TY - JOUR
AU - Livesey, E, J
AB - Abstract Preparing actions to achieve goals, overriding habitual responses, and substituting actions that are no longer relevant are aspects of motor control often assumed to be driven by deliberate top-down processes. In the present study, we investigated whether motor control could come under involuntary control of environmental cues that have been associated with specific actions in the past. We used transcranial magnetic stimulation (TMS) to probe corticospinal excitability as an index of motor preparation, while participants performed a Go/No-Go task (i.e., an action outcome or no action outcome task) and rated what trial was expected to appear next (Go or No-Go). We found that corticospinal excitability during a warning cue for the upcoming trial closely matched recent experience (i.e., cue–outcome pairings), despite conflicting with what participants expected would appear. The results reveal that in an action–outcome task, neurophysiological indices of motor preparation show changes that are consistent with participants learning to associate a preparatory warning cue with a specific action, and are not consistent with the action that participants explicitly anticipate making. This dissociation with conscious expectancy ratings reveals that conditioned responding and motor preparation can operate independently of conscious expectancies about having to act. associative learning, Go/No-Go, motor control, Perruchet effect, transcranial magnetic stimulation Introduction Many events in our daily life are predicted by antecedent environmental cues. However, these predictive relationships are rarely perfect; a predictive cue may be followed by several outcomes, creating uncertainty about which event will follow. For example, the sound of a car horn can alert us to a behavioral error that requires immediate action, such as swerving back into our lane. Alternatively, the sound may be intended for another driver and requires no action on our part. During such situations of outcome uncertainty, motor control is essential for selecting and executing the most appropriate action, given our current circumstances and goals. This form of proactive control is commonly assumed to be driven by deliberate top-down processes (see Verbruggen et al. 2014). However, motor preparation does not always appear to require thoughtful decision-making. Under some conditions, outcome uncertainty can produce conflicting top-down goal-directed expectations about motor preparation and bottom-up cue-elicited motor control (e.g., Perruchet 1985). These demonstrations question the extent to which action selection and execution are under deliberate motor control or are triggered automatically by cues that we have learned to associate with the most appropriate action based on past experiences. The conflict between top-down and bottom-up control can be observed using a cued-response task in which a warning cue is followed by a response cue on half of the trials and by no outcome on the other half of trials (e.g., a Go/No-Go task). In a standard design, the trial sequence is pseudorandomized to allow for runs of 1, 2, 3, and 4 consecutive cue-response trials as well as runs of 1, 2, 3, and 4 consecutive cue-alone trials. When participants are presented with consecutive trials of the warning cue followed by the response cue, the speed of their response increases as the preceding run length increases (e.g., as the number of consecutive Go trials increases). This pattern suggests that the repeated pairing of the warning cue with the action outcome enhances the participant’s preparedness to respond. However, this positive linear trend in response speed is contrasted with a negative linear trend in the same participant’s explicit expectancy for the response cue to appear on the next trial (e.g., Perruchet et al. 2006; Barrett and Livesey 2010; Destrebecqz et al. 2010; Livesey and Costa 2014; Lee Cheong Lem et al. 2015; Lee Cheong Lem et al. 2018). That is, expectancy ratings for what outcome would appear next followed a classic “gambler’s fallacy” (e.g., Tune 1964; Tversky and Kahneman 1971). Thus, as the run of consecutive cue-response trials increased, the response cue was less and less expected (the subjects expected a No-Go trial instead), but, counterintuitively, reaction times to the response cue were faster. The reverse pattern is also observed on cue-alone trials. Across long runs of the warning cue without an action outcome, the response cue was increasingly anticipated, yet reaction time to that response cue when it appeared was slower (see Fig. 1). This dissociation between preparedness to respond and conscious expectation about having to respond has been termed “the Perruchet effect”. Figure 1 Open in new tabDownload slide Schematic representation of the Perruchet effect demonstrating the dissociation between trends in behavior (response speed) and trends in conscious expectancy (for an outcome that signals whether or not to respond, i.e., response cue) as a function of the run length of consecutive preceding trials. Figure 1 Open in new tabDownload slide Schematic representation of the Perruchet effect demonstrating the dissociation between trends in behavior (response speed) and trends in conscious expectancy (for an outcome that signals whether or not to respond, i.e., response cue) as a function of the run length of consecutive preceding trials. In discussions of behavioral control, it is common to refer to processes as “automatic,” “involuntary,” and “bottom-up.” Here, we use the term automatic to refer to an influence on performance that is governed by stimulus and situational factors and their prior involvement in learning, and not by the explicit beliefs and intentions of the individual. An increase in response speed with increasing runs of previous Go trials can be considered automatic insofar as it directly contradicts explicit expectation of the Go signal, which instead decreases across those same runs. This trend in performance is presumably involuntary, unless the participant is intentionally preparing to respond faster when they expect the Go signal less. Acknowledging that there are a number of processes that might produce patterns that conflict with top-down goals, beliefs, and intentions, we refer more generally to such influences as being bottom-up in nature. Perruchet’s dissociation between the strength of responding and conscious expectancy has been demonstrated with eye-blink conditioning (Perruchet 1985; Weidemann et al. 2009; Weidemann et al. 2012; Weidemann and Lovibond 2016), fear learning (Moratti and Keil 2009; McAndrew et al. 2012; Perruchet et al. 2016), and other decision paradigms (Moore et al. 2012; Jiménez and Méndez 2013). Interestingly, this dissociation suggests that there are limits to deliberate motor control, that motor actions may be automatically controlled by bottom-up cue-elicited influences that are independent of (and even in opposition to) top-down goal-directed control. Specifically, response times to an uncertain outcome can be biased by the recent conditioning history of the warning cue that signals that outcome, contrary to an individual’s explicit expectation that the outcome is about to occur. While the evidence for the Perruchet dissociation is very reliable, it remains unclear what specific bottom-up processes modulate the effect. The dissociation suggests that environmental cues can automatically trigger motor preparation that is independent of top-down control based on self-reported expectancies. However, this conclusion relies on an assumption that the performance trends in response times are the direct result of learning or conditioning processes due to the pairing of cue and outcome. Indeed, this explanation was the conventional view proposed for the original result by Perruchet (1985), and has been supported by others (Barrett and Livesey 2010; Perruchet et al. 2016; Lee Cheong Lem et al. 2018). However, some researchers argue that changes in response times are due to non-cognitive performance-related processes, such as simple temporary priming of the outcome and response, that speed (or slow) reaction times depending on how recently a response cue was presented or a response was performed (Mitchell et al. 2010; Weidemann and Lovibond 2016). This alternative explanation of the Perruchet effect casts doubt on the significance of the dissociation for understanding motor control. Therefore, the present study sought to identify the bottom-up processes that operate independently of top-down control to bias behavior in the Perruchet effect. This investigation will provide a unique test of the underlying mechanisms responsible for the dissociation to help determine whether conditioned environmental cues can automatically trigger motor preparation and influence motor control, or whether the dissociation is due to non-cognitive priming effects. There have been several attempts to examine the processes governing motor control in the Perruchet effect. Previous studies have often tackled this question by manipulating the trial structure in a standard Perruchet effect design to examine its effects on the strength of responding as a function of run length. This approach has produced mixed findings (e.g., Weidemann et al. 2016) and is limited in its ability to measure bottom-up effects on motor control only when there are observable effects on behavior, such as changes in reaction time. Innovations in the use of non-invasive brain stimulation offer a solution to this problem. Specifically, transcranial magnetic stimulation (TMS) can be used to measure motor system excitability without the need for a behavioral response. When TMS is applied over the hand region of the primary motor cortex, a motor-evoked potential (MEP) can be recorded in the contralateral hand with electromyography (EMG). The magnitude of MEPs is sensitive to motor preparation (Bestmann and Duque 2016) and differential effects of motor system excitability have been found to increase with learning (Poole et al. 2018; Tran et al. 2019). Therefore, MEPs provide a measure of motor preparedness at the exact time of cortical stimulation, providing an index of the degree of bottom-up influences on motor control (further discussion surrounding MEP amplitudes and motor preparedness can be found in the General Discussion section). A recent study that used TMS to investigate the influence of response history on expectancy and motor preparation showed that motor excitability aligned with response history, even when expectancy had been updated (Verbruggen et al. 2016). In this study, participants were presented with runs of five response trials in strict alternation with runs of five no-response trials. Before the first trial of each run, participants learned to anticipate what the next trial type would be, but MEPs still reflected the previous trial type—remaining high at the beginning of a no-response run (immediately after a Go trial) and remaining low at the beginning of a response run (immediately after a No-Go trial). These results provide preliminary evidence that motor preparation can conflict with top-down control, at least in perfectly predictable environments. Extending this finding, we employed a design inspired by the Perruchet effect, in which a warning cue preceded one of two outcomes in a pseudorandomized sequence, creating outcome uncertainty, and allowing us to test the underlying cause of the dissociation. Specifically, the aim of our study was to use TMS to examine whether motor control is modulated by non-cognitive performance effects (e.g., outcome or response priming) or is triggered automatically by cue-elicited effects. In the present experiment, we used TMS in combination with a Go/No-Go task. A large fixation cross served as the warning cue, with “GO” and “STOP” commands serving as the Go/No-Go response cues (i.e., the outcomes) to which a response was and was not required, respectively. We also measured expectancy ratings about the likelihood that a “GO” or a “STOP” would appear on the next trial (i.e., whether the participant would or would not have to respond on the next trial) during the inter-trial interval (ITI). TMS was triggered on every trial, either during the fixation cross (During-Cue trial) or during the ITI before the fixation cross (Before-Cue trial). Critically, the duration of the ITI was variable and the TMS was timed such that the onset of the trigger was matched between During-Cue and Before-Cue trials for the time since the end of the previous trial (see Fig. 4 in the Procedure section). Systematic variations in the magnitude of MEPs on Before-Cue trials provide an index of motor preparation that is not specific to the warning cue, such as performance effects due to the sensitization of a recent response cue or the priming of recent motor actions. When compared with this Before-Cue control, systematic variations in the magnitude of MEPs on During-Cue trials provide an index of motor preparation that is specific to the warning cue, such as a conditioned response developed over recent cue-response pairings. If reaction time trends in the Perruchet effect are mediated by performance-based priming from the recency of previous outcome presentations or response executions, it is predicted that MEPs on both During- and Before-Cue trials will vary as a function of run length (Fig. 2a). Alternatively, if the Perruchet effect is mediated by learning-based processes from recent cue-outcome pairings, only MEPs on During-Cue trials, and not on Before-Cue trials, will vary as a function of run length (Fig. 2b). Figure 2 Open in new tabDownload slide Predicted results of MEPs as a function of consecutive No-Go (NG) or Go (G) trials based on (a) the performance-based priming hypothesis and (b) the learning-based priming hypothesis. Figure 2 Open in new tabDownload slide Predicted results of MEPs as a function of consecutive No-Go (NG) or Go (G) trials based on (a) the performance-based priming hypothesis and (b) the learning-based priming hypothesis. Materials and Methods Participants Behavioral and TMS data were collected from 47 University of Sydney undergraduate students [mean age = 19.89 years (SD = 4.31)] who received partial course credit in exchange for their participation. We aimed to collect 50 participants but stopped at 47 due to constraints on the testing pool of undergraduate students. Three participants were excluded for not completing the experiment: two participants withdrew due to the frequency/intensity of TMS pulses and one participant’s session was discontinued because they requested a reduced level of stimulation, which was insufficient to produce reliable MEPs. Previous work has shown that a minimum sample of 30 participants is sufficient to detect learning effects with MEPs (Poole et al. 2018). Our target sample (50) was nearly double the minimum number of 30 participants due to some cells in our design having only a small number of trials (i.e., Go and No-Go run lengths of 4–6). All participants completed a TMS safety screening and provided informed consent before commencing the experiment. All procedures were approved by the Human Research Ethics Committee of the University of Sydney. EMG and TMS Surface EMG recorded the MEP activity from the first dorsal interosseus (FDI) muscle of the right hand. In preparation for electrode placement, the skin was cleaned with a small sponge and wiped with 70% v/v isopropyl alcohol swab. Two 10 mm diameter Ag/AgCl electrodes were placed in a belly-tendon arrangement over the FDI muscle along with the ground electrode placed over the ulnar styloid process of the wrist. EMG activity was recorded from 200 ms pre-stimulation to 100 ms post-stimulation using PowerLab 26 T DAQ (ADInstruments, Bella Vista, NSW, Australia). This signal was digitally converted (sampling rate: 4 kHz, bandpass filter: 0.5 Hz–2 kHz, mains filter: 50 Hz, and anti-aliasing) and stored using LabChart software (version 8, ADInstruments) for offline processing. TMS was administered using a Magstim 2002 stimulator and a 70 mm figure-eight coil (Magstim, Whitland, UK) locked in position with an adjustable arm (Manfrotto, Italy). Participants wore an elastic cap marked with the 10/20 EEG electrode locations and rested their head on a chin and forehead rest (UHCOTech HeadSpot, Texas, USA). The coil was held tangentially to the scalp with the handle orientated 45° from the midline. The location of the left-hemisphere motor cortex “hotspot” was determined starting from a spot 5 cm lateral and 1 cm anterior to Cz, the coil was moved around until the maximal MEP was elicited in the FDI. Resting motor threshold (rMT) was determined by finding the lowest stimulation intensity that produced MEPs larger than 50 μV in 5 out of 10 consecutive trials. The stimulation intensity during the experiment was set at 120% of rMT or 110% of rMT for the two participants who requested for the intensity to be reduced. The mean rMT for all participants in the study was 41.38% (SD = 7.53) of maximum stimulator output. Figure 3 Open in new tabDownload slide Example of a trial sequence with runs of Go (G) trials highlighted in dark grey and runs of No-Go (NG) trials highlighted in light grey. The fixation cross (+) represents the warning cue preceding the outcome or response cue. One run consists of an uninterrupted sequence of trials of the same type. Note, for example, that a run of 5 Go trials also includes a run of 4, 3, 2, and 1 Go trial(s) before the next No-Go trial. Figure 3 Open in new tabDownload slide Example of a trial sequence with runs of Go (G) trials highlighted in dark grey and runs of No-Go (NG) trials highlighted in light grey. The fixation cross (+) represents the warning cue preceding the outcome or response cue. One run consists of an uninterrupted sequence of trials of the same type. Note, for example, that a run of 5 Go trials also includes a run of 4, 3, 2, and 1 Go trial(s) before the next No-Go trial. Table 1 Number of runs and TMS trials per block as a function of run length Run length No-Go Go 6 5 4 3 2 1 1 2 3 4 5 6 No. of runs 1 1 2 4 8 16 16 8 4 2 1 1 No. of TMS trials 1 2 4 8 16 32 32 16 8 4 2 1 Run length No-Go Go 6 5 4 3 2 1 1 2 3 4 5 6 No. of runs 1 1 2 4 8 16 16 8 4 2 1 1 No. of TMS trials 1 2 4 8 16 32 32 16 8 4 2 1 For the trial sequence to conform to a binomial distribution with an equal likelihood of Go and No-Go trials, there were necessarily more short runs than long runs. Open in new tab Table 1 Number of runs and TMS trials per block as a function of run length Run length No-Go Go 6 5 4 3 2 1 1 2 3 4 5 6 No. of runs 1 1 2 4 8 16 16 8 4 2 1 1 No. of TMS trials 1 2 4 8 16 32 32 16 8 4 2 1 Run length No-Go Go 6 5 4 3 2 1 1 2 3 4 5 6 No. of runs 1 1 2 4 8 16 16 8 4 2 1 1 No. of TMS trials 1 2 4 8 16 32 32 16 8 4 2 1 For the trial sequence to conform to a binomial distribution with an equal likelihood of Go and No-Go trials, there were necessarily more short runs than long runs. Open in new tab Design Runs of Go and No-Go trials were generated to approximate a binomial distribution. An example portion of the trial sequence is presented in Figure 3 and the numbers of runs in one block of the experiment are presented in Table 1. There were four blocks in the experiment, which were separated with a self-timed break. The sequence of runs within a block was randomized with the constraints that runs of Go and No-Go trials alternated and that the first and last trial of each block was a run length of 1. Each block contained an equal number of During-Cue and Before-Cue trials. The experiment lasted approximately 70 mins and the total testing session (including briefing, set-up, and debrief) lasted approximately 90 mins for each participant. Figure 4 Open in new tabDownload slide Schematic representation of trial structure. The top panel shows the timing of a During-Cue trial in which the TMS pulse was triggered during the fixation cross (warning cue) 100 ms before the response cue (outcome). The bottom panel shows the timing of a Before-Cue trial in which the TMS pulse was triggered during the ITI 100 ms before the fixation cross. All numerical values are in ms and expectancy was pseudo-randomly measured on half of the trials during the ITI. The total ITI for During-Cue trials was 4500–5500 ms and for Before-Cue trial was 5500–6500 ms. Notably, the onset of the TMS pulse was matched across conditions from the end of the last trial. The hand represents a response was required, and a chime sounded for correct responses while a buzz sounded for misses (on “GO”) and FAs (on “STOP”). Figure 4 Open in new tabDownload slide Schematic representation of trial structure. The top panel shows the timing of a During-Cue trial in which the TMS pulse was triggered during the fixation cross (warning cue) 100 ms before the response cue (outcome). The bottom panel shows the timing of a Before-Cue trial in which the TMS pulse was triggered during the ITI 100 ms before the fixation cross. All numerical values are in ms and expectancy was pseudo-randomly measured on half of the trials during the ITI. The total ITI for During-Cue trials was 4500–5500 ms and for Before-Cue trial was 5500–6500 ms. Notably, the onset of the TMS pulse was matched across conditions from the end of the last trial. The hand represents a response was required, and a chime sounded for correct responses while a buzz sounded for misses (on “GO”) and FAs (on “STOP”). Table 2 Mean (SD) number of MEP trials across participants used to compute the raw MEP values at each run length by TMS time factor Run length (max) No-Go Go 4+ (14) 3 (16) 2 (32) 1 (64) 1 (64) 2 (32) 3 (16) 4+ (14) During-Cue 11.64 (2.04) [1.96] 13.55 (2.83) [1.89] 27.34 (4.65) [3.64] 54.02 (9.40) [7.46] 55.25 (6.89) [5.93] 28.11 (4.90) [3.32] 14.05 (2.72) [1.68] 11.64 (2.73) [1.71] Before-Cue 11.95 (2.34) [1.61] 13.64 (3.03) [2.02] 28.18 (4.49) [3.34] 55.20 (6.87) [5.77] 56.30 (7.17) [5.16] 28.05 (4.52) [2.98] 13.91 (2.83) [1.55] 12.48 (2.65) [1.48] Run length (max) No-Go Go 4+ (14) 3 (16) 2 (32) 1 (64) 1 (64) 2 (32) 3 (16) 4+ (14) During-Cue 11.64 (2.04) [1.96] 13.55 (2.83) [1.89] 27.34 (4.65) [3.64] 54.02 (9.40) [7.46] 55.25 (6.89) [5.93] 28.11 (4.90) [3.32] 14.05 (2.72) [1.68] 11.64 (2.73) [1.71] Before-Cue 11.95 (2.34) [1.61] 13.64 (3.03) [2.02] 28.18 (4.49) [3.34] 55.20 (6.87) [5.77] 56.30 (7.17) [5.16] 28.05 (4.52) [2.98] 13.91 (2.83) [1.55] 12.48 (2.65) [1.48] Values in [] indicate the mean number of excluded MEP trials <100 μV. Open in new tab Table 2 Mean (SD) number of MEP trials across participants used to compute the raw MEP values at each run length by TMS time factor Run length (max) No-Go Go 4+ (14) 3 (16) 2 (32) 1 (64) 1 (64) 2 (32) 3 (16) 4+ (14) During-Cue 11.64 (2.04) [1.96] 13.55 (2.83) [1.89] 27.34 (4.65) [3.64] 54.02 (9.40) [7.46] 55.25 (6.89) [5.93] 28.11 (4.90) [3.32] 14.05 (2.72) [1.68] 11.64 (2.73) [1.71] Before-Cue 11.95 (2.34) [1.61] 13.64 (3.03) [2.02] 28.18 (4.49) [3.34] 55.20 (6.87) [5.77] 56.30 (7.17) [5.16] 28.05 (4.52) [2.98] 13.91 (2.83) [1.55] 12.48 (2.65) [1.48] Run length (max) No-Go Go 4+ (14) 3 (16) 2 (32) 1 (64) 1 (64) 2 (32) 3 (16) 4+ (14) During-Cue 11.64 (2.04) [1.96] 13.55 (2.83) [1.89] 27.34 (4.65) [3.64] 54.02 (9.40) [7.46] 55.25 (6.89) [5.93] 28.11 (4.90) [3.32] 14.05 (2.72) [1.68] 11.64 (2.73) [1.71] Before-Cue 11.95 (2.34) [1.61] 13.64 (3.03) [2.02] 28.18 (4.49) [3.34] 55.20 (6.87) [5.77] 56.30 (7.17) [5.16] 28.05 (4.52) [2.98] 13.91 (2.83) [1.55] 12.48 (2.65) [1.48] Values in [] indicate the mean number of excluded MEP trials <100 μV. Open in new tab Procedure The experiment was run on a PC connected to a 24-inch LCD monitor (1920 × 1080 pixel, 60 Hz refresh rate) at a viewing distance of approximately 60 cm. PsychoPy (version 1.83.03) was used to present stimuli and collect response data. Participants were instructed that they were required to respond as quickly as possible on some trials and to withhold responding on other trials. The task was to press down on the RIGHT ENTER key with their right index finger when the word GO appeared but to withhold any response when the word STOP appeared. Each trial began with a fixation cross (1000 ms, acting as the warning cue) before the onset of a response cue, GO or STOP (1000 ms, acting as the outcome cue). If participants responded on time and correctly, a chime sound played as feedback; if participants did not respond on time or responded incorrectly on No-Go trials (i.e., false alarm, FA), a buzz sound played as feedback. No feedback was given for correctly withholding a response on No-Go trials. A schematic of the trial structure is presented in Figure 4. During the ITI following the offset of the response cue and before the onset of the next fixation cross, participants were asked to rate their expectancy of a GO cue on the next trial. A horizontal seven-point rating scale appeared on the screen with a red marker starting in the middle to indicate the participant was “Unsure” what the next response cue would be. Participants could move the marker right as much as three spaces (with the right-arrow key using their left hand) the more they expected a GO cue on the next trial, or they could move the marker left (with the left-arrow key using their left hand) the more they expected a STOP cue. The ends of the scale were labeled with “Very sure STOP” and “Very sure GO”. Participants could move the marker as much as they wanted (or not at all) before a 3000 ms time out. Expectancy was measured on only half of the trials due to a previous finding that expectancy measurements can mask RT trends in a choice response task (Lee Cheong Lem et al. 2015). On trials when expectancy was not measured, the screen remained blank in place of the rating screen. Figure 5 Open in new tabDownload slide Mean expectancy rating (a) and reaction time (b) as a function of the preceding run of consecutive No-Go (NG) or Go (G) trials. Runs of NG4+ and G4+ were collapsed across run lengths of 4–6. Lines represent the two trial types across the TMS timing factor when a single pulse was delivered either during the warning cue or before the warning cue. The horizontal dotted line in a represents the mid-point of the expectancy scale. Error bars represent within-participant standard errors (Morey 2008). Figure 5 Open in new tabDownload slide Mean expectancy rating (a) and reaction time (b) as a function of the preceding run of consecutive No-Go (NG) or Go (G) trials. Runs of NG4+ and G4+ were collapsed across run lengths of 4–6. Lines represent the two trial types across the TMS timing factor when a single pulse was delivered either during the warning cue or before the warning cue. The horizontal dotted line in a represents the mid-point of the expectancy scale. Error bars represent within-participant standard errors (Morey 2008). Single pulse TMS was triggered on every trial, totaling 504 trials. On During-Cue trials, the pulse was triggered during the fixation cross, 100 ms before the onset of the response cue. On Before-Cue trials, the pulse was triggered during the ITI, 100 ms before the onset of the fixation cross. The timing of the TMS pulse was matched across During- and Before-Cue trials such that the time elapsed since the offset of the previous response cue was the same. Therefore, the total ITI time from the response cue offset of the previous trial to the warning cue onset of the current trial varied between 4500 and 5500 ms for During-Cue trials, and 5500 and 6500 ms for Before-Cue trials (see Fig. 4). Data Preparation Custom Python software was used for screening and selecting MEP data (https://github.com/nicolasmcnair/MEPAnalysis). Each trial was visually inspected to exclude atypical MEP wave forms, and pre-TMS movement artifacts in the EMG signal (>50 μV). MEP amplitudes were defined as the highest to lowest point of activity following the TMS pulse before returning to baseline. Any MEP less than 100 μV was treated as a mistrigger and excluded from the analysis (see Table 2 for number of excluded trials). Using a criterion of 50 μV does not change the pattern of results or their statistical significance. Statistical Analysis RT and MEP data were analyzed as a (2) (expectancy measured, expectancy not measured) × (2) (Before-Cue, During-Cue) × (8) (run length) repeated measures analysis of variance (ANOVA, IBM SPSS Statistics for Windows, version 22.0) to check if being asked to make an expectancy judgment during the ITI affected trends in RTs or MEPs. The behavioral (Expectancy and RT) and MEP data were analyzed for linear trends across the entire run length (8 levels) from runs of ≥4 (4–6) No-Go trials up to runs of ≥4 (4–6) Go trials. Runs of 4–6 were collapsed due to the low number of samples of these trial types occurring in a binomial distribution. The behavioral and MEP data were analyzed using separate repeated measures ANOVAs over the factor of TMS time across run lengths. It was not predicted that the factor of TMS time would affect the slopes across run length in the Expectancy or RT data. However, the presence or absence of an interaction between the factor of TMS time and run length in the MEP data will inform the mechanisms underlying the Perruchet effect. The MEP results were also log-normalized for each participant calculated as log[(During-Cue trials)/(Before-Cue trials)] at each run length separately. Baselining the data in this way removes individual variability associated with using absolute MEP magnitudes and provides an index of motor preparation that is specific to the warning cue relative to the background motor system excitability. Follow-up one sample t-tests subsequently compared the normalized data with a baseline value of 0. Results Behavioral Data Expectancy for a GO cue measured during the ITI period decreased linearly with a decreasing number of consecutive No-Go trials and increasing number of consecutive Go trials for both the During- and Before-Cue conditions, F(1,43) = 16.03, P < 0.001, ƞp2 = 0.27 (8 levels collapsing over the TMS time factor). There was no significant linear trend interaction with TMS time, F(1,43) = 1.17, P = 0.286, ƞp2 = 0.03 (Fig. 5a). Measuring expectancy did not significantly interact with linear trends in reaction time (across the 8 run length levels) and TMS time factor (three-way interaction, F(1,43) = 1.42, P = 0.239, ƞp2 = 0.03; full ANOVA reported in Supplementary Material). Therefore, the results and figures are presented pooled across Expectancy factor (measured or not measured). A reaction time advantage was present on Before-Cue trials compared to During-Cue trials, F(1,43) = 54.41, P < 0.001, ƞp2 = 0.56 (Fig. 5b). This benefit may be due to the onset of an earlier TMS pulse on Before-Cue trials signaling the end of the variable ITI and alerting participants to the start of the next trial. Alternatively, the difference could be due to the onset of a later TMS pulse (and associated muscle twitch) on During-Cue trials interfering with the ability to respond to the response cue. Importantly, overall reaction times on Go trials decreased linearly with a decreasing number of consecutive No-Go trials and an increasing number of consecutive Go trials for both the During- and Before-Cue conditions, F(1,43) = 38.13, P < 0.001, ƞp2 = 0.47 (8 levels collapsing over the TMS time factor). There was no significant linear trend interaction with TMS time, F = 0.06, P = 0.801, ƞp2 < 0.01. These behavioral results replicate the Perruchet effect, showing that the speed of responding (inverse of reaction times) followed the opposite trend to GO expectancies. False alarms (FA) were rare but were more common on No-Go trials that immediately followed a Go trial than another No-Go trial (χ(3, N = 136) = 18.42, P < 0.001, Cramer’s V = 0.37). On average participants made 3.97 FAs; nine participants made 0 FAs and one participant made a maximum of 20 FAs. Of the participants who made FAs, Table 3 summarizes the frequency by run length. Table 3 Total FA frequency on No-Go trials across all participants (n = 44), as a function of preceding run length Run length No-Go Go 4+ 3 2 1 1 2 3 4+ Total FAs 1 3 11 19 34 17 26 25 % of No-Go trials 0.08 0.21 0.39 0.34 0.60 0.60 1.85 2.03 Run length No-Go Go 4+ 3 2 1 1 2 3 4+ Total FAs 1 3 11 19 34 17 26 25 % of No-Go trials 0.08 0.21 0.39 0.34 0.60 0.60 1.85 2.03 Note that shorter run lengths are more common than longer run lengths (see Table 1). Open in new tab Table 3 Total FA frequency on No-Go trials across all participants (n = 44), as a function of preceding run length Run length No-Go Go 4+ 3 2 1 1 2 3 4+ Total FAs 1 3 11 19 34 17 26 25 % of No-Go trials 0.08 0.21 0.39 0.34 0.60 0.60 1.85 2.03 Run length No-Go Go 4+ 3 2 1 1 2 3 4+ Total FAs 1 3 11 19 34 17 26 25 % of No-Go trials 0.08 0.21 0.39 0.34 0.60 0.60 1.85 2.03 Note that shorter run lengths are more common than longer run lengths (see Table 1). Open in new tab MEP Data Measuring expectancy did not significantly interact with linear trends in MEPs (across the 8 run length levels) and TMS time factor (three-way interaction, F(1,43) = 0.01, P = 0.935, ƞp2 = 0.00; full ANOVA reported in Supplementary Material). Therefore, the results and figures are presented pooled across Expectancy factor (measured or not measured). The MEP results are shown separately for the During- and Before-Cue conditions in Figure 6a. MEP amplitudes on During-Cue trials increased with a decreasing number of consecutive No-Go trials and increasing number of consecutive Go trials; this linear trend across the entire range of run length (8 levels) was significant, F(1,43) = 23.02, P < 0.001, ƞp2 = 0.35. In contrast, MEP amplitudes on Before-Cue trials remained relatively flat across runs of Go and No-Go trials with a non-significant linear trend across the entire run length, F(1,43) = 1.94, P = 0.171, ƞp2 = 0.05. Critically, the linear trend interaction between TMS timing (During-Cue vs. Before-Cue) and run length was significant, F(1,43) = 8.58, P = 0.005, ƞp2 = 0.17. Figure 6 Open in new tabDownload slide Mean MEP as a function of the preceding Go (G) and No-Go (NG) run length (a) separated by TMS time condition, and (b) log-normalized taking (During-Cue trials)/(Before-Cue trials). Runs of NG4+ and G4+ were collapsed across runs lengths of 4–6. Error markings represent within-participant standard errors (Morey 2008). Figure 6 Open in new tabDownload slide Mean MEP as a function of the preceding Go (G) and No-Go (NG) run length (a) separated by TMS time condition, and (b) log-normalized taking (During-Cue trials)/(Before-Cue trials). Runs of NG4+ and G4+ were collapsed across runs lengths of 4–6. Error markings represent within-participant standard errors (Morey 2008). Figure 7 Open in new tabDownload slide Postulated pattern of MEPs as a function of run length based on the predictions of (a) performance-based priming, (b) learning-based priming, and (c) expectancy-based impulse control [panels (a) and (b) are replicated from Figure 2]. If response trends follow expectancy-based control (c) and align with self-reported expectations about an upcoming response, we should still see modulation on the During-Cue trials. However, importantly, excitability in this condition should never exceed that of the Before-Cue condition. Alternatively, the absolute level of excitability of the During-Cue trials may be more comparable to the values observed in learning-based priming (b), but the function should flatten once it reaches baseline (i.e., the level of the Before-Cue condition). Figure 7 Open in new tabDownload slide Postulated pattern of MEPs as a function of run length based on the predictions of (a) performance-based priming, (b) learning-based priming, and (c) expectancy-based impulse control [panels (a) and (b) are replicated from Figure 2]. If response trends follow expectancy-based control (c) and align with self-reported expectations about an upcoming response, we should still see modulation on the During-Cue trials. However, importantly, excitability in this condition should never exceed that of the Before-Cue condition. Alternatively, the absolute level of excitability of the During-Cue trials may be more comparable to the values observed in learning-based priming (b), but the function should flatten once it reaches baseline (i.e., the level of the Before-Cue condition). The MEP results are also presented normalized to the Before-Cue condition in Figure 6b. These results show the mean log-normalized MEPs of each participant calculated as log[(During-Cue trials)/(Before-Cue trials)] at each run length separately. Normalized MEP amplitude increased with a decreasing number of No-Go trials and increasing number of Go trials; this positive linear trend was significant, F(1,43) = 8.14, P = 0.007, ƞp2 = 0.16. These results reveal that MEP amplitudes became increasingly elevated above baseline with an increasing number of consecutive cue-outcome pairings of Go trials, and became increasingly suppressed below baseline with an increasing number of consecutive cue-outcome pairing of No-Go trials. Follow-up one sample t-tests revealed that log-normalized MEP amplitudes were significantly lower than zero collapsing run lengths at NG4+ and NG3, t(43) = 2.05, P = 0.047, d = 0.30, but non-significantly higher than zero collapsing run lengths at G3 and G4+, t(43) = 1.28, P = 0.208, d = 0.19. Discussion The present study was designed to assess the bottom-up processes contributing to Perruchet’s dissociation between the strength of responding and conscious expectancy. Specifically, we tested whether trends in responding were the product of performance-based or learning-based motor priming effects. To date, behavioral manipulations aimed at resolving this question have produced mixed results and possible alternative explanations for the findings (e.g., Mitchell et al. 2010; Weidemann and Lovibond 2016; Weidemann et al. 2016). Our experiment examined the Perruchet effect using TMS to measure motor preparedness as a novel approach to this problem. The design involved triggering a TMS pulse over the primary motor cortex and measuring MEPs at the same time point relative to the previous trial. A variable ITI allowed us to administer TMS during the presentation of the warning cue before the onset of the response cue (During-Cue), or during the ITI prior to the start of the trial signaled by the warning cue (Before-Cue). The key advantage of using TMS is that it allowed us to probe motor preparation with precise timing without requiring an observable response. This meant that we could directly compare motor preparation either with or without the warning cue present, while still maintaining the partially predictive status of the warning cue. If response trends in the Perruchet effect are mediated entirely by performance-based priming from the recency of previous outcome presentations or response executions, we should see variations in motor preparation on both During- and Before-Cue trials (as illustrated in Fig. 7a). In contrast, if the Perruchet effect is mediated by learning-based priming from recent cue-outcome pairings, we should see variations in motor preparation on During-Cue trials, but not on Before-Cue trials (as illustrated in Fig. 7b). We found a significant linear interaction between run length and TMS timing, indicating that the run length linear trend was stronger for the During- than Before-Cue trials. Our results show that, relative to Before-Cue trials, motor excitability on During-Cue trials was (non-significantly) elevated in the presence of the warning cue following longer runs of Go trials, and (significantly) suppressed in the presence of the warning cue following longer runs of No-Go trials. These results demonstrate that it is the presence of the warning cue—which is conditioned by its pairing with the response cues (i.e., outcomes)—that modulates motor preparedness in anticipation of the upcoming outcome. Specifically, following consecutive pairings of the warning cue followed by the GO cue, participants learn to anticipate a Go trial when presented with the fixation cross and show increased preparedness for the next trial, whereas the converse is true following pairings with the STOP cue on No-Go trials. Critically, this interpretation suggests that bottom-up motor preparation triggered by the warning cue contradicts self-reported top-down expectancies about an upcoming response, which follow the opposite trend. It should be noted that the literature on MEP and expectancy can be complex. Some studies report that anticipation of an upcoming response cue produces increased motor system excitability (“preparedness” hypothesis; e.g., Mars et al. 2007; van den Hurk et al. 2007; Poole et al. 2018), while other studies report that anticipation of an upcoming response cue produces a timed reduction in motor system excitability as a form of preparatory inhibition (Duque et al. 2017), such as “impulse control” before needing to execute an action (Hasbroucq et al. 1997; Touge et al. 1998; McMillan et al. 2004; Davranche et al. 2007; Prabhu et al. 2007; van Elswijk et al. 2007; Duque and Ivry 2009; Duque et al. 2010; Hannah et al. 2018) or “signal:noise enhancement” for excitatory inputs to be more easily detected against a quiescent background (Greenhouse et al. 2015). That is, the preparedness and preparatory inhibition (e.g., impulse control) hypotheses, predict opposing patterns of MEPs in anticipation of an upcoming response or Go trial. The preparedness hypothesis predicts a gradual increase in MEP while the impulse control hypothesis predicts a timed reduction in MEPs. These opposing predictions about trends in MEPs could be problematic for interpreting how the MEP run length data align with participants’ expectations. That is, an alternative interpretation for the results is that the pattern of MEPs on the During-Cue trials are capturing impulse control tendencies that directly align with reported expectations for an upcoming response. Hence, we see lower excitability following longer runs of No-Go trials when participants most likely expect a GO cue on the next trial. While the two hypotheses considered above create some ambiguity when addressing the data from the During-Cue trials alone, the two hypotheses can be distinguished in their predictions for MEPs measured on Before-Cue trials relative to During-Cue trials. According to the impulse control hypothesis, MEPs should always be smaller (impulse control should be greater) after the warning cue to respond has appeared (During-Cue condition) than before that cue appears, regardless of run length. In contrast, the response preparedness hypothesis predicts that During-Cue MEPs should be lower than Before-Cue MEPs after a run of No-Go trials, but During-Cue MEPs should be above Before-Cue MEPs after runs of Go trials (see Fig. 7 for an illustration of these contrasting hypotheses). The linear interaction, as seen in Figure 6, is more consistent with the preparedness hypothesis than the impulse control hypothesis. Additionally, two analyses were performed post-hoc to test the impulse control hypothesis. First, MEP amplitudes on Before-Cue trials were higher following Go trials than No-Go trials, F(1,43) = 6.03, P = 0.018, ƞp2 = 0.18 (collapsed across run length). That is, probing Before-Cue trials at the same time point relative to the end of the previous trial revealed greater excitability overall following Go trials compared to No-Go trials. Since RTs were faster following Go trials than No-Go trials, the larger MEPs following Go trials than No-Go trials at baseline is further evidence that we are not measuring impulse control in our experiment. Second, the positive linear trend on During-Cue Go trials (four levels) was significantly predicted by the size of the difference in MEPs between During-Cue Go trials and Before-Cue Go trials (averaged across the four Go run lengths), r = 0.425, P = 0.004, n = 44. This correlation indicates that the positive linear trend on During-Cue Go trials is driven by participants who show larger MEPs on During-Cue Go trials than Before-Cue Go trials. If impulse control underlies the linear trend on During-Cue Go trials, we should see a negative correlation here, driven by participants who show larger MEPs on Before-Cue Go trials than During-Cue Go trials. It should also be noted that often the conditions (e.g., trial structure and timing of the TMS pulse) for observing preparatory inhibition or impulse control are largely different to the conditions of the current experiment. For example, Hannah et al. (2018) showed that selective suppression of the motor system was related to faster RTs, but this suppression was observed with MEPs measured during the imperative Go stimulus normalized to the warning cue. Here, we probed motor system excitability during the warning cue (During-Cue) normalized to a baseline period (Before-Cue). Thus, despite conflicting with conscious expectancies, variations in RT across runs of Go and No-Go trials can be attributed to changes in motor preparedness as a result of the learned association between the warning cue and the outcome, rather than being solely the product of recent history of presented outcomes and executed responses. This final point is noteworthy; although we find strong support for associative learning in the Perruchet effect, this does not preclude some contributions of performance-based priming effects to the standard dissociation obtained in the Perruchet effect. That is, both mechanisms could be taking place simultaneously and do not mutually exclude the other. Critically though, these results confirm that conditioning plays an important role. Perruchet’s dissociation provides strong evidence that learning about the relationship between events in a dynamic environment can ultimately produce conflicting top-down and bottom-up motor control. However, debate about Perruchet’s dissociation has centered on what causes the response priming observed after a run of trials requiring a response. Many argue that the Perruchet effect is evidence of automatic, bottom-up control in which the participant has learned to anticipate making another response after a run of response trials (and conversely extinguished that learning across a run of no-response trials). However, others argue that the response priming does not involve any learning process but reflects residual activation of a response set that has been recently performed, and so does not demonstrate evidence for conflicting motor control as a result of learning. Our experiment examined the priming mechanism (performance-based or learning-based) underlying conditions that produce a dissociation between responding and explicit expectancy. We found that motor system excitability was modulated in the presence of the warning cue but not in its absence. This result provides compelling evidence that the changes in responding that characterize Perruchet’s dissociation are indeed learned. The Perruchet effect is one of several phenomena that appear to dissociate the behavioral influence of associative learning from decisions based on higher order cognition. It bears some similarities, for instance, to dissociations between feature- and rule-based generalization in complex patterning tasks (e.g., Cobos et al. 2017), where speeded responses appear to be governed by simple associative learning but responses under more relaxed time constraints reveal a pattern consistent with a more elaborate combination rule. The use of this rule-based transfer has also been linked with participants’ ability to make model-based choices in reward learning tasks, effectively overcoming competing habitual tendencies (Don et al. 2016). Although the links between the Perruchet effect and these other phenomena are not yet clear, the effect can be viewed as one involving the development of habitual action tendencies. To this extent, our finding that cue-outcome associations can modulate motor system excitability across trial runs as short as 3 or 4 suggests that under the right conditions, habits can form rapidly. Although recent work has shown that several days of training may be necessary for the expression of habitual behaviors (for instance, Hardwick et al. 2018, using a visuomotor association task), it may still be the case that the development of such tendencies is incremental and continuous, and that the Perruchet effect measures a form of emerging automaticity that produces habitual behavior after extensive experience. In summary, we replicated the Perruchet effect in a cued-response Go/No-Go task (e.g., Perruchet et al. 2006). We found the typical dissociation between top-down goal-directed expectations and bottom-up cue-elicited motor control in a scenario where a warning cue preceded an uncertain outcome. Further, we showed that the bottom-up processes responsible for variations in response speed are based in associative learning and are not simply performance effects. Critically, this finding reveals that motor preparation can be controlled involuntarily by a conditioned stimulus (i.e., the warning cue), even when it opposes explicit belief of the anticipated outcome and the need to act. The results from this study provide novel insights into the limits of goal-directed motor control; even when people have formed conscious expectancies about an upcoming outcome, they do not always adjust their behaviors accordingly and their actions can still be biased by environmental cues. Funding Australian Research Council’s Discovery Projects funding scheme (project number DP160102871 and DP190100410). 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TI - Motor Conflict: Revealing Involuntary Conditioned Motor Preparation Using Transcranial Magnetic Stimulation
JF - Cerebral Cortex
DO - 10.1093/cercor/bhz253
DA - 2020-04-14
UR - https://www.deepdyve.com/lp/oxford-university-press/motor-conflict-revealing-involuntary-conditioned-motor-preparation-njJ4NECcPs
SP - 1
VL - Advance Article
IS - 
DP - DeepDyve
ER -