Darinka Trübutschek

Consciousness is mostly studied as if it were composed of independent snapshots, aggregating reports over trials and experimental contexts, disregarding any temporal aspect. However, almost nothing is known about how conscious experience spontaneously fluctuates in time. To shed light on the temporal dynamics of conscious awareness, we evaluated the temporal autocorrelation of subjective visibility/confidence reports and objective performance across 20 datasets (N= 335). Two widely used measures of consciousness dissociate in temporal autocorrelation under full spatial attention: subjective measures followed an exponential decay function with long timescales (> 5s), whereas objective accuracy exhibited a slow linear decay function. But both measures share a common underlying temporal fluctuation dynamics, suggesting that the strong autocorrelation in subjective measures cannot be fully attributed to response bias or criterion shift, but might reflect a genuinely aperiodic fluctuation of conscious awareness. Causal transcranial magnetic stimulation (TMS) suggests that this temporal dependency of subjective measures is read out in the dorsolateral prefrontal cortex (DLPFC), where a similarly long neural timescale has been demonstrated. Together, these findings establish a temporal constraint for neural models of conscious access: candidate mechanisms should explain not only whether a stimulus becomes consciously accessible, but also how the probability of conscious access evolves over time.

The EEGManyPipelines initiative included 396 scientists (168 teams) who independently analyzed the same dataset to test the same hypotheses, mapping the full landscape of analytical decisions. This captured an underexamined source of variability in research results: data processing choices needed to arrive at an analyzable dataset. Considering full analysis pipelines, ERP variability across teams was equivalent to variability across participants, suggesting that who analyses the data is as important as who provides it. In contrast to prior many-analyst projects, typically explaining less than 10% of variance in estimates, our granular codings of researcher choices explained 53%, 75%, and 51% of the variance in difference waves across three hypotheses, with unusual results partly attributable to deviations from the prototypical pipeline. Despite the heterogeneity in approaches and estimates, teams reached a substantial consensus on effect presence. By being transparent across all analysis stages, researchers can identify uncertainty and still draw meaningful inferences.

The EEGManyPipelines initiative included 396 scientists (168 teams) who independently analyzed the same dataset to test the same hypotheses, mapping the full landscape of analytical decisions. This captured an underexamined source of variability in research results: data processing choices needed to arrive at an analyzable dataset. Considering full analysis pipelines, ERP variability across teams was equivalent to variability across participants, suggesting that who analyses the data is as important as who provides it. In contrast to prior many-analyst projects, typically explaining less than 10% of variance in estimates, our granular codings of researcher choices explained 53%, 75%, and 51% of the variance in difference waves across three hypotheses, with unusual results partly attributable to deviations from the prototypical pipeline. Despite the heterogeneity in approaches and estimates, teams reached a substantial consensus on effect presence. By being transparent across all analysis stages, researchers can identify uncertainty and still draw meaningful inferences.

The perception of time is elastic, often deviating from physical intervals depending on how experience is structured. Yet, what determines how subjective time is constructed remains debated. Here, we tested whether perceived ongoing time is actively constructed from learned event representations rather than a dedicated internal clock. Using a novel pause-adjustment task across three statistical learning experiments, we measured temporal distortions during continuous listening to structured versus unstructured syllable streams. The presence of event structure systematically warped time: pauses were perceived as longer between pseudowords and shorter within pseudowords. This bidirectional temporal warping emerged online and remained stable across pause durations. Enriching these events with semantic meaning eliminated boundary-related dilation while preserving within-event compression. Moreover, physiological tracking of event structure, indexed by pupil dynamics, dissociated from the magnitude of temporal warping. These findings show that subjective time is constructed from hierarchical event representations and depends not only on where events are segmented, but also on how they are represented.

Caractérisation de l'architecture neurocognitive de la mémoire non-consciente: Depuis plus d'un siècle, les neuroscientifiques considèrent la mémoire de travail comme liée à la conscience, les deux fonctions ayant des caractéristiques et des mécanismes cérébraux similaires. Des travaux récents remettent en question cette interprétation en démontrant que des informations non-conscientes peuvent affecter le comportement pendant plusieurs secondes (« vision aveugle »), et suggérant qu'il existe un véritable système de mémoire de travail non-conscient. Nous combinons ici l’étude du comportement, l’imagerie du cerveau à haute résolution temporelle, et la modélisation computationnelle pour tester ces hypothèses. Nous rejetons d’abord plusieurs explications alternatives à la vision aveugle, montrant que celle-ci résulte d'un processus véritablement non-conscient. Nous montrons également que le maintien non-conscient de l’information ne s’accompagne pas nécessairement d'une activité cérébrale soutenue, mais pourrait dépendre d’états cérébraux «silencieux» qui impliquent des changements transitoires dans la connectivité synaptique. Ensuite, nous explorons systématiquement les propriétés clés de la mémoire de travail consciente dans le contexte de la vision aveugle. Même si plusieurs éléments et leur ordre temporel peuvent être stockés de manière non-consciente, la manipulation des représentations nécessite l’accès conscient et une activité neuronale soutenue. Ces résultats défient les théories qui assimilent le maintien de l'information en mémoire de travail à une activité consciente soutenue. Ils contredisent aussi la notion d'une véritable mémoire «de travail» non-consciente. Nous proposons donc l'existence d'une mémoire à court terme "silencieuse".

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science^1-3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts' choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen's d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses⁴. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1–3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts’ choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, where for one claim per study, at least five re-analysts independently re-analysed the original data. The statistical appropriateness of the re-analyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent re-analyses yielded the same result (within a tolerance region of +/- 0.05 Cohen’s d) as the original report; with a four times broader tolerance region, this indicator rose to 57%. Regarding the conclusions drawn, 74% of analyses were reported to arrive at the same conclusion as in the original investigation; 24% to no effects/inconclusive result, and 2% to the opposite effect as in the original investigation. This exploratory study suggests that the common single-path analyses in social and behavioural research should not simply be assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

There is a growing need for large-scale, global studies in response to the replication crisis, which has raised concerns across many scientific fields and prompted efforts to understand the sources of variability in research outcomes. Here we describe the EEGManyPipelines dataset: a many-analyst study aiming to examine how differences in electrophysiology (EEG) data analysis may contribute to differences in the results. We describe data shared and analyzed by 168 analyst teams. The original shared raw data consisted of behavioral data and EEG recordings of 33 participants performing a long-term memory task, together with 8 hypotheses regarding event-related potentials and time-frequency effects. Analyst teams also provided meta-scientific data detailing analysts’ approach to hypotheses: processed EEG data, scripts, and answers to questionnaires. We propose this unique dataset for future use to study analytical flexibility, research culture, and scientific decision-making. It offers a valuable tool for meta-scientific, global and multicenter studies, EEG methodology, and reproducibility studies.

The scope of unconscious processing has long been, and still remains, a hotly debated issue. This is driven in part by the current diversity of methods to manipulate and measure perceptual consciousness. Here, we provide ten recommendations and nine outstanding issues about designing experimental paradigms, analyzing data, and reporting the results of studies on unconscious processing. These were formed through dialogue among a group of researchers representing a range of theoretical backgrounds. We acknowledge that some of these recommendations naturally do not align with some existing approaches and are likely to change following theoretical and methodological development. Nevertheless, we hold that at this stage of the field they are instrumental in evoking a much-needed discussion about the norms of studying unconscious processes and helping researchers make more informed decisions when designing experiments. In the long run, we aim for this paper and future discussions around the outstanding issues to lead to a more convergent corpus of knowledge about the extent – and limits – of unconscious processing.

The scope of unconscious processing has long been, and still remains, a hotly debated issue. This is driven in part by the current diversity of methods to manipulate and measure perceptual consciousness. We provide here ten recommendations and nine outstanding issues about designing experimental paradigms, analyzing data, and reporting the results of studies on unconscious processing. These were formed through dialogue among a group of researchers representing a range of theoretical backgrounds. We acknowledge that some of these recommendations naturally do not align with some existing approaches and are likely to change following theoretical and methodological development. Nevertheless, we hold that at this stage of the field they are instrumental in evoking a much-needed discussion about the norms of studying unconscious processes and helping researchers make more informed decisions when designing experiments. In the long run, we aim for this paper and future discussions around the outstanding issues to lead to a more convergent corpus of knowledge about the extent – and limits – of unconscious processing.