However, their lack of primary sequence and secondary RNA structure conservation, as well as the diversity of host factor requirement to recruit the ribosomal subunits, suggest distinct types of IRES elements. In spite of this heterogeneity, conserved motifs preserve sequences impacting on RNA structure and RNA–protein interactions important for IRES-driven translation. This conservation brings the question of whether IRES elements could consist of basic building blocks, which upon evolutionary selection result in functional elements with different properties. Although RNA-binding proteins (RBPs) perform a crucial role in the assembly of ribonucleoprotein complexes, the versatility and plasticity of RNA molecules, together with their high flexibility and dynamism, determines formation of macromolecular complexes in response to different signals. These properties rely on the presence of short RNA motifs, which operate as modular entities, and suggest that decomposition of IRES elements in short modules could help to understand the different mechanisms driven by these regulatory elements. Here we will review evidence suggesting that model IRES elements consist of the combination of short modules, providing sites of interaction for ribosome subunits, eIFs and RBPs, with implications for definition of criteria to identify novel IRES-like elements genome wide.Beyond the general cap-dependent translation initiation, eukaryotic organisms use alternative mechanisms to initiate protein synthesis. Internal ribosome entry site (IRES) elements are  Key to understanding the relevance of RNA in biological processes has been the development of RNA structure-related methodologies, which has shown the flexible conformation of RNA, and also the modular nature of RNA three-dimensional 3D) architecture . In partD architehe field ,5. In thhe field .The molecular basis of RNA conformational flexibility has been extensively studied in viral RNAs. Generally, SHAPE models largely recapitulate RNA structures predicted by other methods, but also allow identifying unpredicted structural elements, with novel regulatory properties –9. This Here we will focus on RNA motifs conserved in model IRES elements and how this information can be crucial to understand the modular organization of these RNAs, eventually contributing to the accurate prediction of IRES-like motifs in genomes. More detailed information on non-canonical and cap-independent translation mechanisms can be found in previous reviews ,20.2.7G(5′)ppp(5′)N modification (or cap) at the 5′ end (a). This structure mediates initiation of translation via the so-called cap-dependent mechanism that involves the binding of the translation initiation factor (eIF)-4E in a complex with eIF4G and eIF4A to the 5′ end of mRNAs [i, scanning the 5′ UTR until an AUG triplet is found in the appropriate context to start protein synthesis. Joining of the 60S ribosomal subunit follows this step, producing a translation competent complex. It is well documented that strong cellular stresses  severely compromise cap-dependent translation [In all organisms, translation control is a key step in gene expression regulation. Eukaryotic mRNAs usually contain the me 5′ end a. This sof mRNAs . This conslation . Howevernslation ,24.FiguDrosophila melanogaster [Viral mRNAs have developed distinct strategies to overcome the shut-off of cap-dependent protein synthesis induced in virus-infected cells . Hence, nogaster ,32. TherAlternative manners to initiate translation received support from experimental evidence for distinct ribosome composition within the large population of ribosomal particles ,34, also3.b). Picornavirus RNAs contain long, highly structured 5′ UTRs able to recruit the 40S subunit using a cap-independent mechanism [. Yet there are important differences among them, which will be discussed below.The pioneering work performed 30 years ago with picornavirus RNAs, which are naturally uncapped, provided the foundations for mRNA regions termed IRES elements b. Picornechanism ,42. Thisechanism . Cumulatechanism ,44. Notwb). Both types I and II are independent of eIF4E but require the C-terminal region of eIF4G, eIF4A, eIF2 and eIF3 to assemble 48S initiation complexes [The secondary structure and protein interactions of picornavirus IRES elements have been extensively analysed although high-resolution 3D structures are still lacking. Because of their heterogeneity, picornavirus IRES elements are classified into different types, such that each type harbours a common RNA structure core maintained by evolutionary conserved covariant substitutions. Due to their high efficiency and complex requirement of factors, type I and II IRES are prototypes to study internal initiation mechanisms. Type I IRES occurs in enterovirus , and type II IRES in cardiovirus  and aphthovirus (FMDV) b. Both tomplexes . Specifiomplexes . Domain omplexes ,48. Domaomplexes ,50. Domaomplexes ,52. Domaomplexes . This doomplexes , besidesomplexes ,56.In further support of the modular organization of picornavirus IRES elements, structural analysis of the region of type II IRES interacting with eIF4G revealed that the conserved A-pentaloop serves as a docking site for base-pair receptors that requires the concerted action of all subdomains, since subtle changes in the orientation abrogate the interaction with eIF4G . Interesb) contains conserved sequences and stem-loops controlling viral RNA translation, replication and stability [The HCV 5′ UTR b containtability ,59–61. Stability . Domain tability . A distitability . In additability . Remarkatability .The structure–function relationship of the HCV IRES has been studied in great detail using transcripts corresponding to the IRES region alone, in RNA replicons or inserted into artificial constructs within reporter genes . HoweverVarious RNA viruses have been described to contain HCV-like IRES elements that, however, display subtle differences in their RNA conformation, as illustrated by pestivirus, hepacivirus, and type IV picornavirus IRES elements –74. Morei directing translation initiation at a non-AUG triplet [b). The 3D structure of the IGR-ribosome shows that PKI resembles a tRNA/mRNA interaction in the decoding centre of the A site, mimicking a pre-translocation rather than the initiation state of the ribosome [Contrary to hepacivirus and picornavirus, whose positive-strand RNA genome encodes a single long polyprotein, the genome of dicistroviruses is a natural dicistronic mRNA, in which translation of each ORF is governed by distinct IRES with different RNA structure organization and mode of action . Activit triplet   and HCV IRES conformation in solution [Although the presence of stable stem-loops in IRES elements has been determined by several RNA probing methodologies , the nucfigure 2solution , which wsolution . Therefob), while native ribosomes induced additional structural changes within domains 4 and 5 on long timescales (figure 2c). Furthermore, supplementing salt-washed ribosomes with soluble factors, including eIFs and RBPs, restored the RNA conformation of the IRES incubated with native ribosomes [The function of RNA molecules depends on their 3D structure and their ability to acquire distinct conformations . Conversfigure 2ibosomes , reinforibosomes –100. TheExperimental data accumulated over the years from many different laboratories have provided strong evidence for the involvement of RBPs on IRES function ,102. Oned) and the apical region of domain 3 of the FMDV IRES element in vitro. The presence of the FMDV IRES in mRNA expressed in living cells enhanced its association to the ribosomal subunits relative to a cap-mRNA. The enhanced IRES–ribosome association was observed both in normal conditions [The conformational flexibility changes observed by differential SHAPE raise the possibility that the type II IRES contain separate sites for ribosome interaction and eIF binding. In support of this, direct interactions between 40S subunit and the EMCV IRES have been described . These ifigure 2nditions , and alsnditions ,101,102.Therefore, taking into account that short conserved motifs within the type II IRES harbour the capacity to interact with the ribosomal particles, it could be concluded that RNA motifs present in domains 2 and 3 could define a functional building block responsible for the recruitment of ribosomal subunits. The remaining domains 4 and 5 would be mainly responsible for providing the binding sites for eIFs and RBPs, in agreement with previous reports ,57,115. The finding that conserved RNA modules can provide direct IRES–ribosome interaction suggests that it could be possible to design synthetic RNAs with novel functional features, built from a combination of conserved building blocks connected via linker sequences to RNA motifs able to recruit transacting factors. These building blocks, however, should operate in concerted action to provide the correct and hierarchical orientation of the RNA motifs involved in ribosome and trans-acting factors recruitment. In support of the possibility to design synthetic RNAs behaving as IRES-like elements, it is worth mentioning synthetic RNA nanostructures recently designed by relying on the hierarchical formation of recurrent sequence-dependent networks of tertiary interactions . These nAttempts to generate artificial IRES-like elements were previously reported ,120. In 5.Not surprisingly, the existence of cellular IRES elements is expected from the observation that novel mechanisms initially discovered in viruses have been invariably extended to the host cells. Hence, IRES activity has been claimed for a subset of cellular mRNAs ,124. BeyYet the number of cellular IRES elements is rather reduced , especiaWorth mentioning are the efforts to implement specific methods for the computational search of sequences in mRNAs promoting cap-independent translation –134. Earb). Moreover, a number of cellular 5′ UTRs that harbour (CCU)n sequences were reported to contain PTB-dependent IRES elements, raising the possibility that PTB or its interacting protein partners could provide a bridge between the IRES and the ribosome [A follow-up work aimed to develop methodologies for genome-wide computational prediction of IRES elements, which relied on the sequences shared by cap-independent translated mRNAs , resultefigure 1ribosome . As suchribosome –145. Hen6.i. However, the vast majority of IRES elements use more complex strategies involving the concerted action of eIFs and distinct RBPs to capture the 40S subunit, followed by the assembly of the 60S subunit. We would like to propose that complex IRES elements share conserved motifs which could behave as building blocks, enabling interaction with the ribosome. Eventually assisted by eIFs and RBPs, these motifs could contribute to assemble ribosomal subunits in a translationally active complex. Given that RNA is a versatile molecule in structure and function, the presence of distinct conserved motifs opens new avenues for designing engineered IRES elements with novel translation regulation features.Recent studies on IRES–ribosome complex formation have shown the active role of the IRES RNA in manipulating the ribosome. However, the ongoing IRES research is challenging mostly due to the structural diversity of the established viral IRES. In turn, the heterogeneity of IRES elements points to different strategies developed by viruses to exploit the host translation machinery. Data reported over the years have provided insights for different IRES-driven mechanisms to initiate translation. The simplest one involves direct recruitment of the ribosomal subunits mimicking features of the translation machinery, such as tRNAmet


Perceptual choices depend not only on the current sensory input but also on the behavioral context, such as the history of one’s own choices. Yet, it remains unknown how such history signals shape the dynamics of later decision formation. In models of decision formation, it is commonly assumed that choice history shifts the starting point of accumulation toward the bound reflecting the previous choice. We here present results that challenge this idea. We fit bounded-accumulation decision models to human perceptual choice data, and estimated bias parameters that depended on observers’ previous choices. Across multiple task protocols and sensory modalities, individual history biases in overt behavior were consistently explained by a history-dependent change in the evidence accumulation, rather than in its starting point. Choice history signals thus seem to bias the interpretation of current sensory input, akin to shifting endogenous attention toward (or away from) the previously selected interpretation. Decisions are not isolated events, but are embedded in a sequence of choices. Choices, or their outcomes (e.g. rewards), exert a large influence on subsequent decisions . This hoChoice history biases are prevalent in human , monkey By which mechanism are choice history signals incorporated into the formation of a decision? Current models of perceptual decision-making posit the temporal accumulation of sensory evidence, resulting in an internal decision variable that grows with time . When thIt is currently unknown which of those two principal mechanisms accounts for the choice history biases observed in overt behavior. Previous theoretical accounts have postulated a shift in the starting point of the decision variables toward the bound of the previous choice . This isHere, we untangle how history-dependent changes in evidence accumulation and starting point contribute to history biases in overt choice behavior. Across a range of perceptual choice tasks, we found that individual differences in choice repetition are explained by history-dependent biases in accumulation, not starting point. Thus, the interaction between choice history and decision formation seems to be more complex than previously thought: choices may bias later evidence accumulation processes towards (or away from) the previous chosen perceptual interpretation of the sensory input.We fit different bounded-accumulation models to human behavioral data:choices and response times (RT). The DDM estimates model parameters from joint choices and RT distributions, and provides good fits to behavioral data from a large array of two-choice task . We estiWithin the DDM, choice behavior can be selectively biased toward repetition or alternation by two mechanisms: shifting the starting point, or biasing the drift toward (or away from) the bound for the previous choice . These bWe fit different variants of the DDM  to data The DDM fits matched several aspects of the behavioral data . First, rocedure . Third, rocedure  and monorocedure . In fixerocedure , so the rocedure  and mighrocedure .Models with history-dependent biases better explained the data than the baseline model without such history dependence , corroboThe above model comparison pointed to the importance of including a history-dependency into the model. We further examined the ability of each model to explain specific diagnostic features in the data  that disWe used the parameter estimates obtained from the full model (with both history-dependent starting point and drift bias) to investigate how history-dependent variations in starting point and drift bias related to each individual’s tendency to repeat their previous choices. We call each bias parameter’s dependence on the previous choice its ‘history shift’. For instance, in the left vs. right motion discrimination task, the history shift in starting point was computed as the difference between the starting point estimate for previous ‘left’ and previous ‘right’ choices, irrespective of the category of the current stimulus. The history shift in drift bias, but not the history shift in starting point, was robustly correlated to the individual probability of choice repetition  was 0.0473, indicating strong evidence for H0. We thus remain agnostic about the relationship between the history shifts of both parameters.Correlations between estimated history shifts in starting point and drift bias were generally negative  after each choice. It is possible that participants experienced positive feedback as rewarding and (erroneously) assumed that a rewarded choice is more likely to be rewarded on the next trial. Manipulations of reward (probability or magnitude) have been found to change starting point , but migTwo findings refute this idea. First, the same result holds in the two datasets without single-trial outcome feedback . Across Correlations tended to be smaller for previous erroneous choices. However, directly comparing the correlation coefficients between post-correct and post-error trials  did not allow us to refute nor confirm a difference . In sum,Does the history shift in evidence accumulation depend on events from one past trial only? Recent work has exposed long-lasting choice history biases that span several trials and tens of seconds . We thusInclusion of further lags improved the model’s ability to account for the data, up to a lag of 2–4 after which model fits (ΔAIC) began to deteriorate . In 4/6 Previous choices shifted drift bias in line with individual history bias across several trials, whereas starting point did not consistently shift in the direction of history bias. The hybrid models showed that the effect of choice history on drift bias decayed over approximately three past trials , with a In sum, the biasing effect of choice history on evidence accumulation is long-lasting (longer than the effects on starting point), dependent on preceding choices several trials into the past, but independent of their correctness. This analysis corroborates the previous findings from our simpler models focusing on only the preceding trial, and further dissociate the effects of choice history on starting point and evidence accumulation.We next set out to test the generality of our conclusions and gain deeper mechanistic insight into the nature of the dynamic (i.e. time-increasing) bias. We used a variety of bounded-accumulation models with more complex dynamics than the standard DDM. We focused on the preceding trial only, which our previous analyses had identified as exerting the same effect on history bias as the longer lags . These mWe first re-fit the standard DDM where the two biasing parameters were allowed to vary with previous choice see , now usiA previous study of reward effects on speeded decisions reported that reward asymmetries induced supra-linear bias dynamics . TemporaIt has been proposed that decision bounds might collapse over time, implementing an ‘urgency signal’ . Indeed,In the brain, a neural representation of the momentary sensory evidence feeds into a set of accumulators. These consist of circuits of excitatory and inhibitory populations of cortical neurons, which give rise to persistent activity and competitive winner-take-all dynamics . Under cinput best explained the data, among all the models considered , a biased input leaky accumulator can produce a decelerating effective bias. Combination of a biased input with some starting point and or leak bias can further change the dynamics. The key observation is that, regardless of the modeling framework used, we identified an effective bias signal that grew steadily throughout decision formation, in line with the main conclusion drawn from the basic fits of the standard DDM.The reader may notice that these (supra-linear) effective bias dynamics are similar to those predicted by the DDM with a ramping drift bias , left. TOur results are in line with the idea the impact of choice history bias on decision formation grows as a function of elapsed time. This observation might be surprising, as prior information (here: about the previous choice) does not change over time. Yet, previous work has identified a principled rationale for such a time-dependent combination of prior and evidence. When evidence reliability changes from trial to trial, prior information (bias) should be weighted more strongly when sensory evidence is unreliable . This caQuantitative treatments of perceptual decision-making commonly attribute trial-to-trial variability of overt choices to noisy decision computations . Those aHistory biases in perceptual choice have long been known in perceptual psychophysics  and neursequence of categories) or the asymmetry between rewards for both choices. Most of these studies explained the resulting behavioral biases in terms of starting point shifts . But why did participants use choice history as a prior for their decisions? In all our experiments, the sensory evidence was uncorrelated across trials – as is the case in the majority of perceptual choice tasks used in the literature. Thus, any history bias can only reduce performance below the level that could be achieved, given the observer’s sensitivity. It may seem irrational that people use history biases in such settings. However, real-world sensory evidence is typically stable (i.e. auto-correlated) across various timescales . Thus, p10= 0.0473). It is possible that both of the scenarios discussed here conspired to yield the starting point effects observed in model comparisons and individual parameter estimates. Future work is needed to illuminate this issue, for example through manipulating decision speed and/or the delays between subsequent motor responses, and modeling choice-related neural dynamics in motor cortex.While we found that choice history-dependent variations of accumulation bias were generally more predictive of individual choice repetition behavior, the DDM starting point was consistently shifted away from the previous response for a majority of participants  a previously selected category . This isA growing body of evidence points to the interplay of multiple timescales for neural computation in the cortex. One line of behavioral work has revealed effective  evidence accumulation over timescales ranging from a few hundred milliseconds  to severWe analyzed six different datasets, four of which were previously published. These spanned different modalities , decision-relevant sensory features , and tasks (detection or discrimination). In each dataset, the number of participants was determined to allow for robust estimation of the original effects of interest. No participants were excluded from the analyses.Those tasks where the decision-relevant sensory evidence was presented until the observer generated a response were called response time (RT) tasks; those tasks where the sensory evidence was presented for a fixed duration, and its offset cues the observer’s response, were called fixed duration (FD) tasks in line with the terminology from https://doi.org/10.5061/dryad.tb542. The study was approved by the ethics committee of the Leiden University Cognitive Psychology department, and all subjects provided written informed consent before taking part. Twenty-six observers  performed a motion direction (left vs. right) discrimination task. Stationary white dots were presented on a black screen for an interval of 4.3–5.8 s. After this fixation interval, the decision-relevant sensory evidence was presented: some percentage of dots (the ‘motion coherence’ level) moved to the left or the right. The coherence was individually titrated to yield an accuracy level of 85% correct (estimated from a psychometric function fit) before the start of the main experiment, and kept constant afterwards. The moving dots were presented until observers indicated their choice with a button press. After the response, the fixation cross changed color for 700 ms to indicate single-trial feedback. Each observer performed 500 trials of the task in one session. We refer to this task as ‘Visual motion 2AFC (RT)’.These data were previously published , and areThirty-two participants  participated in the study after giving their informed consent. The experiment was approved by the ethical review board of the University Medical Center Hamburg-Eppendorf (PV4714).2, resulting in 118 dots on the screen at each frame. The stimuli were confined to a circle of 2.5° radius, which was placed in the lower half of the visual field at 3.5° from fixation. After a fixation interval of 0.75–1.5 s, random dot motion stimuli  were displayed for 750 ms. Signal dots moved with a speeds of 11.5 degree/s, and noise dots were randomly displaced within the circle on each frame. We used the single-trial dot coordinates to construct time courses of fluctuating external evidence  task in the MEG scanner. White dots were displayed on a gray background screen, with a density of 6 dots/degreehttp://dx.doi.org/10.6084/m9.figshare.4300043. The ethics committee at the University of Amsterdam approved the study, and all observers gave their informed consent before participation. Twenty-seven observers  performed a two-interval motion coherence discrimination task. They viewed two consecutive intervals of random dot motion, containing coherent motion signals in a constant direction towards one of the four diagonals  and judged whether the second test interval (variable coherence) contained stronger or weaker motion than the first reference (constant coherence) interval. After a fixation interval of 0.5–1 s, they viewed two consecutive intervals of 500 ms each, separated by a delay of 300–700 ms. The decision-relevant sensory evidence , was chosen pseudo-randomly for each trial from the set . Observers received auditory feedback on their choice after a delay of 1.5–2.5 s. After continuing to view noise dots for 2–2.5 s, stationary dots indicated an inter-trial interval. Observers self-initiated the start of the next trial . Each observer performed 2500 trials of the task, divided over five sessions. We refer to this task as ‘Visual motion 2IFC (FD) #1’.These data were previously published in Sixty-two participants  participated in the study after screening for psychiatric, neurological or medical conditions. All subjects had normal or corrected to normal vision, were non-smokers, and gave their informed consent before the start of the study. The experiment was approved by the ethical review board of the University Medical Center Hamburg-Eppendorf (PV4648).Observers performed five sessions, of which the first and the last took place in the MEG scanner  and the three sessions in between took place in a behavioral lab . The task was as described above for ‘Visual motion 2IFC (FD) #1’, with the following exceptions. The strength of the decision-relevant sensory evidence was individually titrated to an accuracy level of 70% correct, estimated from a psychometric function fit, before the start of the main experiment and kept constant for each individual throughout the main experiment. Each stimulus was presented for 750 ms. In the MEG sessions, auditory feedback was presented 1.5–3 s after response, and an inter-trial interval with stationary dots started 2–3 s after feedback. Participants initiated the next trial with a button press . In the training sessions, auditory feedback was presented immediately after the response. This was followed by an inter-trial interval of 1 s, after which the next trial started. In this experiment, three sub-groups of observers received different pharmacological treatments prior to each session, receiving placebo, atomoxetine , or donepezil (an acetylcholinesterase inhibitor). These groups did not differ in their choice history bias and were pooled for the purpose of the present study . We refehttps://doi.org/10.6084/m9.figshare.4806559. The ethics committee of the Psychology Department of the University of Amsterdam approved the study. All participants took part after giving their written informed consent. Twenty-nine observers  performed a yes/no contrast detection task. During a fixation interval of 4–6 s, observers viewed dynamic noise . A beep indicated the start of the decision-relevant sensory evidence. On half the trials, a vertical grating was superimposed onto the dynamic noise; on the other half of trials, only the dynamic noise was shown. The sensory evidence  was presented until the observers reported their choice , or after a maximum of 2.5 s. The signal contrast was individually titrated to yield an accuracy level of 75% correct using a method of constant stimuli before the main experiment, and kept constant throughout the main experiment. Observers performed between 480–800 trials over 6–10 sessions. Six observers in the original paper  performed an auditory tone detection task. After an inter-trial interval of 3–4 s, decision-relevant sensory evidence was presented: on half the trials, a sine wave (2 KHz) superimposed onto dynamic noise d′=Φ−formance . We estiWe quantified individual choice history bias in terms of the probability of repeating a choice, termed P(repeat), regardless of the category of the (previous or current) stimulus. This yielded a measure of bias that ranged between 0  and 1 (maximum repetition bias), whereby 0.5 indicated no bias.This section describes the general DDM, with a focus on the biasing mechanisms described in Results and illustrated in nce c2dt . In an uo bounds :(4)y0=z=A drift bias can be implemented by adding a stimulus-independent constant ift bias , to the ift bias . This adWe allowed both bias parameters to vary as a function of observers’ previous choice. These two biasing mechanisms result in the same (asymmetric) fraction of choices, but they differ in terms of the resulting shapes of RT distributions . In previc’ bias  or ‘judgic’ bias .We used hierarchical drift diffusion modeling as implemented in the HDDM toolbox  to fit tFirst, we estimated a model without history-dependence. Overall drift rate, boundary separation, non-decision time, starting point, and drift bias were estimated for each individual . Across-Second, we estimated three different models of history bias, allowing (i) starting point, (ii) drift or (iii) both to vary as a function of the observer’s immediately preceding choice  or error (choice – stimuli) trials. We sign-flipped the weight values for alternators (i.e. those participants with a repetition tendency at lag one < 0.5); this makes all the panels in 2 optimization  for each trial performed by the observers. These predicted patterns for the ‘baseline model’ (without history-dependence) were first used to compare the observed and predicted patterns of choices and RTs .average P(bias)>0.5 is trivial, and would occur for any generative model of history bias. Conditional bias functions instead focus on the effect of choice history bias as a function of time within each trial, the shape of which distinguishes between different bias sources  observer’s RT distribution into five quantiles  and computed the fraction of biased choices within each quantile. The shape of the conditional bias functions for models with e of RTs .For the Visual motion 2AFC (FD) dataset, we used motion energy filtering , we subtracted the average motion energy traces corresponding to ‘up’ vs. ‘down’ choices. The resulting trace represents the excess motion energy that drives choices, over and above the generative stimulus coherence .In the 2AFC (FD) visual motion experiment participants viewed the stimulus for 0.75 s  and could respond only after the stimulus offset. This required specifying the input to the evidence accumulation process. In the models described below, we used separate simulation protocols, based on different assumptions about this input. In the ‘dynamic’ protocol, where the input was the time-varying sensory evidence from each trial, the accumulation process was assumed to start at stimulus onset, and responses could happen during the motion viewing interval. The average activity of the accumulator(s) at stimulus offset served as input for accumulation during the post-offset period. For fitting models using this protocol, empirical RTs were calculated relative to the stimulus onset. Motion energy estimates were used as time-resolved input to the model.By contrast, in the ‘default’ protocol, the motion energy fluctuations were averaged across the viewing interval excluding the filter rise time (i.e. from 250 to 750 s after stimulus offset), and the average motion energy was then used as a single-trial drift rate for the accumulation process. In other words, the accumulation-to-bound dynamics only took place during the post-offset period. Accordingly, when fitting models with this protocol, the empirical RTs were calculated relative to stimulus offset. Using this protocol was necessary for replicating our basic result from the standard DDM fits: For the ‘dynamic’ protocol, any starting point bias would turn into a drift bias because it would feed into accumulation process after stimulus offset, precluding the comparison between the two forms of bias. Thus, we used only the default protocol for the standard DDM fits, which aimed at differentiating between starting point and accumulation biases. For comparison, we also used the same simulation protocol when fitting an extended DDM with a both a constant and a ramping component in the drift bias (see below). We then switched to the more realistic dynamic protocol for the subsequent models with more complex dynamics.The AIC scores of models using the default protocol were generally lower (i.e. better) compared to the respective models that used the dynamic protocol. This difference is likely due to the fact that the dynamic protocol is more constrained by using as input to the models the exact motion energy traces rather than just their mean for each trial. AIC is blind to such latent flexibility differences that do not map onto differences in number of parameters. Thus, AIC may have ‘under-penalized’ models in the default protocol relative to those in the dynamic protocol.In all models and in both simulation protocols, model predictions were derived via Monte Carlo simulation. The variance of the processing noise was set to For all basic DDM variants described in this section, we used the default simulation protocol: the time-averaged motion energy for each trial provided the drift-rate (Naïve DDM. We denote with U was a uniform random variable (rectangular distribution) in the y was described by:y exceeded a (right choice) or surpassed 0 (left choice). The moment that either of these boundaries was crossed plus a non-decision time Above, Starting point DDM. This model was the same as the naïve model but with an extra parameter The variable Drift bias DDM. Same as the naïve model but with an extra biasing parameter Hybrid DDM. This version combined the starting point DDM and drift bias DDM using two biasing parameters.Simple Ramping DDM. This model was the same as the naïve model but with an extra parameter maxt = 300 time-steps, which was the maximum duration that a given trial could run for.Hybrid Ramping DDM. Same as the naïve model but with 2 extra parameters This model thus implemented a drift bias that is nonzero at the start of the trial , with the motion energy time courses serving as input to the accumulation process. To illustrate the details of the dynamic protocol, we next describe how the decision variable was updated in the case of the naïve DDM. The decision variable during the viewing period evolved according to the following differential equation:t. Following stimulus offset (at t = T), after 150 time-steps, the diffusion variable carried on being updated as follows:For all subsequently described models, we used the dynamic simulation protocol  exceeds In the ‘collapsing bounds’ DDM models, a response was generated when the diffusion variable  of the leaky accumulator model in order to account for history biases. These different biasing mechanisms were further crossed with two different bound regimes: static or collapsing bounds, as described for the DDM above.Here, the diffusion variable was initiated according to After stimulus offset, accumulation continued according to:Here, the diffusion variable was initiated according to After stimulus offset accumulation continued according to λ-bias model, accounted for history biases by introducing an asymmetry in the dynamics of evidence accumulation. In this model, we followed a different implementation in order to enable biasing the effective leak (λ) parameter: we reformulated the model to describe two separate accumulators that integrate the sensory evidence. We define the diffusion variable as The third leaky accumulator model we defined, the y).Starting point variability was thus applied only to one accumulator, which was equivalent to applying this variability on their difference (diffusion variable During the viewing period the two accumulators were updated according to:y) is c2, as in the DDM.The variance of the processing noise applied to each accumulator was divided by two such as the processing variance of the accumulators’ difference  exceeded a positive threshold +A response was initiated when the difference between the two accumulators , leading to 6 bins x 3 coherence x 2 response x two history = 72 bins per participant. Denoting the number of empirical observations in a particular bin We fit the extended models using a Quantile Maximal Likelihood (QMPE) approach. Under this approach, empirical RT values are classified into bins defined by the 0.1, 0.3, 0.5, 0.7 and 0.9 quantiles of the RT distribution . RT quantiles were derived separately for the various coherence levels. We excluded the 81% coherence trials and pooled together the 0% and 3% coherence trials as RT quantiles in these trials were not distinguishable. This resulted in quantiles for each of three difficulty levels , for each of the two responses (correct/error), and for two history conditions . In total, we ran 50 of such fitting sessions, each with a different random seed. Second, we chose the best-fitting parameter set from each of the 50 sessions and recomputed the likelihood while replicating 20 synthetic trials for each experimental trial. Third, the five best-fitting of these 50 sets were used as starting points fminsearchbnd, which further refined the local minima of the fit. Fourth, we recalculated the likelihood of the single best parameter set in simulations with 30 synthetic trials for each experimental trial  by averaging parameters across participants with λ > 0, and b) by averaging parameters across participants with λ < 0. Because the time courses were very similar in these two cases, in We simulated various biasing mechanisms within the frameworks of the DDM and the leaky accumulator models. Per biasing mechanism, we simulated 100K traces in timesteps of 10 ms using For the DDM simulations , the maiFor the leaky accumulator simulations , the maithe data . We simuWe then fit DDM models separately to each of the simulated datasets and fit the parameters boundary separation, drift rate, non-decision time, starting point, drift bias and drift rate variability.We quantified across-subject correlations between P(repeat) and the individual history components in DDM bias parameter estimates using Spearman’s rank correlation coefficient ive rate . The difive rate .10 quantifies the evidence in favor of the alternative versus the null hypothesis, where BF10 = 1 indicates inconclusive evidence to draw conclusions from the data. BF10 <1/10 or >10 is taken to indicate substantial evidence for H0 or H1 (We used Bayes factors to quantify the strength of evidence across our different datasets. We first computed the Bayes factor for each correlation (between P(repeat) and the history shift in starting point, and between P(repeat) and the history shift in drift bias) . We thenH0 or H1 .https://doi.org/10.6084/m9.figshare.7268558. Analysis code is also available on GitHub , the authors asked whether choice history modifies the parameters of the drift diffusion model in a reliable and meaningful way. They consistently find that the influence of past choices on future decisions is best explained by a bias in drift rate toward the previously selected decision (regardless of accuracy). While there was evidence of a minor influence of history on the starting point bias, this effect was largely dwarfed by the modulation of the drift rate. The authors contrast their results with prior work suggesting that choice history primarily shifts the starting point of the decision processThis is an extremely well-written paper, supported by a sound, thorough, and sophisticated set of analyses and strikingly clear visualization. The authors' dedication to replicating the computational mechanism is laudable, and the (five-times-replicated) results give strongly convincing evidence that choice history biases the drift-rate in perceptual decision-making. That being said, there are several open questions that remain unresolved.All reviewers were enthusiastic about the paper, however, they also identified several issues that need to be addressed before the study can be accepted.Essential revisions:1) Dual parameter models. All three reviewers thought that there needed to be a more in depth investigation of the dual parameter models.bias?Reviewer 1 pointed out that the authors repeatedly conclude that history shifts in drift rate, but not starting point, account for choice history effects, but they show that a model that lets both of these parameters vary with choice history fits better than either one alone in 5 out of 6 datasets. This is potentially interesting, and the paper would benefit from a more nuanced/accurate interpretation of this result. As it is currently written, the narrative is that either of these mechanisms could account for choice history effects, but only drift rate does. While drift rate seems to have a stronger effect, the authors need to present an intuition about why the model including both as free parameters fits the best. What does the starting point add, that the drift rate doesn't account for? How do the starting point and drift rate interact? Is it the case that one (starting point) accounts for choice history effects on fast RT trials, and the other (drift rate) accounts for effects on slower RT trials? Delving deeper here could make the paper richer. To this point, in Figure 3B, what is the performance of a model including both z and vbias, and most of the rest favor vbias. Does this reflect their parameterization of z? Did the authors explore the possibility that z and vbias might integrate over different timescales? The Materials and methods section reports that they fit the same regressors (X) for vbias and z in the HDDM regression models; if that's not the case, the authors should clarify. z seems to help model performance when it reflects one trial back, why is that not the case for longer timescales?Reviewer 1 also points out that, in Figure 6, for the model that incorporates trial history, it looks like only 2 of 6 datasets are best fit by a model including both z and vReviewer 2 pointed out that the authors appear to treat choice history effects as monolithic with respect to mechanism. That is, they test whether influences of previous choices are universally (across the tasks explored) accounted for by certain model parameters and not others, whether those relate to response repetition versus alternation. This assumption may ultimately be justified, but the paper does not offer support for it from the literature or from the data examined. It would be appropriate to include a set of analyses that split the drift and starting point biases in two, with one set of fit parameters increasing the bias towards a given response after it is chosen, and the other set decreasing that bias.  How does such a model compare to their current version that does not distinguish between these response biases? Do the currently observed relative influences of drift and starting point hold across both directions of bias?Finally, reviewer 3 pointed out that the original model comparisons  show that in 4 of the 6 experiments a dual parameter model best explains behavior. While the authors make a clear case that choice bias drives drift-rate bias changes, it could be that another factor  is also modifying starting point bias. In fact, the previous papers cited above show that accuracy biases boundary height. Given this previous literature, the authors should consider what other factors may independently be manipulating starting point bias (and boundary height).2) Choice history effects. All three reviewers requested a more detailed elaboration of the nature of the choice history effects.bias to each subjects' choice repetition, are they only looking at trials where the current evidence and the previous choice were congruent ? If so, this should be clarified in the text. If not, is this why the z parameter does so poorly? How would these plots look for trials when previous choice and current evidence are congruent vs. incongruent?Reviewer 1 pointed out that, in Figure 4, when the authors compare history shifts in z and vReviewer 2 strongly recommended revising the Introduction and the start of Results to enumerate the choice history biases that have previously been observed, and the approach of the current analyses in clustering these together (this was not clear on first read).Reviewer 2 also pointed out that it wasn't clear what distinction the authors were drawing between the prior reported effects and the current experiments – they say that the current ones "emerge spontaneously and in an idiosyncratic fashion" but estimates of prior probability could also change spontaneously and idiosyncratically. Perhaps the distinction they are drawing  is between environments that naturally engender a rational basis for choice history biases  versus those that do not. This could still predict that the same rational biases that produce starting-point biases in other experiments could result in shorter term history effects in experiments like the current one, if participants treat short runs of the same response as weak evidence for autocorrelation. The current findings suggest that this is not the case for these data. Also it is unclear whether the authors find any cumulative effects of choice history if they look at how many times a given response was given prior to the current response .10's) look consistently smaller for previous error trials than previous correct trials. No direct comparison on the magnitude of these effects is run, thus the actual null hypothesis as to whether they are different was never evaluated.Reviewer 3 pointed out that the authors' lack evidence to justify their claim that the accuracy of the preceding trials does not influence choice history effects on the drift rate. This inference is largely based on the fact that the drift rate correlations with repetition probability pass a significance threshold  both when the previous trial was correct and when it was incorrect . However, the magnitude of the likelihood ratios  Alternative models. Both reviewers 2 and 3 had concerns about possible alternative mechanisms.Reviewer 2 pointed out that the authors currently interpret choice history as being causal on changes in drift across trials, but they do not address potential third variables that could commonly influence both. For instance, could autocorrelated changes in drift rate across trials  drive the observed choice history biases? There isn't a strong intuition that this should be the case but it is a plausible mechanism  and it would be easy enough to simulate this in order to rule it out. Similarly, could an alternate form of autocorrelation that reflects regression-to-the-mean on drift rate  produce these biases?Reviewer 3 pointed out that, several papers have looked at how reinforcement learning mechanisms target specific parameters of accumulation-to-bound processes . The critical difference between these studies and the current project is that all three found evidence that selection accuracy targets the boundary height parameter itself. Not only is it relevant to link the current study to this previous work, but it also begs the question as to why the authors did not test the boundary height parameter as well. The motivation for comparing models featuring starting point and drift-rate bias terms, respectively, makes sense. While the current analyses point to an accuracy-independent effect of choice bias , given the previous findings showing the dependence between of the boundary height and selection accuracy a model testing this effect should be considered. Alternatively, given that the decision boundary is a relevant parameter and accuracy has been shown to modulate the boundary, the authors should give their rationale for its exclusion, in combination with a convincing response to the critique of the accuracy analysis (see below).Frank MJ, Gagne C, Nyhus E, Masters S, Wiecki TV, Cavanagh JF, et al. fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning. J Neurosci. 2015;35(2):485-494Pedersen ML, Frank MJ, Biele G. The drift diffusion model as the choice rule in reinforcement learning. Psychonomic bulletin and review. 2017;24(4):1234-1251.Dunovan K, and Verstynen T. "Errors in action timing and inhibition facilitate learning by tuning distinct mechanisms in the underlying decision process." J Neurosci (2019): 1924-18.4) Leaky competing accumulators. Reviewer 3 had concerns about the LCA simulations. The extension of the findings to models with collapsing bounds is interesting as a robustness analysis. However, the incorporation of the LCA model seems less straightforward. If input bias is the LCA parameter most similar to the drift rate in the DDM, then what are we to make of model 4 (LCA without collapsing bounds) that shows λ-bias being the best model ? It is completely left out of Figure 7C. All in all, this analysis seemed to simply "muddy the water" on the main results.5) Mechanism. Reviewer 3 also requested more details on the proposed mechanism. The idea that changes in a history-dependent drift-rate bias correspond to shifts in endogenous attention from one interpretation to another could be further developed. In particular it seems to imply a more explicit  mechanism for trial-wise changes in drift rate bias. But one could imagine many other mechanisms driving this effect as well (see papers referenced above). As it stands, the connection between shifts in attention and drift-rate bias does seem to be an intuitively plausible explanation (one of many), but a further description of the authors' line of thought would help to convince the reader. Essential revisions:1) Dual parameter models. All three reviewers thought that there needed to be a more in depth investigation of the dual parameter models.Reviewer 1 pointed out that the authors repeatedly conclude that history shifts in drift rate, but not starting point, account for choice history effects, but they show that a model that lets both of these parameters vary with choice history fits better than either one alone in 5 out of 6 datasets. This is potentially interesting, and the paper would benefit from a more nuanced/accurate interpretation of this result. As it is currently written, the narrative is that either of these mechanisms could account for choice history effects, but only drift rate does. While drift rate seems to have a stronger effect, the authors need to present an intuition about why the model including both as free parameters fits the best.Thank you for raising this important point. We reply to each of your specific questions pertaining to this issue in the following. Upfront, we did not intend to claim that only drift bias “accounts for choice history effects” in a general sense. What we do want to claim (and what we think is strongly supported by our data) is more specific: that history-dependent shifts in drift bias, not starting point, explain individual differences in overt choice repetition behavior. Even so, the previous choice consistently shifts the average starting point towards negative values. We now realize that the latter aspect was not sufficiently reflected in our previous presentation of the results. We have now elaborated on the starting point effects by means of (i) additional analyses and (ii) a new paragraph in the Discussion section.Specifically, we added the following new analyses (described in more detail below):- Statistical tests of the average history shift in starting point ;- The relationship between RT and overall repetition bias ;- The average correlation between individual drift bias and starting point estimates for each dataset ;- Simulations of various DDM and leaky accumulator models, and DDM fits of these synthetic data .Furthermore, we added the following Discussion paragraph:“While we found that choice history-dependent variations of accumulation bias were generally more predictive of individual choice repetition behavior, the DDM starting point was consistently shifted away from the previous response for a majority of participants . […] Future work is needed to illuminate this issue, for example through manipulating decision speed and/or the delays between subsequent motor responses, and modeling choice-related neural dynamics in the motor cortex.”What does the starting point add, that the drift rate doesn't account for?Please see the above Discussion paragraph for our general take on this issue. The starting point shift might either reflect a “real mechanism” , or actual limitations of the standard DDM. To explore the second possibility, we have extended our simulations of bounded accumulation models. Figure 7—figure supplement 2 and 3 now show, for each model:1) The conditional bias function when simulating data with varying bias strength;bias, when fitting a hybrid model;2) The recovered parameter estimates for z and vbias-only, and the hybrid model;3) DBIC values from a model without history for z-only, vbias, as estimated from the hybrid models.4) Correlations between synthetic individuals’ P(bias), and the parameter estimates for z and vThis analysis reveals two specific mechanisms that can produce both the lowest BIC for a hybrid model, together with an opposite effect on DDM drift bias and starting point:1) A nonlinearly increasing dynamic bias (i.e. ramping drift bias) in an extended perfect accumulator model ;2) A leak bias in an accumulator model , to which we point in the Discussion paragraph (quoted above).How do the starting point and drift rate interact?10 was 0.0473, indicating strong evidence for H0 . This is now reported in the section "History-dependent accumulation bias, not starting point bias, explains individual differences in choice repetition behavior”. Second, we used simulations  to unravel which modes of decision dynamics may in principle give rise to such a negative correlation: this pattern can arise from a non-linearly increasing dynamic bias , or from a leaky accumulation process with a bias in effective leak .We used two approaches to illuminate this question. First, we correlated the parameter estimates for history shift in starting point with the history shift in drift bias across observers. Correlations were generally negative , the correlation coefficient only reached significance at p < 0.05 in one dataset. The combined Bayes Factor BFIs it the case that one (starting point) accounts for choice history effects on fast RT trials, and the other (drift rate) accounts for effects on slower RT trials? Delving deeper here could make the paper richer.This is a very interesting idea, which we now elaborate on in the Discussion paragraph. All features of the current results may be explained by the combined effects two distinct biasing mechanisms, whose effects superimpose, but contribute differently to fast and slow choices: (i) motor response alternation which will load on starting point and dominate fast choices; and (ii) an accumulation bias, which will load on drift bias and dominate slow choices.Further, the starting point effect may be stereotypical (e.g. hardwired in the motor machinery), which is why it is generally negative for most subjects as we found in our DDM fits. Such a mechanism may be explained by the selective “rebound” of beta-band activity in the motor cortex . By contrast, the drift bias may be more adaptive, varying with subjects’ belief about the structure of the environment, and thus giving rise to the individual differences in repetition behavior we observe in our participants overall.In all our data sets, RTs are long so that the drift bias would be expected to dominate repetition behavior, just as we found. However, this scenario makes specific predictions when sorting trials based on RT: at very short RTs, we should predominantly find choice alternation, which is invariable across all participants, due to the stereotypical starting point shift. Furthermore, the starting point shift should only be caused by the immediately preceding response, whereas the drift bias may be affected by several choices back into the past.In Figure 4—figure supplement 5C, we now test these predictions. This shows the overall probability of choice repetition as a function of RT quantile . As expected, for the dataset which including very short RTs (< 600ms), we do observe a strong choice alternation bias on average across the group. Since not all of our datasets have many trials with such short RTs, we do not want to make too strong claims about this pattern and placed the RT split analysis in the supplement. Future work should systematically test this scenario, for example through manipulating decision speed and/or the delays between subsequent motor responses, and by modeling decision-related neural dynamics in the motor cortex.bias?To this point, in Figure 3B, what is the performance of a model including both z and vbias-only model.The model prediction for the hybrid model CBF is now added into the new Figure 3B. It is overall quite similar to the vbias, and most of the rest favor vbias. Does this reflect their parameterization of z?Reviewer 1 also points out that, in Figure 6, for the model that incorporates trial history, it looks like only 2 of 6 datasets are best fit by a model including both z and vThis is a very perceptive point. Indeed, we also noticed and explored this discrepancy, and concluded that it is due to differences in the model fitting approaches for the two analyses – specifically between standard DDM fits used for all main analyses reported in this paper, and the regression approach used to Figure 6 . The differences persist even after fitting the regression model analogously to the fits in the other figures – i.e. only based on the choice (not correct and incorrect choice) for only lag 1 (data not shown). We believe this is a technical issue, which considers attention by the field of sequential sampling models, but is beyond the scope of the present paper. We have here used the standard fitting approach for all analyses in this paper except for the analysis in Figure 6, which can only be performed with the regression approach. We feel more comfortable to base our selection between competing models on the standard approach in general.The purpose of the comparisons of different regression models is more methodological in nature: to find the best-fitting lag across trials. So, we have now moved this panel from main Figure 6 to Figure 6—figure supplement 1, instead focusing main Figure 6 on a conceptually more informative issue: the differences in the timescales of the history effects on drift and starting point (see reply below).bias might integrate over different timescales?Did the authors explore the possibility that z and vThank you for this very interesting suggestion. We now assess this quantitatively in the main Figure 6A. We fit exponentials to these “history kernels” in order to estimate the timescale of drift bias and starting point effects. Interestingly, the time constant for starting point is lower than the one for drift bias, in particular after error trials. This difference further speaks to the mechanistically distinct nature of these two history effects. We point out this difference in timescale in the Results section  and again refer to it in Discussion.bias and z in the HDDM regression models; if that's not the case, the authors should clarify.The Materials and methods section reports that they fit the same regressors (X) for vWe realized that our Materials and methods section was not sufficiently clear, thank you for pointing this out. We have now clarified our description of the approach (section ‘HDDM regression models’):“We first created a matrix 𝑋, with dimensions trials x 2 * lags, which included pairs of regressors coding for previous stimuli and choices , until (and including) each model’s lag. […] Starting point was defined asz~1+Xz, with a logistic link function 11+e-X.”z seems to help model performance when it reflects one trial back, why is that not the case for longer timescales?We realize that our model comparison visualization did not reflect the results sufficiently clearly. We have now improved the visualization, which shows that there is a consistent relationship between the three models: in those datasets where the hybrid model has the lowest AIC, this is true across several lags (up to lag 2 or 4).See also our reply above (sixth response to point #1): these regression model comparisons are not important conceptually (only used to select a particular lag), and distract from the more important message on the timescale of integration. Thus, we have moved the full set of model comparison indices to Figure 6—figure supplement 1.Reviewer 2 pointed out that the authors appear to treat choice history effects as monolithic with respect to mechanism. That is, they test whether influences of previous choices are universally (across the tasks explored) accounted for by certain model parameters and not others, whether those relate to response repetition versus alternation. This assumption may ultimately be justified, but the paper does not offer support for it from the literature or from the data examined. It would be appropriate to include a set of analyses that split the drift and starting point biases in two, with one set of fit parameters increasing the bias towards a given response after it is chosen, and the other set decreasing that bias.  How does such a model compare to their current version that does not distinguish between these response biases? Do the currently observed relative influences of drift and starting point hold across both directions of bias?In its current form, our model can (and does) capture biases either towards a previously given response (repetition), or away from the previously given response . Indeed, across our groups of observers, we see that the choice repetition probabilities of different individuals lie along a continuum from repeaters to alternators.To investigate whether the effects on DDM parameters are the same within each of these two sub-groups of participants, we split the participants by their overall history bias: repeaters (P(repeat)>0.5) and alternators (P(repeat)>0.5). We then correlated individual repetition probability with each of the history-shifts in each of the two DDM parameters, separately within each sub-group. This was only possible for five out of the six datasets, where there were sufficient numbers of participants in both sub-groups . The main result holds also within each sub-group: history shift in drift bias, not starting point, explains individual differences in choice repetition probability. This further corroborates the notion that we can treat individual choice history bias as lying on a single continuum, rather than arising from two qualitatively distinct mechanisms between these two sub-groups of individuals, or behavioral patterns.This result is now reported in the text as follows:“The same effect was present when individual participants were first split into “Repeaters” and “Alternators” based on P(repeat) being larger or smaller than 0.5, respectively ”. We hope this gets to your point.Finally, reviewer 3 pointed out that the original model comparisons  show that in 4 of the 6 experiments a dual parameter model best explains behavior. While the authors make a clear case that choice bias drives drift-rate bias changes, it could be that another factor  is also modifying starting point bias. In fact, the previous papers cited above show that accuracy biases boundary height. Given this previous literature, the authors should consider what other factors may independently be manipulating starting point bias (and boundary height).As you suggested, we observe that the previous trial’s correctness affects both boundary separation and overall drift rate. This phenomenon of post-error slowing, and its algorithmic basis in the DDM, is present in some of our datasets . However, the results are not highly consistent across datasets, most likely reflecting the fact that our tasks were not conducted under speed pressure, and featured considerable sensory uncertainty.In general, the reviews have indicated to us that we have not sufficiently addressed the link between our current work on choice history biases and previous work on other forms of sequential effects in decision-making, in particular post-error slowing. Correspondingly, we have now further unpacked the conceptual differences between these two distinct forms of sequential effects in the Introduction.Additionally, we have now added an analysis where we allow both post-error slowing  and choice history bias (i.e. previous choice affecting starting point and drift bias). These two processes seem to be relatively independent in these data: the joint fit shows largely similar results of post-error slowing  as well as choice history bias .2) Choice history effects. All three reviewers requested a more detailed elaboration of the nature of the choice history effects.Thank you for this suggestion. We realize that it was not sufficiently clear how our current approach and results differ from the previous work on sequential effects in decision making – specifically from previous work on post-error slowing. We have now changed the Introduction as follows:“Decisions are not isolated events, but are embedded in a sequence of choices. Choices, or their outcomes (e.g. rewards), exert a large influence on subsequent choices . […] Choice history biases vary substantially across individuals .”Results:“Within the DDM, choice behavior can be selectively biased toward repetition or alternation by two mechanisms: shifting the starting point or biasing the drift towards (or away from) the bound for the previous choice . […] History dependent changes in bound separation or mean drift rate may also occur, but they can only change overall RT and accuracy: those changes are by themselves not sufficient to bias the accumulation process toward one or the other bound, and thus towards choice repetition or alternation .”bias to each subjects' choice repetition, are they only looking at trials where the current evidence and the previous choice were congruent ? If so, this should be clarified in the text. If not, is this why the z parameter does so poorly? How would these plots look for trials when previous choice and current evidence are congruent vs. incongruent?Reviewer 1 pointed out that, in Figure 4, when the authors compare history shifts in z and vOur analyses did not distinguish whether previous choice and current stimulus were congruent; all trials were included. This is now clarified in the text:“For instance, in the left vs. right motion discrimination task, the history shift in starting point was computed as the difference between the starting point estimate for previous ‘left’ and previous ‘right’ choices, irrespective of the category of the current stimulus.”The possibility of testing whether the previous choice induces a ‘confirmation bias’ on the next trial, increasing perceptual sensitivity to stimuli that are congruent with the previous choice, is intriguing. In fact, we have previously investigated this same question in the context of a more complex sequential decision task that focused on the interaction between two successive judgments made within the same protracted decision process . In that manuscript, we describe a model-based analysis approach that is specifically designed to tackle your question. However, the 2AFC structure of current datasets is not suited for the consistency-dependent analyses used in this previous work.Specifically, splitting trials by whether previous choices are congruent or incongruent leads to a split that is dominated by repetition vs. alternation trials. For example, in trials where the previous choice was “up” in the up/down discrimination task, and the direction of the current stimulus is also up (‘congruent’), choice repetition is correct, and choice alternation is incorrect. Similarly, for trials where the previous choice was “up” and the current stimulus motion is down (‘incongruent’), choice alternation is correct, whereas choice repetition is incorrect. Because participants chose the correct stimulus category on the majority of trials, the ‘congruent’ condition will be primarily populated by choice repetitions, whereas the ‘incongruent’ condition will be primarily populated by alternations. In sum, this analysis is confounded by participants’ above-chance performance and does not reveal any mechanism of choice history bias.While we agree that this issue is an important direction for future research, it would require different experimental designs that are beyond the scope of our current paper. Again, we point the reviewers (and interested reader) to our recent work on confirmation bias .Reviewer 2 strongly recommended revising the Introduction and the start of Results to enumerate the choice history biases that have previously been observed, and the approach of the current analyses in clustering these together (this was not clear on first read).This has now been addressed in the two new paragraphs (from Introduction and Results sections) quoted above.Reviewer 2 also pointed out that it wasn't clear what distinction the authors were drawing between the prior reported effects and the current experiments – they say that the current ones "emerge spontaneously and in an idiosyncratic fashion" but estimates of prior probability could also change spontaneously and idiosyncratically.There are two crucial ways in which choice history biases differ from previous studies of choice biases that have applied sequential sampling (“bounded accumulation”) models of decision-making . First, in the previous experiments, the biases were experimentally induced ; by contrast, no such experimental manipulation was performed in our study. . Second, when prior probability was manipulated in the previous experiments, this was the probability of the occurrence of a particular target; not the conditional probability of occurrence, given a previous choice . This is the difference between a frequency bias and a transition bias .We have now edited the Discussion paragraph to better explain this difference:“It is instructive to relate our results with previous studies manipulating the probability of the occurrence a particular category  or the asymmetry between rewards for both choices. Most of these studies explained the resulting behavioral biases in terms of starting point shifts . […] By contrast, the choice history biases we studied here emerge spontaneously and in an idiosyncratic fashion , necessitating our focus on individual differences. "Perhaps the distinction they are drawing  is between environments that naturally engender a rational basis for choice history biases  versus those that do not. This could still predict that the same rational biases that produce starting-point biases in other experiments could result in shorter term history effects in experiments like the current one, if participants treat short runs of the same response as weak evidence for autocorrelation. The current findings suggest that this is not the case for these data.This distinction and the one we referred to in the quote above are different. Here, you refer to the distinction between environments with and without serial correlations in the stimuli (more precisely: stimulus categories). Let us elaborate on our assumptions about this here.Based on insights from recent normative models  and our own empirical work , we suspect there is no fundamental difference between task environments that naturally engender choice history bias, and those who don’t. While idiosyncratic choice history biases appear across almost all task environments  they are generally stronger and more consistent across people when the stimulus sequence exhibits correlation structure. In that framework of the above normative models, the existence of history biases in the face of random sequences can be readily explained by assuming that participants bring an internal representation of environmental stability to the lab that is biased toward repetition or alternation. If decision-makers assume that evidence is stable , they should accumulate their past decision variables into a prior for the current trial; if they assume the evidence is systematically alternating, they should accumulate past decision variables with sign flips, yielding alternating priors . Note that a stability assumption would make sense, because natural environments typically have strong autocorrelation  just as one finds experimentally.This is what we mean when we speculate: “… these considerations suggest that participants may have applied a rational strategy, but based on erroneous assumptions about the structure of the environment.”Also it is unclear whether the authors find any cumulative effects of choice history if they look at how many times a given response was given prior to the current response .We investigate these cumulative effects by plotting the probability that a trial is a repetition of the previous choice, conditioned on the sequence of choices that preceded it. So, for lag 0 this equals the average P(repeat) across all trials, for lag 1 we take only those trials following a repetition, for lag 2 we take trials following two consecutive repeats, etc. for longer sequences of repetitions. The same analyses can be performed conditioning on increasingly long sequences of alternations.We observe that, across all observers and datasets, repetition indeed increases following longer sequences of repeats. We refer to this cumulative effect as ‘cumulative repetition bias’. This cumulative bias saturates around 4 consecutive repeats, similar to effects previously reported in humans  and in rats .10's) look consistently smaller for previous error trials than previous correct trials. No direct comparison on the magnitude of these effects is run, thus the actual null hypothesis as to whether they are different was never evaluated.Reviewer 3 pointed out that the authors' lack evidence to justify their claim that the accuracy of the preceding trials does not influence choice history effects on the drift rate. This inference is largely based on the fact that the drift rate correlations with repetition probability pass a significance threshold  both when the previous trial was correct and when it was incorrect . However, the magnitude of the likelihood ratios . In fact, we had also discussed among ourselves prior to submission, but decided against addressing it further due to the associated additional complexity of the analysis.For the data presented in our previous Figure 4, this comparison would be confounded, due to the asymmetries in trial counts for correct and incorrect choices. As shown in in not claim that these effects are identical.To compute the comparison between the correlation coefficients in an unbiased manner, we have now subsampled the post-correct trials to equate the number of post-error trials for each participant and dataset . Based on Bayes factors on the difference in the resulting correlation coefficients, we are not able to strongly refute nor confirm the null hypothesis of no difference . In sum, we observe a significant positive correlation between P(repeat) and drift bias after both error and correct trials, showing that qualitatively similar mechanisms are at play after both outcomes. That said, we do 3) Alternative models. Both reviewers 2 and 3 had concerns about possible alternative mechanisms.Reviewer 2 pointed out that the authors currently interpret choice history as being causal on changes in drift across trials, but they do not address potential third variables that could commonly influence both. For instance, could autocorrelated changes in drift rate across trials  drive the observed choice history biases? There isn't a strong intuition that this should be the case but it is a plausible mechanism  and it would be easy enough to simulate this in order to rule it out. Similarly, could an alternate form of autocorrelation that reflects regression-to-the-mean on drift rate  produce these biases?To address this question, we simulated an ARIMA process that is defined by a parameter c, the auto-correlation at lag 1, and with standard deviation 0.1. We then added this fluctuating pertrial value to the drift rate, rather than using a single fixed drift rate term across trials. In This result is a specific demonstration of the general point (now elaborated in the paper), that changes in mean drift rate (or boundary separation) alone cannot produce any selective bias towards one or the other choice. The mean drift rate v is an unsigned quantity that affects the decision through its multiplication with the stimulus category s:dy = s ∙ v ∙ dt + cdW (1)whereby s is coded as  to represent ‘up’ or ‘down’ stimuli. If v is reduced over time (e.g. due to fatigue), this slows down decisions and leads to more errors  but it won’t by itself produce more choice repetitions or alternations. Some change in either starting point or drift bias is necessary for the latter.Now, it may be possible that a third variable causes drift bias to vary slowly over time, which will then cause choice repetition. This is a viable alternative to the one that choices bias subsequent drift, to give rise to choice repetition . Indeed, both scenarios would give rise to autocorrelations in the (signed) drift across trials. One plausible third variable that might cause such slow changes in drift bias towards one or the other bound is selective attention . We now elaborate on this issue in our paragraph on possible underlying mechanism in our Discussion paragraph on attention.“It is tempting to speculate that choice history signals in these regions cause the same top-down modulation of sensory cortex as during explicit manipulations of attention. […] These ideas are not mutually exclusive and can be tested by means of multiarea neurophysiological recordings combined with local perturbations.”We think that both scenarios are interesting, and that conclusively distinguishing between them will require interventional approaches in future work. We are aware of the limitations of our current correlational approach, and we realize that some statements in the previous version of our manuscript may have suggested a strong causal interpretation. We have now rephrased down all statements of this kind throughout the paper. Thank you for pointing us to this important issue.Reviewer 3 pointed out that, several papers have looked at how reinforcement learning mechanisms target specific parameters of accumulation-to-bound processes . The critical difference between these studies and the current project is that all three found evidence that selection accuracy targets the boundary height parameter itself. Not only is it relevant to link the current study to this previous work, but it also begs the question as to why the authors did not test the boundary height parameter as well. The motivation for comparing models featuring starting point and drift-rate bias terms, respectively, makes sense. While the current analyses point to an accuracy-independent effect of choice bias , given the previous findings showing the dependence between of the boundary height and selection accuracy a model testing this effect should be considered. Alternatively, given that the decision boundary is a relevant parameter and accuracy has been shown to modulate the boundary, the authors should give their rationale for its exclusion, in combination with a convincing response to the critique of the accuracy analysis (see below).Frank MJ, Gagne C, Nyhus E, Masters S, Wiecki TV, Cavanagh JF, et al. fMRI 1037and EEG predictors of dynamic decision parameters during human reinforcement 1038 learning. J Neurosci. 2015;35(2):485-494Pedersen ML, Frank MJ, Biele G. The drift diffusion model as the choice rule in 1105reinforcement learning. Psychonomic bulletin and review. 2017;24(4):1234-1251.Dunovan K, and Verstynen T. "Errors in action timing and inhibition facilitate learning by tuning distinct mechanisms in the underlying decision process." J Neurosci (2019): 1924-18.Thank you for this important point. Figure 4—figure supplement 4 shows two analyses: first (A-C), we test for the pure effect of post-accuracy increases in boundary separation (A) and reductions in mean drift rate (v) – two established sources of post-error slowing . While we observe post-error slowing in some of our datasets, this mainly loads onto the drift rate parameter.We replicate these results when adding choice history bias into the same model (D-F), indicating that choice history biases  and post-error slowing independently shape overall decision dynamics in our data.Post-error slowing has previously been studied within the DDM framework, whereas choice history bias has not – so our focus is on the latter. Importantly, our control analyses show that all our conclusions pertaining to the mechanisms of choice history bias do not depend on whether or not post-error slowing effects are taken into account .4) Leaky competing accumulators. Reviewer 3 had concerns about the LCA simulations. The extension of the findings to models with collapsing bounds is interesting as a robustness analysis. However, the incorporation of the LCA model seems less straightforward. If input bias is the LCA parameter most similar to the drift rate in the DDM, then what are we to make of model 4 (LCA without collapsing bounds) that shows λ-bias being the best model ? It is completely left out of Figure 7C. All in all, this analysis seemed to simply "muddy the water" on the main results.Thank you for pointing this out – we agree that the two types of LCA models were confusing. We have removed model 4 from Figure 7, focusing now on the leaky accumulator models including collapsing bounds.not stationary . Moreover, the leaky accumulator model is attractive in enabling the distinction between two forms of accumulation biases: a bias in the input feeding into the accumulators, or a bias in the accumulation itself. This helps generate more specific mechanistic hypotheses for future neurophysiological tests from the behavioral modelling. Similar points could be made for the collapsing bounds.That said, we view the leaky accumulator model as more than just a “robustness check“. A substantial body of theoretical work  indicates (i) that models with a non-zero leak term provide a more accurate description of decision dynamics and (ii) that leaky evidence accumulation is, in fact, normative under general conditions, in which the sensory evidence is So, while we think use of the DDM is perfectly motivated from its general simplicity and wide use in the field, generalization of our conclusions to models with collapsing bounds and leaky accumulation is important at a conceptual level.5) Mechanism. Reviewer 3 also requested more details on the proposed mechanism. The idea that changes in a history-dependent drift-rate bias correspond to shifts in endogenous attention from one interpretation to another could be further developed. In particular it seems to imply a more explicit  mechanism for trial-wise changes in drift rate bias. But one could imagine many other mechanisms driving this effect as well (see papers referenced above). As it stands, the connection between shifts in attention and drift-rate bias does seem to be an intuitively plausible explanation (one of many), but a further description of the authors' line of thought would help to convince the reader.Thank you for this suggestion. We have now elaborated on this idea in the corresponding Discussion paragraph (seventh paragraph), part of which we have quoted above. Our findings indicate that choice history signals bias the accumulation of subsequent evidence. At a neural level, an accumulation may be implemented in at least three ways: through (i) a bias in the neural input (from sensory cortex) to one of the two neural accumulator populations (in association cortex) encoding either choice; (ii) a bias in the way those populations accumulate their inputs ; or (iii) additional, evidence independent input to one of the accumulators. Scenarios (i) and (ii) precisely match current accounts of the neural implementation of selective attention: a selective boosting of sensory responses to certain features, at the expense of others ; or a stronger impact of certain sensory responses on their downstream target neurons . And, indeed our besting-fitting accumulator model is in line with scenario (i). Now, the selective amplification of certain sensory responses by top-down attention is commonly explained by selective feedback signals from prefrontal and parietal association cortex to sensory cortex  – the same regions that also carry choice history information . So, it is tempting to speculate that choice history biases the state of these association circuits, which then feed back these biases to sensory cortex in much the same way as happens during explicit manipulations of attention.Indeed, while attention is commonly studied in the lab by providing explicit cues, we speculate that an agent’s own choices may be one (among others) important factor that controls the allocation of attention in natural settings, where explicit cues are often not available. Top-down  attention is commonly thought to be accompanied by an awareness of control. We remain agnostic as to whether or not such a sense of control accompanies the spontaneous choice history biases we have studied here. But we feel the functional analogy to attention we have identified here is a potentially important avenue for future research.