Proactive control without midfrontal control signals? The role of midfrontal oscillations in preparatory conflict adjustments

Biological Psychology 148 (2019) 107747

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Proactive control without midfrontal control signals? The role of midfrontal oscillations in preparatory conflict adjustments

T

Jakob Kaiser , Simone Schütz-Bosbach ⁎

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

ARTICLE INFO

ABSTRACT

Keywords: Cognitive control Conflict processing Motor inhibition Neural oscillations

Successful motor control during behavioral conflicts relies on neural adjustments that can occur reactively (i.e., after conflict occurrence) and proactively (i.e., in preparation prior to conflicts). While midfrontal delta/theta oscillations are known to play a role for reactive control, their relevance for proactive control is unclear. Using EEG, we investigated the role of midfrontal oscillations during conflict preparation in a motor conflict task, where a predictive cue either indicated no or an increased likelihood for an action conflict. During conflict preparation, increased conflict likelihood led to a proactive modulation of neural oscillations related to both motor processing (central beta) and sensory processing (posterior alpha). While midfrontal control oscillations significantly increased during conflict occurrence, increased conflict likelihood did not change midfrontal oscillatory activity during conflict preparation. This dissociation suggests that, while midfrontal oscillations are related to reactive conflict adjustments, proactive neural adjustment can be implemented without midfrontal oscillatory control.

1. Introduction Changes in the environment often necessitate changes of our behavior. This is particularly challenging when currently appropriate actions are in conflict with existing prepotent behavioral tendencies, for example when a prepotent action impulse has to be suppressed or be quickly replaced by an alternative response. Cognitive control refers to processes that help to implement neural and behavioral adjustments necessary for resolving action conflicts (Gratton, Cooper, Fabiani, Carter, & Karayanidis, 2017; Ridderinkhof, Forstmann, Wylie, Burle, & van den Wildenberg, 2011). One neural marker of cognitive control are low-frequency oscillations in midfrontal brain regions in the delta-theta (< 7 Hz) range (Cavanagh & Frank, 2014; Cohen, 2014a). Several studies found that the occurrence of behavioral conflicts leads to increases in midfrontal delta-theta power, (Cavanagh, ZambranoVazquez, & Allen, 2012; Nigbur, Ivanova, & Stürmer, 2011; Nigbur, Cohen, Ridderinkhof, & Stürmer, 2012; Töllner et al., 2017). These midfrontal signals are assumed to facilitate top-down control of ongoing sensory and motor processes, for example by increasing interconnectivity between brain areas that are in need of reconfiguration due to the occurrence of a behavioral conflict (Cavanagh & Frank, 2014; van Driel, Ridderinkhof, & Cohen, 2012). So far midfrontal oscillations have mostly been studied during tasks

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requiring reactive control, which refers to conflict resolution processes that are initiated after a conflict has been detected. For example, a stop signal indicating that an intended action has to be interrupted immediately, can trigger increased midfrontal delta-theta power (Harper, Malone, & Bernat, 2014; Mückschel, Dippel, & Beste, 2017; Nigbur et al., 2011; Yamanaka & Yamamoto, 2010). In these cases, midfrontal reactivity has been found to be stronger on trials with successful compared to unsuccessful behavioral adjustments (Wessel & Aron, 2014). This suggest that midfrontal oscillations are involved in reactive control mechanisms. Successful conflict resolution can also be facilitated by preparatory or proactive mechanisms. Proactive control refers to processes that are engaged prior to a conflict situation in order to increase the chances of identifying and overcoming upcoming conflicts (Aron, 2011; Braver, 2012). This can entail preparatory sensory adjustments, such as increased sensory attention in order to quickly detect stimuli that signal conflict situations, as well as preparatory motor adjustments, such as planning for movements that might be needed to adequately react towards conflict signals (Chang, Ide, Li, Chen, & Li, 2017; Kenemans, 2015; Langford, Krebs, Talsma, Woldorff, & Boehler, 2016; Liebrand, Pein, Tzvi, & Krämer, 2017; Muralidharan, Yu, Cohen, & Aron, 2019). Some previous lines of research suggest an involvement of midfrontal oscillations in proactive control processes. First, it has been

Corresponding author at: Leopoldstr. 13, D-80802 Munich, Germany. E-mail address: [email protected] (J. Kaiser).

https://doi.org/10.1016/j.biopsycho.2019.107747 Received 7 January 2019; Received in revised form 26 August 2019; Accepted 26 August 2019 Available online 27 August 2019 0301-0511/ © 2019 Elsevier B.V. All rights reserved.

Biological Psychology 148 (2019) 107747

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found that the midfrontal control signal is related to post-error adjustments in cognitive conflict tasks. Eliciting an incorrect response can lead to increased midfrontal oscillations after the action has been performed (Luu & Tucker, 2001; Luu, Tucker, Derryberry, Reed, & Poulsen, 2003; Luu, Tucker, & Makeig, 2004). Importantly, the strength of midfrontal post-error reactions can be partially predictive of improved performance on the next trial (Cohen & Van Gaal, 2013; Debener, 2005; Gehring, Goss, Coles, Meyer, & Donchin, 1993; Luft, Nolte, & Bhattacharya, 2013; Valadez & Simons, 2018; van Driel et al., 2012). Thus, midfrontal oscillations in this case might facilitate proactive neural and behavioral changes prior to the next trial after an error has indicated that current behavior is not sufficient for task success. Proactive midfrontal oscillations have also been found in cued task switching experiments, where a cue prior to each trial indicated if the upcoming trial belonged to the same or a different type of task than the previous one. A cue indicating a task switch compared to one indicating the repetition of same task type has been shown to lead to increased midfrontal delta/theta (Cooper, Wong, McKewen, Michie, & Karayanidis, 2017; Cunillera et al., 2012). This supports the notion that midfrontal oscillations are involved in adaptive changes already prior to task onset. However, proactive control can potentially refer to a wide range of different cognitive and neural mechanisms (Braver, 2012; Ridderinkhof et al., 2011) and it still remains unclear, whether midfrontal control signals are generally necessary for proactive control adjustments. In everyday life we are often confronted with situations, in which we do not know in advance if a behavioral conflict will arise, and, thus, if a change in our behavioral routine will be necessary or not. For example, even though a traffic sign nearby a school might warn drivers of the increased likelihood that children might run on the street, the actual probability of such an event occurring may still be relatively low. This poses a challenge for the preparation of our actions, since we need to prepare for successful conflict resolution (e.g., being able to hit the brakes in time), while still allowing for the performance of the originally intended action (e.g., driving towards the intended destination) if no conflict occurs after all. Thus, the role of proactive processes in these cases is to allow for a gradual adjustment of ongoing processes aimed at facilitating potential conflict resolution, while taking into account the uncertainty that either the current or a new behavior will be needed. In contrast to proactive adjustments that would have to occur during the preparation for uncertain situations, many previous studies finding proactive midfrontal changes tested scenarios where already during the preparatory phase participants had complete certainty that a behavioral change would be necessary. For task switching experiments, a cue predicting a different task for the upcoming trial allows adjustments specific to the upcoming task already to take place during the preparation, such as the activation of rules and behavioral patterns relevant to the upcoming trial (Cooper et al., 2015, 2017). Concerning post-error adjustments, error feedback could play a similar role in signaling that the current tasks strategy is insufficient and needs to be changed for the next trial in order to ensure task success. Thus, midfrontal oscillations in these cases could be interpreted as enabling immediate changes of ongoing behavioral and cognitive processes, which can take place prior to action onset, since it is clear already during the preparatory phase that past behavior is not appropriate anymore. In order to understand how proactive cognitive control is implemented on a neural level, it would be important to know if midfrontal oscillations are also involved in situations, where neural adjustments happen due to increased conflict likelihood, but without prior certainty if a change in behavior will be necessary or not. The current study investigated this question by using a cued motor conflict task (Liebrand et al., 2017; Liebrand, Kristek, Tzvi, & Krämer, 2018). In order to elicit a behavioral conflict, participants repeatedly performed a frequent (prepotent) action, which occasionally had to be replaced by an alternative (conflict) response. Since the repeated performance of the frequent action induces an automatic action tendency,

successfully enacting the alternative response creates a behavioral conflict, and thus necessitates increased cognitive control (Wessel, 2017). Importantly, each action was preceded by a cue which either indicated full certainty that no conflict response would be needed (certain-cues, 0% chance of conflict), or signaled that a conflict response might be possible (maybe-cues, 25% chance of conflict). Thus, compared to certain-cues, maybe-cues would signal to participants the need for proactive cognitive adjustments in order to successfully react to potential conflict situations, without allowing to know precisely if a change in motor behavior would be necessary or not. This allowed us to test whether a predictive cue indicating increased likelihood of a conflict also elicits increased midfrontal low-frequency oscillations already before the actual conflict occurred. Increased midfrontal delta-theta activity for maybe-cues compared to certain-cues already prior to the onset of the action, would support the view that midfrontal delta-theta oscillations play a general role in proactive neural adjustments during conflict preparations. While the focus of our study was mainly the role of midfrontal delta/theta oscillations, we also measured proactive oscillatory changes related to sensory processing (posterior alpha oscillations) and motor preparation (central beta oscillations). This allowed us to assess whether the conflict cues used in the current study were effective in eliciting proactive neural adjustments, as well as help to clarify in how far conflict preparation modulates neural reactivity related to sensory and motor processing. Concerning sensory processing, successful conflict resolution depends on increased attention towards task-relevant stimuli, for example to quickly identify unexpected task stimuli (Langford et al., 2016). On a neural level, increased visual attention has been found to be related to a decrease in posterior activity in the alpha range (8–12 Hz; Bonnefond & Jensen, 2012; Sadaghiani & Kleinschmidt, 2016). This so-called alpha suppression could signal increased sensitivity towards relevant stimuli or a stronger suppression of potential distractors (Foster & Awh, 2019). Accordingly, one could expect that a cue indicating increased conflict likelihood would lead to stronger preparatory alpha suppression. Concerning motor processing, behavioral conflicts often necessitate an adjustment or outright suppression of an intended motor movement. One established oscillatory measure of motor activation is beta activity (15–25 Hz) over the motor cortex (Crone, Miglioretti, Gordon, & Lesser, 1998; Pfurtscheller, Neuper, Andrew, & Edlinger, 1997). Stronger suppression of central beta oscillations contralateral to the acting hand has been repeatedly found prior and during motor actions (Cheyne, 2013; Kilavik, Zaepffel, Brovelli, MacKay, & Riehle, 2013; Pfurtscheller & Berghold, 1989; Tzagarakis, West, & Pellizzer, 2015). While the strength of preparatory beta decreases is predictive of an increased likelihood that an action will be elicited (Donner, Siegel, Fries, & Engel, 2009), the expectation of having to inhibit a planned action can lead to a preparatory increase in beta activity (Muralidharan et al., 2019). Accordingly, a cue indicating increased likelihood of a motor conflict should lead to proactive modulation of lateralized central beta power. Different types of behavioral conflicts necessitate different types of behavioral adjustments. For example, previous research often distinguished between action inhibition, the outright stopping of a prepotent action impulse, and action switching, the sudden change of a prepotent action impulse towards an alternative response (Boecker, Gauggel, & Drueke, 2013; Mostofsky & Simmonds, 2008). However, so far it is not clear in how far these different types of conflict responses rely on the same, task-independent or different, task-specific neural mechanisms for resolution (Rae, Hughes, Weaver, Anderson, & Rowe, 2014; Wessel & Aron, 2017). Therefore, one additional goal of this study was the comparison of proactive processes between motor inhibition and motor switching. All participants performed two versions of a cued motor conflict task, which only differed in the type of the necessary conflict response. For both action inhibition and switching, the frequent/prepotent action was operationalized as a single button press. For action inhibition, conflict trials necessitated an immediate 2

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suppression of any button press response (i.e. a classical Go/NoGo design). For action switching, conflict trials prompted participants to press a second, alternative button instead (Liebrand et al., 2018). This allowed us to test, if inhibition and switching conflicts differ in their proactive neural activity changes associated with either midfrontal topdown control (midfrontal delta-theta) or adjustments in sensory (posterior alpha) or motor processes (central beta). To summarize, previous studies found midfrontal delta-theta oscillations already prior to the occurrence of control conflicts, suggesting they play a role in the top-down enactment of proactive adjustments of neural processes for conflict resolution. While these studies mostly investigated situations where prior cues specified the kind of behavioral change that had to be implemented, the current study investigated situations where participants were not informed in advance if a prepotent response tendency had to be adjusted but only if the occurrence of a behavioral conflict was more or less likely. By measuring EEG, we could assess if midfrontal delta-theta oscillations would be proactively increased prior to action onset for trials where the occurrence of a conflict was more likely. We also measured previously established oscillatory measures of visual attention (posterior alpha suppression) and motor activation (central beta suppression), since this allowed us to verify that our cue manipulation led to proactive changes in sensory and motor processes. We compared two important types of motor conflicts, action inhibition and action switching, in order to see if different types of potential behavioral conflicts would necessitate different, task-specific proactive adjustments, or if proactive oscillatory proactive processes where indicative of task-independent, general control processes.

performed. The other half of the trials started with a maybe-cue, indicating that there was a 75% chance for a prepotent action, but 25% chance that the conflict action would have to be enacted. Thus, the maybe-cue prompted participants to prepare for a possible behavioral conflict. This design resulted in three types of trials: certain-prepotent trials (prepotent actions following a certain-cue), maybe-prepotent trials (prepotent actions following an maybe-cue), and maybe-conflict trials (conflict actions following an maybe-cue). The experiment consisted of two tasks, inhibition and switching, which only differed in the type of conflict action. On prepotent trials in both tasks, participants had to press the down-arrow button with their right index finger (Go-action). For the inhibition task, conflict trials prompted participants to withhold any button press (No-Go action). For the switching task, conflict trials prompted participants to pressing the left-arrow button instead of the down-arrow button with their right index finger (Switch-Go action). Thus, conflict actions in the switching task differed from prepotent actions only with respect to the location of the button on the computer keyboard, while the effector (right index finger) remained the same. The inhibition and switching tasks were presented in two separate blocks. The order of the two tasks was counterbalanced between participants. Each task consisted of 800 trials: 400 certain-prepotent trials, 300 maybe-prepotent trials and 100 maybe-conflict trials. All three types of trials were presented in randomized order. Before each task, participants completed 16 test trials which were repeated in case task instructions were not followed correctly. Each task was subdivided into 8 blocks. After each block participants received feedback about their mean reaction times and error rates. For every trial, the predictive cue was shown for 100 ms, followed by a blank screen for 1000 ms. After this preparatory phase, the action signal was presented for 100 ms. Starting with the onset of the action signal participants had 500 ms to react. A warning message was shown for 500 ms if participants reacted incorrectly towards the action signal or already pressed a button before the action signal was shown. Before the start of the next trial, a blank screen was shown for a random duration between 1500 and 2000 ms.

2. Method 2.1. Participants Participants were 33 students taking part for course credit or financial reimbursement. Four participants were excluded because the number of retainable trials was too low due to either a high number of behavioral errors or EEG noise artefacts (see below). This resulted in a sample of 29 students (18 female) with a mean age of 25.2 (SD = 3.9).

2.4. Data preprocessing

2.2. Apparatus and measurement setup

The recorded EEG data was filtered (high-pass: 0.75 Hz, low-pass: 100 Hz) and referenced to an average of all electrodes. For seven participants, 1–3 exceedingly noisy electrodes were excluded. The trial data was cut into epochs from −2600 ms to +1500 ms around the onset of the action signal. The epochs were chosen to be longer than the time window of interest (see below), in order to avoid edge artifacts during the time-frequency calculation. Independent component analysis was used to identify components represent eye blinks or other noise artefacts clearly unrelated to brain activity, leading to a mean removal of 2.9 (SD = 1.5) components. Electrodes which were previously excluded were replaced after component removal with spherical interpolations via the EEGLAB function pop_interp(). Trials with a deflection exceeding +−90 μV were deleted (mean = 9.37%, SD = 8.75%). Additionally, trials were removed if participants pressed a button before the onset of the action signal or responded incorrectly towards the signal. Participants were excluded if they did not retain at least 50 trials in every condition of each task after exclusions. Overall, the mean rate of trials retained per participant and task was 346.6 (SD = 38.8) for the certain-prepotent condition, 264.2 (SD = 28.5) for the maybe-prepotent condition, and 79.8 (SD = 11.1) for the maybeconflict condition. Oscillatory power was calculated in Fieldtrip (Oostenveld, Fries, Maris, & Schoffelen, 2011). In order to reduce volume conduction effects in the EEG signal, a surface Laplacian filter was applied to the signal via the Fieldtrip function ft_scalpcurrentdensity(), using the spline method with a Legendre polynomial degree of 10. Time-frequency power was calculated for a time window from −1500 ms to +700 ms

The experimental display was shown on a 24-inch monitor with a distance of approximately 90 cm from the participants. Stimuli for action signals were three white shapes (square, circle, triangle) which were randomly assigned towards the three possible actions (Go, No-Go, Switch-Go). Stimuli for the two predictive cues (certain/maybe) were a brown or blue cross, with a counterbalanced assignment between colour and cue type across participants. All stimuli were presented with a visual angle of 0.8° in the middle of the screen on a grey background. EEG was recorded with 65 active electrode and one additional ground electrode using a BrainVision QuickAmp amplifier. Electrodes were positioned in accordance with international 10–20 system, with the FCz electrode functioning as an online reference. The signal was recorded with a 500 Hz sampling rate and a 0.016 Hz–250 Hz bandpass filter. 2.3. Procedure Fig. 1 shows a schematic overview of the experimental task. On every trial participants had to react towards an action signal. On a majority of trials, the action signal indicated the same action (prepotent trials). Occasionally a different action signal prompted a response which diverged from the prepotent action, and thus induced a behavioral conflict between the prepotent action tendency and the less-frequent conflict response. Before the action signal, a predictive cue was presented: On half of the trials a certain-cue was shown, indicating that there was a 100% chance that the prepotent action would have to be 3

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Fig. 1. Schematic representation of the experimental design. Percentages show the likelihood of different types of trials. The inhibition and switching task were presented in separate blocks.

relative to the action signal onset. Frequency estimation was performed with Morlet-Wavelets from 2 to 40 Hz in 1-Hertz steps with a linearly spaced cycle length from 3 to 10 cycles, using the function ft_freqanalysis(). Gradually increasing the number of cycles from low to high frequency bands leads to better comparability between oscillatory changes of different frequencies with respect to their temporal precision, since the duration of one oscillatory cycle is shorter for higher frequencies than for lower frequencies (Cohen, 2014b, Chapter 13). Average power between 300 and 100 ms prior to trial onset were used to baseline-correct power values via decibel conversion (dB values = 10 *log10[power/baseline]).

2.6. Statistical analysis In order to determine the time intervals for our main analysis, we first employed a statistical procedure to identify those time points at which the predictive cue led to differences in oscillatory activity. This procedure was conducted for each conflict task (inhibition/switching) separately, in order to allow for the possibility that either task might show a different pattern of effects. For each dependent measure, we extracted participants’ average activity starting from the preparatory cue up to the end of the action phase (−1.1 to 0.7 s relative to the action onset) in 40-ms averaged intervals, resulting in 45 time bins. Since our main interest was to identify potential effects of the predictive cues, we compared the activity between maybe-cue trials and certaincue trials with two-sided t-tests for each time point. First, we identified all time bins with statistically significant t-values. Importantly, we only retained significant bins, if at least one directly adjacent time point showed a significant t-value in the same direction (i.e., positive or negative t-values) as well. This additional criterion decreases the likelihood of Type-1 errors, since it is less likely to obtain two false positives in directly adjacent time bins by chance. We adjusted the critical p-value based on the adjacency criterion and the number of time bins according to the formula pcrit = alpha level/number of times bins as previously used (cf. Van der Lubbe, Bundt, & Abrahamse, 2014 for a formal discussion of this approach; s. also Talsma, Wijers, Klaver, & Mulder, 2001; Van der Lubbe, de Kleine, & Rataj, 2019). For the current study this led to a critical p value of p = .0236 for individual time bins. Using this approach, we identified time intervals consisting of adjacent data points with significant cue-related changes for every dependent measure. For the main analysis, activity of each identified time interval was averaged separately for the preparation phase, i.e., after the cue and prior to the action signal, and the action phase after the action signal onset. In some cases, the identified time windows stretched over both the preparation and action phase. Since it is crucial for our interpretation to distinguish proactive from reactive neural changes, we decided in these cases to only include time bins up to 100 ms prior to action onset for the average values of the preparation phase, and only time bins starting at least 100 ms after the action onset for the averages of the action phase. While not imposing this restriction led to essentially the same pattern of results, restricting the time windows in this manner allows for a clearer dissociation of proactive and reactive neural changes. For each dependent measure, separate ANOVAs were performed for the preparation phase and the action phase. For the preparation phase, the ANOVA factors were TASK (inhibition/switching) and CUE TYPE (certain/maybe). For the main phase, the factors were TASK (inhibition/switching) and TRIAL TYPE (certain-prepotent /maybe-prepotent/maybe-conflict). In cases where violations of sphericity were found, Greenhouse Geiser corrections for ANOVAs were applied, with corrected p-values and original degrees of freedom being reported. Bonferroni-correction was applied for p-values of post-hoc comparisons.

2.5. Selection of regions of interests Analysis of the EEG data was focused on three aspects of oscillatory reactions: posterior alpha suppression as a measure of visual attention (8–12 Hz at electrodes PO7/PO8/PO3/PO4/O1/O2), left-lateralized central beta suppression as an indicator of right-hand motor activation (15–30 Hz at CP3/CP1/C3/C1), and midfrontal delta/theta as a measure of frontal control processes (2–7 Hz at FCz/Cz/FC1/FC2). The choice of frequency bands and regions of interest was based on previous literature. For sensor-based analysis of midfrontal oscillations, most previous studies relied on sensors around the FCz electrode since it commonly shows peak activation in the delta-theta range for behavioral conflicts (e.g. Cavanagh et al., 2012; Chang et al., 2017; Cohen & Donner, 2013; Nigbur et al., 2011; Vissers, Ridderinkhof, Cohen, & Slagter, 2018). In a similar vein, our electrode selection for posterior alpha and central beta oscillations matches closely the choice of previous studies concerning visual processing and motor activation (see for example for posterior alpha: de Vries, van Driel, & Olivers, 2017; Krämer et al., 2013; Liebrand et al., 2017; for central beta: Fischer, Nigbur, Klein, Danielmeier, & Ullsperger, 2018; Burle, van den Wildenberg, Spieser, & Ridderinkhof, 2016; Picazio et al., 2014). Inspection of time-frequency plots (Fig. 2) as well as topographical plots (Fig. 3–5) confirmed that each chosen site showed a marked modulation of the frequency bands of interest during the action phase. Note that activity in any frequency band is usually not confined to one specific site only, for example due to propagation of oscillatory waves across the cortex. Importantly, the left-lateralized central sensors showed a strong decrease in activity of both alpha and beta oscillations. This is in line with previous research showing that motor activation leads to suppression within both the beta and alpha frequency range contralateral to the acting limb (Cheyne, 2013; Pfurtscheller et al., 1997). However, previous research indicates that, compared to alpha oscillations in the lateral-central area, the beta band is more sensitive to motor-related processes (Tzagarakis et al., 2015). Therefore, we chose to focus our analysis of lateralized central activity on the beta range, but report results for lateralized central alpha activity as well for comparison. 4

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Fig. 2. Time-Frequency maps for a) posterior, b) contralateral central, and c) midfrontal electrodes. Selected electrodes for each region of interest are marked with blue stars on the topographical schematics on the right-hand side of each time-frequency map. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

We also report Bayes factors (BF), which quantify the evidence for the alternative hypothesis (the existence of differences between conditions) versus evidence for the null hypothesis (no condition differences). Bayes factors estimate the ratio of evidence for the alternative hypothesis relative to evidence for the null hypothesis. Thus, BF < 1

favor the null hypothesis, BF > 1 favor the alternative hypothesis, with values close to 1 indicating that there is no conclusive evidence in either direction (Jarosz & Wiley, 2014; Van de Schoot et al., 2014). While the interpretation of Bayes factors is not principally bound to a specific cutoff value, it has been suggested that BF > 3 could be interpreted as 5

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Fig. 3. Changes in posterior alpha oscillations (8–12 Hz at PO7/PO8/PO3/PO4/O1/O2) depending on trial type for both inhibition and switching. a) Time course over the whole trial starting with cue onset at -1.1. Dashed line indicates onset of the action signal. Shaded areas around condition lines show standard errors adjusted for within-subject designs (Cousineau, 2005). Grey areas indicate time intervals of interest for preparation and action phase. b) Topographical distributions of oscillatory activity separately for both marked areas representing preparation and action phase.

strong evidence for the alternative hypothesis, while BF < .33 could be seen as strong evidence for the null hypothesis (cf. Jarosz & Wiley, 2014). All statistical tests were calculated in R using the packages ezANOVA, effsize and BayesFactor.

in task demand. Mean reaction times are shown in Table 1. Since for inhibition no reaction times exists for the maybe-conflict trials (as no action is performed here), reaction time data was analyzed separately for the switching and inhibition task. For switching, we found a main effect of TRIAL TYPE, F(2, 56) = 238.9, p < .001, p2 = 0.90, BF > 106. Maybe-conflict trials lead to significantly increased reaction times compared to both maybe-prepotent trials, t(28) = 11.60, p < .001, d = 2.15, BF > 106, and certain-prepotent trials, t(28) = 20.25, p < .001, d = 3.76, BF > 106. Additionally, maybe-prepotent compared to certain-prepotent trials lead to significantly slower reactions, t (28) = 11.41, p < .001, d = 2.12, BF > 106. For inhibition, maybeprepotent compared to certain-prepotent trials lead to significantly slower reactions, t(28) = 10. 8, p < .001, d = 2.01, BF > 106. Thus, in both tasks the presence of the cue influenced participants’ behavior even when no conflict occurred, with maybe- compared to certain-cues slowing down reaction times.

3. Results 3.1. Behavioral results Mean error rates are shown in Table 1. The ANOVA of error rates revealed a significant main effect of TRIAL TYPE, F(2, 56) = 110.1, p < .001, p2 = 0.80, BF > 106, but no significant effect of TASK, F (1,28) = 0.2, p = .63,

2 p

= 0.008, BF = 0.16, and no TRIAL TYPE *

TASK interaction, F(2,56) = 0.1, p = .82, p2 < 0.001, BF = 0.10. For both inhibition and switching, maybe-conflict trials lead to an increased number of errors compared to maybe-prepotent trials, inhibition: t (28) = 7.36, p < .001, d = 1.37, BF > 106; switching: t(28) = 8.50, p < .001, d = 1.58, BF > 106, as well as compared to certain-prepotent trials, inhibition: t(28) = 7.57, p < .001, d = 1.41, BF > 106; switching: t(28) = 9.75, p < .001, d = 1.81, BF > 106. Additionally, for both inhibition and switching error rates between maybe-prepotent and certain-prepotent trials were not significantly different; inhibition: t (28) = 2.12, p = .13, d = 0.39, BF = 1.37; switching: t(28) = 2.21, p = .11, d = 0.41, BF = 1.60. Thus, for both tasks errors were more likely for conflict trials. The absence of a TASK main effect or interaction indicates that inhibition and switching did not significantly differ

3.2. Posterior alpha Time plots of posterior alpha are shown in Fig. 3. Note that increased alpha suppression, meaning lower alpha levels, is commonly interpreted as a measure of increased visual activity (Clayton, Yeung, & Cohen Kadosh, 2017). For both tasks, we found time windows with significant effects of the cue manipulation starting in the preparation phase and extending up to the end of the action phase (inhibition: -0.38 6

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Fig. 4. Changes in lateralized central beta oscillations (15–25 Hz at CP3/CP1/C3/C1) depending on trial type for both inhibition and switching. a) Time course over the whole trial starting with cue onset at -1.1. Dashed line indicates onset of the action signal. Shaded areas around condition lines show standard errors. Grey area indicates time interval of interest for the preparation phase. For the action phase, +-200 ms around condition-wise minima after the action signal were extracted for topographical plots to account for temporal differences in peak activity between conditions. b) Topographical distributions of oscillatory activity separately for preparation and action phase.

– 0.7 s; switching: -0.34 – 0.7 s) For the preparation phase, posterior alpha showed a significant main effect of CUE TYPE, F(1, 28) = 17.60, p < .001, p2 = 0.39, BF = 380.59, but no significant main effect of TASK, F(1, 28) = 0.53, p = .47, p2 = 0.02, BF = 0.27, and no CUE TYPE*TASK interaction, F

stronger alpha suppression in both task, inhibition: t(28) = −6.89, p < .001, d = 1.28, BF = 89,296.37, switching: t(28) = −5.37, p < .001, d = 1.00, BF = 2127.78. To conclude, the maybe-cue compared to the certain-cue lead to stronger alpha suppression already before the action signal onset, suggesting increased visual attention in anticipation of a potential behavioral conflict. Additionally, action signals following an maybe-cue lead to increased alpha suppression, with signals indicating the need for a conflict action inducing the strongest changes in posterior alpha.

(1, 28) = 0.47, p = .50, p2 = 0.02, BF = 0.26. Thus, inhibition and switching did not differ significantly in their preparatory effects on posterior alpha. Alpha suppression during the preparation phase was significantly stronger after the maybe-cue compared with the certaincue for both inhibition, t(28) = -3.98, p < .001, d = 0.74, BF = 69.19, and switching, t(28) = -3.44, p = .002, d = 0.64, BF = 19.59. For the action phase, posterior alpha showed a significant effect of TRIAL TYPE, F(2,56) = 78.87, p < .001, p2 = 0.74, BF > 106, no

3.3. Lateralized central beta Fig. 4 shows time plots of lateralized central beta. Note that increased beta suppression i.e. lower beta levels, are usually seen as an indicator of increased motor activation (Alegre et al., 2003). Time regions with significant effects of the cue manipulation differed between inhibition and switching (cf. Fig. 4). For switching, one time window emerged stretching from the preparatory phase to the end of the action phase (−0.62 – 0.70 s). For inhibition, two separate time regions with significant effects emerged, one during the early preparation phase (−0.70 s – −0.38 s), and one at the end of the action phase (0.42 s – 0.7 s). However, as can be seen from the time plot, the second time range is indicative of faster rebounds of beta deflections after actions in the certain-prepotent trials, reflecting the faster reaction times in this condition (cf. Behavioral results). Thus, since the temporal

main effect of TASK, F(1,28) = 0.01, p = .91, p2 < 0.001, BF = 0.17, and only a marginal TRIAL TYPE*TASK interaction, F(2,56) = 2.77, p = .09, p2 = 0.09, BF = 0.25. As in the preparation phase, inhibition and switching after action signal did not differ significantly in their alpha power. For both tasks, alpha suppression was stronger for maybe-conflict compared to maybeprepotent trials, inhibition, t(28) = -8.59, p < .001, d = 1.60, BF > 106, switching: t(28) = −8.53, p < .001, d = 1.58, BF > 106, as well as maybe-conflict compared to certain-prepotent trials, inhibition: t(28) = −9.74, p < .001, d = 1.81, BF > 106, switching: t(28) = −7.68, p < .001, d = 1.43, BF = BF > 106. Additionally, maybeprepotent compared to certain-prepotent trials showed significantly

7

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Fig. 5. Changes in midfrontal delta-theta oscillations (2–7 Hz at FCz/Cz/FC1/FC2) depending on trial type for both inhibition and switching. a) Time course over the whole trial starting with cue onset at -1.1. Dashed line indicates onset of the action signal. Shaded areas around condition lines show standard errors. Grey area indicates time interval of interest for the preparation phase. For the action phase, +-200 ms around condition-wise maxima after the action signal were extracted for topographical plots to account for temporal differences in peak activity between conditions. b) Topographical distributions of oscillatory activity separately for preparation and action phase.

p < .001, d = 0.75, BF = 79.13. For switching, the maybe compared to the certain-cue lead to significantly more beta suppression, t(28) = −4.47, p < .001, d = 0.83, BF = 226.96. During the action phase, switching showed stronger beta suppression for maybe-conflict compared to both maybe-prepotent trials, t(28) = −7.30, p < .001, d = 1.36, BF > 106, and certain-prepotent trials, t(28) = −7.47, p < .001, d = 1.39, BF > 106. Maybe-prepotent trials lead stronger beta suppression than certain-prepotent trials, t(28) = −4.98, p < .001, d = 0.93, BF = 804.55. For inhibition, our comparison of individual time bins between conditions had shown no evidence for significant differences in beta suppression directly during action execution. However, the earlier beta rebound in the certainprepotent trials leaves open the possibility that differences in timing between the conditions obscure differences in maximum beta suppression. In order to explore this possibility, we calculated for each participant the condition-wise minimum of beta power during the action phase and extracted a +-200 ms interval around the peak as a measure of maximum beta suppression. Thus, the resulting values in this case are not influenced by differences in timing between conditions. Comparison of the peak beta deflections lead to the same pattern of results as before for the switching task. For inhibition, maybe-conflict trials showed marginally significantly weaker beta suppression than maybe-prepotent trials, t(28) = 2.25, p = .098, d = 0.42, BF = 1.71, and no significant difference compared to certain-prepotent trials, t(28) = 0.79, p = .44, d = 0.15, BF = 0.26. Maybe-prepotent compared to certain-prepotent

Table 1 Mean error rates in percentages and mean reaction times in ms (with standard deviations in brackets) for all conditions. Error Rates

Reaction Times

Trial Type

Trial Type

Task

maybeconflict

maybeprepotent

certainprepotent

maybeconflict

maybeprepotent

certainprepotent

Inhibition

11.5 % (7.9) 12.0 % (8.4)

2.2 % (4.3) 2.4 % (2.9)

1.7 % (3.6) 1.7 % (3.4)

–

340.9 (27.5) 368.0 (30.2)

241.1 (65.1) 253.2 (68.4)

Switching

458.5 (42.1)

differences in this case do not reflect actual differences in the strength of beta suppression during action execution, this second time window was not analyzed further. Comparison between inhibition and switching for the preparation phase showed no significant effect of CUE TYPE, F(1,28) = 2.95, p = .10, p2 = 0.10, BF = 0.32, no significant effect of TASK, F (1,28) = 1.89, p = .18,

2 p

= 0.06, BF = 1.02, but a significant CUE

TYPE * TASK interaction, F(1,28) = 37.89, p < .001, p2 = 0.58, BF = 65.79. This indicates that inhibition and switching differed in their preparatory beta response: For inhibition, the maybe- compared to the certain-cue lead to significantly less beta suppression, t(28) = 4.04, 8

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trials showed significantly stronger beta suppression, t(28) = −2.69, p = .01, d = 0.50, BF = 3.94. Overall, there was no strong evidence for significant changes in beta power after the occurrence of an inhibitory conflict. To conclude, the maybe-cue influenced beta suppression for both tasks already during preparation, leading to less proactive beta suppression for inhibition and more proactive beta suppression for switching. Only for switching, we found conflict actions compared to prepotent actions lead to a significant increase in beta suppression during action execution, probably indicating increased motor activation for sudden motor changes.

action phase, showed a significant effect of TRIAL TYPE, F (2,56) = 75.46, p < .001, p2 = 0.76, BF > 106, no significant main

effect of TASK, F(1,28) = 0.12, p = .73, p2 = 0.004, BF = 0.17, and a significant TRIAL TYPE*TASK interaction F(2,56) = 44.70, p < .001, 6 2 p = 0.61, BF > 10 . For both tasks, midfrontal delta-theta increases were stronger for maybe-conflict compared to maybe-prepotent trials, inhibition: t(28) = 10.13, p < .001, d = 1.88, BF > 106, switching: t (28) = 2.84, p = .03, d = 0.53, BF = 5.33, as well as for maybe-conflict compared to certain-prepotent trials, inhibition: t(28) = 11.62, p < .001, d = 2.16, BF > 106, switching: t(28) = 7.15, p < .001, d = 1.33, BF > 106. Maybe-prepotent compared to certain-prepotent trials showed a significantly stronger midfrontal oscillation for inhibition, t(28) = 2.75, p = 0.03, d = 0.51, BF = 4.46, and a significantly stronger increase for switching, t(28) = 5.69, p < .001, d = 1.06, BF = 4639.81. We tested if the differences in timing of the oscillatory responses between the conditions (cf. Fig. 5) might have influenced the analysis of conflict-induced increases by calculating for each participant the condition-wise maximum deflection during the action phase and extracting a +-200 ms interval around this peak. Analysis of this timing-independent measure of power increases led to the same results as reported above, with the exception that the difference between maybe-prepotent and certain-prepotent trials were not significant for inhibition, t(28) = -0.70, p > .90, d = 0.13, BF = 0.25, and only marginally significant for switching: t(28) = 2.32, p = .08, d = 0.43, BF = 1.94. This indicates that the maybe-cue signaling the possibility of a conflict was not sufficient for inducing clearly significant midfrontal oscillatory increases during the action phase. Instead, only the occurrence of an actual conflict lead to significantly stronger delta-theta increases during the action phase. As the TRIAL TYPE*TASK interaction indicated a difference in strength of the effect between the two tasks, we compared the conflict induced increase in midfrontal oscillations (maybe-conflict – certainprepotent) between inhibition and switching. Increases due to conflict occurrence were significantly higher for inhibition than for switching: t (28) = 7.16, p < .001, d = 1.33, BF > 106. Thus, midfrontal deltatheta responses were stronger for inhibition than for switching conflicts. To conclude, significant conflict-related increases of midfrontal oscillations were present in both tasks during the action phase. While certain-cues lead to an earlier onset of midfrontal oscillations (reflecting the earlier onset of action execution for certain-prepotent trials), maybe-cues indicating the possibility of a conflict did not lead to midfrontal oscillatory increases during the preparation phase.

3.4. Lateralized central alpha While our analysis of motor-related activity was focused on central beta oscillations, we also found a marked modulation of alpha oscillations in the same region (cf. Fig. 3). Using the same analysis parameters as for central beta activity, we tested for effects within the alpha range on lateralized central electrodes. This led to essentially the same pattern of results as for modulations of beta oscillations: For the preparation phase, maybe-cues compared to certain-cues led to weaker alpha suppression for inhibition, t(28) = 3.52, p = .002, d = 0.65, BF = 23.65, but stronger alpha suppression for switching, t(28) = −4.33, p < .001, d = 0.80, BF = 158.42. For the action phase, we again extracted +− 200 intervals around condition-wise maxima to account for systematic differences in response speed between conditions. Switching showed stronger alpha suppression for maybe-conflict compared to both maybe-prepotent trials, t(28) = −5.46, p < .001, d = 1.01, BF = 2659.65, and certain-prepotent trials, t(28) = −4.83, p < .001, d = 0.90, BF = 550.76. Maybe-prepotent trials led to only marginally stronger alpha suppression than certain-prepotent trials, t(28) = −2.29, p = .09, d = 0.42, BF = 1.83. For inhibition, no significant differences in alpha activity emerged during the action phase, all p’s > .15. Thus, both during the preparation, as well as during the action phase, lateralized central electrodes showed a similar modulation of both alpha and beta power. 3.5. Midfrontal delta/theta Fig. 5 shows the temporal development of midfrontal delta-theta activity over the trial period. For both inhibition and switching, we found two time windows with significant differences with a similar temporal distribution, with one window emerging at the end of the preparation (inhibition: −0.30 s – 0.14 s; switching: −0.30 s – 0.18 s), and a second time window emerging during the action phase (inhibition: 0.14 s – 0.7 s; switching: 0.18 s – 0.62 s). For the time region at the preparation phase, the maybe-cue compared to the certain-cue lead to less oscillatory activity in both task, inhibition: t(28) = −3.13, p = .004, d = 0.58, BF = 9.84, switching: t(28) = −2.81, p = .01, d = 0.52, BF = 5.00. This would indicate evidence for a decrease in midfrontal delta/theta for maybe compared to certain-cues, and therefore the opposite of the expected increase in midfrontal oscillations during conflict anticipation. However, as can be seen from the time plot (Fig. 5), the differences during the preparation phase are due to the earlier onset of midfrontal oscillation increases following the certaincue compared to the maybe-cue. Thus, this difference is not indicative of conflict-induced increases prior to action onset (which should have resulted in higher theta power values for maybe- compared to certaincues in this time range), but rather reflects the quicker reaction times of reactive processes enabled by certainty about the to-be-executed action (cf. Behavioral results). Testing the directed hypothesis that the maybecue compared to the certain-cue should lead to an increase in midfrontal Theta via one-sided t- tests indicated strong evidence for the absence of an effect in both tasks, inhibition: p > .90, BF = 0.055; switching: p > .90, BF = 0.059. Comparison of average midfrontal oscillatory increases during the

3.6. Differentiation between midfrontal delta and theta activity Our analysis of midfrontal oscillations entailed both the delta and theta frequency band, as conflict-related increases were observed throughout this range (Fig. 2). Since some previous studies of midfrontal control focused exclusively on the theta range, we tested if the conflict-related response differed between the delta and theta band. For this purpose, we separately extracted delta (2–3 Hz) and theta (4–7 Hz) frequency responses for the same time windows as in the main analysis. For each task, we run an ANOVA for the preparation phase with the factor CUE TYPE and FREQUENCY (delta/theta), as well as an ANOVA for the action phase with the factor TRIAL TYPE and FREQUENCY. Since this analysis mostly replicates the effects of our experimental manipulation already reported above, we here only discuss effects related to the addition of the factor FREQUENCY (delta/theta). For the preparation phase, we found a main effect of FREQUENCY for both inhibition, F(1,28) = 11.02, p = .01, p2 = 0.28, BF = 206.07, and switching, F(1,28) = 25.16, p < .001, p2 = 0.47, BF = 89,309.81, but no significant CUE TYPE*FREQUENCY interaction in either task, inhibition F(1,28) = 0.003, p = .96, p2 < 0.001, BF = 0.25, switching, F (1,28) = 0.74, p = .40, 9

2 p

= 0.03, BF = 0.28. The main effect of

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FREQUENCY indicated that, independent of cue type, delta activity was stronger than theta activity, for inhibition: mean delta activity =0.21 dB (SD = 0.55), mean theta activity: = -0.08 dB (SD = 0.59), for switching: delta =0.39 dB (SD = 0.42), theta = - 0.02 dB (SD = 0.50). Importantly, the absence of an interaction indicates that the cue had a similar effect on both delta and theta during preparation for inhibitory and switching conflicts. For the main phase, the inhibition task showed a main effect of FREQUENCY, F(1,28) = 10.45, p = 0.003, p2 = 0.27, BF = 10.86, and a significant TRIAL TYPE*FREQUENCY interaction, F(2,56) = 28.95, p < .001, p2 = 0.51, BF = 39.29. This suggests that the effect of inhibition conflicts differed between delta and theta in their magnitude. In order to compare the magnitude of conflict-induced change between the two frequency bands, we subtracted the mean activity of certainprepotent from maybe-conflict trials separately for both delta and theta activity as a measure of conflict-related increases in each frequency band. Conflict-induced increases were stronger for delta than for theta, t (28) = 6.13, p < .001, d = 1.14, BF = 14,033.13, This indicates that midfrontal delta activity was more sensitive to the occurrence of inhibition conflicts than theta conflicts. For switching, we found a marginally significant main effect of FREQUENCY, F(1,28) = 2.97, p = 0.10, p2 = 0.10, BF = 1.07, and a marginally significant TRIAL

average of the delta-theta range, the peak of experimentally induced oscillatory activity within a frequency band can differ between individuals (Haegens, Cousijn, Wallis, Harrison, & Nobre, 2014). This interindividual variability could potentially lower the sensitivity for detecting conflict-related changes in midfrontal oscillations. We therefore repeated the cluster-based permutation, but instead of averaging over the whole delta/theta range, we included only one frequency band per participant. More specifically, for each participant we choose the frequency with the maximum deflection during the action phase of the maybe-conflict trials. Since in these trials conflict-related increases in delta/theta oscillations were most pronounced, this should maximize the chance to select for each participant the frequency band most sensitive to conflict-induced changes. The mean peak frequency was 4.00 Hz (SD = 1.23) for the inhibition task and 3.66 Hz (SD = 1.18) for the switching task. Results of cluster-based permutation revealed similar clusters to the analysis of average frequency bands. Each task showed one negative cluster prior to action onset, inhibition: -0.45 – 0.17 s, (tmass = -1248.85, p < .001), switching: -0.14 – 0.22 s, (tmass = -798.21, p = .002). Additionally, each task showed one positive cluster after action onset, inhibition: 0.21 – 0.7 s, (tmass = 2048.26, p < .001), switching: 0.28 – 0.7 s, (tmass = 1190.40, p < .001). There were no other significant clusters in any of the two tasks. To conclude, analysis of participant-specific peak frequencies supports the conclusion that conflict-induced increases in midfrontal low-frequency activity only occur during the action phase, but not during conflict preparation.

TYPE*FREQUENCY interaction, F(2,56) = 3.46, p = .053, p2 = 0.11, BF = 0.30. There was no significant difference between conflict-related increases between the delta and theta band in the switching task, t(28) = -0.43, p = .67, d = 0.08, BF = 0.21. Thus, delta and theta were not differentially modulated by the occurrence of switching conflicts. Overall, our analysis suggests that conflict-related effects on midfrontal activity occurred in both the delta and theta range, with delta reactivity potentially being more sensitive to inhibition conflicts.

4. Discussion The current study employed a cued conflict task in order to investigate if the increased possibility of a motor conflict lead to a proactive increase of midfrontal low-frequency oscillations. Behaviorally, a cue predicting the possibility of a motor conflict (maybe-cues) compared to a cue indicating no conflict (certain-cues) lead to slower reaction times even when no actual conflict occurred, suggesting that an increased chance of a conflict resulted in more cautious behavior. This was accompanied by neural adjustments that arose prior to the actual action onset in both sensory and motor areas. First, the anticipation of possible conflicts led to stronger posterior alpha suppression, suggesting increases in visual attention. Second, increased likelihood of an action conflict led to changes in central beta power, indicating changes in motor preparation. The direction of conflict-related changes in motor activity depended on the type of potential conflict, with significantly less beta suppression for inhibition and stronger beta suppression for action switching. Importantly, while we observed significantly increased midfrontal delta/theta during the occurrence of an action conflict, the mere anticipation of a potential conflict did not lead to increased midfrontal delta-theta oscillations during the preparatory phase. This suggests that proactive control processes due to increased conflict likelihood is not necessarily reflected in midfrontal oscillatory low-frequency activity. Many previous studies found evidence that midfrontal delta/theta activity is involved in reactive cognitive control processes during the occurrence of a conflict (e.g. Cavanagh et al., 2012; Nigbur et al., 2011; Töllner et al., 2017). The current results are consistent with these findings: Both conflicts necessitating sudden inhibition, as well as conflicts necessitating a sudden change of intended motor actions lead to significant increases in midfrontal oscillations after conflict occurrence. Some previous experiments also found evidence for midfrontal delta/theta increases in preparation of control-demanding situations (Cooper et al., 2015, 2017; Luft et al., 2013; van Driel et al., 2012). This has led to the suggestion that midfrontal oscillations not only play a role in reactions towards conflict, but also in proactive adjustments which help to prepare for successful conflict resolution. The current findings show that increased anticipation of a potential conflict is not sufficient to elicit increases in midfrontal oscillations prior to action onset. This is especially noteworthy, since our results show clear

3.7. Cluster-based permutation analysis Our analysis indicated conflict-related increases of midfrontal oscillations during the action phase but not during the preparation phase. We determined time windows for analysis via statistical tests for individual time bins, controlled for multiple comparisons. In order to ensure that our choice of statistical method did not preclude us from identifying potential effects on midfrontal delta/theta, we also tested for cue-related differences in midfrontal activity (maybe-cue – certaincue) over the whole trial interval via cluster-based permutation (Groppe, Urbach, & Kutas, 2011; Maris, 2012). We used the permutation algorithm implemented in Fieldtrip function ft_freqstatistics, based on two-sided t-tests and 2000 random iterations for the creation of a nonparametric test distribution (cf. Maris & Oostenveld, 2007, for a detailed explanation of the procedure). For both inhibition and switching, one negative cluster emerged prior to action onset, inhibition; -0.3 – 0.16 s, (tmass = -950.70, p = .004), switching: -0.34 – 0.19 s, (tmass = -1084.53, p < .001). This indicates increased delta-theta levels for certain-cues compared to maybe-cues due to the earlier action onset for certain actions, as reported above. Additionally, for both inhibition and switching, we found one positive cluster with an onset after the action signal, inhibition: 0.2 – 0.7 s, (tmass = 2228.28, p < .001), switching: 0.23 – 0.64 s, (tmass = 1454.87, p < .001). This is consistent with the increases in midfrontal activity during the action phase for conflict-related trials. There were no further significant clusters in any of the two tasks. To summarize, results of the clusterbased permutation are highly similar to our findings based on multiple comparison of individual time-bins. They confirm that conflict-related increases in midfrontal low-frequency activity occurred only in the action phase, but not proactively in the preparation phase. 3.8. Participant-specific midfrontal frequency peaks While our analysis of midfrontal oscillations was based on an 10

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evidence for proactive neural adjustments of both sensory and motor processes. This demonstrates that proactive neural adjustments aimed at successful conflict resolution can take place without increases in power of midfrontal oscillatory activity. Thus, while midfrontal control signals seem to be crucial for top-down adjustments during reactive control, proactive changes can be implemented without their occurrence. While the current findings indicate an absence of preparatory increases in midfrontal oscillations, some previous studies found that preparatory neural adjustments were accompanied by increases in midfrontal low-frequency power. There are, however, some major differences between previous studies and the current report. One study comparing high conflict-likelihood cues with low conflict-likelihood cues (similarly to maybe and certain-cues in the current study) found increases of midfrontal theta activity during the preparation phase (Ryman et al., 2018). However, this study employed an unequal ratio of high and low conflict-likelihood cues, with high-conflict likelihood cues appearing less frequently. Conversely, in the current study high conflict-likelihood cues and low conflict-likelihood cues appeared equally often. Surprising events and deviations from expected patterns are known to increase midfrontal theta activity (Harper, Malone, & Iacono, 2017; Wessel & Aron, 2013). Accordingly, increases in midfrontal activity in Ryman et al. (2018) might signal the relative novelty of a lessfrequent cue type. Additionally, a number of previous studies found increased midfrontal oscillatory activity after cues that signaled certainty in terms of the definitive need, instead of only an increased chance for behavioral or cognitive adjustments. For example, error feedback, as well as the mere anticipation of aversive outcomes can lead to an increase in midfrontal theta prior to the next trial (van Driel et al., 2012; van Noordt et al., 2018). Here, negative action outcomes could signal the urgent need for behavioral and neural adjustments. In a similar vein, it has been shown that cues indicating a change of the current task induced proactive increases in midfrontal activity (Cooper et al., 2019). Importantly, these proactive effects of task-switching cues have been found to be absent in cases where the cues do not specify the exact type of the new task for the upcoming trial (Cooper et al., 2017). To summarize, proactive midfrontal activity was observed in contexts where a) it was evident that the upcoming task necessitated a change in behavior to previous trials, and b) participants already knew the type of action to perform on the upcoming trial (Cooper et al., 2017). Thus, in these cases the preparation phase allows for more specific adjustments of ongoing sensory and motor processes. Accordingly, proactive increases in midfrontal control activity could be confined to contexts that evoke the preparation of specific behavioral routines. Conversely, the maybe-cues in the current experiment signaled increased conflict likelihood but did not provide certainty as to whether a conflict would occur. Therefore, participants did not know in advance which action they would have to execute and thus have to prepare for. Accordingly, in the current experiment the preparatory phase only allowed for general proactive adjustments, which might increase the chances of flexible behavioral change during the action phase, but not for the preparation of a specific behavioral routine signaled in advance. Overall, this suggests that midfrontal oscillations are related to the occurrence of an actual need for cognitive and behavioral change, but adjustments in anticipation of potential change are not reflected in a change in midfrontal control signals. The presence or absence of midfrontal delta-theta oscillations during preparatory processes appears to depend on the foreknowledge about the type of challenge that has to be met in the upcoming task. While preparation for potential action conflicts did not elicit increased midfrontal delta-theta oscillations, it led to proactive oscillatory changes in both sensory and motor areas. Concerning sensory processing, the possibility of a control conflict compared to the certainty that no conflict would occur lead to significantly stronger posterior alpha suppression. This is in line with previous studies

interpreting posterior alpha suppression as a marker of visual attention (Bonnefond & Jensen, 2012; Clayton et al., 2017). When the type of tobe-performed action is known in advance, a cursory sensory processing of the action signal is sufficient for successful task fulfilment. Conversely, the possibility of having to suppress or change one’s intended action necessitate a more precise identification of the action signal in order to select the correct course of action (Boehler et al., 2009; Langford et al., 2016). While changes in visual attention did not differ between the inhibition and switching task, we found significant differences between the tasks for lateralized central beta as a measure of motor activation: Beta suppression during the preparation phase was stronger during the anticipation of a potential need to change one’s motor movement (switching task), but became weaker during the anticipation of outright motor stopping (inhibition task). Previous studies showed that, in the absence of any potential motor conflict, a to-be-performed motor action is known to increase motor suppression both before and during the action (Alegre et al., 2003; Cheyne, 2013; Kilavik et al., 2013; van Wijk, Beek, & Daffertshofer, 2012). In the current experiment, stronger beta suppression during anticipation of action switching appears to indicate increased top down control over motor processes in order to avoid the impulsive execution of the prepotent standard movement. Conversely, the lower beta suppression during conflict anticipation in the inhibition task might be related to a reduction in motor readiness, which would potentially facilitate the outright stopping at action onset. This interpretation is consistent with previous studies that found a reduction of beta suppression during action inhibition (Alegre et al., 2004; Swann et al., 2009; Zhang, Chen, Bressler, & Ding, 2008). A recent study investigated central beta suppression in a bimanual motor task, where on some trials one of two simultaneous motor responses had to be inhibited (Muralidharan et al., 2019). It was found that higher beta levels (i.e., less beta suppression) contralateral to the to-be-inhibited hand predicted improved ability to selectively stop the target response without impeding the still to-be-performed action with the other hand. This finding supports the view that preparatory beta modulations in the current study represent proactive motor adjustments. However, it is noteworthy, that in the current study the inhibition-related beta modulation could only be clearly detected during the preparation phase while during the action phase inhibition only led to a small, non-significant reduction in beta suppression. It might be possible that a reactive modulation of beta reactivity is not necessary for motor inhibition in cases where proactive adjustments of motor-related beta already take place during conflict preparation. However, further research is needed to clarify the functional significance of preparatory beta modulations for inhibition and action switching that we observed here. For example, while the current study focused on the implementation of successful motor adjustments, a future experiment could compare successful with unsuccessful attempts to resolve motor conflicts (e.g. via the stop-signal paradigm, cf. Swann et al., 2009; Wessel & Aron, 2014), in order to test if the degree of proactive motor beta modulation predicts an increased likelihood of successfully adjusting one’s motor response during conflict tasks. Note that our analysis of the theta, beta, and alpha band activity was restricted to a-priori selected brain regions and is therefore not exhaustive concerning the function of these frequency bands for motor conflict tasks. Importantly, the role of individual frequency bands is very likely not restricted to single brain areas or functions (for a review concerning alpha-band frequencies see Sadaghiani & Kleinschmidt, 2016; for beta-band frequencies: Khanna & Carmena, 2015). For example, while we investigated suppression of beta oscillations over the lateralized central cortex as a measure of motor activation, inhibition conflicts have also been related to an increase in beta power at the right inferior frontal cortex (Swann et al., 2009; Wagner, Wessel, Ghahremani, & Aron, 2018). This is likely to reflect activity in the subthalamic nucleus which is assumed to be a part of an inhibitionspecific network (Aron, Robbins, & Poldrack, 2014; Aron, Herz, Brown, 11

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Forstmann, & Zaghloul, 2016; Wessel & Aron, 2013; Zavala, Zaghloul, & Brown, 2015). Concerning alpha, we focused our investigation on posterior alpha oscillations, since previous research indicates that activity in this area is strongly influenced by modulations in visual attention (Thut, Nietzel, Brandt, & Pascual-Leone, 2006; Van Diepen, Foxe, & Mazaheri, 2019). However, modulations of alpha oscillations are not exclusively related to the processing of visual stimuli. For example, it has been suggested that alpha activity plays a more general role in facilitating top-down communication for task involving executive control (Hanslmayr, Gross, Klimesch, & Shapiro, 2011; Sadaghiani et al., 2012). Additionally, as in previous studies, we observed lateralized alpha suppression contralateral to the acting limb, suggesting an involvement of alpha activity in sensory-motor coordination (cf. Cheyne, 2013). We chose to mainly focus on beta oscillations as a measure of motor-related activity, since previous studies suggested that beta compared to alpha modulations might be a more sensitive measure of motor activation (Alegre et al., 2003; Tzagarakis et al., 2015). Analysis of alpha activity over the contralateral central cortex in the current study showed mostly analogous effects of conflict anticipation and execution, albeit with smaller effect sizes compared to beta activity in some cases. Importantly, further research is needed to integrate findings concerning oscillatory changes in different brain areas during motor conflicts. Concerning the choice of frequency bands, it has been suggested that subranges within both alpha and beta might represent functionally dissociable processes (Klimesch, 1999; Lobier, Palva, & Palva, 2018; van Wijk et al., 2016). We chose not to subdivide these frequency bands, as our main goal was to obtain an overall measure of sensory and motor activation. Our results show that the chosen frequency ranges were clearly sensitive towards both proactive and reactive processes for both alpha and beta oscillations. However, it would be worthwhile to investigate the potential differentiation between high and low subranges within the traditionally defined alpha and beta bands during proactive control conflicts, in order to better understand the function of these oscillatory modulations for behavioral and cognitive control. Our analysis was based on averages of electrode activity. It has been suggested that neural sources of cognitive phenomena can be more effectively isolated with source separation methods such as independent component analysis (ICA, cf. Stone, 2002) or generalized eigenvalue decomposition (GED, cf. Cohen, 2017; Muralidharan et al., 2019). For example, a recent study found that frontal theta activity might consist of at least two independent components with different local orientations (Töllner et al., 2017). In this study, only the component with a midfrontal peak appeared to be sensitive to the occurrence of cognitive conflicts, which is in line with the electrode selection chosen for the current study. Importantly, given that cognitive control is probably related to complex intercommunication within frontal brain regions, applying source separation methods to proactive control tasks could help to develop a more detailed picture of neural dynamics during conflict preparations. To conclude, while results of previous studies suggested that midfrontal oscillations might be crucial for topdown control of both reactive and proactive conflict adjustments, the current experiment demonstrates that proactive neural adjustments due to increased conflict likelihood can occur without the involvement of proactive midfrontal control signals. This result helps to both specify the functional role of midfrontal oscillations, as well as sheds light on how the brain enables effective preparation for behavioral conflicts.

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Data accessibility Data and materials of this study are archived online at https://osf. io/8vrp2/?view_only=66dc9498c4d247d4b2a28e6ac9c57cb4. Declaration of Competing Interest The authors have no conflict of interest. 12

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