based on permutation analysis (p < .05). Vertical

968 dashed line indicates time of motor response.

969

41