Beyond Conflict Monitoring: Dorsolateral Prefrontal Cortex Mediates Congruency Sequence Effects via Post-Congruent Suppression

Beyond Conflict Monitoring: Dorsolateral Prefrontal Cortex Mediates Congruency Sequence Effects via Post-Congruent Suppression | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 August 2025 V1 Latest version Share on Beyond Conflict Monitoring: Dorsolateral Prefrontal Cortex Mediates Congruency Sequence Effects via Post-Congruent Suppression Authors : Man Li 0009-0003-2415-731X , Zixin Gong , Jie Chen , Mengjie Feng , Ginger Zeng , Meng Sun , Fengqiong Yu , Cuihong Li , and Nan Li [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175648009.90764747/v1 159 views 105 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Cognitive control, a key mechanism for goal-directed behavior, adaptively adjusts in complex environments. This adaptability is reflected in the congruency sequence effect (CSE). Traditional conflict monitoring theory posits that the dorsolateral prefrontal cortex (dlPFC) enhances cognitive control after a conflict (incongruent) event, driving the CSE. However, recent evidences suggest dlPFC involvement in adaptive adjustments following a non-conflict (congruent) event rather than an incongruent event. We proposed an ”action selection and carry-over” hypothesis, positing that the dlPFC contributes to the CSE by suppressing interference after a congruent event. To verify this hypothesis, Thirty-four healthy participants performed a modified Wisconsin Card Sorting Task with a rule-switch preparatory phase to amplify interference processing. Transcranial direct current stimulation (tDCS) was applied to inhibit left dlPFC during the task, examining its causal role in modulating the CSE. The CSE was found to increase during the switch preparatory phase compared to control conditions. tDCS-induced dlPFC inhibition further amplified this effect. Crucially, this modulation stemmed from an altered conflict processing after a congruent event, not after an incongruent event. These findings suggest that the dlPFC supports the CSE via suppressing residual interference after a congruent event, rather than post-incongruent control adaptation, thus favor the ”action selection and carry-over” hypothesis over conflict monitoring accounts. Our results offer new insights into the neuropsychological mechanism of adaptive cognitive control. Article title Beyond Conflict Monitoring: Dorsolateral Prefrontal Cortex Mediates Congruency Sequence Effects via Post-Congruent Suppression Author names Man Li 1 , Zixin Gong 1 , Jie Chen 1 , Mengjie Feng 1 , Ginger Qinghong Zeng 3 , Meng Sun 2,1 , Fengqiong Yu 1 , Cuihong Li 2,1 , Nan Li 2,1 Affiliations 1 School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui, China. 2 Department of Psychology and Sleep Medicine, the Second Hospital of Anhui Medical University, Hefei, Anhui, China. 3 Institute of Advanced Technology, University of Science and Technology of China, Hefei, Anhui, China Corresponding author Nan Li, [email protected] , [email protected] Cuihong Li, [email protected] Present address 81 Meishan Road, Shushan District, Hefei, Anhui, 230032, China Abstract Cognitive control, a key mechanism for goal-directed behavior, adaptively adjusts in complex environments. This adaptability is reflected in the congruency sequence effect (CSE). Traditional conflict monitoring theory posits that the dorsolateral prefrontal cortex (dlPFC) enhances cognitive control after a conflict (incongruent) event, driving the CSE. However, recent evidences suggest dlPFC involvement in adaptive adjustments following a non-conflict (congruent) event rather than an incongruent event. We proposed an ”action selection and carry-over” hypothesis, positing that the dlPFC contributes to the CSE by suppressing interference after a congruent event. To verify this hypothesis, Thirty-four healthy participants performed a modified Wisconsin Card Sorting Task with a rule-switch preparatory phase to amplify interference processing. Transcranial direct current stimulation (tDCS) was applied to inhibit left dlPFC during the task, examining its causal role in modulating the CSE. The CSE was found to increase during the switch preparatory phase compared to control conditions. tDCS-induced dlPFC inhibition further amplified this effect. Crucially, this modulation stemmed from an altered conflict processing after a congruent event, not after an incongruent event. These findings suggest that the dlPFC supports the CSE via suppressing residual interference after a congruent event, rather than post-incongruent control adaptation, thus favor the ”action selection and carry-over” hypothesis over conflict monitoring accounts. Our results offer new insights into the neuropsychological mechanism of adaptive cognitive control. Keywords adaptive cognitive control, congruency sequence effect, conflict monitoring hypothesis, dorsolateral prefrontal cortex, cognitive flexibility Highlights 1. Congruency sequence effect was driven by adaptive control after non-conflict but not conflict. 2. Suppressing dlPFC altered post-non-conflict adjustment beyond conflict monitoring hypothesis. 3. ’Action selection and carry-over’ hypothesis was validated explaining adaptive cognitive control. Introduction Cognitive control refers to a set of mechanisms that enable goal-directed behavior in complex or conflicting environments, representing top-down mental processes. This function is pivotal when focused attention is required, or when reliance on instinct, intuition, or automatic responses proves unwise, insufficient, or impossible (Bognar et al., 2024; Diamond, 2013; Burgess & Simons, 2005; Espy, 2004; Miller & Cohen, 2001). As cognitive control consumes cognitive resources, the human brain does not maintain high levels of cognitive control continuously but rather demonstrates adaptive regulation according to dynamic environment, which is referred to as adaptive cognitive control, representing cognitive flexibility. Cognitive flexibility is closely associated with frontal lobe functions (Fiebelkorn & Kastner, 2020). Clinical populations with frontal lobe impairments, including Alzheimer’s disease, Parkinson’s disease, schizophrenia, and obsessive-compulsive disorder, consistently exhibit compromised cognitive flexibility as a core deficit (Jalal et al., 2023; Lange et al., 2018; Townley et al., 2020; Tyburski et al., 2021). However, how the frontal cortex is involved in adaptive cognitive control and thus contributes to cognitive flexibility remains obscure. In experimental settings, cognitive control is typically represented in congruency effect (Stroop, 1935; Simon & Wolf, 1963; Eriksen & Eriksen, 1974), which is referred to as the phenomenon that the behavior is compromised when the task irrelevant information interferes with the task relevant information (e.g., naming the color of a word ‘red’ inked with green), comparing to when they are not (e.g., naming the color of a word ‘red’ inked with red). Larger congruency effect indicates greater interference from irrelevant information, reflecting lower level of cognitive control. Moreover, empirical evidences have demonstrated that the congruency effect is amplified when the previous trial is congruent but compressed when it is incongruent (Gratton et al., 1992; Kerns et al., 2004; Atalay & Inan, 2017; Yang et al., 2019; Wang et al., 2021), which suggests the level of cognitive control is adaptively adjusted based on historical conflict experiences. This phenomenon is referred to as congruency sequence effect (CSE), and considered to represent adaptive cognitive control (Egner, 2007). Over the past two decades, the Conflict Monitoring Hypothesis (CMH) has served as the predominant framework for the psychological and neural mechanisms underlying the CSE (Botvinick et al., 1999, 2001, 2004). This hypothesis posits that the anterior cingulate cortex (ACC) detects conflict (interference between task relevant and irrelevant information) in the task and relays this signal to the dorsolateral prefrontal cortex (dlPFC). Accordingly, the dlPFC upregulates cognitive control level to suppress task-irrelevant information afterward, thus inducing a smaller congruency effect for the trial after an incongruent trial, which explains the CSE. Neuroimaging studies have provided evidences supporting the involvement of the ACC in detecting the conflict (Egner et al., 2008; Egner & Hirsch, 2005; Kerns et al., 2004), and the dlPFC in subsequent adaptive adjustment (Kim et al., 2014; MacDonald et al., 2000; van Veen & Carter, 2005). However, recent studies have challenged the completeness of the CMH. For trials of neutral condition, in which the task-irrelevant information does not indicate any valid behavioral response (e.g., naming the color of a word ‘cup’ inked with red), Compton et al. (2012) argued that both the congruent and neutral trials lacked conflict, thus via the CMH framework, these trials would exert similar influence on the congruency effect of the subsequent trial. However, their experiments revealed larger congruency effect after a congruent trial compared to a neutral trial. Similar results have been obtained by other studies (Berger et al., 2019; Lamers & Roelofs, 2011). More interestingly, Compton et al. (2012) and Lamers & Roelofs (2011) did not find difference between the congruency effect after a neutral trial and that after an incongruent trial, which was inconsistent with the CMH. Besides, studies with non-invasive brain stimulation cast doubt on the role of the dlPFC in adaptive adjustment after an incongruent event as suggested by the CMH. Friehs et al. (2020) perturbed the oscillation of the dlPFC with repeated transcranial magnetic stimulation (rTMS) during a Stroop task, and observed an increased CSE due to a specific increase of the congruency effect after a congruent trial. Similarly, with continuous theta-burst stimulation (cTBS) on the dlPFC, Xu et al. (2024) found altered congruency effect following a congruent trial rather than an incongruent trial. Our earlier work applying transcranial direct current stimulation (tDCS) on the left dlPFC (Li et al., 2021) likewise revealed a selective modulation of the congruency effect of post-congruent trial rather than that of post-incongruent trial. Collectively, behavioral results involving neutral trials and causal evidences with brain stimulation suggests that the CMH, especially the role of the dlPFC in the framework, could not explain the CSE, thus underscoring a need of a refined model to elucidate the mechanism of adaptive cognitive control in the CSE. Based on recent evidences, we previously proposed an assumption of “action selection and carry-over” involved in the CSE (Li et al., 2021). Specifically, we assumed that the task-irrelevant information was not fully inhibited during action selection in a congruent trial since an individual could generate the response according to either relevant or irrelevant information, while the task-irrelevant information should be effectively suppressed to guide to a correct response in an incongruent trial. The residual processing for the task-irrelevant information, especially at a high level for a congruent trial, would be carried over to the subsequent trial, inducing an increase of the congruency effect for the post-congruent trial. We further assumed that the dlPFC might spontaneously suppress the residual activation for the task-irrelevant information, which is especially essential after a congruent trial. Thus, interventions inhibiting the dlPFC (Friehs et al., 2020; Li et al., 2021) might impair the suppression, resulting in an amplified CSE due to an increased congruency effect after a congruent trial. While recent evidences implicate a specific role of dlPFC in cognitive control adjustment after a congruent trial, they could not demonstrate that the adjustment would be indeed due to a residual processing of task-irrelevant information after a congruent trial, thus could not verify our “action selection and carry-over” hypothesis. In the current study, we focused on this issue with a task-rule switching paradigm, which contained a special phase when participants were instructed to prepare for a rule switch while keep abiding by the current rule. Previous studies suggested that the processing level of potential new rules (i.e., interfering information in our paradigm) would be amplified during switch preparation (Karayanidis et al., 2023; Kessler & Rozanis, 2023; Kiesel et al., 2010; Liu & Zhang, 2020; Shi et al., 2014). This paradigm was combined with tDCS on the left dlPFC. If the role of dlPFC in the CSE was indeed associated with the activation of task-irrelevant information after a congruent trial, tDCS intervention effect would specifically manifest in a change of the congruency effect after a congruent trial during the rule-switch preparatory phase, which in turn alter the CSE. 2 Materials and Method 2.1 Participants Thirty-eight participants (26 females; age of 20.6 ± 1.3 years old ranging from 17 to 23; years of education of 14.8 ± 0.8 ranging from 13 to 16) were recruited for the current experiment. All participants were right-handed and had no color blindness or weakness. Written informed consent was obtained from each participant after the procedure had been fully explained, prior to the experiment. The study protocol was in accordance with the guidelines of the Declaration of Helsinki and approved by the local ethics committee (IRB number: 83220373). Data of four subjects were excluded due to low accuracy in the main task (see Data Analysis), leaving 34 valid subjects (23 females; age of 20.6 ± 1.4 years old ranging from 17 to 23; years of education of 14.8 ± 0.9 ranging from 13 to 16). 2.2 Behavioral paradigm We implemented a modified Wisconsin Card Sorting Test (WCST) paradigm coded with Psychtoolbox in MATLAB (2021b, The MathWorks, Inc., Natick, Massachusetts, United States). Task stimuli consisted of colored arrows, with four colors (red, green, blue, yellow) and four arrow orientations (top-left, bottom-right, bottom-left, top-right), which were mapped to the corresponding response direction with the joystick of a PlayStation Dualsense controller (Figure 1a). We split the combinations of color and arrow orientation into two groups: red and green were only paired with top-left and bottom-right arrows (RG group), while yellow and blue were only paired with bottom-left and top-right arrows (BY group). Trials from the RG and BY groups were shown alternatively in the task. At the presentation of the stimulus, participants were instructed to move the joystick of the game controller with their right thumb to the corresponding direction according to the color or arrow orientation (Figure 1b). Similar with the classical paradigm of WCST, the valid rule (color or arrow) for a specific trial was not explicitly informed, and the participant should figure out the valid rule by their response and feedback. Each block consisted of 12 mini-blocks of color rule and 12 mini-blocks of arrow rule starting with a color mini-block that was instructed to the participant explicitly, with 210 trials in total lasting no more than 8 min. For each mini-block, we split the trials into four phases including the rule confirmation phase, rule stable phase, switch alert phase, and switch preparatory phase (Figure 1c). Rule confirmation phase consisted of 2 incongruent trials, to inform the participant the changed rule. Rule stable phase consisted of 4 trials for color mini-blocks (48 trials in total for one block) and three for arrow mini-block (36 trials in total for one block). The single trial after rule table phase was defined as switch alert phase, in which the cue for rule-switching preparation might occur. The trial number in switch preparatory phase varied across mini-blocks, ranging from 1 to 4 trials for color mini-blocks (30 trials in total for one block) and from 1 to 3 trials for arrow mini-blocks (24 trials in total for one block). Trial numbers of congruent and incongruent trials were balanced for each phase type of rule stable phase, switch alert phase and switch preparatory phase across color mini-blocks as well as arrow mini-blocks. Different cues were shown for the phases, forming two types of blocks (Figure 1c): fully instructed rule switch (FiRS) block and partially instructed rule switch (PiRS) block. In the FiRS block, participants were explicitly informed at the specific trial when the valid rule switched, while in the PiRS block, participants were only given a rough period for the occurrence of rule switch. It is notable that the trial number in the switch preparatory phase was not fixed, but varied across the mini-blocks. This was to make the rule switch period for the PiRS block more unpredictable, thus enhancing participants’ intrinsic preparation for the rule switch. Meanwhile, we adopted an asymmetry length for the color and arrow mini-blocks, since in the current study we mainly focused on the results from the color rule. Decreasing the trial number in arrow condition was to increase the sampling rate during the limited period of tDCS session. In each task session, the participant first completed a short protocol to adjust the sensitivity of the joystick. Specifically, the participant was instructed to push the joystick to the maximum position of the four directions in the main task, and to keep for 2 s before releasing the joystick. The average deviation of joystick position from the default position was calculated, with 50% of this deviation defined as the threshold of valid response in the formal task. This method was to normalize joystick response habit across individuals and joystick sensitivity of the PlayStation Dualsense controller. Afterward, the task paradigm was instructed to the participant with several practice blocks to learn the mapping rule between color/arrow and response. All participants were guaranteed to achieve no less than 80% for the accuracy of mapping rule, after which they completed 3 blocks of the main task, with a 1.5 min rest in between the blocks. During the main task, tDCS were implemented. Researchers have argued that to investigate the mechanism of the CSE, the paradigm should be designed in a sophisticated way to exclude the possible confounding in interpreting the phenomenon (Braem et al., 2019). In our paradigm, we adopted a feature space of four colors and four arrow orientations, and the trials from the RG and BY groups were presented alternatively. This setting enabled us to eliminate the confounding of feature integration in explaining the CSE (Hommel, 2004; Mayr et al., 2003). Moreover, we also balanced the trial numbers for the congruent and incongruent trials, and the trial numbers for the four congruency sequences (congruent to congruent, congruent to incongruent, incongruent to congruent, and incongruent to incongruent). Nevertheless, for the rule confirmation phase, since we applied two incongruent trials to inform the subject the change of the valid rule which we believe is necessary in the WCST, in the rule stable phase, the trial numbers of incongruent to congruent and incongruent to incongruent sequence were slightly more than those of the other two sequences. We hereby argued that our design included a minimal confounding of contingency learning (Schmidt & De Houwer, 2011), and item-level learning (Hommel, 2004; Mayr et al., 2003) in explaining the CSE in our paradigm. 2.3 tDCS procedure To investigate the role of the dlPFC in the CSE, we used High-Definition tDCS with a 4 × 1 electrode array design (Soterix Medical Inc., Woodbridge,New Jersey, United States). There were five 1 x 1 cm ring electrodes for the device. One electrode was the stimulation electrode, placed at F3 with an electroencephalography recording cap according to the international 10–10 system. The other four electrodes were the reference electrodes, placed at AFz, F7, C5 and FCz, respectively (Figure 2a). This tDCS montage was considered to be able to intervene the function of left dlPFC, as simulated in the software of simNIBS (Thielscher et al., 2015) (Figure 2b). In the current study, we used cathodal current of 2 mA for the stimulation electrode, and anodal current of 0.5 mA for each of the reference electrodes. With cathodal current stimulation, the dlPFC was assumed to be inhibited (Chan et al., 2023; Stagg & Nitsche, 2011). For real stimulation, tDCS was applied to the dlPFC for no more than 30 minutes, with the current rising to 2 mA within 30 seconds, maintaining at 2 mA throughout the experiment, and then decreasing to 0 mA in 30 seconds. For sham stimulation, the electrode placement was the same as that in real stimulation, except that the current reached 2 mA but was only maintained for 30 seconds before returning to 0 mA in 30 seconds. All participants received four tDCS sessions (Figure 2c): two real and two sham stimulation sessions. The two real stimulation sessions were applied in two successive days corresponding to the FiRS and PiRS blocks, respectively. The two sham stimulation sessions were similarly given. There was a one-week interval between real and sham stimulations to prevent carry-over effects. The order of real and sham sessions and block types for the task were counterbalanced across participants. 2.4 Data Analysis The paradigm of current study contained six main factors: the congruency of current trial, the congruency of previous trial, experimental phase (rule stable phase and switch preparatory phase), block type (FiRS and PiRS), tDCS protocol (real and sham), and valid rule (color and arrow). Before statistical analysis, we excluded data of subjects with relatively low behavioral performance. Specifically, for the data of each subject, we calculated the accuracy for each combination of the six factors above, resulting in 64 accuracies. Data of subjects with any of these accuracies less than 25% (chance level) was excluded, leaving 34 valid subjects to the formal analysis. For the valid subjects, we calculated the accuracy and response time (RT) for the combinations of main factors of the experiment. For accuracy, trials with RT less than 200 ms or following an incorrect trial, with a proportion of 12.9 ± 6.2% in the two phases of color rule and a proportion of 4.6% ± 3.0% in the two phases of arrow rule for each subject) were excluded. These criteria were to eliminate effects from involuntary responses (Liu et al., 2013; Ye & Damian, 2023) and post-error cautiousness (Cho et al., 2009; Danielmeier & Ullsperger, 2011; Li et al., 2024). For RT, incorrect current trials were additionally excluded. In the formal analysis, we mainly focused on color mini-blocks in which color was task-relevant and arrow was task-irrelevant, since the color rule is an arbitrary stimulus-response mapping and the arrow rule is a prepotent mapping (but see Table S1 and Figure S1 for results of arrow mini-blocks). This focus was consistent with previous studies on cognitive control in which the prepotent mapping served as the interference factor (Bognar et al., 2024; Tang et al., 2021; Yang et al., 2021, 2022). Meanwhile, we specifically analyzed the switch preparatory phase, since this was the essence arrangement in our paradigm and the phase distinguished the FiRS and PiRS blocks while the rule stable phase did not. Accordingly, the accuracy and response time (RT) for the switch preparatory phase in color mini-blocks were entered into a four-way repeated measure ANOVA including factors of the congruency of current trial, the congruency of previous trial, block type and tDCS protocol. To investigate the origin of the modulation effect on the CSE, we further conducted two-way ANOVA including block type and tDCS protocol on the congruency effect after a congruent trial and that after an incongruent trial. We did not include the rule stable phase and switch preparatory phase within a single ANOVA model, since there was a systematic temporal order for the two phases in our design which introduced a potential effect of practice. In our design, the switch preparatory phase in the FiRS blocks served as a better reference condition for that in the PiRS blocks. Nevertheless, as a contrast, we also conducted ANOVA separately on the data of rule stable phase. All the analysis above were conducted in MATLAB (2021b, The MathWorks, Inc., Natick, Massachusetts, United States). 3 Results For the switch preparatory phase of color mini-blocks, with a four-way ANOVA including factors of the congruency of current trial, the congruency of previous trial, block type (FiRS and PiRS) and tDCS protocol (cathode and sham), we were interested in the congruency effect indicated by the main effect of the congruency of current trial; the CSE indicated by the interaction between the congruency of current trial and that of previous trial; the modulation effect of rule-switching preparation on the CSE indicated by the three-way interaction of the congruency of current trial, the congruency of previous trial and block type; and the interacted modulation effect of rule-switching preparation and tDCS on the CSE indicated by the four-way interaction of the congruency of current trial, the congruency of previous trial, block type and tDCS protocol. ANOVA on both accuracy and RT revealed the congruency effect via a main effect of congruency of current trial (accuracy: F (1, 33) = 111.85, p < 0.001, partial η 2 = 0.772; RT: F (1, 33) = 91.46, p < 0.001, partial η 2 = 0.735), with better performance of the congruent trials (accuracy: 95.0% ±4.7 %; RT: 628 ± 44 ms) than that of the incongruent trials (accuracy: 80.7% ± 8.6 %; RT: 664 ± 47 ms). There was also a CSE in accuracy and RT respectively indicated by a significant two-way interaction between the congruency of previous trial and that of current trial (accuracy: F (1, 33) = 54.16, p < 0.001, partial η 2 = 0.621; RT: F (1, 33) = 25.40, p < 0.001, partial η 2 = 0.435), with larger congruency effect after a congruent trial (accuracy: 20.0% ± 10.6%; RT: 47 ± 25 ms) than that after an incongruent trial (accuracy: 8.5% ± 7.3%; RT: 25 ± 25 ms). Induction of the rule-switching preparation significantly modulated the CSE, revealed by a three-way interaction among the congruency of previous trial, that of current trial and block type for accuracy ( F (1, 33) = 21.31, p < 0.001, partial η 2 = 0.392) and marginally for RT ( F (1, 33) = 3.81, p = 0.060, partial η 2 = 0.104). tDCS interacted with the block type on its modulation on the CSE, suggested by a four-way interaction among the congruency of previous trial, that of current trial, block type and tDCS protocol for accuracy ( F (1, 33) = 6.988, p = 0.013, partial η 2 = 0.175) but not for RT ( F (1, 33) = 0.238, p = 0.629, partial η 2 = 0.007). See Table S2 and Figure S2 for other statistical effects. To illustrate the origin of the three-way and four-way interactions in accuracy above, we calculated the congruency effect after a congruent trial and that after an incongruent trial, and then conducted two-way ANOVA with factors of block type and tDCS protocol on these metrics respectively (Figure 3). For the congruency effect after a congruent trial and that after an incongruent trial, there was both a main effect of block type (after congruent: F (1, 33) = 66.19, p < 0.001, partial η 2 = 0.667; after incongruent: F (1, 33) = 10.31, p = 0.003, partial η 2 = 0.238), with larger congruency effect in the PiRS block (after congruent: 30.4% ± 15.9%; after incongruent: 12.7% ±12.4%) than that in the FiRS block (after congruent: 9.6% ± 9.1%; after incongruent: 4.3% ± 8.1%), respectively. These results suggested that preparation for rule switching in PiRS block amplified the processing level of task-irrelevant information. No main effect of tDCS protocol were found for the congruency effect either after a congruent trial ( F (1, 33) = 0.429, p = 0.517, partial η 2 = 0.013) or after an incongruent trial ( F (1, 33) = 0.912, p = 0.347, partial η 2 = 0.027). However, the interaction between block type and tDCS protocol was significant for the congruency effect after a congruent trial ( F (1, 33) = 5.879, p = 0.021, partial η 2 = 0.151) rather than that after an incongruent trial ( F (1, 33) = 1.554, p = 0.221, partial η 2 = 0.045). Post hoc analysis showed that cathodal tDCS on the dlPFC marginally increased the congruency effect after a congruent trial in the PiRS block ( t (33) = 1.901, p = 0.066), while did not alter that in the FiRS block ( t (33) = -1.133, p = 0.265). Furthermore, post hoc analysis on the CSE showed that cathodal tDCS increased the CSE in the PiRS block ( t (33) = 2.468, p = 0.019) but not in the FiRS block ( t (33) = -0.794, p = 0.433). These results suggest that, for the CSE during the switch preparatory phase, the interacted modulation effect of tDCS and rule-switching preparation cue indicated by the four-way interaction in accuracy, were attributed to the modulation for the congruency effect after a congruent trial rather than after an incongruent trial. Moreover, we also analyzed the rule stable phase for the color mini-blocks, which was assumed to be comparable between the PiRS and FiRS blocks according to the paradigm design (also see Table S2 and Figure S2). Four-way ANOVA showed a significant main effect of the congruency of current trial for both accuracy ( F (1, 33) = 69.71, p < 0.001, partial η 2 = 0.679) and RT ( F (1, 33) = 51.89, p < 0.001, partial η 2 = 0.611), with better performance for the congruent trial (accuracy: 94.7% ± 3.8%; RT: 624 ± 46 ms) than for the incongruent trial (accuracy: 85.0% ± 8.0%; RT: 656 ± 45 ms), indicating the congruency effect. There was also an interaction between the congruency of current trial and that of previous trial for both accuracy ( F (1, 33) = 31.66, p < 0.001, partial η 2 = 0.490) and RT ( F (1, 33) = 50.00, p < 0.001, partial η 2 = 0.602), with larger congruency effect after a congruent trial (accuracy: 13.6% ± 9.2%; RT: 44 ± 22 ms) than that after an incongruent trial (accuracy: 5.8% ± 6.2%; RT: 19 ± 32 ms), indicating the CSE. However, we did not found any significance for the two-way interaction between the congruency of current trial and block type (accuracy: F (1, 33) = 2.376, p = 0.133, partial η 2 = 0.067; RT: F (1, 33) = 1.121, p = 0.298, partial η 2 = 0.033) or for the three-way interaction among the congruency of previous trial, that of current trial and block type (accuracy: F (1, 33) = 0.163, p = 0.688, partial η 2 = 0.005; RT: F (1, 33) = 0.144, p = 0.706, partial η 2 < 0.004), indicating no difference in the congruency effect or CSE between the PiRS and FiRS blocks. Meanwhile there was no significance for the two-way interaction between the congruency of current trial and tDCS protocol (accuracy: F (1, 33) = 0.296, p = 0.590, partial η 2 = 0.009; RT: F (1, 33) = 1.413, p = 0.243, partial η 2 = 0.041) or for the three-way interaction among the congruency of previous trial, that of current trial and tDCS protocol (accuracy: F (1, 33) = 0.346, p = 0.560, partial η 2 = 0.010; RT: F (1, 33) = 1.584, p = 0.217, partial η 2 = 0.046), indicating that tDCS did not modulate the congruency effect or the CSE in the rule stable phase. Moreover, tDCS did not interact with the block type on their influence on the CSE, indicated by the non-significant four-way interactions among the congruency of previous trial, that of current trial, block type and tDCS protocol (accuracy: F (1, 33) = 0.230, p = 0.628, partial η 2 = 0.007; RT: ( F (1, 33) = 3.414, p = 0.074, partial η 2 = 0.094). Results above were depicted in an equivalent two-way ANOVA style for simplicity (Figure 3). These results suggested that there were indeed congruency effect and the CSE during the rule stable phase, but the CSE did not differ between the PiRS and FiRS blocks which was consistent with our design. Notably, the CSE in this period was not modulated by tDCS, which was in contrast to the situation of the switch preparatory phase. 4 Discussion 4.1 dlPFC modulates the CSE via suppressing irrelevant rule after congruent event The current study investigated the role of the dlPFC in the CSE, particularly its involvement in suppressing task-irrelevant rules following a congruent trial. With a modified paradigm for the WCST, we examined how the CSE was modulated by an explicit induction of rule-switching preparation and cathodal tDCS on the dlPFC. As a predominant framework in explaining the mechanism of the CSE, the CMH assumes that the CSE is attributed to an adaptive increase of cognitive control level associated with the dlPFC after detecting a conflict event (Botvinick et al., 1999, 2001, 2004). However, we previously found that interfering the function of dlPFC with tDCS altered the congruency effect after a congruent trial rather than that after an incongruent trial, which was inconsistent with the CMH (Li et al., 2021). The specificity of the congruency effect after a congruent trial in the CSE has also been implicated by previous behavioral studies (Compton et al., 2012; Lamers & Roelofs, 2011) and TMS studies (Friehs et al., 2020). We previously proposed that there might be an additional mechanism in the CSE, an ‘action selection and carry-over’ hypothesis (Li et al., 2021). Specifically, the action selection refers to the process of generating the correct response in a trial with potential distractor information. In a congruent trial, there would be a low demand on action selection since both relevant and irrelevant information indicates the correct response. In an incongruent trial, the irrelevant information should be effectively inhibited so as to generate the correct response. The residual of compromised irrelevant information after action selection would be carried over to the next trial, inducing a higher behavioral interference after a congruent trial comparing to that after an incongruent trial. Meanwhile, we assumed that the dlPFC would be able to suppress the carry-over effect of irrelevant information, as a spontaneous control after a congruent trial rather than after an incongruent trial, since for the later case the irrelevant information has already been eliminated in previous action selection. Unlike the CMH, our assumption could explain the specificity of the congruency effect after a congruent trial in the CSE as well as the evidences from the brain stimulation studies. Previous studies have been unable to directly demonstrate a link between the level of task-irrelevant information processing and the role of the dlPFC in the CSE. The present study addressed this issue by explicitly introducing a rule-switching preparation phase within a WCST paradigm. Our results showed that, during this preparatory phase, the congruency effect following both congruent and incongruent trials was markedly amplified, indicating that the rule-preparation stage indeed elevated the processing level of irrelevant information. This finding aligns with prior evidence demonstrating that rule-switching preparation boosts the activation of the to-be-switched rules (Karayanidis et al., 2023; Kessler & Rozanis, 2023; Kiesel et al., 2010; Liu & Zhang, 2020; Shi et al., 2014). We note that although the congruency effects after both congruent and incongruent trials were enlarged, their difference, namely the congruency sequence effect (CSE), was also increased in the preparatory phase of PiRS block. This means that the increment in the congruency effect after a congruent trial exceeded the increment after an incongruent trial. However, it should be noted that this pattern alone is insufficient to distinguish the conflict-monitoring hypothesis (CMH) from our “action selection and carry-over” hypothesis. According to CMH, the elevated processing of task-irrelevant information during the rule-switching preparatory phase generates a stronger conflict signal on incongruent trials, thereby engaging a higher level of cognitive control that more strongly suppresses the irrelevant information in the next trial. Under this account, CMH operates as a compensatory control mechanism available only after incongruent trials, while no conflict signal is elicited after congruent trials and the compensatory mechanism cannot be recruited, resulting in a larger alteration of the congruency effect after congruent than after incongruent trials. Meanwhile, our “action selection and carry-over” hypothesis also explain this result in a natural way. The task-irrelevant information is sufficiently suppressed in an incongruent trial but not in a congruent trial, and its processing status is carried over to the next trial. Thus, the preparation of the to-be-switched rule in the preparatory phase interferes more with the trial after a congruent trial than after an incongruent one, predicting larger CSE in the PiRS block. However, the tDCS modulation result supported our hypothesis rather than the CMH. According to the CMH, cathodal tDCS on the dlPFC, which has been assumed to inhibit its function (Chan et al., 2023; Stagg & Nitsche, 2011), would attenuate its adaptive adjustment, inducing an increase of the congruency effect after an incongruent trial and consequently a decreased CSE, especially during the switch preparatory phase when the compensative cognitive control is assumed to be effective. However, our result showed the opposite case: cathodal tDCS on the left dlPFC during the switch preparatory phase did not change the congruency effect after an incongruent trial but altered the congruency effect after a congruent trial, contributing to an increased CSE. Nevertheless, this result could be explained by our ‘action selection and carry-over’ hypothesis in which we assumed that the dlPFC might be especially essential in suppressing the irrelevant information after a congruent trial. Moreover, it is noteworthy that in our paradigm, we adopted two groups of stimuli (RG and BY) and presented them alternatively. Thus, the tDCS modulation effect were not due to the preserved irrelevant stimulus itself (e.g., maintenance of a specific arrow direction) but rather the processing of feature dimension (i.e., arrow rule). This result indicate that the ‘carry-over’ effect in our hypothesis is not restricted for a residual activation of a specific irrelevant feature after a congruent trial, but extend to feature dimension. Accordingly, the dlPFC could suppress this untimely activated irrelevant feature dimension specifically after a congruent trial. This finding benefit from the alternative presentation of stimulus groups in our paradigm, which was not used in previous CSE studies with brain stimulation (Friehs et al., 2020; Gbadeyan et al., 2016; Li et al., 2021; Xu et al., 2024). Our results could not be explained by the prevalent frameworks that are associated with the CSE other than the CMH. The feature integration theory assumed that the CSE could be explained via updating the integrated event file formed by the relevant and irrelevant information (Hommel, 2004). The validity of this hypothesis has been shown in paradigms with a 2:2 stimulus-response mapping (e.g., two colors and two key responses) (Hommel, 2004; Kühn et al., 2011; Moeller & Frings, 2014; Zmigrod & Hommel, 2010). However, the paradigm in the current study included a 4:4 stimulus-response mapping which was split into two groups presented alternatively. Previous researchers argued that this design could eliminate the effect of feature integration in the paradigm (Braem et al., 2019; S. Kim & Cho, 2014; Schmidt & Weissman, 2014; Weissman et al., 2014). The temporal learning account explained the CSE with a mechanism involving a correlated response threshold across successive trials (Schmidt & Weissman, 2016). If our results were attributed to this process, the induction of rule-switching preparation and its interaction with tDCS would have changed both the congruency effect after a congruent trial and that after an incongruent trial, which was not the case in our results. 4.2 A state-dependent nature of dlPFC’s role in adaptive cognitive control Although our results in the switch preparatory phase support our ‘action selection and carry-over’ hypothesis, we are not arguing that this mechanism would be the only valid framework for the CSE. Like the CMH, feature integration theory and temporal learning account, the ‘action selection and carry-over’ hypothesis might be part of the complicated psychological basis of adaptive cognitive control. Our results from the rule stable phase shed preliminary light on this account. Trials of the rule stable phase showed little modulation effect on the congruency effect as well as the CSE with tDCS on the dlPFC, which was in contrast to those of switch preparatory phase. These results indicated that the role of the dlPFC in the CSE might not keep in a sustained style throughout the task, but might be related to a specific cognitive environment. Human brain might be able to recruit the dlPFC in a state that intrusion of distractor information is easy to cause a mistake (rule switching preparatory phase), and leave the brain area in an idle mode in a state when there is enough external clue informing what to do. Indeed, in our paradigm, during the rule stable phase, and the switch preparatory phase in the FiRS block, the participant was provided the external clue indicating a sustained valid rule as the previous trial, which might decrease the necessity of recruiting the dlPFC. Thus, we tend to interpret the working style of the dlPFC in the CSE as state dependent, which might be part of a higher order system in charge of the cognitive flexibility. This system could determine the mode of the dlPFC in the CSE as an adaptive adjustment node according to the previous conflict event as supposed in the CMH, or as a spontaneous cognitive controller to specifically inhibit the irrelevant information after a congruent event as supposed in our ‘action selection and carry-over’ hypothesis, or as an idle mode when the external information is rich enough as in the rule stable phase. The state dependent nature of the dlPFC in cognitive control as well as the broader network has been implicated by previous studies. Menon & D’Esposito (2022) emphasized that implementing cognitive control in dynamic environments relies on the flexible organization of prefrontal cortical networks. Ryman et al. (2019) found reduced dlPFC engagement during proactive control but heightened activation during reactive control, underscoring the context-sensitive nature of PFC operations. The adaptive cognitive control might involve multiple areas such as superior frontal cortex in expecting conflict events (Martínez-Molina et al., 2024) and anterior prefrontal cortex in arbitrating the functional style of the dlPFC (Badre & D’Esposito, 2007; Badre & Nee, 2018; Nee & D’Esposito, 2016). 4.3 Debates and limitations Due to the notorious complexity of the frameworks in explaining the CSE, details in the paradigm design might influence the psychological strategy of the participants used in the task, thus confounding the CSE results (Braem et al., 2019; Duthoo et al., 2014). Moreover, so far the studies on the causal role of the brain on the CSE are scarce, many of which adopted a unique paradigm design. Thus, it is not surprising that there have been debates or even contradicted results within the field. Brain lesion studies in macaques (Buckley et al., 2009) and humans (Boschin et al., 2017) showed that lesions of the dlPFC decreased the CSE, which contrasts with the present results. The two studies adopted an analog of WCST in which the subject selected the target by matching currently valid feature with a sample stimulus. The matching process requires visual searching within the stimulus array which might exaggerate the influence of conflict event, leading to a CMH-like strategy in the subjects. With the similar paradigm, Boschin et al. (2017) applied rTMS on the left dlPFC to disrupt its oscillation when human subjects were completing the task, and found a decreased CSE, which accorded with the CMH. However, their detailed analysis revealed that the rTMS modulation effect on the CSE was not due to a change in the performance after an incongruent trial, which was inconsistent with the CMH. One tDCS study with flanker task also provided partially supportive evidence for the CMH: anodal tDCS on the dlPFC increased the CSE, but not attributed to the congruency effect either after a congruent trial or after an incongruent one (Gbadeyan et al., 2016). Note that Gbadeyan’s paradigm design and analysis could not rule out the confounding of feature integration in the CSE. In contrast, two recent studies on the CSE adopted Stroop task without feature integration confounding revealed that the Stroop effect after a congruent trial was altered by online rTMS and offline cTBS on the right dlPFC, respectively (Friehs et al., 2020; Xu et al., 2024), which might be comparable to our results. Struggling with a detailed analysis to exclude possible confounding, our recent tDCS study implicated the involvement of the dlPFC in the CSE for the post-congruent trial (Li et al., 2021). With a carefully designed paradigm, the current study provided evidence suggesting a novel role of the dlPFC in the CSE via inhibiting the irrelevant information after a congruent trial. Collectively, the role of the dlPFC in the CSE might be related to the specific paradigm design, which might be explained by its function of state dependent style. More studies are required to illustrate how a specific functional style in the dlPFC is determined in accordance with the cognitive environment. Considering the necessary arrangement in the WCST, our paradigm included a feedback phase which was not ordinary in previous studies. Our design could not exclude the impact from a possible process of credit assignment triggered by the feedback (Krausz et al., 2023; McDougle et al., 2016; Tang et al., 2024). However, tDCS was not effective in the rule stable phase which also included the feedback, indicating that the credit assignment by the feedback alone could not recruit the dlPFC in the CSE. Previous behavioral and brain stimulation studies using paradigms without feedback have also shown the specific contribution from the post-congruent trial associated with the dlPFC in the CSE (Friehs et al., 2020; Xu et al., 2024), which further ease the influence of feedback in our result. Nevertheless, future research with more sophisticated design could help to eliminate this caveat. Finally, we did not investigate the tDCS modulation effect for the right dlPFC, thus could not disclose the factor of laterality of the dlPFC in our ‘action selection and carry-over’ hypothesis, which requires future work. 5 Conclusion In summary, with tDCS and a modified WCST paradigm, the current study suggested that the involvement of dlPFC in the CSE might be inhibiting the irrelevant information after a congruent event, which indicated the validity of the ‘action selection and carry-over’ hypothesis, as a complementary perspective to the CMH for the neuropsychological mechanism of adaptive cognitive control. Our results also implicated that the dlPFC might function in a state dependent way, which contributes to human cognitive flexibility. Further investigation of the specific role of dlPFC in the post-congruent and post-incongruent events would help to evaluate the ability of adaptive cognitive control of the clinical groups more elaborately. Data and code availability statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Funding statement This work was supported by the Natural Science Research Project of Anhui Educational Committee, China [grant number 2024AH050676]; the Translational Medicine Research Program of Anhui Translational Medicine Institute, China [grant number 2023zhyx-C17]; the Basic and Clinical Collaborative Research Advancement Program of Anhui Medical University, China [grant number 2021xkjT029]; and the National Natural Science Foundation of China [grant number 32200856, 32371134]. Conflict of interest The authors declare no conflict of interest. Acknowledgement The authors are grateful to all subjects for their participation in the study. Author Contribution Man Li: Data curation; Formal analysis; Methodology; Validation; Visualization; Writing – original draft; Writing – review & editing. Zixin Gong: Data curation; Investigation; Project administration; Software; Visualization. Jie Chen: Data curation; Formal analysis; Investigation; Methodology. Mengjie Feng: Investigation; Methodology; Software. Ginger Qinghong Zeng: Methodology; Writing – review & editing. Meng Sun: Resources; Supervision; Writing – review & editing. Fengqiong Yu: Funding acquisition; Resources; Supervision; Writing – review & editing. Cuihong Li: Conceptualization; Funding acquisition; Methodology; Project administration; Resources; Supervision; Writing – review & editing. Nan Li: Conceptualization; Formal analysis; Funding acquisition; Methodology; Project administration; Resources; Supervision; Visualization; Writing – original draft; Writing – review & editing References Atalay, N. B., & Inan, A. B. (2017). Repetition or alternation of context influences sequential congruency effect depending on the presence of contingency. Psychological Research , 81 (2), 490–507. https://doi.org/10.1007/s00426-016-0751-8 Badre, D., & D’Esposito, M. (2007). Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. Journal of Cognitive Neuroscience , 19 (12), 2082–2099. https://doi.org/10.1162/jocn.2007.19.12.2082 Badre, D., & Nee, D. E. (2018). Frontal cortex and the hierarchical control of behavior. Trends in Cognitive Sciences , 22 (2), 170–188. https://doi.org/10.1016/j.tics.2017.11.005 Berger, A., Fischer, R., & Dreisbach, G. (2019). It’s more than just conflict: The functional role of congruency in the sequential control adaptation. Acta Psychologica , 197 , 64–72. https://doi.org/10.1016/j.actpsy.2019.04.016 Bognar, M., Szekely, Z., Varga, M. A., Nagy, K., Spinelli, G., Hartanto, A., Majeed, N. M., Chen, N. R. Y., Gyurkovics, M., & Aczel, B. (2024). Cognitive control adjustments are dependent on the level of conflict. Scientific Reports , 14 (1), 3116. https://doi.org/10.1038/s41598-024-53090-4 Boschin, E. A., Brkic, M. M., Simons, J. S., & Buckley, M. J. (2017). Distinct roles for the anterior cingulate and dorsolateral prefrontal cortices during conflict between abstract rules. Cerebral Cortex (New York, N.Y.: 1991) , 27 (1), 34–45. https://doi.org/10.1093/cercor/bhw350 Boschin, E. A., Mars, R. B., & Buckley, M. J. (2017). Transcranial magnetic stimulation to dorsolateral prefrontal cortex affects conflict-induced behavioural adaptation in a wisconsin card sorting test analogue. Neuropsychologia , 94 , 36–43. https://doi.org/10.1016/j.neuropsychologia.2016.11.015 Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review , 108 (3), 624–652. https://doi.org/10.1037/0033-295x.108.3.624 Botvinick, M. M., Cohen, J. D., & Carter, C. S. (2004). Conflict monitoring and anterior cingulate cortex: An update. Trends in Cognitive Sciences , 8 (12), 539–546. https://doi.org/10.1016/j.tics.2004.10.003 Botvinick, M. M., Nystrom, L. E., Fissell, K., Carter, C. S., & Cohen, J. D. (1999). Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature , 402 (6758), 179–181. https://doi.org/10.1038/46035 Braem, S., Bugg, J. M., Schmidt, J. R., Crump, M. J. C., Weissman, D. H., Notebaert, W., & Egner, T. (2019). Measuring adaptive control in conflict tasks. Trends in Cognitive Sciences , 23 (9), 769–783. https://doi.org/10.1016/j.tics.2019.07.002 Buckley, M. J., Mansouri, F. A., Hoda, H., Mahboubi, M., Browning, P. G. F., Kwok, S. C., Phillips, A., & Tanaka, K. (2009). Dissociable components of rule-guided behavior depend on distinct medial and prefrontal regions. Science (New York, N.Y.) , 325 (5936), 52–58. https://doi.org/10.1126/science.1172377 Burgess, P. W., & Simons, J. S. (2005). Theories of frontal lobe executive function: Clinical applications. In P. W. Halligan & D. T. Wade (Eds.), The Effectiveness of Rehabilitation for Cognitive Deficits (1st ed., pp. 211–232). Oxford University PressOxford. https://doi.org/10.1093/acprof:oso/9780198526544.003.0018 Chan, M. M. Y., Choi, C. X. T., Tsoi, T. C. W., Shea, C. K. S., Yiu, K. W. K., & Han, Y. M. Y. (2023). Effects of multisession cathodal transcranial direct current stimulation with cognitive training on sociocognitive functioning and brain dynamics in autism: A double-blind, sham-controlled, randomized EEG study. Brain Stimulation , 16 (6), 1604–1616. https://doi.org/10.1016/j.brs.2023.10.012 Cho, R. Y., Orr, J. M., Cohen, J. D., & Carter, C. S. (2009). Generalized signaling for control: Evidence from postconflict and posterror performance adjustments. Journal of Experimental Psychology. Human Perception and Performance , 35 (4), 1161–1177. https://doi.org/10.1037/a0014491 Compton, R. J., Huber, E., Levinson, A. R., & Zheutlin, A. (2012). Is “conflict adaptation” driven by conflict? Behavioral and EEG evidence for the underappreciated role of congruent trials. Psychophysiology , 49 (5), 583–589. https://doi.org/10.1111/j.1469-8986.2012.01354.x Danielmeier, C., & Ullsperger, M. (2011). Post-error adjustments. Frontiers in Psychology , 2 . https://doi.org/10.3389/fpsyg.2011.00233 Diamond, A. (2013). Executive functions. Annual Review of Psychology , 64 , 135–168. https://doi.org/10.1146/annurev-psych-113011-143750 Duthoo, W., Abrahamse, E. L., Braem, S., Boehler, C. N., & Notebaert, W. (2014). The congruency sequence effect 3.0: A critical test of conflict adaptation. PLOS ONE , 9 (10), e110462. https://doi.org/10.1371/journal.pone.0110462 Egner, T. (2007). Congruency sequence effects and cognitive control. Cognitive, Affective, & Behavioral Neuroscience , 7 (4), 380–390. https://doi.org/10.3758/CABN.7.4.380 Egner, T., Etkin, A., Gale, S., & Hirsch, J. (2008). Dissociable neural systems resolve conflict from emotional versus nonemotional distracters. Cerebral Cortex (New York, N.Y.: 1991) , 18 (6), 1475–1484. https://doi.org/10.1093/cercor/bhm179 Egner, T., & Hirsch, J. (2005). Cognitive control mechanisms resolve conflict through cortical amplification of task-relevant information. Nature Neuroscience , 8 (12), 1784–1790. https://doi.org/10.1038/nn1594 Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics , 16 (1), 143–149. https://doi.org/10.3758/BF03203267 Espy, K. A. (2004). Using developmental, cognitive, and neuroscience approaches to understand executive control in young children. Developmental Neuropsychology , 26 (1), 379–384. https://doi.org/10.1207/s15326942dn2601_1 Fiebelkorn, I. C., & Kastner, S. (2020). Functional specialization in the attention network. Annual Review of Psychology , 71 , 221–249. https://doi.org/10.1146/annurev-psych-010418-103429 Friehs, M. A., Klaus, J., Singh, T., Frings, C., & Hartwigsen, G. (2020). Perturbation of the right prefrontal cortex disrupts interference control. NeuroImage , 222 , 117279. https://doi.org/10.1016/j.neuroimage.2020.117279 Gbadeyan, O., McMahon, K., Steinhauser, M., & Meinzer, M. (2016). Stimulation of dorsolateral prefrontal cortex enhances adaptive cognitive control: A high-definition transcranial direct current stimulation study. The Journal of Neuroscience , 36 (50), 12530–12536. https://doi.org/10.1523/JNEUROSCI.2450-16.2016 Gratton, G., Coles, M. G., & Donchin, E. (1992). Optimizing the use of information: Strategic control of activation of responses. Journal of Experimental Psychology. General , 121 (4), 480–506. https://doi.org/10.1037//0096-3445.121.4.480 Hommel, B. (2004). Event files: Feature binding in and across perception and action. Trends in Cognitive Sciences , 8 (11), 494–500. https://doi.org/10.1016/j.tics.2004.08.007 Jalal, B., Chamberlain, S. R., & Sahakian, B. J. (2023). Obsessive-compulsive disorder: Etiology, neuropathology, and cognitive dysfunction. Brain and Behavior , 13 (6), e3000. https://doi.org/10.1002/brb3.3000 Karayanidis, F., Hawkins, G. E., Wong, A. S. W., Aziz, F., Hunter, M., & Steyvers, M. (2023). Jointly modeling behavioral and EEG measures of proactive control in task switching. Psychophysiology , 60 (7), e14241. https://doi.org/10.1111/psyp.14241 Kerns, J. G., Cohen, J. D., MacDonald, A. W., Cho, R. Y., Stenger, V. A., & Carter, C. S. (2004). Anterior cingulate conflict monitoring and adjustments in control. Science (New York, N.Y.) , 303 (5660), 1023–1026. https://doi.org/10.1126/science.1089910 Kessler, Y., & Rozanis, M. (2023). Task cues are quickly updated into working memory as part of their processing: The multiple-cue task-switching paradigm. Psychonomic Bulletin & Review , 30 (2), 643–651. https://doi.org/10.3758/s13423-022-02186-x Kiesel, A., Steinhauser, M., Wendt, M., Falkenstein, M., Jost, K., Philipp, A. M., & Koch, I. (2010). Control and interference in task switching—A review. Psychological Bulletin , 136 (5), 849–874. https://doi.org/10.1037/a0019842 Kim, C., Johnson, N. F., & Gold, B. T. (2014). Conflict adaptation in prefrontal cortex: Now you see it, now you don’t. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior , 50 , 76–85. https://doi.org/10.1016/j.cortex.2013.08.011 Kim, S., & Cho, Y. S. (2014). Congruency sequence effect without feature integration and contingency learning. Acta Psychologica , 149 , 60–68. https://doi.org/10.1016/j.actpsy.2014.03.004 Krausz, T. A., Comrie, A. E., Kahn, A. E., Frank, L. M., Daw, N. D., & Berke, J. D. (2023). Dual credit assignment processes underlie dopamine signals in a complex spatial environment. Neuron , 111 (21), 3465-3478.e7. https://doi.org/10.1016/j.neuron.2023.07.017 Kühn, S., Keizer, A. W., Colzato, L. S., Rombouts, S. A. R. B., & Hommel, B. (2011). The neural underpinnings of event-file management: Evidence for stimulus-induced activation of and competition among stimulus-response bindings. Journal of Cognitive Neuroscience , 23 (4), 896–904. https://doi.org/10.1162/jocn.2010.21485 Lamers, M. J. M., & Roelofs, A. (2011). Attentional control adjustments in eriksen and stroop task performance can be independent of response conflict. Quarterly Journal of Experimental Psychology (2006) , 64 (6), 1056–1081. https://doi.org/10.1080/17470218.2010.523792 Lange, F., Brückner, C., Knebel, A., Seer, C., & Kopp, B. (2018). Executive dysfunction in parkinson’s disease: A meta-analysis on the wisconsin card sorting test literature. Neuroscience and Biobehavioral Reviews , 93 , 38–56. https://doi.org/10.1016/j.neubiorev.2018.06.014 Li, N., Wang, Y., Jing, F., Zha, R., Wei, Z., Yang, L., Geng, X., Tanaka, K., & Zhang, X. (2021). A role of the lateral prefrontal cortex in the congruency sequence effect revealed by transcranial direct current stimulation. Psychophysiology , 58 (5), e13784. https://doi.org/10.1111/psyp.13784 Li, Q., Wang, J., Meng, Z., Chen, Y., Zhang, M., Hu, N., Chen, X., & Chen, A. (2024). Decoding the task specificity of post-error adjustments: Features and determinants. NeuroImage , 297 , 120692. https://doi.org/10.1016/j.neuroimage.2024.120692 Liu, H., & Zhang, Q. (2020). Response inhibition in the task-switching paradigm. Biological Psychology , 156 , 107954. https://doi.org/10.1016/j.biopsycho.2020.107954 Liu, P., Yang, W., Chen, J., Huang, X., & Chen, A. (2013). Alertness modulates conflict adaptation and feature integration in an opposite way. PLOS ONE , 8 (11), e79146. https://doi.org/10.1371/journal.pone.0079146 MacDonald, A. W., Cohen, J. D., Stenger, V. A., & Carter, C. S. (2000). Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science (New York, N.Y.) , 288 (5472), 1835–1838. https://doi.org/10.1126/science.288.5472.1835 Martínez-Molina, M. P., Valdebenito-Oyarzo, G., Soto-Icaza, P., Zamorano, F., Figueroa-Vargas, A., Carvajal-Paredes, P., Stecher, X., Salinas, C., Valero-Cabré, A., Polania, R., & Billeke, P. (2024). Lateral prefrontal theta oscillations causally drive a computational mechanism underlying conflict expectation and adaptation. Nature Communications , 15 (1), 9858. https://doi.org/10.1038/s41467-024-54244-8 Mayr, U., Awh, E., & Laurey, P. (2003). Conflict adaptation effects in the absence of executive control. Nature Neuroscience , 6 (5), 450–452. https://doi.org/10.1038/nn1051 McDougle, S. D., Boggess, M. J., Crossley, M. J., Parvin, D., Ivry, R. B., & Taylor, J. A. (2016). Credit assignment in movement-dependent reinforcement learning. Proceedings of the National Academy of Sciences of the United States of America , 113 (24), 6797–6802. https://doi.org/10.1073/pnas.1523669113 Menon, V., & D’Esposito, M. (2022). The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology , 47 (1), 90–103. https://doi.org/10.1038/s41386-021-01152-w Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience , 24 , 167–202. https://doi.org/10.1146/annurev.neuro.24.1.167 Moeller, B., & Frings, C. (2014). Long-term response-stimulus associations can influence distractor-response bindings. Advances in Cognitive Psychology , 10 (2), 68–80. https://doi.org/10.5709/acp-0158-1 Nee, D. E., & D’Esposito, M. (2016). The hierarchical organization of the lateral prefrontal cortex. eLife , 5 , e12112. https://doi.org/10.7554/eLife.12112 Ryman, S. G., El Shaikh, A. A., Shaff, N. A., Hanlon, F. M., Dodd, A. B., Wertz, C. J., Ling, J. M., Barch, D. M., Stromberg, S. F., Lin, D. S., Abrams, S., & Mayer, A. R. (2019). Proactive and reactive cognitive control rely on flexible use of the ventrolateral prefrontal cortex. Human Brain Mapping , 40 (3), 955–966. https://doi.org/10.1002/hbm.24424 Schmidt, J. R., & De Houwer, J. (2011). Now you see it, now you don’t: Controlling for contingencies and stimulus repetitions eliminates the gratton effect. Acta Psychologica , 138 (1), 176–186. https://doi.org/10.1016/j.actpsy.2011.06.002 Schmidt, J. R., & Weissman, D. H. (2014). Congruency sequence effects without feature integration or contingency learning confounds. PLOS ONE , 9 (7), e102337. https://doi.org/10.1371/journal.pone.0102337 Schmidt, J. R., & Weissman, D. H. (2016). Congruency sequence effects and previous response times: Conflict adaptation or temporal learning? Psychological Research , 80 (4), 590–607. https://doi.org/10.1007/s00426-015-0681-x Shi, Y., Meindl, T., Szameitat, A. J., Müller, H. J., & Schubert, T. (2014). Task preparation and neural activation in stimulus-specific brain regions: An fMRI study with the cued task-switching paradigm. Brain and Cognition , 87 , 39–51. https://doi.org/10.1016/j.bandc.2014.03.001 Simon, J. R., & Wolf, J. D. (1963). Choice reaction time as a function of angular stimulus-response correspondence and age. Ergonomics , 6 (1), 99–105. https://doi.org/10.1080/00140136308930679 Stagg, C. J., & Nitsche, M. A. (2011). Physiological basis of transcranial direct current stimulation. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry , 17 (1), 37–53. https://doi.org/10.1177/1073858410386614 Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology , 18 (6), 643–662. https://doi.org/10.1037/h0054651 Tang, J. C. Y., Paixao, V., Carvalho, F., Silva, A., Klaus, A., da Silva, J. A., & Costa, R. M. (2024). Dynamic behaviour restructuring mediates dopamine-dependent credit assignment. Nature , 626 (7999), 583–592. https://doi.org/10.1038/s41586-023-06941-5 Tang, Y., Chen, Z., Jiang, Y., Zhu, C., & Chen, A. (2021). From reversal to normal: Robust improvement in conflict adaptation through real-time functional near infrared spectroscopy-based neurofeedback training. NEUROPSYCHOLOGIA , 157 , 107866. https://doi.org/10.1016/j.neuropsychologia.2021.107866 Thielscher, A., Antunes, A., & Saturnino, G. B. (2015). Field modeling for transcranial magnetic stimulation: A useful tool to understand the physiological effects of TMS? Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference , 2015 , 222–225. https://doi.org/10.1109/EMBC.2015.7318340 Townley, R. A., Graff-Radford, J., Mantyh, W. G., Botha, H., Polsinelli, A. J., Przybelski, S. A., Machulda, M. M., Makhlouf, A. T., Senjem, M. L., Murray, M. E., Reichard, R. R., Savica, R., Boeve, B. F., Drubach, D. A., Josephs, K. A., Knopman, D. S., Lowe, V. J., Jack, C. R., Petersen, R. C., & Jones, D. T. (2020). Progressive dysexecutive syndrome due to alzheimer’s disease: A description of 55 cases and comparison to other phenotypes. Brain Communications , 2 (1), fcaa068. https://doi.org/10.1093/braincomms/fcaa068 Tyburski, E., Mak, M., Sokołowski, A., Starkowska, A., Karabanowicz, E., Kerestey, M., Lebiecka, Z., Preś, J., Sagan, L., Samochowiec, J., & Jansari, A. S. (2021). Executive dysfunctions in schizophrenia: A critical review of traditional, ecological, and virtual reality assessments. Journal of Clinical Medicine , 10 (13), 2782. https://doi.org/10.3390/jcm10132782 van Veen, V., & Carter, C. S. (2005). Separating semantic conflict and response conflict in the stroop task: A functional MRI study. NeuroImage , 27 (3), 497–504. https://doi.org/10.1016/j.neuroimage.2005.04.042 Wang, T., Chen, X., Pan, W., Xiao, Q., & Chen, A. (2021). The neural network underlying individual differences in conflict adaptation effect. Biological Psychology , 164 , 108150. https://doi.org/10.1016/j.biopsycho.2021.108150 Weissman, D. H., Jiang, J., & Egner, T. (2014). Determinants of congruency sequence effects without learning and memory confounds. Journal of Experimental Psychology: Human Perception and Performance , 40 (5), 2022–2037. https://doi.org/10.1037/a0037454 Xu, P., Lin, F., Alimu, G., Zhang, J., Jin, Z., & Li, L. (2024). The important role of the right dorsolateral prefrontal cortex in conflict adaptation: A combined voxel-based morphometry and continuous theta burst stimulation study. Journal of Cognitive Neuroscience , 36 (6), 1172–1183. https://doi.org/10.1162/jocn_a_02155 Yang, G., Wang, K., Nan, W., Li, Q., Zheng, Y., Wu, H., & Liu, X. (2022). Distinct brain mechanisms for conflict adaptation within and across conflict types. Journal of Cognitive Neuroscience , 34 (3), 445–460. https://doi.org/10.1162/jocn_a_01806 Yang, G., Xu, H., Li, Z., Nan, W., Wu, H., Li, Q., & Liu, X. (2021). The congruency sequence effect is modulated by the similarity of conflicts. Journal of Experimental Psychology: Learning, Memory, and Cognition , 47 (10), 1705–1719. https://doi.org/10.1037/xlm0001054 Yang, Z., Zheng, Y., Yang, G., Li, Q., & Liu, X. (2019). Neural signatures of cooperation enforcement and violation: A coordinate-based meta-analysis. SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE , 14 (9), 919–931. https://doi.org/10.1093/scan/nsz073 Ye, W., & Damian, M. F. (2023). Effects of conflict in cognitive control: Evidence from mouse tracking. Quarterly Journal of Experimental Psychology (2006) , 76 (1), 54–69. https://doi.org/10.1177/17470218221078265 Zmigrod, S., & Hommel, B. (2010). Temporal dynamics of unimodal and multimodal feature binding. Attention, Perception & Psychophysics , 72 (1), 142–152. https://doi.org/10.3758/APP.72.1.142 Supplementary Material File (figure1.docx) Download 561.84 KB File (figure2.docx) Download 4.82 MB File (figure3.docx) Download 98.24 KB Information & Authors Information Version history V1 Version 1 29 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Man Li 0009-0003-2415-731X Anhui Medical University View all articles by this author Zixin Gong Anhui Medical University View all articles by this author Jie Chen Anhui Medical University View all articles by this author Mengjie Feng Anhui Medical University View all articles by this author Ginger Zeng University of Science and Technology of China Institute of Advanced Technology View all articles by this author Meng Sun Anhui Medical University View all articles by this author Fengqiong Yu Anhui Medical University View all articles by this author Cuihong Li Anhui Medical University View all articles by this author Nan Li [email protected] Anhui Medical University View all articles by this author Metrics & Citations Metrics Article Usage 159 views 105 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Man Li, Zixin Gong, Jie Chen, et al. Beyond Conflict Monitoring: Dorsolateral Prefrontal Cortex Mediates Congruency Sequence Effects via Post-Congruent Suppression. Authorea . 29 August 2025. DOI: https://doi.org/10.22541/au.175648009.90764747/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175648009.90764747/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00c8a486942c13d',t:'MTc3OTYyODIyMg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();