About

Bruce McCandliss is a Professor at the Piggott Family Graduate School of Education at Stanford University, affiliated with the Symbolic Systems Program within the School of Humanities and Sciences. His academic and research focus includes biomedical applications, learning neurosciences, and related interdisciplinary fields. He is involved in advising students within these areas and contributes to the academic community through his teaching and research activities at Stanford.

Research topics

• Psychology
• Medicine
• Cognitive psychology
• Audiology
• Developmental psychology
• Neuroscience

Selected publications

• Toward precision EEG: assessing the reliability of individual-level ERPs across EEG systems

  Frontiers in Human Neuroscience · 2026-04-09

  articleOpen access

  Introduction Event-related potentials (ERPs) are among the most established tools for studying the neural mechanisms of perception and cognition. Advancing toward precision EEG, however, places new demands for a better understanding of how reliable neural markers are at the individual subject level. Methods We conducted two complementary experiments using an auditory oddball paradigm with three sounds (Standard, Target, and Novel) to examine the reliability of N100 and P300 components. In Experiment 1, we assessed consistency at both the group and individual levels across four EEG systems: one research-grade wired system (BioSemi) and three mobile devices (Smarting, DSI-24, and EPOC X). In Experiment 2 , we used a test–retest design to evaluate within-participant reliability over time. Results Results from Experiment 1 show that at the group level, all EEG systems demonstrated the canonical N100 and P300 components; however, the EPOC X system showed a significantly reduced signal-to-noise ratio compared to the others. At the individual level, temporal and spatial clustering analyses showed that N100 and P300 components were detectable in most individuals (70–85%), with additional significant responses appearing outside this range. We further calculated the similarity of individual responses across participants (“typicality index”), which revealed highly consistent responses to Standard and Novel sounds, alongside divergent patterns of responses to Targets. In Experiment 2, results indicated high within-participant consistency of response patterns for all three stimuli, demonstrating that individual ERPs remain reliably stable over time, even when they deviate from canonical group-level patterns. Conclusion The current study contributes to the ongoing discussion regarding the utility and reliability of ERP-based metrics for precision imaging and highlights important methodological considerations for their practical implementation.

  PublisherDOI

• Auditory attention reorganizes the phase alignment of neural oscillations

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-26

  articleOpen accessSenior author

  Abstract Auditory attention enables the selection of behaviorally relevant sounds in dynamic environments, supporting the flexible allocation of neural resources over time. Although neural entrainment has been proposed as a mechanism for temporal prediction in audition, human studies have largely emphasized changes in response strength, leaving unresolved whether attention reorganizes the temporal alignment of entrained activity across hierarchical cortical networks. Here, we introduce the Selective Temporal Alignment of Components (STAC) framework to dissociate stimulus-driven and attention-controlled dynamics using non-invasive EEG. In a series of experiments across two independent adolescent cohorts (n = 79), Reliable Components Analysis (RCA) revealed two dissociable entrained networks with distinct spatial, functional, and attentional profiles: a sensory-driven network that remained tightly stimulus-locked and a frontal-auditory network that exhibited systematic attention-dependent phase shifts. These phase dynamics were consistent across independent cohorts and stable within individuals, and critically, predicted performance on a standardized neuropsychological measure of auditory attention. Together, these findings establish selective temporal alignment as a robust and behaviorally relevant neural mechanism underlying auditory attentional control.

  PublisherOA PDFDOI

• Math and self-control

  Open MIND · 2026-01-01

  otherOpen accessSenior author

  This study uses data from the Adolescent Brain Cognitive Development (ABCD) Study to investigate the developmental relationship between mathematics achievement and self-control. Prior work has established that self-control is associated with academic performance, but important questions remain about whether these associations differ across academic domains, which dimensions of self-control are most relevant, and how academic performance and self-control influence one another over time. The present registration focuses specifically on the developmental direction of association between mathematics achievement and self-control-related processes over time. The central question is whether stronger self-control predicts later academic success, whether stronger mathematics achievement predicts later self-control, or whether the association is bidirectional. We hypothesize that the developmental relationship between mathematics achievement and self-control-related processes will be bidirectional.

  PublisherDOI

• Auditory attention reorganizes the phase alignment of neural oscillations

  Research Square · 2026-04-16

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Math and Self-Control: The Role of Inhibitory Control Across Methods

  Open MIND · 2026-01-01

  otherOpen accessSenior author

  Building on our prior work establishing a dynamic relationship between self-control (measured via parent&self-reported self-control and behavioral problem indicators) and math(SMARTE) in the ABCD Study, the present study focuses on one cognitive component of self-control: inhibitory control. While our previous analyses relied on temperament/personality questionnaires and behavioral checklists as repeated proxies of self-control, the present study leverages task-based cognitive measures of inhibitory control, specifically the NIH Toolbox Flanker task.

  PublisherDOI

• Cortical Latency Predicts Reading Fluency from Late Childhood to Early Adolescence

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Steady-state EEG captures how elementary classroom instruction drives plasticity for novel visual words

  npj Science of Learning · 2025-11-20

  articleOpen accessSenior author

  Early readers encounter thousands of printed words in children's books. The frequency with which they see each word shapes both neural and behavioral responses. Teachers also introduce novel written words through short, intensive learning experiences. Here we combined steady-state visual evoked potentials (SSVEP), corpus-based word frequency counts, and a novel two-week classroom "learning sprint" to examine and compare these two forms of experience-dependent plasticity. Cortical responses at 4 Hz to contrasts between real words of varying frequency (high: on average 1000 per million; medium: on average 200 per million) and pseudowords were sensitive to corpus-based frequency estimates-marking the first such finding using SSVEP. Strikingly, newly acquired low-frequency words (<1 per million)-taught in a child's own classroom versus counterbalanced words taught in two other classrooms-elicited cortical responses nearly identical to those evoked by high-frequency words versus pseudowords. Furthermore, 1 Hz responses to new vocabulary learning was linked to individual differences in reading skills, including word decoding and rapid automatic naming. Together, these findings highlight the causal impact of authentic instruction and the value of neuroscience-informed methods in education research.

  PublisherOA PDFDOI

• Cortical latency predicts reading fluency from late childhood to early adolescence

  Developmental Cognitive Neuroscience · 2025-10-22

  articleOpen accessSenior authorCorresponding

  Progressive development of reading comprehension fluency from late childhood to early adolescence is remarkably linked to changes in the temporal dynamics of visual word recognition. EEG/ERP based measures of how an individual participant's cortical timing for visual word recognition change over development are limited by low reliability. We present a novel approach to this challenge that individually models cortical latency to visual word forms by extracting phase values from Steady-State Visual Evoked Potentials (SSVEPs) for each participant. The resulting precise and reliable timing information for neural signatures underlying visual word form processes help account for the development of fluent reading comprehension. Typically developing readers (n=68), aged 8-15 years, viewed streams of four-character stimuli presented at 3 Hz, which evoked large significant power spikes from every participant. Linear phase by frequency functions across harmonics at 3, 6, and 9 Hz were consistent with a delay model, indicating a mean latency of 170 ms. Subject-level latencies revealed (a) high internal consistency (r=.94); (b) stability across variations in character-level (letters, unfamiliar pseudo-characters) and word-form level (words, nonwords, pseudofont strings) manipulations; (c) a linear relationship with age; and most remarkably, (d) a strong relationship with individual variation in the fluency of reading comprehension, that was (e) mediated by word naming speed. Results suggest a promising new approach for investigating the neural basis of reading development across several levels of processes, with temporal precision at the individual level that holds translational significance for promoting population-level fluency in reading comprehension.

  PublisherDOI

• Toward precision EEG: Assessing the reliability of individual-level ERPs across EEG Systems

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-11

  preprintOpen access

  Abstract Event-related potentials (ERPs) are among the most established tools for studying the neural mechanisms of perception and cognition. Advancing toward precision EEG, however, places new demands for a better understanding of how reliable neural markers are at the individual subject level. We conducted two complementary experiments to examine the reliability of N100 and P300 components in an auditory oddball paradigm with three sounds (Standard, Target, and Novel). In Experiment 1 , we evaluated the consistency at both the group level and the individual level across four EEG systems: one research-grade wired system (BioSemi) and three mobile devices—Smarting, DSI-24, and EPOC X. At the group level, all systems demonstrated the canonical N100 and P300 components; however, the EPOC X system showed a significantly reduced signal-to-noise ratio compared to the others. At the individual level, temporal and spatial clustering analyses showed that N100 and P300 components were detectable in most individuals (70–85%), with additional significant responses appearing outside this range. We further calculated the similarity of individual responses across participants (“typicality index”), which revealed highly consistent responses to Standard and Novel sounds, alongside divergent patterns of responses to Targets. In Experiment 2 , we assessed the within-participant reliability of N100 and P300 using a test–retest design. Results indicated high within-participant consistency of response patterns for all three stimuli, demonstrating that individual ERPs remain reliably stable over time, even when they deviate from canonical group-level patterns. The current study contributes to the ongoing discussion regarding the utility and reliability of ERP-based metrics for precision imaging and highlights important methodological considerations for their practical implementation.

  PublisherOA PDFDOI

• Cortical latency predicts reading fluency from late childhood to early adolescence

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-01

  preprintOpen accessSenior authorCorresponding

  Abstract The development of fluent literacy skills from childhood to adolescence is strongly constrained by the temporal dynamics of word recognition. Capturing the neural basis of these subtle timing changes in word recognition has remained challenging with EEG measures that lack reliability at the individual subject level. Here, we leverage phase information from Steady-State Visual Evoked Potentials (SSVEPs) to derive precise and reliable temporal dynamics of neural signatures underlying visual word form recognition at the individual level and examine their relationship to reading fluency and comprehension. Typically developing readers (N = 68), aged 8–15 years, viewed a stream of four-character stimulus strings presented at 3 Hz. Significant SSVEP signals emerged for nearly all participants. Signals at 3, 6, and 9 Hz harmonics exhibited a phase pattern consistent with a delay model, indicating a mean latency of approximately 170 milliseconds. Individual variations in latencies demonstrated (a) high internal consistency ( R = .94); (b) stability across variations in letter string forms (familiar words, nonwords with familiar letters, nonwords with unfamiliar pseudo-characters); (c) a linear relationship with age; and most remarkably, (d) a predictive relationship with individual variation in reading fluency and reading comprehension. These results establish SSVEP visual word form latency as a promising approach for investigating the neural basis of reading development, paving the way for future translational applications in education and offering potential solutions to broader societal challenges in promoting population-level reading fluency.

  PublisherOA PDFDOI

Recent grants

• Biological Bases of Alphanumeric Learning Interventions

  NSF · $1.6M · 2004–2009

• NIH Grant R01DC007694

  NIH · $3.5M · 2013

Frequent coauthors

• Jamshid Ghajar

  Stanford University

  37 shared

• Rachel Kolster

  Janssen (United States)

  33 shared

• Ranjeeta Sarkar

  30 shared

• Megan M. McClelland

  30 shared

• Pratik Mukherjee

  University of California, San Francisco

  27 shared

• Robert D. Zimmerman

  Cornell University

  26 shared

• Carl E. Johnson

  Digital Research Alliance of Canada

  26 shared

• Michael I. Posner

  26 shared

Education

• Ph.D, Psychology

  University of Oregon

  1997

Awards & honors

• Glushko Prize for Excellence in Undergraduate Research in Sy…
• Barwise Award for Distinguished Contributions to Symbolic Sy…
• Symbolic Systems Distinguished Teaching Award