Human primary motor cortex represents evidence for a perceptual decision before motor response

Perceptual decision making involves a complex network of brain regions including premotor and motor cortices. Premotor areas activate in proportion to the available evidence, thus anticipating the movements required to indicate the still developing decision. Conversely, primary motor cortex is thought to execute planned movements indicating a completed decision by innervating muscles. Recent results question this strict division of labour among premotor and primary motor areas, but the exact role of primary motor areas in perceptual decision making remains unclear. Here we tested the hypothesis that human primary motor cortex follows the ups and downs of available evidence during decision making. We used stimuli changing randomly every 100 ms to induce fast variations in the available decision evidence throughout single trials. The stimuli were chosen such that participants had to observe typically more than 5 stimulus changes before being able to make a confident decision. This enabled us to investigate corresponding changes in brain signals within trials. We correlated the stimulus-induced varying evidence as predicted by an ideal observer model with the ongoing neuronal signals measured by magnetoencephalography. This approach provided us with unprecedented statistical precision for identifying brain areas that represent decision evidence. We found that the primary motor cortex of humans indeed represents decision evidence, at least 500 milliseconds before the actual response, confirming that it is not just executing, but also anticipating decisions.


Introduction 31
During perceptual decision making observers judge the state of their environment. Already while the 32 observer makes the decision, brain areas presumably related to motor planning such as premotor 33 cortex and the frontal eye fields represent evidence for the decision (Gold & Shadlen, 2007;Hanks & 34 Summerfield, 2017). In contrast, primary motor cortex has traditionally been only associated with the 35 execution of movements through the suitable activation of muscles (Kalaska & Rizzolatti, 2012). This 36 view suggests that the primary motor cortex is only marginally involved in the decision making process 37 by signalling the outcome of the decision in the form of a motor command. 38 This strict functional role of primary motor cortex as a motor control device has come under strain in 39 the recent past. In monkeys, there are single primary motor cortex neurons whose firing rate appears 40 to track the decision evidence shown on a screen before committing to a motor response (Thura & 41 Cisek, 2014). In humans, lateralised oscillatory signals, for example, in the beta band measured with 42 magnetoencephalography (MEG) exhibit choice predictive build-up that is thought to mirror the 43 increasing evidence for a decision and the sources of these oscillations have been located in dorsal 44 premotor and primary motor cortex ( Gardelle, Scholl, & Summerfield, 2012). The advantage of this approach, relative to previous ones, is 59 that the induced evidence fluctuations enable more specific predictions about the time course of 60 measured evidence signals and therefore increase the power and specificity of the corresponding 61 analyses. Applying this approach to MEG measurements we, therefore, expected to be able to identify 62 areas in the human brain that specifically represent decision evidence while the decision develops. 63 Although the focus of our analyses was on primary motor cortex, the approach also allowed us to test 64 for representations of decision evidence across the whole cerebral cortex. 65 Using source reconstruction of the MEG data, we found significant correlations with decision evidence 66 in primary motor cortex 300 to 500 ms after the onset of a new stimulus element. Critically, these 67 correlations were distinct from the motor signal related to a participant's actual motor response. This 68 finding confirms that human primary motor cortex represents decision evidence while making 69 perceptual decisions and not only executes decisions. Apart from primary motor cortex, we identified 70 the posterior cingulate cortex as a brain area with consistent representations of decision evidence. 71

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While MEG was recorded, 34 human participants observed a single white dot on the screen changing 73 its position every 100 ms and had to decide whether a left or a right target (two yellow dots) was the 74 centre of the white dot movement (Figure 1). Participants indicated their choice with a button press 75 using the index finger of the corresponding hand. The distance of the target dots on the screen was 76 chosen in behavioural pilots so that participants had an intermediate accuracy around 75% while being 77 told to be as accurate and fast as possible. The average median response time across participants was 78 1.1 s with an average accuracy of 78% (cf. Figure 1). 79 80 An ideal observer model for inference about the target given a sequence of single dots has been 81 described before ( Participants integrate evidence provided by single dot positions to make decisions 88 As the task required and the model predicted, participants made their decision based on the provided 89 evidence. In Figure 2 we show this as the correlation of participants' choices with momentary and 90 accumulated evidence. Momentary evidence was mildly correlated with choices throughout the trial 91 (correlation coefficients around 0.3) while the correlation between accumulated evidence and choices 92 increased to a high level (around 0.7) as more and more dot positions were presented. This result 93 indicates that participants accumulated the momentary evidence, here the x-coordinate of the dot, to 94 make their choices. In contrast, as expected, the y-coordinates had no influence on the participants' 95 choices as indicated by correlation coefficients around 0 ( Figure 2B). 96 Figure 1. Course of events within a trial in the single dot task (A) and behaviour of individual participants (B). Each trial started with the presentation of a fixation cross, followed by the appearance of the two yellow targets after about 1 s. 700 ms after the appearance of the targets the fixation cross disappeared and a single white dot was presented at a random position on the screen (drawn from a 2D-Gaussian distribution centred on one of the targets). Every 100 ms the position of the white dot was changed to a new random draw from the same distribution. Participants were instructed to indicate the target which they thought was the centre of the observed dot positions. After 25 dot positions (2.5 s) without a response, a new trial was started automatically, otherwise a new trial started with the response of the participant. Average behaviour (accuracy and median response time) for each of the 34 participant is shown in B.

97
Naturally, momentary and accumulated evidence can correlate quite strongly, because the last sampled 98 x-coordinate has a strong influence on the accumulated evidence (Supplementary Figure 1). Therefore, 99 we here only report results for momentary decision evidence and mean momentary evidence 100 whenever writing "evidence" in the text unless stated explicitly otherwise. As Figure 2 shows, and as 101 expected, the momentary evidence is more clearly dissociated from the final choice of the participants 102 than the accumulated evidence. This feature of the momentary evidence will be useful below in 103 separating effects related to decision evidence from effects related to the final choice. 104 MEG signals correlate with evidence at specific time points after stimulus update 105 For the analysis of the MEG data we used regression analyses computing event-related regression 106 coefficients ( As a first result, we found that correlations between momentary evidence and MEG signals followed a 116 stereotypical temporal profile after each dot position update (cf. Supplementary Figure 2). Therefore, 117 we performed an expanded regression analysis where we explicitly modelled the time from each dot 118 position update, which we call 'dot onset' in the following. To exclude the possibility that effects 119 signalling the button press motor response influence the results of the dot onset aligned analysis, we 120  only included data up until at least 200 ms prior to the participant response of each trial into this  121  analysis.  122   We first identified time points at which the MEG signal correlated most strongly with the momentary  123  evidence. To do this we performed separate regression analyses for each time point from dot onset,  124  magnetometer sensor and participant, computed the mean regression coefficients across participants,  125 took their absolute value to yield a magnitude and averaged them across sensors. Figure 3 shows that 126 the strongest correlations between decision evidence and magnetometer signals occurred at 120 ms, 127 180 ms and in a prolonged period from roughly 300 to 500 ms after dot onset. In contrast, correlations 128 with the control, that is, the dot y-coordinates, were significantly lower in this period from 300 to 500 129 ms (two-tailed Wilcoxon test for absolute average coefficients across all sensors and times within 300-130 500 ms, W = 382781, p << 0.001). 131

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The sensor topographies shown in Figure 3 also indicate for the decision evidence a progression of the 133 strongest correlations from occipital to central sensors while y-coordinate correlations remained 134 spatially at occipito-parietal sensors. 135 A motor-posterior cingulate network of brain areas correlating with decision evidence 136 We reconstructed source currents along the cerebral cortex for each participant and subsequently 137 repeated our regression analysis on the estimated sources. Specifically, we performed source 138 reconstruction on the preprocessed MEG data using noise-normalised minimum norm estimation 139 show the corresponding values for data which were randomly permuted across trials before statistical analysis. Black dots indicate time points for which the sensor topography is shown below the plot. These topographies directly display the grand average regression coefficients at the indicated time with negative (blue) and positive (red) values. (A) The decision evidence has strong correlations with the magnetometer signal at 120 ms, 180 ms and from about 300 ms to 500 ms after dot onsets. (B) The y-coordinate correlations are visibly and significantly weaker than for the evidence, but there are two prominent peaks from about 120 ms to 210 ms and at 320 ms after dot onset. There is no sustained correlation with the y-coordinate beyond 400 ms and the topographies of magnetometers differ strongly between evidence and ycoordinates. Specifically, the evidence exhibits centro-parietal topographies whereas the y-coordinate exhibits strong correlations only in lateral occipito-parietal sensors.
. CC-BY 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/350876 doi: bioRxiv preprint based on all MEG sensors (Dale et al., 2000;Gramfort et al., 2013Gramfort et al., , 2014. Further, we aggregated 140 estimated values by averaging across sources within 180 brain areas defined by a recently published 141 brain atlas (Glasser et al., 2016). This resulted in average time courses for each experimental trial in 142 each of the 180 brain areas defined per hemisphere for each participant. We then repeated the 143 expanded regression analysis on these source-reconstructed time courses instead of on MEG sensors. 144 Following the summary statistics approach we identified time points and areas with significant second-145 level correlations by performing t-tests across participants and applying multiple comparison correction 146 using false discovery rate (Benjamini & Hochberg, 1995) simultaneously across all time points and brain 147 areas. 148 In the time window from 300 to 500 ms after dot onset significant correlations with decision evidence 149 occurred predominantly in bilateral primary motor, somatosensory and posterior cingulate cortices, as 150 shown in Figure 4. 151 152 Spatial pattern of correlations in motor cortex is similar to activation for response 153 Having established the involvement of primary motor and somatosensory cortices in representing the 154 momentary evidence we tried to further clarify the nature of these effects. In a first step, we compared 155 the found spatial pattern of correlations with the response-specific (button press) activations in motor 156 areas. To do this we computed standard event-related averages centred on the response time of the 157 participants in source space. Additionally, to provide for a high spatial resolution, we repeated the 158 expanded regression analysis directly on sources of premotor and motor areas without averaging across 159 sources within area. For the comparison of spatial patterns resulting from both analyses we selected 160 those time points for each analysis which had the strongest effects in terms of second-level t-values in 161 bilateral primary motor cortex (area 4). These time points were 490 ms after dot onset for the analysis 162 centred on dot onset and 30 ms after the response for the response-aligned averages (cf. 163 Supplementary Figure 3). The resulting spatial patterns are shown in Figure 5 where one can see that 164 the sources of both effects have the same location in dorsal premotor and primary motor cortex. 165 Importantly, as we excluded in the dot onset centred analysis all time points earlier than 200 ms before 166 the motor response, this result indicates that the areas in the brain that were involved in the execution 167 of the button presses also represented decision-relevant information before the button press. 168 Figure 4. Primary motor, somatosensory and posterior cingulate cortices exhibit significant correlation with decision evidence in the time window of 300 to 500 ms after dot onset. Only brain areas with at least one significant correlation (p < 0.01, FDR corrected) within the time window are coloured. Colours show average second-level t-values where the average is taken over time points between 300 to 500 ms. The five areas with the strongest correlations (in that order) in the left hemisphere were (specified as Brodmann areas with subdivisions as defined in (Glasser et al., 2016); indicated by borders around areas): area 4, 3a, v23ab, 31pd, 3b and in the right hemisphere: v23ab, 7m, 31pd, 31pv, d23ab. All effects are listed in Supplementary Table 2.  7   169 Correlations with decision evidence in primary motor cortex occur well before the 170 response 171 In the previous, dot onset aligned analyses we excluded all data of time points later than 200 ms before 172 the response. This allowed us to exclude the possibility that response-related effects around the 173 response time influenced the found effects. To further investigate how early before the response 174 correlations with decision evidence occurred in primary motor cortex we conducted a response-aligned 175 regression analysis. To increase the statistical power of the analysis, similar to the expanded regression 176 analysis used above, we repeated the analysis for different assumed delays between a dot position 177 update on the screen and the neural effect. We then averaged regression coefficients across delays 178 from 300 to 500 ms within participant before conducting the second-level analysis. This means that an 179 effect at time point -500 ms from the response reflects a correlation of the signal 500 ms before the 180 response with dot positions that were visible on the screen 800 to 1000 ms before the response. See 181 Methods for a detailed description of the procedure. 182  The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/350876 doi: bioRxiv preprint

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The peaks (orange line) just after the response (dotted line) in Figure 6 reflect the motor response 191 typically observed for primary motor cortex. Around these time points left primary motor cortex is 192 strongly activated for a right button press and the right primary motor cortex is strongly activated for a 193 left button press, but not the other way around. This is also reflected in corresponding peaks in the 194 evoked signal (cf. Supplementary Figure 3). This motor response results in an increase in correlation 195 with decision evidence, because i) on average the evidence provided by dot positions will point towards 196 the correct choice, as expressed in the correlation between evidence and choice (cf. Figure 2) and ii) 197 the motor response signal is rather strong such that it leads to a larger correlation with decision 198 evidence as compared to pre-response time points. 199 Signals in posterior cingulate cortex correlate with decision evidence at early and late 200 time points after dot onset 201 In addition to motor cortex our results in Figure 4 identify the posterior cingulate cortex as another key 202 brain region involved in representing decision evidence. While posterior cingulate cortex has been 203 associated with perceptual decision making before ( to 500 ms after dot onset as in Figure 4. We found significant correlations in posterior cingulate cortex 209 already at 120 ms after dot onset, similarly as for early visual areas such as the primary visual cortex 210 (Supplementary Table 1). Further, the signal in posterior cingulate cortex exhibited correlations with 211 decision evidence around 180 ms after dot onset (cf. Figure 7), although these did not become 212 significant after correcting for multiple comparisons across the shown time points and brain areas (in 213 Figure 7A and B). 214 Figure 6. Correlations with decision evidence and dot y-coordinates in left and right primary motor cortex (area 4) aligned to response time. Strongest correlations with decision evidence (orange) occurred just after the response and can be attributed to response-related motor processes (see main text). However, evidence-correlations can be observed already 500 ms and earlier before the response. For comparison, y-coordinate-correlations are shown in green. Lines depict mean regression coefficients across participants together with a band of uncertainty showing twice the standard error of the mean (see Methods for details). Small coloured dots indicate time points at which the corresponding regressor differed significantly from 0 after multiple comparison correction for the number of time points across both hemispheres (FDR α = 0.01, no significant effects for the y-coordinate).
. CC-BY 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/350876 doi: bioRxiv preprint Among the effects in posterior cingulate cortex, area v23ab (roughly the ventral part of BA 23) stands 215 out, because it consistently exhibited relatively strong correlations (magnitude of second-level t-values 216 > 2) with decision evidence at the three time periods 120 ms, 180 ms, and 300 to 500 ms after dot 217 onset ( Figure 7B). In general, the time course of the correlation strength followed that of the grand 218 average shown in Figure 3A, but the sign of the correlation switched between 120 and 180 ms ( Figure  219 7B). This means that initially the signal in area v23ab increased for dot positions shown on the ipsilateral 220 side of the screen while at later time points we observed larger signals for dot positions on the 221 contralateral side. 222 223 For the perceptual control variable, the y-coordinate, an area in posterior cingulate cortex exhibited 224 the strongest correlations across time from dot onset (right POS2). However, these correlations were 225 relatively small after about 220 ms and specifically after 400 ms from dot onset and generally followed 226 the corresponding time course of the whole-brain correlation strength shown in Figure 3. 227 Discussion 228 We have investigated the involvement of motor areas in the human brain during a perceptual decision  (Glasser et al., 2016) shown in a medial view of the inflated cerebral cortex. For the decision evidence (B) and y-coordinate (C) we selected the brain areas with the highest mean absolute correlations across time from dot onset. These were both located in posterior cingulate cortex (evidence: right v23ab, y-coordinate: right POS2). The panels show time courses for the corresponding regression coefficients (β) for individual participants (light grey) and their second-level mean (black). Dots above the traces indicate time points at which the second-level mean differed significantly from 0 (p < 0.01, uncorrected).
. CC-BY 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/350876 doi: bioRxiv preprint motor response and occurred several hundreds of milliseconds before the actual response. These 236 results, therefore, confirm that the human primary motor cortex does not merely execute completed 237 decisions, but also prepares motor responses in proportion to the available decision evidence. Apart 238 from motor areas we additionally found posterior cingulate cortex to represent decision evidence. 239 While the correlations with evidence occurred in motor cortex only in the time window from 300 to 240 500 ms post-stimulus, correlations in posterior cingulate cortex occurred additionally already at 120 241 and 180 ms. 242 To manipulate decision evidence in our task we changed the position of a single dot presented on a 243 screen. Only the x-coordinates of these dot positions represented decision evidence while the decision-244 irrelevant y-coordinates acted as a perceptual control variable. We have shown that correlations of 245 brain signals with the perceptual control variable, in contrast to decision evidence, were strongly 246 diminished in the period from 300 to 500 ms after dot onset. This suggests that the brain ceases to 247 represent perceptual information that is behaviourally irrelevant around this time and that brain areas precision. Specifically, we were able to show that human primary motor cortex not only increases its 259 activity in preparation for a response, as, for example, measured in lateralised readiness potentials 260 (Smulders & Miller, 2012), or shown for a motion discrimination task (Donner et al., 2009), but the 261 average source currents in primary motor cortex tend to rise and fall with decision evidence on a 262 timescale of 100 ms. According to our results, these decision evidence signals in primary motor cortex 263 are about an order of magnitude weaker than the response-related signal in primary motor cortex (cf. 264 Figure 6 pre-response correlations versus post-response peak). 265 One potential caveat of our correlation results in primary motor cortex is: Could it be that we only 266 observed evidence correlations in primary motor cortex, because participants actually executed micro-267 movements that tried to track the perceptual stimulus? Especially, did participants try to follow dot 268 movements on the screen with their eyes, or did their fingers slightly move over the corresponding 269 buttons, when the dot was shown on the respective side of the screen? Although we cannot completely 270 exclude this possibility we deem it unlikely, because: i) Dots were shown only very centrally at visual 271 angles within about 10° visual angle with most dots within 5° diameter from fixation meaning that most 272 dots were well within the foveal visual field. ii) The spatial pattern of early evidence correlations 273 corresponds to that of later button press responses ( Figure 5) What is the functional role of primary motor cortex, beyond its obvious role of causing the movement? 284 The present results support the notion that primary motor cortex continuously prepares for the 285 execution of alternative actions in proportion to their behavioural relevance before committing to a 286 choice, as it has been found for single neurons in monkeys (Thura & Cisek, 2014). Such an interpretation 287 would be congruent with the affordance competition hypothesis (Cisek, 2007) which describes 288 movement generation as a dynamic process in which possible actions compete with each other and are 289 continuously refined in a loop between sensory and motor related areas while frontal areas provide 290 contextual modulation. 291 Given that primary motor cortex itself, as the area in cerebral cortex that is most directly associated 292 with the execution of movements, represents decision evidence, one may ask whether the eventual 293 choice is also made in primary motor cortex and not, for example, in more frontal regions. Possible 294 neural implementations of a suitable mechanism have been proposed (Wang, 2008). In these, different 295 pools of neurons in one brain area compete until a threshold is crossed and one pool decisively signals 296 the choice. Our results are compatible with such a mechanism, but one can ultimately not exclude the 297 possibility that the decision is formed somewhere else in the brain and primary motor cortex only 298 represents the results of this process. We also observed some correlations with decision evidence in 299 premotor regions (Figure 4 and Figure 5), but these tended to be weaker than in primary motor cortex 300 (Supplementary Table 2). We did not find any correlations of sufficient strength to pass the correction 301 for multiple comparisons in other frontal areas (Supplementary Table 1). Although this may suggest that 302 primary motor cortex plays a more important role in the decision making process than these areas, the 303 lack of correlation may also be explained by the sensitivity profile of MEG measurements across 304 cerebral cortex. It has, for example, previously been noted that "only areas with a macroscopic 305 contralateral motor bias were apt to signal subjects' choices", when measured with MEG (Donner et  306 al., 2009). That we were able to identify additional areas representing decision evidence, specifically 307 the posterior cingulate cortex, supports the strength and increased power of our approach compared 308 to previous ones, but the precise limits of which representations of decision evidence in the brain can 309 and cannot be detected with MEG has still to be determined. 310 Based on single neuron recordings in dorsal premotor cortex, primary motor cortex and the basal 311 ganglia of monkeys, it has been suggested that perceptual decisions are made in premotor or primary 312 motor cortex while the basal ganglia eventually invigorate the movement selected in motor cortex 313 (Thura & Cisek, 2017). While these findings, as ours, have been obtained with perceptual decision 314 making tasks in which sensory evidence immediately maps to a button press or reaching movement, it 315 is an interesting future research question how representations of evidence across motor areas change, 316 when the response mapping is only revealed after the decision, for example with a sufficiently large 317 delay after the offset of stimulus presentation (Filimon, Philiastides, Nelson, Kloosterman, & Heekeren, 318 2013; Liu & Pleskac, 2011). In this situation the brain cannot frame decision making as a competition 319 between specific actions and may represent decision evidence in different coordinates and in different 320 brain areas than primary motor cortex. 321 The correlations found in posterior cingulate cortex rivalled in strength those of primary motor cortex, 322 or even exceeded it (Supplementary Table 2). Further, while in motor areas these correlations occurred 323 only from about 300 ms after the evidence first became available, we also observed significant 324 correlations much earlier, around 120 ms and 180 ms, in posterior cingulate cortex and specifically in 325 the ventral part (Figure 7). We speculate that the first time point of significant correlations around 120 326 ms reflects early sensory processing of dot positions, because we found the by far largest effects at this 327 -300 ms to 2500 ms from the first dot onset (zero). Another ICA was applied to these epoched data in 424 order to check for additional artefacts and confirm typical neural topographies from the components. 425 The ICA reconstructed data and original data were compared and inspected in order to ensure only 426 artefactual trials were excluded. Before statistical analysis we used MNE-Python v0.15.2 (Gramfort et 427 al., 2013(Gramfort et 427 al., , 2014 to downsample the data to 100 Hz (10 ms steps) and perform baseline correction for 428 each trial where the baseline value was the mean signal in the period from -300 ms to 0 ms (first dot 429 onset). 430 Source reconstruction 431 We reconstructed the source currents underlying the measured MEG signals using noise-normalised 432 minimum norm estimation (Dale et al., 2000) implemented in the MNE software. To create participant-433 specific forward models we semi-automatically co-registered the head positions of participants with 434 the MEG coordinate frame while at the same time morphing the participants' head shape to that of 435 Freesurfer's fsaverage by aligning the fsaverage head surface to a set of head points recorded for each 436 participant. We defined a source space along the white matter surface of the average subject with 4098 437 equally spaced sources per hemisphere and an approximate source spacing of about 5 mm (MNE's 438 "oct6" option). For minimum norm estimation we assumed a signal-to-noise ratio of 3 (lambda2 = 0.11). 439 We estimated the noise covariance matrix for noise normalisation ( all of these data points across trials and inferred regression coefficients on these expanded data sets. 481 Note that this approach can equally be interpreted as statistical inference over how strongly the 482 sequence of momentary evidence caused by the dot updates is represented in the signal at 100 ms 483 wide steps with a delay given by the chosen time from dot onset. 484 These analyses included two regressors of interest: decision evidence (x-coordinate) and y-coordinate 485 of the associated dots. We additionally included the following nuisance regressors: an intercept 486 capturing average effects, the absolute values of x-and y-coordinates, perceptual update variables for 487 x-and y-coordinates  defined as the magnitude of the change from one dot position 488 to another and accumulated values of x-and y-coordinates. Because we found that the accumulated 489 values can be strongly correlated with the individual x-and y-coordinates (cf. Supplementary Figure 1), 490 we only used accumulated values up to the previous dot in the regressor. For example, if a data point 491 was associated with the y-coordinate of the 4 th dot, the accumulated regressor would contain the sum 492 of only the first three y-coordinates. This accumulated regressor is equal to the regressor resulting from 493 Gram-Schmidt orthonormalisation of the full sum of y-coordinates with respect to the last shown y-494 coordinate. The accumulated evidence regressor was derived from the ideal observer model as the log 495 posterior odds of the two alternatives, but this was almost 100% correlated with the simple sum of x-496 The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/350876 doi: bioRxiv preprint coordinates. The small differences between model-based accumulated evidence and sum of x-497 coordinates after normalisation resulted from a small participant-specific offset representing the 498 overall bias of the participant towards one decision alternative. 499 Identification of significant source-level effects 500 To identify significant correlations between regressors of interest and source signals we followed the 501 summary statistics approach (Friston, Ashburner, Kiebel, Nichols, & Penny, 2006) and performed two-502 sided t-tests on the second level (group-level, t-tests across participants). We corrected for multiple 503 comparisons across time points and brain areas by controlling the false discovery rate using the 504 Benjamini-Hochberg procedure (Benjamini & Hochberg, 1995). Specifically, for identifying significant 505 effects reported in Figure 4 we corrected across 25,340 tests covering 70 time points (0 to 690 ms from 506 dot onset in 10 ms steps) and 362 brain areas (180 brain areas of interest per hemisphere plus one 507 collection of sources per hemisphere that fell between the area definitions provided by the atlas). We 508 report all significant effects of this analysis in Supplementary Table 1. 509 Response-aligned analysis 510 511 In Figure 6 we report time courses of group-level regression coefficients aligned to trial-specific 512 response times of participants. To estimate the impact of dot positions on the signal in primary motor 513 cortex (area 4) we associated each time point from the response in a trial with the dot position that 514 was visible on the screen a fixed temporal distance (delay, Figure 9) before that time point. This delay 515 implemented a hypothesis of when after a dot position change we would observe the effects in the 516 signal in the form of correlations. For each time point from the response and participant and given a 517 fixed delay we estimated regression coefficients for regressors x-coordinate, y-coordinate, perceptual 518 updates for x and y and intercept across trials of individual participants. We further repeated this for 519 all delays from 300 to 500 ms, because of our previous finding that evidence correlations were strong 520 on average across the brain in this time period after dot onset. We then averaged regression 521 coefficients across delays within participants, thus considering participant-level variation. We 522 computed group-level statistics using two-sided t-tests over the averaged coefficients and corrected 523 for multiple comparisons across time points and the two hemispheres with the Benjamini-Hochberg 524 procedure with α = 0.01. After this correction, only the evidence and intercept (response-aligned 525 average) had group-level coefficients significantly different from 0 (evidence effects shown in Figure 6, 526 significant intercept effects: 10-50 ms in left M1, -420 ms in right M1). Figure 6 depicts the mean 527 coefficients across participants for each time point before the trial-specific response together with a 528 band of uncertainty with a width of twice the standard error of the mean above and below the mean.  Numerical values plotted in Figure 4 658 Supplementary   Values are mean (across brain areas) magnitude of second-level regression coefficients (β). Shown are effects for all used regressors (excluding the intercept). All regressors were derived from the dot positions. Regressors derived from xcoordinates (evidence) are shown in blue while y-coordinate regressors are shown in orange. For reference we also plotted regression coefficients obtained from permuted data as dotted lines. We included 4 different measures in our analysis: 'momentary' (evidence) are the original x-and y-coordinates (cf. Figure 3), 'accumulated' corresponds to summed coordinates, but only up to the previous dot (see Methods), 'absolute' are the absolute coordinates measuring only the displacement of the dot from 0 in both directions and 'perceptual update' is the absolute difference between the latest and the previous dot positions (cf. Wyart et al., 2012). Only the regressors respecting the sign of the coordinates (momentary and accumulated) exhibit strong effects. Note that we shifted the effects for the accumulated regressors 100 ms to the right to account for them being defined for the previous dot position instead of the current ones as for the other regressors. Also, the effects measured with the accumulated regressors, although accumulated and momentary regressors were uncorrelated, are a mixture of effects attributable to raw x-, y-coordinates and their cumulative sum, because accumulated regressors of dot d-1, although being uncorrelated to momentary regressors of dot d, are correlated with momentary regressors of dot d-1 and earlier.
. CC-BY 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/350876 doi: bioRxiv preprint