Perceptual decisions made in the face of uncertain sensory evidence are often biased by previous stimuli, choices, and choice outcomes (Gold et al., 2008; Busse et al., 2011; de Lange et al., 2013; Akaishi et al., 2014; Fischer and Whitney, 2014; Fründ et al., 2014; Abrahamyan et al., 2016; Pape and Siegel, 2016; St John-Saaltink et al., 2016; Fritsche et al., 2017; Hwang et al., 2017; Urai et al., 2017; Braun et al., 2018). In most standard laboratory tasks, the environmental state (i.e., stimulus category) is uncorrelated across trials. In that context, such history biases tend to impair performance (Abrahamyan et al., 2016). However, when the environmental state exhibits some stability across trials, as is common for natural environments (Yu and Cohen, 2009; Glaze et al., 2015), trial history biases substantially improve performance (Braun et al., 2018). Previous behavioral work shows that humans and other animals flexibly adapt their trial history bias to the correlation structure of the environment (Abrahamyan et al., 2016; Kim et al., 2017; Braun et al., 2018; Hermoso-Mendizabal et al., 2020).

How do adaptive history biases influence the formation of subsequent decisions? Prominent models conceptualize the decision formation as the accumulation of noisy sensory evidence into a decision variable (DV) that grows with time until a bound for one of the choice alternatives is crossed and the corresponding action is initiated (Bogacz et al., 2006; Gold and Shadlen, 2007; Ratcliff and McKoon, 2008; Brunton et al., 2013; Ossmy et al., 2013). In such accumulator models, history biases may shift the starting point of the DV before evidence onset and/or bias the evidence accumulation per se. Behavioral modeling indicates that individual differences in the idiosyncratic history biases occurring in random environments are better explained by biases of evidence accumulation than by starting point biases (Urai et al., 2019). Such effects have neither been assessed for adaptive biases in structured (stable or systematically alternating) environments nor have they been unraveled at the neural level. We hypothesized that adaptive history biases translate into a biased build-up rate (accumulation bias) of neural signatures of the DV, more so than an offset before decision formation (starting point), in a fashion that depends on the correlation structure of the environment.

Neural signals exhibiting functional properties of the DV have been observed in parietal and frontal cortical areas involved in action planning in both primates (Shadlen and Kiani, 2013; Peixoto et al., 2021) and rodents (Hanks et al., 2015; Brody and Hanks, 2016). Specifically, when choices are reported with hand movements, hallmark signatures of the DV are evident in motor preparatory activity in primate (human and monkey) premotor and primary motor (M1) cortex. In human motor cortex, this selective motor preparatory activity is expressed in a suppression of ongoing beta-band oscillations contralateral to the upcoming hand movement, accompanied by an enhancement of gamma-band power (Crone et al., 1998a; Crone et al., 1998b; Donner et al., 2009) and likely spiking activity. While the origin of this beta-power suppression remains under study (Sherman et al., 2016; Little et al., 2019) we here use it as a functional marker of the DV encoded in local patters of spiking activity: Like this spiking activity (Shadlen and Kiani, 2013; Peixoto et al., 2021), the beta-band suppression (i) encodes the specific choice that will later be reported, (ii) gradually builds up during decision formation, with a rate that scales with evidence strength, and (iii), in reaction time tasks, converges on a common level just before action execution (Donner et al., 2009; O’Connell et al., 2012; Wyart et al., 2012; de Lange et al., 2013; Fischer et al., 2018; Wilming et al., 2020; Murphy et al., 2021).

We combined a canonical decision-making task, discrimination of the net motion direction of dynamic random dot patterns (Gold and Shadlen, 2007), with a systematic manipulation of the environmental correlation structure. Our manipulation was motivated from an ecological perspective (Mobbs et al., 2018), specifically, the insights that (i) natural environments are commonly structured and (ii) history biases that change flexibly with this environmental structure are a hallmark of adaptive behavior. Because previous work on such correlated environments was purely behavioral (Abrahamyan et al., 2016; Kim et al., 2017; Braun et al., 2018; Hermoso-Mendizabal et al., 2020), the neural signatures of adaptive history biases have remained unknown. Although several previous studies have identified neural correlates of history biases in standard perceptual choice tasks (i.e., using unstructured environments) (Talluri et al., 2021), all but one study performed in monkeys (Mochol et al., 2021) focused on static representations of the bias in ongoing activity preceding the new decision. Therefore, it has remained unknown whether such a dynamic bias during evidence accumulation exists in the human brain.

Single-trial behavioral modeling uncovered the resulting history-dependent biases as well as their flexible adjustment to the environmental correlation structure. Relating the model-inferred time-varying history bias to magnetoencephalography (MEG) measurements of the pre-trial baseline state and subsequent build-up rate of action-selective motor cortical population activity identified a neural signature of this adaptive bias in the latter, not the former. In sum, we show that the sign and rate of the build-up of a selective neural marker of DV during evidence accumulation track a dynamic history bias that is adapted to the environmental structure.

Human participants (N=38) performed a random dot motion (up vs. down) discrimination task with varying levels of motion strength spanning psychophysical threshold (Figure 1A, Materials and methods). We alternated the task, in pseudo-random order, between three different sensory environments with distinct repetition probabilities of stimulus categories (i.e., motion directions) across trials, referred to as neutral, repetitive, and alternating, respectively (Figure 1B). These three environments were characterized by approximately equal fractions of upward and downward motion stimuli, and they were presented in blocks of 99 trials each, separated by pauses (Materials and methods). Participants were not informed about the existence of these different environments and received outcome feedback after each choice.

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Large-scale cortical dynamics of task processing

The behavioral results reported above indicate that participants adjusted their history bias to the environmental statistics, which, in turn, boosted their performance. How did these (partly) adaptive history biases affect the formation of subsequent decisions, more specifically: the dynamics of the underlying DV in the brain? Our concurrent collection of whole-brain MEG data during this task enabled us to address this question. We combined source reconstruction with established anatomical atlases and spectral analysis to characterize the cortical dynamics involved in our task across several pre-defined cortical regions known to be involved in visual processing and action planning (Wilming et al., 2020; Murphy et al., 2021).

We first identified established task-related modulations of MEG power during decision formation. Control analyses indicated minor leakage between the source estimates for neighboring cortical regions, but negligible leakage for more distant regions (Figure 2—figure supplement 1; Materials and methods). Importantly, our analysis revealed distinct functional profiles for several regions, in line with previous work (Siegel et al., 2011; Wilming et al., 2020; Murphy et al., 2021; Urai and Donner, 2022): modulations in visual cortical regions that scaled with motion coherence and encoding of the evolving action plan in frontal (motor and premotor) and parietal cortical regions (Figure 2).

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In line with previous work (Siegel et al., 2007), gamma-band power (~60–100 Hz) in visual cortex was enhanced while low-frequency power (<30 Hz) was suppressed relative to baseline during motion viewing (Figure 2A); both components of the visual responses scaled with motion coherence, predominantly in dorsal visual cortical areas V3A/B, IPS0-3, and the MT+ complex (Figure 2B). Concomitantly with these responses to visual motion, activity lateralization predicting the subsequent choice (left vs. right button) built up in downstream (anatomically more anterior) parietal and motor cortical areas (Figure 2C). Again in line with previous work (Donner et al., 2009; de Lange et al., 2013; Pape and Siegel, 2016; Wilming et al., 2020; Murphy et al., 2021), this action-selective activity build-up was a suppression of beta-band (12–36 Hz) power contra- vs. ipsilateral to the upcoming movement, and robustly expressed in the M1 hand area (Figure 2C and Figure 4A). This signal, referred to as ‘motor beta lateralization’ in the following, has been shown to exhibit hallmark signatures of the DV, specifically: (i) selectivity for choice and (ii) ramping slope that depends on evidence strength (Siegel et al., 2011; Murphy et al., 2021; O’Connell and Kelly, 2021).

This signal reached statistical significance earlier for correct than error trials and during the stimulus interval it ramped to a larger amplitude (i.e., more negative) for correct trials (Figure 2—figure supplement 2, left). But the signal was indistinguishable in amplitude between correct and error trials around the time of the motor response (Figure 2—figure supplement 2, right). We also confirmed the dependence of the ramping of the motor beta lateralization on evidence strength using a single-trial regression also including the history bias that we report in the section Adaptive history bias shapes the build-up of action-selective motor cortical activity below.

In sum, we replicated well-established signatures of visual motion processing and action-selective motor preparation in our current MEG data – for the latter signal in particular, some hallmark signatures of the DV: selectivity for choice, dependence of slope on evidence strength, and dependence of behavioral accuracy. In what follows, we systematically quantify the impact of the adaptive history bias on two fundamental aspects of this neural DV marker: its baseline level at the start of the decision process and its build-up during decision formation.

No consistent modulation of baseline state of action-selective activity by environmental context and trial history

Previous human MEG work indicates that the motor cortical baseline beta-power state is flipped relative to its state just before the previous choice, a phenomenon referred to as ‘beta rebound’ (Pfurtscheller et al., 1996; Pape and Siegel, 2016; Urai and Donner, 2022) that was also evident in our data (Figure 3A, collapsed across all three environments). Recent MEG studies of human perceptual decision-making have linked this phenomenon to either overt choice alternation (Pape and Siegel, 2016) or alternating starting points inferred from drift diffusion model fits (Urai and Donner, 2022). We, therefore, wondered if and how the baseline level of motor beta lateralization depended on the different sensory environments or on the history bias in specific trials.

Figure 3

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If this baseline lateralization state ‘inherited from’ the previous choice was involved in mediating the effect of the adaptive bias on choice, it might be expected to be reduced in repetitive vs. alternating environments, thus reducing subjects’ tendency to alternate in the former. Instead, the beta rebound effect (i.e., increased power contralateral vs. ipsilateral to previous choice) was about equally strong in all three environments (Figure 3B). We found no evidence for its modulation by sensory environment (repetitive vs. neutral: p=0.1511; repetitive vs. alternating: p=0.5667; neutral vs. alternating: p=0.0789; all two-sided permutation tests).

We then related the baseline M1 beta lateralization to the adaptive history bias. To this end, we adapted a single-trial regression procedure from recent monkey physiology work (Mochol et al., 2021) to relate the time-varying history bias to neural data in order to test if this bias modulated the baseline motor beta lateralization on a trial-by-trial basis. We used each individual’s time course of single-trial history bias estimated through the behavioral model (positive values for bias toward upward, Figure 1E) as predictors for the single-trial motor beta lateralization, whereby lateralization was assessed relative to the hand coding for up-choices in a given block (Materials and methods). This procedure took the impact of both previous stimuli and previous choices into account, estimated with an individually optimized number of lags. Thus, the single-trial bias estimates were largely independent of assumptions about the sources of the single-trial bias (stimuli, choices, lags). However, because the model was fit and applied separately to data from different environments, the resulting time course of single-trial bias estimates captured the context-dependent, adaptive bias components described in the preceding section. An involvement of the motor baseline state in the implementation of history bias predicts a stronger baseline beta-suppression contralateral to the hand favored by the bias, with a magnitude that scales with the strength of the bias. In other words, this scenario predicts significant negative beta coefficients, regardless of the environment.

We found no such effect when the analysis was run across all three environments (Figure 3C, ‘all’), again inconsistent with the notion of a generally bias-encoding neural signal. We did find an effect of the single-trial bias on the baseline beta lateralization state in the alternating environment when analyzed selectively (Figure 3C; p=0.0003, two-tailed permutation test). Such an effect was, however, not present for either of the other two environments (Figure 3C; all: p=0.4465; repetitive: p=0.1343; neutral: p=0.6571; two-tailed permutation tests). Overall, the results of our analyses of the baseline beta lateralization suggest the beta rebound from the previous trial may help promote choice alternation when performing in an alternating context, but does not generally encode adaptive stimulus history biases.

Adaptive history bias shapes the build-up of action-selective motor cortical activity

The analyses from the previous section assessed the dependence of the starting point (i.e., baseline level) of a neural DV-proxy on environmental context and adaptive history bias. Behavioral modeling has shown that idiosyncratic history biases in a variety of tasks in random environments are accounted for by history-dependent biases in the build-up (i.e., drift) of the DV, rather than in its starting point (Urai et al., 2019). We, therefore, next asked whether the adaptive history biases identified here might shape the build-up rate of our neural proxy of the DV during decision formation.

To test this idea, we used two complementary approaches both of which again exploited our model-inferred single-trial bias estimates. In one of those approaches, we grouped the single-trial bias estimates into three equally spaced bins, with two bins containing strong biases of opposite direction (up vs. down) and the middle bin containing trials with little bias (Figure 1E). We used the ‘up’ and ‘down’ bins to visualize the impact of the history bias on the ramping of the neural DV, by computing the time course of beta lateralization contra- vs. ipsilateral to the button-press for the direction of the bin-wise bias (Materials and methods). The behavioral choice was, by definition, correlated with both, the single-trial bias and the action-selective motor beta lateralization (Figure 2C and Figure 4A). This could yield a correlation between bias and motor cortical lateralization even in the absence of any direct effect of bias on motor beta lateralization. To isolate a genuine effect of the bias on our neural DV, we subsampled the data from the up and down bias bins to yield an equal number of upward and downward choices within each bin (Materials and methods). For each bin, we then computed the time course of beta lateralization relative to the button-press for the bias and collapsed the resulting time courses across bins. This procedure isolated the impact of the model-inferred history bias on the neural DV, independent of the choice. The resulting time course ramped into the direction of the single-trial bias, reaching statistical significance at about 700 ms after motion onset, before the end of the decision interval (Figure 4B). We used linear regression to estimate the slope of the ramp for an interval that exhibited clear linear ramping in the average motor beta lateralization across all trials (gray-shaded in Figure 4A, Materials and methods). As expected, the slope was smaller than zero (p=0.048; one-tailed permutation test against zero; Figure 4C, left), indicating that the time-varying history bias contributed to the build-up of action-selective motor cortical activity during decision formation. We obtained qualitatively identical results as in Figure 4B and C when first removing (using linear regression) the beta-rebound from the previous trial (Figure 3A) from the time course of the beta-band lateralization (data not shown).

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Second, we again fit a single-trial regression model, now to simultaneously quantify the impact of the history bias and current evidence on the dynamics of the neural DV in a time-variant fashion. We ran two separate regression models, one on the amplitude of motor beta lateralization for a range of time windows, the other on the slope of motor beta lateralization, assessed locally in time for the same time windows; in both cases, lateralization was again assessed relative to the hand coding for up-choices in a block (Materials and methods). An impact of the adaptive history bias on the ramping of motor cortical activity would predict a specific effect of the history bias, over and above the effect of the sensory evidence, on both read-out measures, in particular on the ramping slopes. Specifically, it predicts negative beta weights, reflecting steeper downward slope (i.e., stronger suppression) for stronger biases.

We found a clear and expected effect of current sensory evidence on motor beta lateralization, with a steeper downward slope for stronger evidence (Figure 4E). This dependence of the ramping of the motor beta lateralization on current motion strength is in line with previous work (de Lange et al., 2013) and confirms that the motor beta lateralization reflects hallmarks of a neural DV. Critically, and in line with our hypothesis, the same was true for the effect of the history bias: a stronger bias produced a stronger and steeper motor beta lateralization toward the direction of the bias (Figure 4D). The bias effect on the lateralization amplitude reached significance during the decision interval (from about 320 to about 720 ms after motion onset; Figure 4D, black line), and the corresponding impact on the ramping slope was significant even earlier, during the first half of the decision interval (starting at about 150 ms after the motion onset; Figure 4D, gray line). Combined, these two effects indicate that a strong bias on a given trial constituted an early force on the M1 ramping dynamics, pushing the signal into the direction of the bias even before the current evidence exerted its effect (compare with gray lines in Figure 4E); the M1 lateralization amplitude later during the decision interval reflected the bias more strongly on trials, for which the bias was strong than those, for which the bias was weak.

Our analysis of the ramping slopes in Figure 4C (left) estimated the slope for a longer (and later) time interval than the one, for which the single-trial regression in Figure 4D yielded significant slope effects. We, thus, repeated the above analysis also for the earlier time window derived from the single-trial regression results Figure 4C (right). Also, for this window did we find a robust effect of the bias estimate on the ramping slope (p=0.0158; one-tailed permutation test against zero). Taken together, model-independent and model-based analyses provided convergent evidence for the dependence of action-selective cortical ramping activity during decision formation on the time-varying, context-dependent history biases.

It has long been known that the history of preceding choices and stimuli biases perceptual judgments of the current stimulus (Fernberger, 1920). Recent behavioral modeling showed that at least part of such history biases reflect time-varying expectations that are flexibly adapted to the environmental structure (Abrahamyan et al., 2016; Braun et al., 2018; Hermoso-Mendizabal et al., 2020). Such dynamically varying expectations, largely ignored in standard neurophysiological studies of perceptual decision-making, may be a key driver of sensory-guided behavior in ecological settings (Mobbs et al., 2018). How adaptive expectations shape the neural dynamics underlying decision-making has remained unknown. Here, we addressed this issue by combining a standard task from the neurophysiology of decision-making (Gold and Shadlen, 2007; Siegel et al., 2011) with systematic manipulations of the environmental stability as well as single-trial, model-based MEG assessment of cortical decision dynamics. This revealed that the history-dependent, dynamic expectations boosted participants’ behavioral performance and selectively altered the build-up sign and rate, not (consistently) the pre-trial baseline level, of an established neurophysiological proxy of the DV: action-selective preparatory population activity in their motor cortex.

While participants’ history biases in a random environment (i.e., uncorrelated stimulus sequences) were largely idiosyncratic, as widely observed (Akaishi et al., 2014; Urai et al., 2019; Urai and Donner, 2022), we found that one component of these biases lawfully shifted between stable (frequent category repetitions) and systematically alternating environments and improved participants’ performance. It is instructive to compare this adjustment of history bias with the one observed in a previous study using a similar manipulation of environmental statistics (Braun et al., 2018). In that previous study, participants did not receive outcome feedback and thus remained uncertain about the category of the previous stimulus. Correspondingly, the history bias adjustment was evident in the impact of their previous choices (rather than previous stimulus categories), and most strongly of those made with high confidence (i.e., correct and fast). By contrast, in the current study, participants could deterministically infer the true category of the previous stimulus from the feedback. Correspondingly, we found that their history bias adjustment to the different environments was now governed by the previous stimulus category. Together, the findings from both studies support the notion that human subjects can use different types of internal signals to build up history-dependent expectations in an adaptive fashion.

The observation of an effective behavioral adjustment to differentially structured environments in participants’ steady-state behavior raises the question of how (and how quickly) they learned the different environmental structures. Our behavioral modeling approach required many trials, which precludes the assessment of the temporal evolution of the bias (i.e., weight) adjustment during the blocks of a given sensory environment. This issue should be addressed in future work, using models capable of learning environmental parameters such as transition probabilities (Yu and Cohen, 2009; Meyniel et al., 2016; Glaze et al., 2018; Hermoso-Mendizabal et al., 2020).

Previous work has characterized neural signals underlying idiosyncratic history biases in contexts where these biases would be maladaptive. Such signals were observed in several brain areas and in different formats. In a continuous spatial working memory task, activity-silent codes in prefrontal cortex during mnemonic periods seem to promote memory reactivations, which mediate serial memory biases (Barbosa et al., 2020). Studies of perceptual forced choice tasks have found signatures of persistent population activity reflecting the previous choice in posterior parietal cortex (Morcos and Harvey, 2016; Hwang et al., 2017; Scott et al., 2017; Urai and Donner, 2022), prefrontal cortex (Mochol et al., 2021), and motor cortex (Pape and Siegel, 2016; Urai and Donner, 2022). Specifically, human MEG work showed that history-dependent modulations of parietal cortical activity in the gamma-band spanned the intervals between trials and mediated idiosyncratic choice repetition biases (Urai and Donner, 2022). Such an effect was not observed for the motor beta-rebound that was similarly sustained into the next decision interval (Urai and Donner, 2022). Importantly, none of these studies quantified the build-up rate of action-selective motor cortical activity on the subsequent trial.

Idiosyncratic history biases are reflected in a persistent baseline state of action-selective neural population activity in monkey prefrontal cortex, during decision formation accompanied by a subtler modulation of the build-up rate (Mochol et al., 2021). Another human MEG study derived an action-independent proxy of the neural DV from sensor-level MEG data that required two successive judgments within a trial (Rollwage et al., 2020). The initial decision biased the subsequent build-up of that DV-proxy in a manner that depended on the consistency of new evidence with the initial decision and the confidence in that initial decision. Critically, no previous study has investigated the flexible and performance-increasing history biases that we have manipulated and studied here.

Our current results resemble the results from block-wise manipulations of the probability of a specific stimulus category (i.e., not of transitions across stimulus categories): this also biases the build-up of saccade-selective activity in monkey posterior parietal cortex (Hanks et al., 2011), just like what we found here for hand movement-selective motor cortical activity in humans, albeit with strong, but lawful, trial-by-trial variations in our current setting (Figure 1E). It is tempting to interpret both as downstream expressions of perceptual expectations in cortical circuitry involved in action planning. Indeed, modulating the build-up of an evolving DV by prior expectations can be useful in accumulation-to-bound models when reliability of the evidence varies from decision to decision (Hanks et al., 2011; Moran, 2015). Whether or not the neural signatures of idiosyncratic history biases studied in previous work have similar cognitive content and underlying mechanisms remains an open question.

Our results indicate that dynamic and adaptive expectations bias the dynamics of neural signatures of action planning during decision formation. How are these expectations implemented in upstream neural populations, so as to yield the selective changes in M1 ramping dynamics observed here? One possibility is that history biases the state of sensory cortex (Nienborg and Cumming, 2009; St John-Saaltink et al., 2016), for example via feedback from cortical areas involved in decision formation (Wimmer et al., 2015). Another possibility is that the expectations shape the read-out of sensory evidence by the evidence accumulator, with preferential accumulation of evidence that matches the expectation, in line with active inference (Friston, 2010). Yet another possibility is that the evidence accumulator receives non-sensory input from brain regions encoding history information in a sustained fashion (Talluri et al., 2021; Urai and Donner, 2022). In all these different schemes, dynamic expectations would need to be constructed in a highly flexible, context-dependent fashion in order to give rise to the adaptive biasing of action-selective activity observed here.

Raw MEG data are available at https://www.fdr.uni-hamburg.de/record/13475. Source reconstructed MEG data are available at https://www.fdr.uni-hamburg.de/record/13197. Behavioral data is available at https://www.fdr.uni-hamburg.de/record/13517. All data is available under Creative Commons Attribution 4.0 International License. The ethics protocol disallows sharing MRI data via a public repository. Data may be shared however within the context of a collaboration. No proposal is needed. In order to obtain the data, please email Anke Braun (anke.braun86@gmail.com) and Tobias H. Donner (t.donner@uke.de). The code and data immediately underlying all main and figure supplements are publicly available on https://github.com/DonnerLab/2023_BraunA_Adaptive_biasing_of_action-selective_cortical_build-up_activity_by_stimulus_history (copy archived at DonnerLab, 2023a) under GNU General Public LicenseVersion 2. The logistic regression model with history bias was fitted using a toolbox from Fründ et al., 2014, which is publicly available under https://bitbucket.org/mackelab/serial_decision/src/master/. Preprocessing of MEG data was done using a Fieldtrip pipeline from Urai and Donner, 2022, which is publicly available on https://github.com/DonnerLab/2022_Urai_choicehistory_MEG (copy archived at DonnerLab, 2022). Source reconstruction of MEGdata was done using pymeg (Wilming et al., 2020), which is publicly available under https://github.com/DonnerLab/pymeg (copy archived at DonnerLab, 2023b).

1. 1. Braun A
   2. Donner THD

   (2023) Forschungsdatenrepositorium Uni Hamburg

   Source Reconstructed MEG Data for Adaptive biasing of action-selective cortical build-up activity by stimulus history.

   https://doi.org/10.25592/uhhfdm.13196
2. 1. Braun A
   2. Donner THD

   (2023) Forschungsdatenrepositorium Uni Hamburg

   Raw MEG Data for Adaptive biasing of action-selective cortical build-up activity by stimulus history.

   https://doi.org/10.25592/uhhfdm.13474
3. 1. Braun A
   2. Donner THD

   (2023) Forschungsdatenrepositorium Uni Hamburg

   Behavioral data for Adaptive biasing of action-selective cortical build-up activity by stimulus history.

   https://doi.org/10.25592/uhhfdm.13516

1. 1. Abrahamyan A
   2. Silva LL
   3. Dakin SC
   4. Carandini M
   5. Gardner JL

   (2016) Adaptable history biases in human perceptual decisions

   PNAS 113:E3548–E3557.

   https://doi.org/10.1073/pnas.1518786113

   • PubMed
   • Google Scholar
2. 1. Akaishi R
   2. Umeda K
   3. Nagase A
   4. Sakai K

   (2014) Autonomous mechanism of internal choice estimate underlies decision inertia

   Neuron 81:195–206.

   https://doi.org/10.1016/j.neuron.2013.10.018

   • PubMed
   • Google Scholar
3. 1. Barbosa J
   2. Stein H
   3. Martinez RL
   4. Galan-Gadea A
   5. Li S
   6. Dalmau J
   7. Adam KCS
   8. Valls-Solé J
   9. Constantinidis C
   10. Compte A

   (2020) Interplay between persistent activity and activity-silent dynamics in the prefrontal cortex underlies serial biases in working memory

   Nature Neuroscience 23:1016–1024.

   https://doi.org/10.1038/s41593-020-0644-4

   • PubMed
   • Google Scholar
4. 1. Bogacz R
   2. Brown E
   3. Moehlis J
   4. Holmes P
   5. Cohen JD

   (2006) The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks

   Psychological Review 113:700–765.

   https://doi.org/10.1037/0033-295X.113.4.700

   • PubMed
   • Google Scholar
5. 1. Braun A
   2. Urai AE
   3. Donner TH

   (2018) Adaptive history biases result from confidence-weighted accumulation of past choices

   The Journal of Neuroscience 38:2418–2429.

   https://doi.org/10.1523/JNEUROSCI.2189-17.2017

   • PubMed
   • Google Scholar
6. 1. Brody CD
   2. Hanks TD

   (2016) Neural underpinnings of the evidence accumulator

   Current Opinion in Neurobiology 37:149–157.

   https://doi.org/10.1016/j.conb.2016.01.003

   • PubMed
   • Google Scholar
7. 1. Brunton BW
   2. Botvinick MM
   3. Brody CD

   (2013) Rats and humans can optimally accumulate evidence for decision-making

   Science 340:95–98.

   https://doi.org/10.1126/science.1233912

   • PubMed
   • Google Scholar
8. 1. Busse L
   2. Ayaz A
   3. Dhruv NT
   4. Katzner S
   5. Saleem AB
   6. Schölvinck ML
   7. Zaharia AD
   8. Carandini M

   (2011) The detection of visual contrast in the behaving mouse

   The Journal of Neuroscience 31:11351–11361.

   https://doi.org/10.1523/JNEUROSCI.6689-10.2011

   • PubMed
   • Google Scholar
9. 1. Crone NE
   2. Miglioretti DL
   3. Gordon B
   4. Lesser RP

   (1998a) Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band

   Braina 121:2301–2315.

   https://doi.org/10.1093/brain/121.12.2301

   • PubMed
   • Google Scholar
10. 1. Crone NE
    2. Miglioretti DL
    3. Gordon B
    4. Sieracki JM
    5. Wilson MT
    6. Uematsu S
    7. Lesser RP

    (1998b) Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization

    Brain 121:2271–2299.

    https://doi.org/10.1093/brain/121.12.2271

    • PubMed
    • Google Scholar
11. 1. Dale AM

    (1999) Optimal experimental design for event-related fMRI

    Human Brain Mapping 8:109–114.

    https://doi.org/10.1002/(SICI)1097-0193(1999)8:2/3<109::AID-HBM7>3.0.CO;2-W

    • PubMed
    • Google Scholar
12. 1. de Gee JW
    2. Colizoli O
    3. Kloosterman NA
    4. Knapen T
    5. Nieuwenhuis S
    6. Donner TH

    (2017) Dynamic modulation of decision biases by brainstem arousal systems

    eLife 6:e23232.

    https://doi.org/10.7554/eLife.23232

    • PubMed
    • Google Scholar
13. 1. de Lange FP
    2. Rahnev DA
    3. Donner TH
    4. Lau H

    (2013) Prestimulus oscillatory activity over motor cortex reflects perceptual expectations

    The Journal of Neuroscience 33:1400–1410.

    https://doi.org/10.1523/JNEUROSCI.1094-12.2013

    • PubMed
    • Google Scholar
14. 1. Donner TH
    2. Siegel M
    3. Fries P
    4. Engel AK

    (2009) Buildup of choice-predictive activity in human motor cortex during perceptual decision making

    Current Biology 19:1581–1585.

    https://doi.org/10.1016/j.cub.2009.07.066

    • PubMed
    • Google Scholar
15. Software

    1. DonnerLab

    (2022) 2022_Urai_Choicehistory_Meg, version swh:1:rev:ec05db4c27b67df9f318e9706f23ac28f904660a

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:98ca0aa690541884ce354f0f2c578091ec46ed52;origin=https://github.com/DonnerLab/2022_Urai_choicehistory_MEG;visit=swh:1:snp:7d1c0e3cc11ee731c2b32ddb4c85c79292ea7b83;anchor=swh:1:rev:ec05db4c27b67df9f318e9706f23ac28f904660a
16. Software

    1. DonnerLab

    (2023a) 2023_BraunA_Adaptive_biasing_of_action-selective_cortical_build-up_activity_by_stimulus_history, version swh:1:rev:f7cdb7fe875ec220d1458e2645ff181cca4fa9c8

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:405d31b1a95e3043667b35cf05c4fe2f85a4fdbf;origin=https://github.com/DonnerLab/2023_BraunA_Adaptive_biasing_of_action-selective_cortical_build-up_activity_by_stimulus_history;visit=swh:1:snp:4822372adb610e572c84a4e473b03c9073d761a4;anchor=swh:1:rev:f7cdb7fe875ec220d1458e2645ff181cca4fa9c8
17. Software

    1. DonnerLab

    (2023b) Pymeg, version swh:1:rev:a76d1050d9910803857a00feacf13a10a07c191e

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:593be9a0a108aa79adae7334f22f9595403c3117;origin=https://github.com/DonnerLab/pymeg;visit=swh:1:snp:04b82e33a6a4cbf97d3ccb3ab9eb6e4897a8f415;anchor=swh:1:rev:a76d1050d9910803857a00feacf13a10a07c191e
18. Book

    1. Efron B
    2. Tibshirani R

    (1998)

    An Introduction to the Bootstrap

    Boca Raton: Chapman & Hall/CRC.

    • Google Scholar
19. 1. Fernberger SW

    (1920) Interdependence of judgments within the series for the method of constant stimuli

    Journal of Experimental Psychology 3:126–150.

    https://doi.org/10.1037/h0065212

    • Google Scholar
20. 1. Fischer J
    2. Whitney D

    (2014) Serial dependence in visual perception

    Nature Neuroscience 17:738–743.

    https://doi.org/10.1038/nn.3689

    • PubMed
    • Google Scholar
21. 1. Fischer AG
    2. Nigbur R
    3. Klein TA
    4. Danielmeier C
    5. Ullsperger M

    (2018) Cortical beta power reflects decision dynamics and uncovers multiple facets of post-error adaptation

    Nature Communications 9:5038.

    https://doi.org/10.1038/s41467-018-07456-8

    • PubMed
    • Google Scholar
22. 1. Fischl B
    2. Sereno MI
    3. Dale AM

    (1999) Cortical surface-based analysis

    NeuroImage 9:195–207.

    https://doi.org/10.1006/nimg.1998.0396

    • PubMed
    • Google Scholar
23. 1. Friston K

    (2010) The free-energy principle: a unified brain theory?

    Nature Reviews. Neuroscience 11:127–138.

    https://doi.org/10.1038/nrn2787

    • PubMed
    • Google Scholar
24. 1. Fritsche M
    2. Mostert P
    3. de Lange FP

    (2017) Opposite effects of recent history on perception and decision

    Current Biology 27:590–595.

    https://doi.org/10.1016/j.cub.2017.01.006

    • PubMed
    • Google Scholar
25. 1. Fründ I
    2. Wichmann FA
    3. Macke JH

    (2014) Quantifying the effect of intertrial dependence on perceptual decisions

    Journal of Vision 14:9.

    https://doi.org/10.1167/14.7.9

    • PubMed
    • Google Scholar
26. 1. Glaze CM
    2. Kable JW
    3. Gold JI

    (2015) Normative evidence accumulation in unpredictable environments

    eLife 4:e08825.

    https://doi.org/10.7554/eLife.08825

    • PubMed
    • Google Scholar
27. 1. Glaze CM
    2. Filipowicz ALS
    3. Kable JW
    4. Balasubramanian V
    5. Gold JI

    (2018) A bias–variance trade-off governs individual differences in on-line learning in an unpredictable environment

    Nature Human Behaviour 2:213–224.

    https://doi.org/10.1038/s41562-018-0297-4

    • Google Scholar
28. 1. Gold JI
    2. Shadlen MN

    (2007) The neural basis of decision making

    Annual Review of Neuroscience 30:535–574.

    https://doi.org/10.1146/annurev.neuro.29.051605.113038

    • PubMed
    • Google Scholar
29. 1. Gold JI
    2. Law CT
    3. Connolly P
    4. Bennur S

    (2008) The relative influences of priors and sensory evidence on an oculomotor decision variable during perceptual learning

    Journal of Neurophysiology 100:2653–2668.

    https://doi.org/10.1152/jn.90629.2008

    • PubMed
    • Google Scholar
30. 1. Gramfort A
    2. Luessi M
    3. Larson E
    4. Engemann DA
    5. Strohmeier D
    6. Brodbeck C
    7. Parkkonen L
    8. Hämäläinen MS

    (2014) MNE software for processing MEG and EEG data

    NeuroImage 86:446–460.

    https://doi.org/10.1016/j.neuroimage.2013.10.027

    • PubMed
    • Google Scholar
31. 1. Hanks TD
    2. Mazurek ME
    3. Kiani R
    4. Hopp E
    5. Shadlen MN

    (2011) Elapsed decision time affects the weighting of prior probability in a perceptual decision task

    The Journal of Neuroscience 31:6339–6352.

    https://doi.org/10.1523/JNEUROSCI.5613-10.2011

    • PubMed
    • Google Scholar
32. 1. Hanks TD
    2. Kopec CD
    3. Brunton BW
    4. Duan CA
    5. Erlich JC
    6. Brody CD

    (2015) Distinct relationships of parietal and prefrontal cortices to evidence accumulation

    Nature 520:220–223.

    https://doi.org/10.1038/nature14066

    • PubMed
    • Google Scholar
33. 1. Hermoso-Mendizabal A
    2. Hyafil A
    3. Rueda-Orozco PE
    4. Jaramillo S
    5. Robbe D
    6. de la Rocha J

    (2020) Author Correction: Response outcomes gate the impact of expectations on perceptual decisions

    Nature Communications 11:3470.

    https://doi.org/10.1038/s41467-020-17071-1

    • PubMed
    • Google Scholar
34. 1. Hwang EJ
    2. Dahlen JE
    3. Mukundan M
    4. Komiyama T

    (2017) History-based action selection bias in posterior parietal cortex

    Nature Communications 8:1242.

    https://doi.org/10.1038/s41467-017-01356-z

    • PubMed
    • Google Scholar
35. 1. Kim TD
    2. Kabir M
    3. Gold JI

    (2017) Coupled decision processes update and maintain saccadic priors in a dynamic environment

    The Journal of Neuroscience 37:3632–3645.

    https://doi.org/10.1523/JNEUROSCI.3078-16.2017

    • PubMed
    • Google Scholar
36. 1. Little S
    2. Bonaiuto J
    3. Barnes G
    4. Bestmann S

    (2019) Human motor cortical beta bursts relate to movement planning and response errors

    PLOS Biology 17:e3000479.

    https://doi.org/10.1371/journal.pbio.3000479

    • PubMed
    • Google Scholar
37. 1. Meyniel F
    2. Maheu M
    3. Dehaene S

    (2016) Human inferences about sequences: A minimal transition probability model

    PLOS Computational Biology 12:e1005260.

    https://doi.org/10.1371/journal.pcbi.1005260

    • PubMed
    • Google Scholar
38. 1. Mobbs D
    2. Trimmer PC
    3. Blumstein DT
    4. Dayan P

    (2018) Foraging for foundations in decision neuroscience: insights from ethology

    Nature Reviews. Neuroscience 19:419–427.

    https://doi.org/10.1038/s41583-018-0010-7

    • PubMed
    • Google Scholar
39. 1. Mochol G
    2. Kiani R
    3. Moreno-Bote R

    (2021) Prefrontal cortex represents heuristics that shape choice bias and its integration into future behavior

    Current Biology 31:1234–1244.

    https://doi.org/10.1016/j.cub.2021.01.068

    • PubMed
    • Google Scholar
40. 1. Moran R

    (2015) Optimal decision making in heterogeneous and biased environments

    Psychonomic Bulletin & Review 22:38–53.

    https://doi.org/10.3758/s13423-014-0669-3

    • PubMed
    • Google Scholar
41. 1. Morcos AS
    2. Harvey CD

    (2016) History-dependent variability in population dynamics during evidence accumulation in cortex

    Nature Neuroscience 19:1672–1681.

    https://doi.org/10.1038/nn.4403

    • PubMed
    • Google Scholar
42. 1. Murphy PR
    2. Wilming N
    3. Hernandez-Bocanegra DC
    4. Prat-Ortega G
    5. Donner TH

    (2021) Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments

    Nature Neuroscience 24:987–997.

    https://doi.org/10.1038/s41593-021-00839-z

    • PubMed
    • Google Scholar
43. 1. Nienborg H
    2. Cumming BG

    (2009) Decision-related activity in sensory neurons reflects more than a neuron’s causal effect

    Nature 459:89–92.

    https://doi.org/10.1038/nature07821

    • PubMed
    • Google Scholar
44. 1. O’Connell RG
    2. Dockree PM
    3. Kelly SP

    (2012) A supramodal accumulation-to-bound signal that determines perceptual decisions in humans

    Nature Neuroscience 15:1729–1735.

    https://doi.org/10.1038/nn.3248

    • PubMed
    • Google Scholar
45. 1. O’Connell RG
    2. Kelly SP

    (2021) Neurophysiology of human perceptual decision-making

    Annual Review of Neuroscience 44:495–516.

    https://doi.org/10.1146/annurev-neuro-092019-100200

    • PubMed
    • Google Scholar
46. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:156869.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
47. 1. Ossmy O
    2. Moran R
    3. Pfeffer T
    4. Tsetsos K
    5. Usher M
    6. Donner TH

    (2013) The timescale of perceptual evidence integration can be adapted to the environment

    Current Biology 23:981–986.

    https://doi.org/10.1016/j.cub.2013.04.039

    • PubMed
    • Google Scholar
48. 1. Pape AA
    2. Siegel M

    (2016) Motor cortex activity predicts response alternation during sensorimotor decisions

    Nature Communications 7:13098.

    https://doi.org/10.1038/ncomms13098

    • PubMed
    • Google Scholar
49. 1. Peixoto D
    2. Verhein JR
    3. Kiani R
    4. Kao JC
    5. Nuyujukian P
    6. Chandrasekaran C
    7. Brown J
    8. Fong S
    9. Ryu SI
    10. Shenoy KV
    11. Newsome WT

    (2021) Decoding and perturbing decision states in real time

    Nature 591:604–609.

    https://doi.org/10.1038/s41586-020-03181-9

    • PubMed
    • Google Scholar
50. 1. Pfurtscheller G
    2. Stancák A
    3. Neuper C

    (1996) Post-movement beta synchronization. A correlate of an idling motor area?

    Electroencephalography and Clinical Neurophysiology 98:281–293.

    https://doi.org/10.1016/0013-4694(95)00258-8

    • PubMed
    • Google Scholar
51. 1. Ratcliff R
    2. McKoon G

    (2008) The diffusion decision model: theory and data for two-choice decision tasks

    Neural Computation 20:873–922.

    https://doi.org/10.1162/neco.2008.12-06-420

    • PubMed
    • Google Scholar
52. 1. Rollwage M
    2. Loosen A
    3. Hauser TU
    4. Moran R
    5. Dolan RJ
    6. Fleming SM

    (2020) Confidence drives a neural confirmation bias

    Nature Communications 11:2634.

    https://doi.org/10.1038/s41467-020-16278-6

    • PubMed
    • Google Scholar
53. 1. Rouder JN
    2. Speckman PL
    3. Sun D
    4. Morey RD
    5. Iverson G

    (2009) Bayesian t tests for accepting and rejecting the null hypothesis

    Psychonomic Bulletin & Review 16:225–237.

    https://doi.org/10.3758/PBR.16.2.225

    • PubMed
    • Google Scholar
54. 1. Scott BB
    2. Constantinople CM
    3. Akrami A
    4. Hanks TD
    5. Brody CD
    6. Tank DW

    (2017) Fronto-parietal cortical circuits encode accumulated evidence with a diversity of timescales

    Neuron 95:385–398.

    https://doi.org/10.1016/j.neuron.2017.06.013

    • PubMed
    • Google Scholar
55. 1. Shadlen MN
    2. Kiani R

    (2013) Decision making as a window on cognition

    Neuron 80:791–806.

    https://doi.org/10.1016/j.neuron.2013.10.047

    • PubMed
    • Google Scholar
56. 1. Sherman MA
    2. Lee S
    3. Law R
    4. Haegens S
    5. Thorn CA
    6. Hämäläinen MS
    7. Moore CI
    8. Jones SR

    (2016) Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

    PNAS 113:E4885–E4894.

    https://doi.org/10.1073/pnas.1604135113

    • PubMed
    • Google Scholar
57. 1. Siegel M
    2. Donner TH
    3. Oostenveld R
    4. Fries P
    5. Engel AK

    (2007) High-frequency activity in human visual cortex is modulated by visual motion strength

    Cerebral Cortex 17:732–741.

    https://doi.org/10.1093/cercor/bhk025

    • PubMed
    • Google Scholar
58. 1. Siegel M
    2. Engel AK
    3. Donner TH

    (2011) Cortical network dynamics of perceptual decision-making in the human brain

    Frontiers in Human Neuroscience 5:21.

    https://doi.org/10.3389/fnhum.2011.00021

    • PubMed
    • Google Scholar
59. 1. St John-Saaltink E
    2. Kok P
    3. Lau HC
    4. de Lange FP

    (2016) Serial dependence in perceptual decisions is reflected in activity patterns in primary visual cortex

    The Journal of Neuroscience 36:6186–6192.

    https://doi.org/10.1523/JNEUROSCI.4390-15.2016

    • PubMed
    • Google Scholar
60. 1. Stolk A
    2. Todorovic A
    3. Schoffelen JM
    4. Oostenveld R

    (2013) Online and offline tools for head movement compensation in MEG

    NeuroImage 68:39–48.

    https://doi.org/10.1016/j.neuroimage.2012.11.047

    • PubMed
    • Google Scholar
61. 1. Talluri BC
    2. Braun A
    3. Donner TH

    (2021) Decision making: How the past guides the future in frontal cortex

    Current Biology 31:R303–R306.

    https://doi.org/10.1016/j.cub.2021.01.020

    • PubMed
    • Google Scholar
62. 1. Urai AE
    2. Braun A
    3. Donner TH

    (2017) Pupil-linked arousal is driven by decision uncertainty and alters serial choice bias

    Nature Communications 8:14637.

    https://doi.org/10.1038/ncomms14637

    • PubMed
    • Google Scholar
63. 1. Urai AE
    2. de Gee JW
    3. Tsetsos K
    4. Donner TH

    (2019) Choice history biases subsequent evidence accumulation

    eLife 8:e46331.

    https://doi.org/10.7554/eLife.46331

    • PubMed
    • Google Scholar
64. 1. Urai AE
    2. Donner TH

    (2022) Persistent activity in human parietal cortex mediates perceptual choice repetition bias

    Nature Communications 13:6015.

    https://doi.org/10.1038/s41467-022-33237-5

    • PubMed
    • Google Scholar
65. 1. van den Brink RL
    2. Wynn SC
    3. Nieuwenhuis S

    (2014) Post-error slowing as a consequence of disturbed low-frequency oscillatory phase entrainment

    The Journal of Neuroscience 34:11096–11105.

    https://doi.org/10.1523/JNEUROSCI.4991-13.2014

    • PubMed
    • Google Scholar
66. 1. Van Veen BD
    2. van Drongelen W
    3. Yuchtman M
    4. Suzuki A

    (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

    IEEE Transactions on Bio-Medical Engineering 44:867–880.

    https://doi.org/10.1109/10.623056

    • PubMed
    • Google Scholar
67. 1. Wang XJ

    (2008) Decision making in recurrent neuronal circuits

    Neuron 60:215–234.

    https://doi.org/10.1016/j.neuron.2008.09.034

    • PubMed
    • Google Scholar
68. 1. Wang L
    2. Mruczek REB
    3. Arcaro MJ
    4. Kastner S

    (2015) Probabilistic maps of visual topography in human cortex

    Cerebral Cortex 25:3911–3931.

    https://doi.org/10.1093/cercor/bhu277

    • PubMed
    • Google Scholar
69. 1. Wilming N
    2. Murphy PR
    3. Meyniel F
    4. Donner TH

    (2020) Large-scale dynamics of perceptual decision information across human cortex

    Nature Communications 11:5109.

    https://doi.org/10.1038/s41467-020-18826-6

    • PubMed
    • Google Scholar
70. 1. Wimmer K
    2. Compte A
    3. Roxin A
    4. Peixoto D
    5. Renart A
    6. de la Rocha J

    (2015) Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

    Nature Communications 6:6177.

    https://doi.org/10.1038/ncomms7177

    • PubMed
    • Google Scholar
71. 1. Wyart V
    2. de Gardelle V
    3. Scholl J
    4. Summerfield C

    (2012) Rhythmic fluctuations in evidence accumulation during decision making in the human brain

    Neuron 76:847–858.

    https://doi.org/10.1016/j.neuron.2012.09.015

    • PubMed
    • Google Scholar
72. 1. Yu A
    2. Cohen JD

    (2009)

    Sequential effects: Superstition or rational behavior

    Advances in Neural Information Processing Systems 21:1873–1880.

    • Google Scholar

Author details

1. Anke Braun

   1. Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
   2. Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Neurosciences, Berlin, Germany
   3. Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Child and Adolescent Psychiatry, Berlin, Germany

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   anke.braun86@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1946-7765

2. Tobias H Donner

   1. Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
   2. Bernstein Center for Computational Neuroscience, Charité – Universitätsmedizin Berlin, Berlin, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   t.donner@uke.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7559-6019

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