Cognitive neuroscience is a highly dynamic field that continuously expands our understanding of the brain and cognition. New methods and approaches to data collection and analysis, driven by both focused empirical studies and innovative meta-analytic approaches, are shaping the field. This symposium brings together early career researchers from transdisciplinary backgrounds to highlight emerging trends in psychological and neuroimaging research.

Hutabarat et al. will present findings on how humans escape biologically relevant lethal threats, highlighting the use of wireless virtual reality to study behavior in naturalistic settings. Deng et al. will introduce a novel Magnetic Resonance Imaging-compatible walking-like pedal device to examine differences in cognitive-motor dual-task performance between young and older adults. This methodology can sensitively detect early cognitive and motor decline and its neural underpinnings in healthy older adults. Heydari et al. will present the Iranian Emotional Face Database, an online, validated dataset of Middle Eastern faces designed to enhance cross-cultural research on emotion recognition. She will also discuss facial color variations in shaping emotional and health judgments and address how emotional dynamics influence social decision-making in economic contexts and cooperative behavior. Shifting to meta-scientific approaches, Short et al. will demonstrate how machine learning can enhance sampling efficiency in large Electroencephalography preprocessing multiverse analyses. Finally, Kristanto et al. will explore how a systematic digital knowledge space may advance emerging research trends in precision neuroimaging.

Taken together, these presentations illustrate new research approaches across cognitive neuroscience and discuss how advanced empirical and meta-analytical methods can benefit the field.

When confronted with a threat, animals often display rapid, species-specific defensive behaviors. However, human-specific defensive behaviors remain largely unknown and are challenging to investigate due to ethical constraints in studying real-life threat scenarios. Understanding these behaviors is however crucial, as individual variation in human defense is hypothesized to predict psychiatric risk. Here, we utilize a wireless virtual reality platform in which participants forage for fruits in a naturalistic environment and must evade or escape from biologically relevant lethal threats. Across three independent experiments, we show that when escaping to a fixed shelter location, humans exhibit a specific dominant movement sequence that differs from those observed in many other mammals. Humans tend to turn their head towards an approaching threat and then continue a body turn into the same direction, often using the contralateral foot first, before running away. Furthermore, we discuss deviations from this dominant pattern and identify predictors of unsuccessful escape. We anticipate that these results will allow quantifying the impact of modulating factors, help elucidate the neural control of escape, and assess deviations in clinical conditions.

Cognitive-motor dual tasks are widely used to assess cognitive and motor decline, as well as fall risk in older adults. While behavioral studies on dual-tasking are established, neuroimaging investigations remain limited due to the technical challenges of measuring walking-like activity in magnetic resonance imaging (MRI) environments. This study introduces an approach to capture gait-like foot movements during cognitive-motor dual tasks in MRI settings while mitigating head motion artifacts.

We applied an MRI-compatible pedal device that enables precise measurement of walking-like foot movement during a cognitive-motor dual-task paradigm. Forty-three older (aged 50–80) and twenty younger adults (aged 20–39) participated. To minimize head motion, a plastic forehead band was applied, allowing participants to perceive and self-regulate their head movements while pedaling. Post-acquisition, we used a quality control pipeline for automated MRI data evaluation and denoising implemented within the CONN toolbox. Functional MRI data underwent scrubbing, motion regression and aCompCor. Whole-brain task-based functional connectivity was computed across 164 HPC-ICA networks and Harvard-Oxford atlas regions and compared between older and younger adults.

The use of the forehead plastic band effectively minimized head motion. After scrubbing, the mean framewise displacement was reduced to 0.15 mm, comparable to resting-state studies. Group-level whole-brain functional connectivity analysis revealed that older adults exhibited weaker connectivity primarily in the basal ganglia, a key region involved in motor learning and control.

Our study demonstrates the feasibility of an MRI-compatible cognitive-motor dual-task paradigm with effective head motion control and highlights the basal ganglia's role in age-related decline of dual-task performance.

Cognitive neuroscience increasingly relies on high-quality datasets and refined methodologies to study emotion and face perception across cultures. A major challenge in this field is the underrepresentation of Middle Eastern facial stimuli in existing databases, limiting the generalizability of findings. To address this gap, I present the Iranian Emotional Face Database (IEFD)—a validated dataset of Middle Eastern faces with systematically categorized emotional expressions. In line with open science principles, the database and validation materials are freely available to researchers upon request. The database was validated through an online rating framework, where participants assessed emotional intensity, accuracy, valence, and genuineness, ensuring its reliability for cross-cultural research.

Beyond categorical emotions, subtle visual cues such as facial color variations play a crucial role in emotional and health-related judgments. Our study investigates how color-based facial cues influence emotion and vitality perception, extending previous findings on the role of skin tone variations in social cognition. Using controlled image manipulations and behavioral assessments, we provide insights into how facial color dynamics shape emotional processing.

These advancements offer valuable tools for researchers studying face and emotion recognition, bridging gaps in cross-cultural affective science. By combining validated stimulus development with systematic perceptual analysis, this work highlights the importance of diverse datasets and reproducible methodologies in cognitive neuroscience.

The high degree of analytical flexibility in electroencephalography (EEG) preprocessing presents a major challenge for replicability. Numerous defensible data processing pipelines exist for the same dataset to answer the same research question, creating uncertainty about the robustness of a result to variations in data processing choices. In a multiverse analysis, all equally defensible pipelines are computed and the robustness of the result to these variations is reported. In neuropsychology, the large number of defensible pipelines may make exhaustive computation impractical. In such cases, a representative subset of pipelines must be sampled, and robustness is reported for the sample. However, different sampling methods may yield different robustness results, introducing what we term multiverse sampling uncertainty. We present the first application of active learning to sample from a large EEG multiverse analysis and use that sample to estimate the full multiverse. Specifically, we computed 528 pipelines quantifying the Late Positive Potential (LPP) in an emotion classification task predicting extraversion scores from LPP amplitude, and compared random, stratified, and active learning sampling approaches in terms of the representativeness of the distribution of model fits to that of the full multiverse. Our results highlight variability between sampling methods, with the active learning most closely representing the median model fit of the full multiverse. We discuss the need for representative pipeline sampling in EEG multiverse analyses, and highlight active learning as a promising approach. We provide an open-source script to facilitate reporting of multiverse sampling uncertainty, and to improve replicability in large-scale EEG multiverse analyses.

An emerging trend in neuroimaging research is to optimize the individuality of neural and behavioral measures to improve the strength and robustness of brain-behavior associations. Various methods have been proposed, yet a lack of systematic evaluation has led to fragmented research and increased researcher degrees of freedom, potentially exacerbating the replication crisis. To address this, we propose a knowledge space: a structured platform for collecting and integrating studies, as seen in initiatives such as the Earth System Model and METEOR projects. Given the growing importance of individualized neuroimaging, the establishment of a systematic knowledge space is expected to enhance coherence and accelerate scientific progress in the field.

This talk will present emerging features of a systematic knowledge space for individualized neuroimaging. First, we will review key advancements in individuality-focused neuroimaging methods, such as individualized brain networks. Next, the concept of a knowledge space will be outlined, detailing its essential components and the ideal environment for its implementation. As a foundational step, we will present a systematic taxonomy derived from literature mining on individuality methods in neuroimaging, forming the first component of the knowledge space. Additionally, we will present an empirical study that evaluates and compares these methods. The results will serve as a second component of the knowledge space, aiming to map associations between methods and their empirical outcomes.

Taken together, this talk will highlight two key points: the rise of individuality-focused neuroimaging research, and a novel systematic approach to making this research trend more cohesive and impactful.