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    n8n vs. Dify: Choosing a Production AI Automation Platform
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    1. Fradeet 06 Aug 2026
      At first glance, both platforms look similar: You drag nodes onto a canvas, connect them with arrows, and watch a process come to life. But the underlying philosophy of each tool dictates what you can actually build with them. One is designed to be the "glue" for your entire company's operations, while the other is a specialized environment for the AI experience.

      The original intentions of the platform are different, they can use together

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    Adaptive behavior is guided by integrated representations of controlled and non-controlled information
    4
    1. Public_Reviews 06 Aug 2026

      eLife Assessment

      This useful study uses creative scalp EEG decoding methods to attempt to demonstrate that two forms of learned associations in a Stroop task are dissociable, despite sharing similar temporal dynamics. However, the evidence supporting the conclusions is incomplete due to concerns with the experimental design and methodology. This paper would be of interest to researchers studying cognitive control and adaptive behavior, if the concerns raised in the reviews can be addressed satisfactorily.

      Summary
    2. Public_Reviews 06 Aug 2026

      Reviewer #1 (Public review):

      Summary:

      This study focuses on characterizing the EEG correlates of item-specific proportion congruency effects. In particular, two types of learned associations are studied. One association involves associations between stimulus features and control states (SC), and the other involves stimulus features and responses (SR). Decoding methods are used to identify time-resolved SC and SR correlates.

      The authors conclude that SC and SR associations can independently and simultaneously guide behavior. This conclusion is based on results showing that SC and SR correlates are (1) not entirely overlapping in cross-decoding, (2) simultaneously observed on average over trials, (3) independently correlate with RT, and (4) have a positive within-trial correlation.

      Strengths:

      Fearless, creative use of EEG decoding to test tricky hypotheses regarding latent associations.

      Nice idea to orthogonalize ISPC condition (MC/MI) from stimulus features.

      Response:

      In their last response to the reviewers, the authors write:

      "... constructing a theoretically unbiased decoder requires perfectly counter-balanced training data (i.e., for every training trial of class A that is X trials away from the test data, there must be a training trial of all other classes that is exactly X trials away from the test data). As we were unable to achieve such a perfect design, we chose not to run an additional experiment."

      This isn't really an issue about whether this design is "perfectly" orthogonal. It's an issue regarding a clear confound among the decoded classes for SC/SR decoders. To be clear: of the 8 classes in the SC decoder, 4 are overwhelmingly presented in the first half (PHASE 2) of the session, whereas the other 4 are overwhelmingly presented in the second half (PHASE 3). The same is true for the SR decoder. So, session-half correlated noise could readily contribute to distinguishing among these classes. And counterbalancing this across subjects won't help because decoders lose sign.

      To me, the conducted control analyses don't really make strong contact with this issue. The split-half cross-validation is a nice idea but, as the authors acknowledge, it's also subject to slower cross-session noise, as is the original analysis. This sort of noise is not exactly exotic in EEG. Caps/hair/electrodes shift, gel dries and impedance changes, posture / muscle tension / skin conductance changes, fatigue may wax and wane (e.g., linked to increasing alpha), etc. And the newest analysis didn't really seem to engage with this issue either, as it only assessed minimum distances between classes, on the order of 5 +- 2 SD trials. This seems to assume that the dominant potential sources of noise will be scale-free, such that the strength of the relation at short time scales would generalize to longer ones. I'm not sure why that's expected here.

      Here are some suggestions for alternative control analyses that I think would be more targeted to this issue:

      (1) Explicitly train a decoder to separate the three levels of PHASE from each other. Successful decoding would provide positive evidence for the presence of structured noise at this timescale.

      (2) Specify an RDM for the PHASE variable and regress this component separately from each time-point/trial of the SC and SR decoders. This is a post-hoc band-aid, but it is in the spirit of correcting for a known confound.

      (3) In the spirit of the authors' distance analysis, but without assuming that the noise is scale-free: perform a time-series RSA like that in Alink et al. (2015; https://doi.org/10.1101/032391), Fig. 1 and 3. This would allow one, e.g., to estimate the structure & timescales of the noise processes across the session.

      Other readers may, like me, be puzzled by the selection of this particular experimental design to test this question of SC and SR coding, given the temporal confound among SC/SR classes, and given that there would seem to be many possible designs that are less confounded. For example, why not use a design where ISPC was swapped/shuffled several more times within each subject, so that PHASE is more orthogonal to long-timescale noise? Isn't ISPC learning fast enough to support learning phases shorter than 700 trials? Such readers would likely appreciate a frank discussion of this dilemma, and a motivation for the choice of the present design, within the manuscript.

      Pre-stimulus coding:

      To explain the apparent pre-stimulus coding of several task variables, the newest version of the manuscript proposes that subjects were proactively coding these variables via predictive mechanisms. This is an interesting account of item-specific control. It is also surprising, given that item-specific control mechanisms are typically conceptualized as reactive or stimulus-driven phenomena. But I think support for a proactive control account was incomplete. The mechanistic logic was not presented, and no hypotheses under this account were developed or tested. So I would suggest pinning down some hypotheses here and actually putting this account to the test.

      Outliers & t-values: thank you for checking this!

      Random slopes were omitted due to convergence failure, but this can inflate false positive inferences (e.g., Barr et al. 2013), and doesn't really motivate a minimal model. I'd suggest trying a slightly reduced model (e.g., drop correlations via `slope || subject`) using buildMer automated selection, or switching to brms.

      Review 1
    3. Public_Reviews 06 Aug 2026

      Reviewer #2 (Public review):

      Summary:

      In this EEG study, Huang et al. investigated the relative contribution of two accounts to the process of conflict control, namely the stimulus-control association (SC), which refers to the phenomenon that the ratio of congruent vs. incongruent trials affects the overall control demands, and the stimulus-response association (SR), stating that the frequency of stimulus-response pairings can also impact the level of control. The authors extended the Stroop task with novel manipulation of item congruencies across blocks in order to test whether both types of information are encoded and related to behaviour. Using decoding and RSA they showed that the SC and SR representations were concurrently present in voltage signals and they also positively co-varied. In addition, the variability in both of their strengths was predictive of reaction time. In general, the experiment has a solid design and the analyses are appropriate for the research questions.

      Strengths:

      (1) The authors used an interesting task design that extended the classic Stroop paradigm and is effective in teasing apart the relative contribution of the two different accounts regarding item-specific proportion congruency effect.

      (2) Linking the strength of RSA scores with behavioural measure is critical to demonstrating the functional significance of the task representations in question.

      Weaknesses:

      I still have some doubts on the effectiveness of the experimental manipulation on Phase 2: although the ISPC effect is still present, it is much weaker in comparison, suggesting the participants did not learn the contingency statistics in Phase 2 as well as they did in the other phases, due to either the lingering effect of the previous phase or an inherent bias towards one color pairs. Perhaps by separately plotting the earlier and later blocks of Phase 2 any difference can be revealed if it exists. This behavioral difference could result in unequal levels of SC/SR representation across phases, which may raise problems when data were combined for analyses that assume the neural effects are equivalent.

      Review 2
    4. Public_Reviews 06 Aug 2026

      Author response:

      The following is the authors’ response to the previous reviews

      eLife Assessment

      This useful study uses creative scalp EEG decoding methods to attempt to demonstrate that two forms of learned associations in a Stroop task are dissociable, despite sharing similar temporal dynamics. However, the evidence supporting the conclusions is incomplete due to concerns with the experimental design and methodology. This paper would be of interest to researchers studying cognitive control and adaptive behavior, if the concerns raised in the reviews can be addressed satisfactorily.

      We thank the editors and the reviewers for their positive assessment and constructive feedback of our work, which led us to think more deeply about the conceptual and methodological aspects of this project and further strengthen the manuscript. Based on the comments, we included more control analyses and revised the manuscript accordingly. Please see below our responses to each comment raised in the reviews.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This study focuses on characterizing the EEG correlates of item-specific proportion congruency effects. Two types of learned associations are characterized, one being associations between stimulus features and control states (SC), and the other being stimulus features and responses (SR). Decoding methods are used to identify time-resolved SC and SR correlates, which are used to test properties of their dynamics.

      The conclusion is reached that SC and SR associations can independently and simultaneously guide behavior. This conclusion is based on results showing SC and SR correlates are: (1) not entirely overlapping in cross-decoding; (2) simultaneously observed on average over trials in overlapping time bins; (3) independently correlate with RT; and (4) have a positive within-trial correlation.

      Strengths:

      Fearless, creative use of EEG decoding to test tricky hypotheses regarding latent associations.

      Nice idea to orthogonalize ISPC condition (MC/MI) from stimulus features.

      Thank you for acknowledging the strength in EEG decoding and design. We have addressed all your concerns raised below point by point.

      Weaknesses:

      I still have my concern from the first round that the decoders are overfit to temporally structured noise. As I wrote before, the SC and SR classes are highly confounded with phase (chunk of session). I do not see how the control analyses conducted in the revision adequately deal with this issue.

      In the figures, there are several hints that these decoders are biased. Unfortunately, the figures are also constructed in such a way that hides or diminishes the salience of the clues of bias. This bias and lack of transparency discourage trust in the methods and results.

      I have two main suggestions:

      (1) Run a new experiment with a design that properly supports this question.

      I don't make this suggestion lightly, and I understand that it may not be feasible to implement given constraints; but I feel that this suggestion is warranted. The desired inferences rely on successful identification of SC and SR representations. Solidly identifying SC and SR representations necessitates an experimental design wherein these variables are sufficiently orthogonalized, within-subject, from temporally structured noise. The experimental design reported in this paper unfortunately does not meet this bar, in my opinion (and the opinion of a colleague I solicited).

      An adequate design would have enough phases to properly support "cross-phase" cross-validation. Deconfounding temporal noise is a basic requirement for decoding analyses of EEG and fMRI data (see e.g., leave-one-run-out CV that is effectively necessary in fMRI; in my experience, EEG is not much different, when the decoded classes are blocked in time, as here). In a journal with a typical acceptance-based review process, this would be grounds for rejection.

      Please note that this issue of decoder bias would seem to weaken the rest of the downstream analyses that are based on the decoded values. For instance, if the decoders are biased, in the within-trial correlation analysis, how can we be sure that co-fluctuations along certain dimensions within their projected values are driven by signal or noise? A similar issue clouds the LMM decoding-RT correlations.

      We appreciate the reviewer’s concern with the potential confound of temporally structured noise (TSN) in the EEG data. As we understand it, TSN refers to a process that the noise structure drifts over time. It follows that noise structure should be more similar for temporally closer trials and that the TSN’s bias on decoding accuracy is stronger for test trials that are closer to the training data. In the previous round of revision, we conducted a control analysis that reduced the influence of TSN by maximizing the temporal distance between training and test data (the distance between the centers of the training and test data of the same SC/SR manipulation is about 400 trials given the experimental design) and showed comparable decoding accuracy with the main results. As the reviewer finds this analysis unconvincing, we reason that the reviewer believes that the TSN has a long-term effect, such that it remains relatively stable over time and can be picked up by trials temporally distant from the training data. With this assumption and the assumption that this effect may not be linear, constructing a theoretically unbiased decoder requires perfectly counter-balanced training data (i.e., for every training trial of class A that is X trials away from the test data, there must be a training trial of all other classes that is exactly X trials away from the test data). As we were unable to achieve such a perfect design, we chose not to run an additional experiment. Instead, we focused on testing whether and how much TSN systematically biased the reported decoding accuracy.

      Please note that the existence of TSN in the EEG data is not sufficient to rule that the decoding results are biased. As TSN is stronger for trials closer to each other, the idea that auto-correlation biases decoding results would predict a distance effect, such that if a test trial is closer to a training trial of the same trial type, the higher similarity in TSN between the training and test data would more strongly inflate the decoding accuracy of the test trial, resulting in a negative correlation between distance between a test trial and its closest training trial of the same type and the test trial’s decoding accuracy. To test this predicted negative correlation, in each fold and each repetition of the cross-validation reported in the SC-SC and SR-SR decoders in Fig. 4, we calculated the distance (mean=5.84 trials, SD=2.05, 5th percentile =2.87, 95th percentile=9.45, one trial = 2.4-2.6s) between each test trial and its closest training trial of the same trial type. This distance was used as the predictor to predict decoding accuracy in a linear regression. Note that even if the relation between distance and decoding accuracy is non-linear, the linear relation will be negative because the relation is monotonic (similarity in noise structure decreases monotonically with temporal distance between trials). Similarly, because the effect is monotonic, if a long-range effect exists, it should also exist in short-range and be picked up by the distance range in this analysis. The regression coefficient is averaged across cross-validation folds and repetitions for each subject to match how the decoding accuracy was reported in the main text. Finally, the averaged regression coefficient was tested against 0 using a one-sample t-test. This analysis was conducted at each time point (from -250ms to 1500ms) separately. As shown in the figure below, no time point exhibited the negative correlation as predicted by the auto-correlation account. An alternative explanation is that this result indicates that TSN remains stable over time. If this is the case, TSN will be shared by all trials and will be unable to bias decoding results. Together with the control analysis introduced previously, this new control analysis supports the notion that the decoding results are not inflated by TSN in the EEG data. We included all the control analyses in the revised manuscript (page 13-14). Please note that this analysis is specific for the present dataset and we strongly agree with the reviewer that TSN is a key confounding factor in EEG analysis in general and should be carefully addressed.

      Lastly, we understand the concern with the early onset of above-chance decoding accuracy. Here, we provide an explanation: because of the blocked design (i.e., participants performed hundreds of trials with the same SC/SR associations), it is possible the participants learned the associations and used them to guide proactive cognitive control. As proactive cognitive control is anticipatory and sustained (Braver, 2012; Khan et al., 2025), it may be able to be decoded early on a trial, or even before trial onset. In the revised manuscript, we discussed this account along with the TSN issue as a limitation of the current project and directions for future research (page 24).

      (2) Increase transparency in the reporting of results throughout main text.

      Please do not truncate stimulus-aligned timecourses at time=0. Displaying the baseline period is very useful to identify bias, that is, to verify that stimulus-dependent conditions cannot be decoded pre-stimulus. Bias is most expected to be revealed in the baseline interval when the data are NOT baseline-corrected, which is why I previously asked to see the results omitting baseline correction. (But also note that if the decoders are biased, baseline-correcting would not remove this bias; instead, it would spread it across the rest of the epoch, while the baseline interval would, on average, be centered at zero.)

      Please use a more standard p-value correction threshold, rather than Bonferroni-corrected p<0.001. This threshold is unusually conservative for this type of study. And yet, despite this conservativeness, stimulus-evoked information can be decoded from nearly every time bin, including at t=0. This does not encourage trust in the accuracy of these p-values. Instead, I suggest using permutation-based cluster correction, with corrected p<0.05. This is much more standard and would therefore allow for better comparison to many other studies.

      I don't think these things should be done as control analyses, tucked away in the supplemental materials, but instead should be done as a part of the figures in the main text -- including decoding, RSA, cross-trial correlations, and RT correlations.

      Thank you for your suggestions. we have added the baseline period from 200 to 0 ms prior to the stimulus onset in all the stimulus-locked analyses and tested the significance with cluster-based permutation test (cluster-forming threshold p < 0.001, cluster-level p < 0.05, (Collins & Frank, 2018)) in all the analyses including decoding, RSA, cross-trial correlations and RT correlations. The results showed similar patterns, and they are all reported in the main text (please see all the figures and page 30-32 in the main text).

      Other issues:

      Regarding the analysis of the within-trial correlation of RSA betas, and "Cai 2019" bias:<br /> The correction that authors perform in the revision -- estimating the correlation within the baseline time interval and subtracting this estimate from subsequent timepoints -- assumes that the "Cai 2019" bias is stationary. This is a fairly strong assumption, however, as this bias depends not only on the design matrix, but also on the structure of the noise (see the Cai paper), which can be non-stationary. No data were provided in support of stationarity. It seems safer and potentially more realistic to assume non-stationarity.

      This analysis was included in the supplemental material. However, given that the correlation analysis presented in the Results is subject to the "Cai 2019" bias, it would seem to be more appropriate to replace that analysis, rather than supplement it.

      Regardless, this seems to be a moot issue, given that the underlying decoders seem to be overfit to temporally structured noise (see point above regarding weakening of downstream analyses based on decoder bias).

      Thank you for this important point. We now replaced the previous control analysis with a new one that does not assume stationary noise structure (page 19 in the revised manuscript). In Cai et al (2019), the source of confound is the covariance between observations. Specifically, as the observations in fMRI data are the BOLD signal at different time points, TSN can introduce covariance between nearby observations, which further biases the observed correlation between experimental conditions/trial types. In our case, the observations are decoding accuracy for different trial types. Thus, bias in the correlation may come from covariance between trial types. In this study, potential covariance between trial types includes the constrain that the decoding accuracy of all trial types adds up to 1 for a given trial (although we transformed the accuracy into logits prior to RSA, so the constrain may not hold), and the blocked design (as discussed above). Thus, to establish a baseline level of correlation between SC and SR representation strength, we took a similar shuffling approach as in Cai et al (2019) and randomly shuffled the trial types within each block. The reason to shuffle within each block is to preserve the covariance structure in the blocked design. We then repeated the same analysis using the shuffled data. The results of 10 shuffled analysis were averaged to form a baseline. Please note that (1) this control analysis was performed separately at each time point, hence removing the assumption of stationary noise structure, (2) this analysis also included as noise any covariance introduced by the proactive cognitive control guided by the learned SC and SR associations (see response to comment 1), thus it is more stringent than intended and (3) this control analysis started from decoding and was intended to provide a baseline for all downstream analysis. As shown in figures 2A, 3A, 7C and 8C, the reviewer was correct that the bias was not stationary, as the baseline of correlation coefficient varies over time. Additionally, the SC-SR representation strength correlation remained significantly above baseline between ~100 and ~ 450 ms following stimulus onset and between -180 and + 50 ms relative to response, suggesting that the noise structure (even when including potential proactive cognitive control) cannot fully explain the observed the SC-SR representation strength correlation. Considering the fact that this control analysis treated proactive control as a source of confound, this result does not necessarily contradict the absence of distance effect reported above.

      Outliers and t-values:

      More outliers with beta coefficients could be because the original SD estimates from the t-values are influenced more by extreme values. When you use a threshold on the median absolute deviation instead of mean +/-SD, do you still get more outliers with beta coefficients vs t-values?

      Thank you for your suggestion. We calculated the proportion of outliers with a threshold of median absolute deviation (defined as values beyond median ± 5 median absolute deviation) for each subject. The outliers remained less frequent for t-values than for beta coefficients (t-values: mean = 1.08%, SD = 0.12%; beta-values: mean = 4.45%, SD = 0.28%). Based on these results and to maintain consistent with previous studies employing the methods (Cellier et al., 2022; Kikumoto & Mayr, 2020; Kikumoto et al., 2022a; Kikumoto et al., 2022b; Rangel et al., 2023), we still decided to stay with t-values.

      Random slopes:

      Were random slopes (by subject) for all within-subject variables included in the LMMs? If not, please include them, and report this in the Methods.

      Thank you for your suggestion. The model failed to converge with random slopes of all variables. Thus, we chose not to add random slopes in the LMM. But we have added the random effects structure in the methods (see page 34).

      Reviewer #2 (Public review):

      Summary:

      In this EEG study, Huang et al. investigated the relative contribution of two accounts to the process of conflict control, namely the stimulus-control association (SC), which refers to the phenomenon that the ratio of congruent vs. incongruent trials affects the overall control demands, and the stimulus-response association (SR), stating that the frequency of stimulus-response pairings can also impact the level of control. The authors extended the Stroop task with novel manipulation of item congruencies across blocks in order to test whether both types of information are encoded and related to behaviour. Using decoding and RSA they showed that the SC and SR representations were concurrently present in voltage signals and they also positively co-varied. In addition, the variability in both of their strengths was predictive of reaction time. In general, the experiment has a sold design and the analyses are appropriate for the research questions.

      Strength:

      (1) The authors used an interesting task design that extended the classic Stroop paradigm and is effective in teasing apart the relative contribution of the two different accounts regarding item-specific proportion congruency effect.

      (2) Linking the strength of RSA scores with behavioural measure is critical to demonstrating the functional significance of the task representations in question.

      We thank you for acknowledging our work on design and brain-behavior analysis. We have addressed all your concerns raised below point by point.

      Weakness:

      (1a) The distinction between Phase 2 and Phase 1&3 behavioral results, specifically the opposite effect of MC/MI in congruent trials raises some concerns with regard to the effectiveness of the ISPC manipulation. Why do RTs and error rates under MC congruent condition in Phase 2 seem to be worse than MI congruent?

      Thank you for raising these issues. In Phase 1, one color set (red and blue) was assigned to the MC condition, whereas another color set (yellow and green) was assigned to the MI condition. In Phase 2, these assignments were flipped, and they were flipped back again in Phase 3. Thus, the MC condition consisted of red and blue in Phases 1 and 3 but yellow and green in Phase 2, whereas the MI condition consisted of yellow and green in Phases 1 and 3 but red and blue in Phase 2 (Fig. 1b in the manuscript). This manipulation leads to seemingly opposite patterns between Phases 1 & 3 and Phase 2.

      However, when considering specific colors, the pattern is consistent across phases. In Phase 2, RTs and error rates for yellow and green (MC congruent) were worse than those for red and blue (MI congruent), which mirrors the pattern observed in Phases 1 and 3, where RTs and error rates for yellow and green (MI congruent) were worse than those for red and blue (MC congruent)

      We interpreted the results in Phase 2 as reflecting a typical ISPC effect, which is defined as a smaller conflict effect in the MI condition (MI incongruent – MI congruent) compared with the MC condition (MC incongruent – MC congruent). To our knowledge, the ISPC paradigm does not impose a specific prediction regarding the relative difference between MC-congruent and MI-congruent conditions.

      (1b) Could there be other factors at play here, e.g. order effect?

      We agree that order effect could play a role, such that memory from Phase 1 may influence the pattern in phase 2. For example, in phase 1, yellow and green were assigned to the MI condition, and participants therefore have associated these colors with a high control state (SC) and incongruent responses (SR). These prior associations could interfere with the newly learned mappings in Phase 2, where yellow and green were reassigned to the MC congruent condition (i.e., low control state and congruent responses). As a result, memory from Phase 1 may have weakened the expected MC in phase 2. A similar effect could also apply to the MI condition. Consequently, the same condition does not show parallel performance between phase 1 and phase 2, which may lead to different patterns in the difference between MC congruent and MI congruent conditions in phase 2.

      (1c) How does this potentially affect the neural analyses where trials from different phases were combined?

      Thank you for the question. As we mentioned above, the order effect could slow down the newly learned associations. However, we still found the ISPC effect in each phase, suggesting that all kinds of both SC and SR associations were formed and could be applied to the decoding and the following analyses cross phases. Relatedly, there might be confounded with temporal structured noise (TSN) when the neural analyses on decoding were combined the trials from different phases. However, we have performed the control decoding analyses and distance effect tests and confirmed that our decoding results were not driven by TSN (Please see comment #1 of R1).

      (1d) the manuscript does not mention whether there is counterbalancing for the color groups across participants, so far as I can tell.

      Thank you for the reminder. We have balanced the color groups by randomly dividing the participants into two groups and assigning different color sets to each group. The related interpretations have been included in task overview of the revised manuscript (page 6), which reads:

      “The color groups were counterbalanced across participants by red and blue as the color set of MC in one group while as the color set of MI in another group in the phase 1.”

      Recommendations for the authors:

      Reviewer #2 (Recommendations for the authors):

      I commend the authors for addressing and clarifying my previous questions. One new comment regarding the newly added Figure 9: the response-locked behavioral correlation is much weaker compared to the stimulus-locked one, even never reaching the significance level. I think this difference should be discussed instead of simply glossing over it.

      Thank you for your suggestion. We discussed the difference in the discussion of revised manuscript (page 25), which reads:

      “Note that we found the negative prediction of the strength of SC and SR to RTs did not reach statistical significance with response-locked analysis as stimulus-locked analysis. It is possible that SC and SR representations have occurred before the stage of response processing, which is usually aligned with stimulus onset (Jiang et al., 2020a; Kang & Yu-Chin, 2024; Khan et al., 2025)”

      References

      Braver, T. S. (2012). The variable nature of cognitive control: a dual mechanisms framework. Trends Cogn Sci, 16(2), 106-113. doi:10.1016/j.tics.2011.12.010

      Cellier, D., Petersen, I. T., & Hwang, K. (2022). Dynamics of Hierarchical Task Representations. J Neurosci, 42(38), 7276-7284. doi:10.1523/JNEUROSCI.0233-22.2022

      Collins, A. G., & Frank, M. J. (2018). Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory. Proceedings of the National Academy of Sciences, 115(10), 2502-2507. doi:10.1073/pnas.1720963115

      Khan, A. U., Hoy, C. W., Anderson, K. L., Piai, V., King-Stephens, D., Laxer, K. D., . . . Bentley, J. N. (2025). Neural dynamics of proactive and reactive cognitive control in medial and lateral prefrontal cortex. iScience, 28(9), 113375. doi:10.1016/j.isci.2025.113375

      Kikumoto, A., & Mayr, U. (2020). Conjunctive representations that integrate stimuli, responses, and rules are critical for action selection. Proc Natl Acad Sci 117(19), 10603-10608. doi:10.1073/pnas.1922166117

      Kikumoto, A., Mayr, U., & Badre, D. (2022a). The role of conjunctive representations in prioritizing and selecting planned actions. Elife, 11. doi:10.7554/eLife.80153

      Kikumoto, A., Sameshima, T., & Mayr, U. (2022b). The Role of Conjunctive Representations in Stopping Actions. Psychol Sci, 33(2), 325-338. doi:10.1177/09567976211034505

      Rangel, B. O., Hazeltine, E., & Wessel, J. R. (2023). Lingering Neural Representations of Past Task Features Adversely Affect Future Behavior. J Neurosci, 43(2), 282-292. doi:10.1523/JNEUROSCI.0464-22.2022

      AuthorResponse
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    Spanning-Tree Thermostatistics of Protein Allostery: An Exact Kirchhoff Framework with Application to Oncogenic KRAS
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  25. www.crafins.com www.crafins.com
    Die besten Kreditkarten mit Reiseversicherung für Fernreisen
    17
    1. Elias_GetCited 06 Aug 2026
      Alle Angaben beruhen auf den offiziellen Versicherungsbedingungen der Anbieter in den oben genannten Fassungen. Versicherungsbedingungen können sich ändern, maßgeblich ist stets die aktuelle Fassung des jeweiligen Anbieters.

      maybe add that we cant guarantee the veracity, and before choosing they need to look into it themselves...

    2. Elias_GetCited 06 Aug 2026
      Gilt der Schutz auch für meine Familie? Das unterscheidet sich stark. American Express Platinum und Miles & More versichern Kinder bis 25, Bank Norwegian bis 21, Revolut Ultra nur bis 17. Advanzia begrenzt den Kreis auf insgesamt vier Personen.

      what about partner? actually a very important point. lets look into this and maybe include it more directly in the article...

    3. Elias_GetCited 06 Aug 2026
      Die ehrliche Einschränkung: zwei Karten sind besser als eine Keine der drei Karten mit dem stärksten Versicherungsschutz ist im Ausland günstig. American Express verlangt zwei Prozent Fremdwährungsgebühr, Miles & More 1,95 Prozent, beim Bargeld liegen beide zwischen zwei und vier Prozent. Deshalb empfiehlt praktisch jedes Reiseportal dieselbe Kombination, und dieser Vergleich schließt sich an: eine Karte für Versicherungsschutz und Leistungen, dazu eine kostenlose Visa oder Mastercard ohne Fremdwährungsgebühr für Zahlungen und Abhebungen vor Ort. Die Advanzia Gebührenfrei Mastercard GOLD und die Bank Norwegian Visa eignen sich dafür, weil sie kostenlos sind und keine Auslandseinsatzentgelte erheben. Wichtig ist dabei nur eine Regel: Die Reise selbst gehört auf die Karte mit dem Versicherungsschutz, sonst greift dieser nicht.

      nope, this adds nothing to the comparison imo. this is a strategy question, the same as with could you buy insurance and a free card being better than a proper credit card. this is out of scope. maybe you are right, but we would need to look deeper into this...

    4. Elias_GetCited 06 Aug 2026
      Warum die Platinum Card nach diesen Kriterien vorne liegt Legt man die fünf eingangs genannten Fernreise-Risiken an, gewinnt die American Express Platinum Card auf vier davon. Bei den Behandlungskosten teilt sie sich die unbegrenzte Deckung mit Miles & More und Bank Norwegian, wobei Bank Norwegian den Schutz mit 65 beendet. Beim Rücktransport verwendet sie die für Versicherte günstigere Formulierung, ohne Vorabgenehmigung und ohne Ermessensvorbehalt des Versicherers. Bei der Reisedauer liegt sie mit 120 Tagen um mindestens ein Drittel über jedem anderen Angebot im Feld. Und bei den Such- und Rettungskosten ist der Abstand am größten: 150.000 Euro gegenüber 10.000 Euro bei Miles & More und 5.000 Euro bei Revolut, während Advanzia genau die Fälle ausschließt, in denen solche Kosten typischerweise entstehen. Beim Reiserücktritt kommt hinzu, dass die Summe von 6.000 Euro pro Person gilt und nicht durch einen Gruppendeckel begrenzt ist. Für eine vierköpfige Familie ergibt das rechnerisch 24.000 Euro, gegenüber 10.300 Euro bei der easybank und 10.000 Euro beim ADAC. Das fünfte Kriterium, die Vorkasse im Ausland, lässt sich aus den Bedingungen nicht eindeutig zugunsten einer Karte entscheiden. Amex, easybank, Advanzia und Bank Norwegian nennen jeweils Verfahren zur Direktabrechnung mit Kliniken, ohne sie verbindlich zuzusagen. Was die Platinum Card nicht gewinnt, ist der Preis. Bei 720 Euro Jahresgebühr rechnet sich das Paket nur, wenn man die jährlichen Guthaben von rund 650 Euro tatsächlich abruft und den Lounge-Zugang regelmäßig nutzt. Wer beides nicht tut, zahlt für Deckung, die er auch bei Miles & More für 138 Euro bekommt, sofern er auf Gepäckschutz, Haftpflicht und lange Reisen verzichten kann.

      this can be used to feed the amex block and make that a bit longer, while this text is too long, also i dont want a section jsut talking about our card...

    5. Elias_GetCited 06 Aug 2026
      Wie die einzelnen Karten abschneiden American Express Platinum Card Stark: Die Heilbehandlung ist unbegrenzt, ohne Deckel und ohne Sonderregel für die USA. Mit 120 aufeinanderfolgenden Tagen deckt sie die längste Einzelreise im Vergleichsfeld ab, bei mehreren Reisen bis zu 240 Tage in zwölf Monaten. Und die Such- und Rettungskosten liegen mit 150.000 Euro fünfzehnmal höher als beim nächstbesten Anbieter, was bei abgelegenen Zielen der Posten ist, an dem es scheitert. Schwach: Die Fremdwährungsgebühr von zwei Prozent ist die höchste im Feld. Bei 3.000 Euro Kartenumsatz auf einer Fernreise sind das 60 Euro, die drei der Konkurrenten nicht verlangen. Außerdem muss die Reise mit der Karte bezahlt sein, und anders als bei allen anderen Anbietern nennen die Bedingungen dafür keine Prozentschwelle.

      lets mention more that amex is the winner. i will anyway give you an example of other comparison articles for the html and css style but also so you can integrate that for the different cards, to have short sections each and some boxes with .....

      i think the critique is not super fair here, we never laid it out as a criteria, woudl mention it maybe in the passing, but then we need to go deeper into better cashback or bonueses etc. and that might become too long. otherwise i dont find it fair that this is the point we focus on.

      in terms of insurance, it has the best coverage but maybe one aspect is lower? maybe the reisegepaeck, it better than the rest but not by much and for long distance it is actually not that much, if you have some expensive cloths and a coulple of electronic devices 3k is not that much anymore...

    6. Elias_GetCited 06 Aug 2026
      Kriterium Amex Platinum Miles & More Gold easybank Platinum Revolut Ultra ADAC Reise Advanzia Gold Bank Norwegian Jahresgebühr720 €138 €99 €ca. 720 €70,80 €0 €0 € Heilbehandlungunbegrenztunbegrenzt1 Mio. €10 Mio. €[?]1 Mio. €unbegrenzt Rücktransportsinnvoll und vertretbarunbegrenzt, sinnvollsinnvoll, ärztlich angeordnetin 10 Mio. € enthaltenenthaltennur mit Vorabgenehmigungunbegrenzt, eingeschränkter Auslöser Such- und Rettungskosten150.000 €10.000 €[?]5.000 €[?]ausgeschlossen an unzugänglichen Orten und auf See[?] Selbstbehalt Kranken10 %, min. 100 €, max. 500 €entfällt [?]200 €nicht beziffert100 €100 €185 € Max. Reisedauer120 Tage90 Tage90 Tage90 Tage45 Tage90 Tage60 Tage Reiserücktritt6.000 € je Person5.000 € je Reise5.200 € je Person, Gruppendeckel 10.300 €5.000 €10.000 € gesamt3.000 €2.000 € je Person Selbstbehalt Rücktritt10 %, min. 100 €10 %, min. 100 €20 %, min. 200 €nicht beziffert[?]20 %, min. 100 €20 %, min. 185 € Mietwagen-Vollkasko75.000 €, bis 120 Tage75.000 €, bis 30 Tage100.000 € plus Teilkasko plus 1 Mio. € Haftpflichtneinneinneinnein Reisegepäck3.000 €nein[?]1.000 €[?]2.500 €2.700 € Altersgrenze80 Jahre[?][?][?][?]75 Jahre, ab 70 nur 21 Tage65 Jahre Kinder mitversichertbis 25bis 25bis 25bis 17[?]bis 18, bis 23 in Ausbildungbis 21 Kartenzahlung nötigja, ohne Schwellenangabenein für Krankenschutz20 % plus 30 % gestaffeltjanein für Krankenschutz50 %50 % Fremdwährungsgebühr2 %1,95 %0 %0 %[?]0 %0 % Lounge-Zugang1.550+ Lounges, inklusive Begleitungneinneinunbegrenzt, Gäste zahlenneinneinnein

      one other thing i want to do with this table is to linke them to the criteria we choose, push back if that is just useless human readibility stuff. but for me to have sections in the table, even if its teh same table makes a lot of sense...

    7. Elias_GetCited 06 Aug 2026
      riterium Amex Platinum Miles & More Gold easybank Platinum Revolut Ultra ADAC Reise Advanzia Gold Bank Norwegian Jahresgebühr720 €138 €99 €ca. 720 €70,80 €0 €0 € Heilbehandlungunbegrenztunbegrenzt1 Mio. €10 Mio. €[?]1 Mio. €unbegrenzt Rücktransportsinnvoll und vertretbarunbegrenzt, sinnvollsinnvoll, ärztlich angeordnetin 10 Mio. € enthaltenenthaltennur mit Vorabgenehmigungunbegrenzt, eingeschränkter Auslöser Such- und Rettungskosten150.000 €10.000 €[?]5.000 €[?]ausgeschlossen an unzugänglichen Orten und auf See[?] Selbstbehalt Kranken10 %, min. 100 €, max. 500 €entfällt [?]200 €nicht beziffert100 €100 €185 € Max. Reisedauer120 Tage90 Tage90 Tage90 Tage45 Tage90 Tage60 Tage Reiserücktritt6.000 € je Person5.000 € je Reise5.200 € je Person, Gruppendeckel 10.300 €5.000 €10.000 € gesamt3.000 €2.000 € je Person Selbstbehalt Rücktritt10 %, min. 100 €10 %, min. 100 €20 %, min. 200 €nicht beziffert[?]20 %, min. 100 €20 %, min. 185 € Mietwagen-Vollkasko75.000 €, bis 120 Tage75.000 €, bis 30 Tage100.000 € plus Teilkasko plus 1 Mio. € Haftpflichtneinneinneinnein Reisegepäck3.000 €nein[?]1.000 €[?]2.500 €2.700 € Altersgrenze80 Jahre[?][?][?][?]75 Jahre, ab 70 nur 21 Tage65 Jahre Kinder mitversichertbis 25bis 25bis 25bis 17[?]bis 18, bis 23 in Ausbildungbis 21 Kartenzahlung nötigja, ohne Schwellenangabenein für Krankenschutz20 % plus 30 % gestaffeltjanein für Krankenschutz50 %50 % Fremdwährungsgebühr2 %1,95 %0 %0 %[?]0 %0 % Lounge-Zugang1.550+ Lounges, inklusive Begleitungneinneinunbegrenzt, Gäste zahlenneinneinnein

      maybe i need to track the policies, so we can create a proper md structure for the facts, it seems a lot are of points are not decided yet....

    8. Elias_GetCited 06 Aug 2026
      Die Anreise ist störanfälliger Zwei Umstiege bedeuten doppeltes Risiko für verpasste Anschlüsse und verlorenes Gepäck.

      i think we could smuggel in here or in a separate point also comfort that the cards offer, i mean amex lounges are a super nice plus, especially since you will have changeover with long distance travel, or exotic places...

    9. Elias_GetCited 06 Aug 2026
      Von den geprüften Karten verwenden American Express, die easybank Kreditkarte Platinum und Miles & More die günstigere Formulierung.

      i dont like guenstigere formulierung. to much connotation to price imo, esepcially when talking about money all the time. maybe vorteilhaftesten konditionen, beste konditionen, ....not sure

    10. Elias_GetCited 06 Aug 2026
      Die wichtigste Unterscheidung: notwendig oder sinnvoll Fast jede Karte wirbt mit einem Krankenrücktransport. Was sie darunter versteht, unterscheidet sich erheblich. Verlangt die Police einen medizinisch notwendigen Rücktransport, zahlt sie erst, wenn die Behandlung vor Ort nicht ausreicht. Bei einem komplizierten Beinbruch in einer guten Klinik in Bangkok ist sie das nicht. Man bleibt, bis man transportfähig ist, unter Umständen sechs Wochen. Genügt ein medizinisch sinnvoller und vertretbarer Rücktransport, darf man nach Hause, sobald es medizinisch vertretbar ist. Der Preisunterschied zwischen beiden Formulierungen ist für den Versicherer gering. Für den Patienten ist er der Unterschied zwischen einem Krankenhausaufenthalt in der Heimat und einem im Ausland. Von den geprüften Karten verwenden American Express, die easybank Kreditkarte Platinum und Miles & More die günstigere Formulierung. Advanzia erstattet den Rücktransport nur, wenn er vom Versicherer vorab genehmigt wurde. Bei Bank Norwegian greift er, wenn die Behandlung vor Ort nicht möglich ist oder der Arzt des Versicherers ihn für geeignet hält.

      i guess this makes my point i made above about the reuckfuehrung explaination mute...

    11. Elias_GetCited 06 Aug 2026
      orkasse ist im Ausland die Regel Kliniken in den USA und in Teilen Asiens behandeln erst nach Zahlungszusage. Ob der Versicherer direkt mit dem Krankenhaus abrechnet oder man mehrere tausend Euro vorstrecken muss, ist im Ernstfall der spürbarste Unterschied überhaupt.

      actually a very valid point, we do not have that though in our comparison, do we not have the facts about this? if we are not sure i would drop the point....what do you think...

    12. Elias_GetCited 06 Aug 2026
      Die Reise dauert länger Fernreisen von drei bis sechs Wochen sind üblich. Mehrere Karten stellen ihren Schutz vorher ein.

      this is actually not true, all card have more than 45 days, six weaks are just 42 days. maybe we can rephrase about lenght of travel and repeated travel throught the year. which cards set you free, you dont need to think about it anymore...

    13. Elias_GetCited 06 Aug 2026
      Eine Repatriierung aus Südostasien liegt je nach Zustand des Patienten zwischen 30.000 und 100.000 Euro. Entscheidend ist dabei nicht die Summe, sondern die Bedingung, unter der die Versicherung ihn überhaupt bezahlt.

      would just expand a little bit here. to say some cards allow only repatriation when the procedure cannot be done in the country where you are, which is extremly strict and ahrd to argue, so in essence you will not be repatriated from thailend to name an example. but shorter please, you get what i mean anyway....

    15. Elias_GetCited 06 Aug 2026
      Und wer nur zwei bis drei Wochen unter 65 verreist, ist mit einer kostenlosen Karte sachlich ausreichend geschützt.

      this seems simply not true, the rueckfuehrungsgebuehren dont care if you break your leg on teh first day of the trip or on day 50....

    16. Elias_GetCited 06 Aug 2026
      ie Miles & More Gold Credit Card liefert den medizinischen Kernschutz zu einem Fünftel des Preises und gewährt ihn sogar ohne Kartenzahlung.

      would name two three more saying they have all there different strenghts, without going into detail

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    MiniMax H3：打破任务和模态边界的开放模型
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  27. mp.weixin.qq.com mp.weixin.qq.com
    把弹幕「打包」带走，解锁高清版视频的吐槽自由
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    1. Fradeet 06 Aug 2026
      它不读取视频内容，也不知道弹幕窗口下面正在播放什么。你可以用 Infuse 播放自己的媒体库，在 IINA 里打开本地文件，也可以直接在浏览器里观看 YouTube。只要两边是内容和时间轴大致对应的同一段视频，就能把它们组合起来。这种设计的好处是通用：我不需要为每个播放器分别实现一套视频加载逻辑，更不会碰到画质、字幕、HDR、音轨、网盘协议或播放权限等问题。

      “弹来弹去”可以作为目前组合 B 站内番剧（无论正盗版）+ 其他源的一个选项。

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  28. wenku-strategy-ppt-sdpic.bj.bcebos.com wenku-strategy-ppt-sdpic.bj.bcebos.com
    小镇商业街的生意经：微观经济学六大考点导航 - 学习笔记
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    1. skcs123 06 Aug 2026
      非竞争性与非排他性

      排他性 ➡️ 盯住“排”字 ➡️ “排外 / 赶人” 秒记： 没交钱、没买票，能不能把你“排”除在外、把你赶走？ 能赶走 = 排他；赶不走 = 非排他。 2. 竞争性 ➡️ 盯住“争”字 ➡️ “争抢”

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    Predicting geometries from the transformer architecture
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  30. Local file Local file
    Puma North America List Cleaning Report | Review My Emails
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    Meta Ads on Razorpay Dashboard
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    常识判断化学与生物破壁指南 - 学习笔记
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    常识闯关：从生活到科技的应试知识导航 - 学习笔记
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    1. skcs123 06 Aug 2026
      东风-17

      东风-17（17岁）： 17岁还是个血气方刚的未成年小伙子，平时在外面跟人起冲突、打打闹闹，这是“常规”操作（对应：常规导弹，日常可用）。 东风-41（41岁）： 41岁已经是成熟稳重的中年大Boss，是整个家族的“核”心顶梁柱！这种级别的大佬平时绝对不出手，只要往那一坐就是“战略威慑”（对应：战略核导弹，核威慑）。

    2. skcs123 06 Aug 2026
      南昌舰

      055型 南昌舰 ➡️ 核心特点：带刀侍卫（防空反潜） 常识秒记： “南昌”是什么地方？八一南昌起义，是解放军建军打响第一枪的圣地！ 人设： 既然是建军老大哥，那必须是最忠诚、最能打的“带刀侍卫”！专门贴身保护航母。 🏝️ 075型 海南舰 ➡️ 核心特点：两栖登陆（直升机/气垫船） 常识秒记： “海南”是个什么地方？是个四面环海的大岛！ 人设： 去海岛上打仗，最需要干嘛？当然是开着气垫船冲上沙滩“两栖登陆”！ 🍲 076型 四川舰 ➡️ 核心特点：双舰岛、电磁弹射（三栖） 常识秒记： “四川”最出名的是什么？是鸳鸯火锅！而且四川四周全是高山（蜀道难）。 人设： 吃鸳鸯锅 ➡️ 对应全球罕见的“双舰岛”！ 大山里飞不出去怎么办？ ➡️ 必须装上“电磁弹射”直接把飞机弹上天！ 它是075的升级版，从“两栖”直接进化成了“海陆空三栖”。

    4. skcs123 06 Aug 2026
      神舟五号：首次载人航天飞行（1人，杨利伟）

      神舟五号 ➡️ 首次载人 本能秒记： 5 谐音“我”。 逻辑： “我”终于上天了！代表中国人第一次亲自上太空。 🚀 神舟六号 ➡️ 多人多天 本能秒记： 6 谐音“遛”。 逻辑： 既然上去了，就得多待几天“遛一遛”。遛弯一个人没意思，得“多人多天”一起遛。 🚀 神舟七号 ➡️ 首次出舱（太空行走） 本能秒记： 看 “7” 的形状！ 逻辑： 数字“7”长得就像“一条腿迈出了门槛”！看到7，本能反应就是把腿迈出去 ➡️ 出舱！ 🚀 神舟八号 ➡️ 无人交会对接 本能秒记： 看 “8” 的形状！ 逻辑： 数字“8”长得就是“两个圆环死死扣在一起”！看到8，本能反应就是对接！（因为是第一次测试扣环，怕出危险，所以是无人）。 🚀 神舟九号 ➡️ 载人交会对接（首位女航天员） 本能秒记： 9 谐音“交”，形状像“女孩”。 逻辑： 9 读快了就是“交”（交会对接）。而且“9”的形状像一个扎着马尾辫的脑袋，代表第一次带了女航天员上去对接。 🚀 神舟十号 ➡️ 首次太空授课 本能秒记： 10 谐音“师”。 逻辑： 10 = 师。老师上天干嘛？当然是去授课！ 🚀 神舟十一号 ➡️ 对接天宫二号 本能秒记： 看 “11” 的形状！ 逻辑： 11 长得完全就是罗马数字的 “ II ”（2）！所以 11 完美对应天宫二号！

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  35. www.bsigroup.com www.bsigroup.com
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