Schedule of Events | Symposia

Symposium Session 4 - Deploying Attention in Real-World Learning Environments within Individual Minds: Contributions from Precision Imaging and Educational Neuroscience.

Symposium Session 4: Sunday, March 30, 2025, 1:30 – 3:30 pm EDT, Constitution B

Chair: Bruce McCandliss¹; ¹Stanford University
Presenters: Arielle Keller, Tyler Moore, Audrey Luo, Elina Visoki, Mārtinš Gatavinš, Alisha Shetty, Zaixu Cui, Yong Fan, Eric Feczko, Audrey Houghton, Hongming Li, Allyson Mackey, Oscar Miranda-Dominguez, Adam Pines, Russell Shinohara, Kevin Sun, Damien Fair, Theodore Satterthwaite, Ran Barzilay, Adi Korisky, Madison Bunderson, Neha Rajagopalan, Vani Dewan, Ailey Crow, Radhika Gosavi, Liz Toomarian, Blair Kaneshiro, Bruce McCandliss, Elana Zion-Golumbic, Ofek Ben Abu, Orel Levy, Ido Davidesco, Ella Ofek-Geva, Sarah Gilmore, Vishal Easwar, Charles Wasserman, Mary Kate Coburn

Cognitive neuroscience investigations of attention are increasingly expanding to grapple with several forms of complexity. Neural measures are moving beyond a 'one-size-fits-all' model toward ‘precision imaging’ which enables researchers to capitalize on subject-level variations in brain anatomy and functional localization. These more personalized approaches are especially important as the field expands to study changes in attention over development, and deployment of attention in complex environments, such as educational settings. Classrooms present complex, dynamic environments that place demands on sustained and selective attention, as students navigate relevance across multiple modalities as they select speech from noise, relevant from irrelevant visual stimuli, and navigate the social world. Combining the approaches of precision imaging of individuals and studying the deployment of attention in such complex environments such as real-world educational contexts can help drive deeper insights into how attention supports learning and cognitive engagement. This symposium will bring together several recent advancements in brain imaging and mobile EEG studies of attention to show how they can enhance our understanding of the diverse attentional strategies students employ in both lab and naturalistic learning scenarios. Presentations will focus on the benefits of leveraging results at the individual level to emphasize how precision neuroscience and data-driven approaches can reveal the varied paths through which students manage attention, moving beyond group-based models. Ultimately, this symposium aims to uncover how we can utilize personalized approaches to pave the way to a better understanding of attention mechanisms that modulate learning in complex environments.

Presentations

Precision neuroscience for studies of individual differences in youth environments and cognition

Arielle Keller^1,2, Tyler Moore³, Audrey Luo³, Elina Visoki³, Mārtinš Gatavinš³, Alisha Shetty³, Zaixu Cui⁴, Yong Fan³, Eric Feczko⁵, Audrey Houghton⁵, Hongming Li³, Allyson Mackey⁵, Oscar Miranda-Dominguez⁵, Adam Pines⁶, Russell Shinohara³, Kevin Sun³, Damien Fair⁵, Theodore Satterthwaite³, Ran Barzilay³; ¹University of Connecticut, ²Connecticut Institute for the Brain and Cognitive Sciences, ³University of Pennsylvania, ⁴Chinese Institute for Brain Research, Beijing, China, ⁵University of Minnesota, ⁶Stanford University

Each human brain is unique in its physical and functional characteristics. Despite this observation, many human neuroscience studies still rely on a “one-size-fits-all” approach to functional brain mapping, relying on the assumption that all brains share a 1:1 correspondence between structure and function. Recently, the field has begun to shift toward the use of precision neuroscience techniques to derive brain measures that capture the unique features of each individual. This shift is critically important for studies of cognition in classroom settings for two reasons. First, group-averaged brain atlases are typically defined in adult populations, making standard group atlas approaches especially problematic for studies of school-aged children and adolescents. Recent developmental studies have shown that, not only does functional brain network organization tend to be highly variable in youth, it also tends to change over the course of development. Second, many cognitive functions like attention, memory, and executive functioning that are essential for effective classroom learning rely on functional brain networks that tend to exhibit the most inter-individual heterogeneity of all large-scale brain networks. In this talk, I will introduce the goals of precision neuroscience, showcase current methodological approaches for personalized brain mapping, and highlight results from two recent studies linking inter-individual variability in functional brain organization with individual differences in cognitive functioning in youth. I will also describe theoretical models for how environmental contexts may shape person-specific cognitive neurodevelopment. Together, these studies underscore the future potential of precision neuroscience for better understanding attention and learning in the classroom.

Precision Imaging EEG Approaches Unveil Selective Auditory Attention in Middle School Kids

Adi Korisky¹, Madison Bunderson¹, Neha Rajagopalan¹, Vani Dewan¹, Ailey Crow^1,2, Radhika Gosavi^1,2, Liz Toomarian^1,2, Blair Kaneshiro¹, Bruce McCandliss¹; ¹Stanford University, ²Synapse School, Menlo Park

Maintaining goal-directed attention in environments with multiple stimuli requires effective filtering of sensory distractors. Individual differences in attention strategies are crucial, as they influence how each person responds to competing information, ultimately impacting learning outcomes. To explore these individual-level differences in attention, we used Reliable Components Analysis (RCA), a data-driven approach inspired by precision imaging that computes a unique spatial filter for each individual. To connect the existing literature on RCA to a natural learning environment, we implemented a within-subject design in a local K-8 school as part of a long-term research-practice partnership. Forty middle school-aged children participated in a selective attention task featuring multimodal steady-state stimuli interwoven with more naturalistic stimuli recorded by their social studies teacher. Our findings revealed consistent individual-level attention patterns across two sessions (r = 0.48, p < 0.02), indicating that students employed stable strategies to process auditory stimuli in a complex environment. Auditory attention decoding, using RCA-derived spatial filters, demonstrated significantly higher neural power during the audio-attend condition, showcasing RCA's potential to capture attention dynamics in individual students. Additionally, we examined how these individual patterns could identify attentional fluctuations while listening to natural stimuli, such as speech, in noisy settings. By integrating data from both controlled and naturalistic contexts, we utilized RCA-derived patterns to decode attention over time, exploring how laboratory-established neural markers of attention translate to real-world educational scenarios.

The effect of ecological disturbances on neural responses and speech tracking of the teacher during real-life classroom learning

Elana Zion-Golumbic¹, Ofek Ben Abu¹, Orel Levy¹; ¹Bar Ilan University, Israel

Real-life classrooms can be notoriously noisy. Sounds from both outside and inside the classroom can cause severe disturbances to the lesson and distract students’ and teachers’ attention. Although the detrimental effects of noise and irrelevant sounds have been studied extensively in highly controlled settings, few studies have investigated how learning and neural processing are affected by irrelevant sounds under ecological conditions. Here we present a unique data set, collected as part of a research-practice partnership with a local high-school, where we leveraged mobile EEG technology to study students’ neural activity (9-11th grade) as they engaged in realistic classroom learning. Validating the premise of this field-based neuroscience study, we demonstrate that neural tracking of the teachers’ speech can be reliably measured in over 80% of individual students, even in these real-life conditions. Moreover, we show that both behavior (comprehension of lesson elements) and neural speech tracking are reduced in the presence of background “chatter” in the classroom, reflecting its disruptive nature. We also investigated how performance and neural speech tracking relate to more “general” neural metrics associated with attention, such as alpha- and beta-power, and how they change throughout the course of the lesson, potentially capturing the effects of fatigue or fluctuations in attention. This novel dataset demonstrates the transformative potential of using mobile EEG for advancing our understanding of perception and attention in real-life situations, particularly in school contexts where maintaining attention and avoiding distraction are such pivotal (and difficult) cognitive feats.

Attentional fluctuations in real-world learning

Ido Davidesco¹, Ella Ofek-Geva², Sarah Gilmore², Vishal Easwar¹, Charles Wasserman², Mary Kate Coburn²; ¹Boston College, ²University of Connecticut

Maintaining attention during lectures is highly demanding, as attentional states naturally fluctuate between externally focused and internally generated thoughts (external and internal attention, respectively). These fluctuations are implicit and challenging to capture through self-report or observational methods. Additionally, they tend to vary between individuals, making precision neuroscience techniques highly suitable for studying them. A key open question in the field is the relationship between internal attentional states and learning: Does shifting attention internally support or hinder learning? To address this question, 100 undergraduate students viewed a lecture from an introductory biology course. In the experimental condition, lecture segments were interspersed with periods of internal attention, during which participants were instructed to silently reflect on a lecture-related prompt for one minute. In the control condition, participants simply pressed a button to advance from one lecture segment to the next. Attentional states were assessed through electroencephalography (EEG) and eye-tracking data. Behaviorally, participants in the experimental condition demonstrated better learning outcomes than those in the control group. Analysis of the EEG data revealed that alpha-band (8-12 Hz) power was higher during uninterrupted lectures compared to those interspersed with internal attention periods. These results suggest that incorporating structured opportunities for internal attention can enhance attentiveness and improve learning. The long-term goal of this research is to identify neural and gaze-based markers that can track attentional fluctuations in individual learners.