Niko A. Busch

The earliest stage of visual memory is often characterized as pre-attentive, yet recent work suggests that spatial attention and pre-stimulus alpha oscillations both shape iconic memory at its onset. Whether these influences reflect a common inhibitory mechanism or contribute independently is unknown. We combined an endogenous spatial pre-cue (valid, invalid, neutral) with an analysis of pre-stimulus alpha power in a partial-report paradigm, at target-to-post-cue onset asynchronies (SOAs) of 0, 120, and 1240 ms, while recording EEG from 45 participants. Behaviorally, perceptual sensitivity ( d ^′ ) showed a cost-dominated signature: invalid cues impaired performance relative to both neutral and valid cues at the 0 and 120 ms SOAs, with no benefit of valid over neutral cues. The pre-stimulus EEG mirrored this asymmetry: at 120 ms, stronger alpha power ipsilateral to the target predicted higher d ^′ , whereas contralateral alpha had no effect at any SOA, indicating suppression of the irrelevant hemifield rather than facilitation of the target. This ipsilateral effect was additive with cue validity rather than interactive, and it persisted in neutral trials without any directional cue. Iconic memory is thus continuously biased by the inhibitory state of early visual cortex, to which endogenous attention and spontaneous excitability fluctuations contribute through separable channels. Significance Statement: Whether the earliest stage of visual memory depends on attention has long been debated. We show that the same asymmetry with which spatial attention shapes iconic memory—misdirecting attention impairs memory, but correctly directing it yields no benefit—also appears in EEG alpha power. Stronger prestimulus alpha power over the hemisphere ipsilateral to the target improves memory, while activity over the target-processing hemisphere does not. This alpha effect occurs even without any spatial cue, showing that it does not depend on deliberate attention. Because the cue-driven and spontaneous alpha effects add together rather than interact, they reflect two separable routes to the same outcome: suppression of irrelevant input. Early visual memory is thus continuously gated by the inhibitory state of visual cortex.

Human long-term memory for visual scenes is remarkably robust, yet the neural mechanisms supporting memory encoding and retrieval remain poorly understood when both processes must operate at the same time. For instance, this might happen when we encounter a familiar place while simultaneously forming new memories of this encounter. We investigated electrophysiological correlates of visual recognition memory using a continuous recognition task (CRT), in which participants judged a continuous stream of scene photographs as previously seen or new, such that encoding and retrieval occurred in parallel on every trial. To make recognition particularly demanding, stimuli were drawn from only four scene categories. Thirty-one participants performed the task while EEG was recorded, and we analyzed canonical ERP markers of retrieval (mid-frontal FN400, 300-550 ms; late parietal effect, LPE, 550-800 ms) and encoding (subsequent memory effect, SME) as a function of stimulus repetition and lag between consecutive presentations. FN400 showed robust old/new effects for both repetitions, whereas LPE differences emerged only at the second repetition. While FN400 amplitude was insensitive to lag, LPE amplitude decreased systematically with increasing lag, mirroring the behavioral pattern of declining accuracy and slower responses. A significant SME emerged selectively for images subsequently recognized on both repetitions, indicating that the SME in continuous recognition is specific for the most robustly encoded items and reflects the strength of encoding. Together, these findings show that canonical ERP markers of recognition memory are preserved even when encoding and retrieval operate concurrently, but their expression depends on how often and how recently an item has previously been encoded – parameters that can be flexibly manipulated within the CRT. This demonstrates that the CRT is sensitive to fine-grained temporal dynamics of memory formation and retrieval that could be missed under standard single-repetition designs.

Attention facilitates stimulus processing by selecting specific locations (spatial attention) or features (feature-based attention). It can be sustained on a given location or feature or reoriented between locations or between features, enabling attentional exploration. Sustained attention has long been associated with alpha (8–12 Hz) oscillations, whereas exploratory attention has been more recently proposed to rely on theta (4–7 Hz) oscillations. However, whether theta oscillations universally index exploration across spatial and nonspatial dimensions remains unclear as prior evidence stems from disparate paradigms and attentional manipulations. Here, we systematically examined attentional exploration of stimulus dimensions (location/feature) in human participants (male/female) using EEG and a cueing paradigm during the (1) precue-to-stimulus (first attentional orienting) and (2) poststimulus (during stimulus processing) trial periods. In the precue-to-stimulus period, multivariate decoding revealed rhythmic modulations of neural information content: alpha-band rhythms emerged during sustained attention regardless of dimension, whereas theta-band rhythms were observed selectively when attention was sustained on features but required exploration of spatial locations. Poststimulus analyses confirmed this link: theta power increased during invalid trials that required attentional reorienting relative to valid trials, and this effect predicted faster reaction times and higher accuracy. Together, these findings clarify the functional role of neural oscillations in attentional orienting and exploration, highlighting theta as a signature of spatial exploration rather than a domain-general mechanism.

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.

Some images are consistently remembered better than others, suggesting that memorability reflects intrinsic image properties. We tested whether within-category distinctiveness underlies this effect. Across three experiments ( N = 477), participants categorized indoor scenes previously rated for subjective typicality and then completed recognition memory tests. Typical scenes were categorized faster and more accurately, but were remembered worse and showed a more liberal response bias than atypical scenes. These opposing effects were robust across categories. To link subjective typicality to visual representations, we quantified image distinctiveness using a convolutional neural network (CNN). Across layers, CNN-derived distinctiveness closely tracked human typicality judgments and predicted both categorization speed and memorability, with strongest effects in higher, semantic layers. Critically, the memory advantage for atypical scenes persisted even when most images were atypical, ruling out rarity within the experimental context. Together, the results show that intrinsic scene memorability reflects an image’s position within a category-specific representational space.

The role of spatial attention in iconic memory – an early interface between visual perception and short-term memory – remains poorly understood. Across two experiments run in 2025, we investigated how endogenous spatial attention modulates iconic memory using a partial-report paradigm. In both exper-iments, pre-cues manipulated the allocation of spatial attention before stimulus onset. Stimulus arrays were then briefly presented to one hemifield and followed by a post-cue that probed iconic memory at varying delays after stimulus offset. In Experiment 1 (N = 47), valid attentional cues improved perfor-mance at short delays. Performance was modeled with an exponential decay function to dissociate effects on initial stimulus availability at short SOAs, the rate of iconic decay, and later transfer to working mem-ory. This analysis indicated that valid pre-cues increased initial stimulus availability relative to invalid pre-cues. In Experiment 2 (N = 66), a neutral pre-cue condition was added, and post-cues were pre-sented at three delays (0, 120, and 1240 ms). This revealed that performance differences were driven by attentional costs at invalidly cued locations, with no detectable benefits at validly cued locations relative to neutral cues. Together, these results show that spatial attention modulates the earliest measurable phase of iconic memory by shaping the initial sensory trace. The cost-dominated pattern suggests that attention primarily suppresses information at unattended locations rather than enhancing representa-tions at attended locations. This finding challenges the view of iconic memory as a pre-attentive sensory store and indicates that attentional selection operates earlier than previously assumed. Significance Statement: The influence of attention on iconic memory – a high-capacity, ultra-brief sensory store – remains highly debated. We demonstrate that endogenous spatial attention modulates iconic memory at its earliest measurable stage. Critically, this modulation reflects an attentional cost at unattended locations rather than a benefit at attended locations, suggesting that spatial attention acts through inhibitory suppression of irrelevant sensory input. These findings challenge models proposing that early sensory representation is categorically attention-free and establish a suppressive role for attention in shaping visual short-term memory at its earliest stage.

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.

Multiverse analysis has emerged as a valuable tool for increasing transparency and assessing robustness in empirical research across many scientific disciplines. By systematically exploring multiple defensible analytical paths, researchers can uncover how different decisions in data processing, model specification, and estimation impact results. However, while multiverse analysis holds significant promise, it also presents several challenges that researchers must navigate. This paper draws on interdisciplinary perspectives and knowledge to provide procedural guidance on design, reporting and interpretation of multiverse results. We discuss diverse data features, levels of precision, preregistration, and computational resources. We aim to produce cohesion across disciplines in multiverse analysis practices, perspectives and terminology, and contribute to ongoing efforts to improve the robustness and reproducibility of scientific results across disciplines.