About

Kara D Federmeier is a Professor in the Department of Psychology and the Program in Neuroscience at the University of Illinois, and a full-time faculty member at the Beckman Institute for Advanced Science and Technology, where she leads the Illinois Language and Literacy Initiative. She received her Ph.D. in Cognitive Science from the University of California, San Diego in 2000. Her research in the Cognition and Brain Lab examines the neurobiological basis of meaning, focusing on how world knowledge from multiple modalities is organized in the brain and how this information is integrated and accessed in various contexts within hundreds of milliseconds. Her work employs human electrophysiological techniques, behavioral methods, eye movement analysis, and brain imaging to explore how semantic information is structured by modality and stimulus type, how it is utilized during language comprehension by different age groups, and how the two hemispheres of the brain access and use this information.

Research topics

• Artificial Intelligence
• Computer Science
• Psychology
• Cognitive psychology
• Linguistics
• Mathematics
• Statistics
• Natural Language Processing
• Neuroscience
• Physics
• Cognitive science
• Chemistry
• Speech recognition
• Medicine
• Internal medicine

Selected publications

• EEG Hyperscanning Study of Alcohol’s Effect on Neural Feedback in a Time Estimation Task

  OSF Preprints (OSF Preprints) · 2026-03-05

  otherSenior author

  For decades, researchers have sought to uncover alcohol’s emotionally reinforcing properties to better understand the mechanisms that drive addiction. Yet laboratory studies, particularly those involving participants drinking alone, have consistently yielded weak support for alcohol as a mood-enhancing substance. Many report no appreciable effects, and some even observe negative impacts on mood (Fairbairn & Sayette, 2014; Sayette, 1993). This pattern highlights the failure of isolated lab consumption to model alcohol reinforcement effectively. In contrast, social drinking contexts tell a different story. Outside the laboratory, most alcohol is consumed in the presence of others, and such social contexts amplify alcohol’s positive effects—improving mood and reducing stress significantly more than solitary drinking (Single & Wortley, 1993; Fairbairn & Sayette, 2014). These effects are further heightened by fundamental human drives for connection (Baumeister & Leary, 1995), and the belief in alcohol’s ability to enhance social interaction strongly predicts later alcohol use disorder (Jones et al., 2002). A groundbreaking study by Sayette and colleagues (2012) demonstrated this effect: moderate alcohol consumption during unstructured social interaction among strangers yielded robust increases in positive affect, stress relief, and nonverbal synchrony. This suggests that laboratory paradigms must simulate realistic social contexts to capture alcohol’s true reinforcing power. Our study leverages this socially potent paradigm and extends it via EEG hyperscanning to explore the neural mechanisms underlying alcohol’s effects in social interactions with both familiar and unfamiliar individuals (Redcay & Schilbach, 2019). Unlike prior experiments with unacquainted groups, we recruit friend dyads and pair each person with a stranger during a time-estimation task. Participants engage in rounds as both performer (Player) and observer, either alongside a stranger or alone, under either alcohol or control conditions. By examining ERPs, inter-brain synchrony, and slow-wave activity, we aim to clarify whether alcohol enhances social reward through reduced social anxiety (Moberg & Curtin, 2009), diminished self-monitoring (Steele & Josephs, 1990), or both. In sum, this study uses a rigorous, socially-informed hyperscanning approach to identify the neurocognitive pathways through which alcohol exerts its social rewarding effects, knowledge that is critical for understanding the mechanisms that contribute to alcohol misuse.

  Publisher

• Shifting expectations: when knowledge-based predictions and linguistic context collide

  Frontiers in Language Sciences · 2026-03-20

  articleOpen accessSenior author

  To cope with the demands of language comprehension, young adults often actively engage in prediction of upcoming information—which may be more or less successful depending on each individual's specific knowledge. However, limited research has directly investigated the link between existing knowledge and real-time mechanisms of prediction. Here, we focus on a specific knowledge domain, the fictional world of Harry Potter (HP). Participants with varying degrees of HP knowledge read sentences about general topics and then about HP, each containing a predictable, unexpected-but-plausible, or implausible critical word, while we recorded event-related brain potentials. As expected, HP knowledge modulated N400 amplitudes (an ERP known to index availability of word meaning) to predictable words in HP sentences. HP knowledge also modulated late frontal positivities (LFPs; associated with shifting meaning interpretation upon encountering prediction violations) to unexpected-but-plausible words. The extent to which domain knowledge modulated both N400s and LFPs to unexpected-but-plausible continuations depended on how generally well-known the content in the sentence was. High-knowledge individuals showed reduced initial facilitation (i.e., larger N400 amplitudes) for unexpected-but-plausible words when the sentence contents were generally well-known (compared to less well-known), suggesting that they used their domain knowledge to “override” a more generic interpretation. They additionally showed a greater frontal positivity when sentence contents were less (compared to more) well known, suggesting a willingness to consider alternate interpretations when knowledge is weaker and/or more uncertain—but less so when knowledge is strong. We conclude that possessing relevant knowledge may shape predictive processes during language comprehension, suggesting people may shift their “mode” of language processing depending on existing knowledge and comprehension demands.

  PublisherDOI

• The Neural and Behavioral Correlates of Music Memorability

  2026-05-09

  articleOpen access
  PublisherOA PDFDOI

• Exploring neural markers of incentive salience and real-world drinking among individuals with alcohol use disorder

  Physiology & Behavior · 2026-02-25

  articleOpen accessSenior author

  Alcohol cue salience is theorized to play a mechanistic role in alcohol use disorder (AUD), yet links between neural cue reactivity and naturalistic drinking remain undercharacterized. This study combined laboratory event-related potential (ERP) measures with two weeks of ambulatory assessment to evaluate whether alcohol-cue P3b relates to real-world drinking and individual differences in AUD severity. Heavy drinking participants (52 % Female; Ages 21-32) were recruited from the local community. Participants completed two weeks of ecological momentary assessment with continuous transdermal alcohol monitoring and attended three laboratory visits scheduled at one-week intervals. At the final study visit, participants completed an EEG visual oddball task involving the presentation of both infrequent alcohol and non-alcohol beverage target images and frequent household-object standards. Alcohol images elicited significantly larger P3b amplitudes than non-alcohol images across the sample (N = 47), b = 2.13, p = .002. Critically, this alcohol-specific P3b enhancement was concentrated among individuals with pronounced AUD (moderate-severe, N = 20). Objective transdermally-measured ambulatory drinking further moderated neural cue reactivity in the pronounced AUD group: more binge-level days, b = 0.51, p = .020, and higher peak estimated consumption, b = 34.09, p = .044, were associated with stronger alcohol-specific P3b responses. In contrast, neither retrospective baseline nor in-vivo ambulatory self-reports of drinking demonstrated consistent associations. Together, findings indicate that alcohol cue-elicited P3b is (a) sensitive to clinically meaningful severity distinctions and (b) larger among individuals with heavier real-world drinking as captured with objective sensors, supporting its utility as a neurocognitive marker with ecological validity for understanding individual differences in AUD.

  PublisherDOI

• ERP effects at encoding: Image memorability or recognition success?

  Cognitive Affective & Behavioral Neuroscience · 2026-04-21

  articleOpen accessSenior author

  The subsequent memory effect (SME) refers to neural patterns (e.g., in EEG or fMRI) at encoding that predict later memory performance. In N400-based SMEs, for example, items later remembered elicit less negative N400 amplitudes at encoding compared to items later forgotten. These effects have traditionally been interpreted as reflecting idiosyncratic neural states during encoding-in the case of the N400, states related to semantic activation-that influence episodic encoding success. However, recent work on memorability, a stable, item-level property indicating the population-level likelihood that an image will be remembered, has shown that high (compared to low) memorability images elicit less negative N400 amplitudes, suggesting that memorability is linked to more targeted semantic mapping. This raises the question of whether encoding-related effects are more tied to intrinsic stimulus properties or in-the-moment encoding variability. The present study examined both factors in tandem: ERPs were recorded while participants viewed images varying in memorability and were later classified by recognition outcome (hit vs. miss). Analyses revealed that N400 amplitudes were significantly predicted by memorability scores even when controlling for subsequent memory performance. Memorability also predicted Late Positive Complex SMEs. These findings suggest that neural activity traditionally associated with later memory success may capture item-level properties rather than transient encoding states. Consequently, memorability appears to be a key driver of differences in memory performance, challenging interpretations of SMEs as purely state-dependent and highlighting the importance of considering intrinsic stimulus characteristics when evaluating effects correlated with memory success.

  PublisherOA PDFDOI

• Late Positive Potentials as an Index of “Desirably Difficult” Learning Processes Engaged During Language Comprehension: <scp>ERP</scp> Evidence From Studies of Domain Knowledge

  Psychophysiology · 2026-03-29

  articleOpen accessSenior author

  Electrophysiological studies of language comprehension have primarily examined the kind of information that comes to mind, and when, as people process words and build message-level understanding. However, less is known about the factors that allow people to commit the message-level information to memory for future use. One promising marker of such explicit memory processes is the late positive component (LPC), an event-related brain potential (ERP) effect linked to recollection in the memory literature and predictive of memory performance at timescales ranging from minutes to months. Here, we examine LPCs, alongside N400 brain potentials (sensitive probes of implicit semantic processing), to investigate the hypothesis that domain knowledge influences explicit memory mechanisms during comprehension. We re-analyzed three existing datasets in which young adults with varying domain knowledge about a fictional book series read short descriptions of fictional "facts" about that domain while ERPs were recorded. As predicted, correct completions of these facts elicited larger LPCs as a function of individuals' overall domain knowledge. We also assessed item-level difficulty using completion norms from an independent peer group. More difficult facts engendered larger LPCs-but only in individuals with relatively greater domain knowledge. By contrast, N400 amplitudes, reflecting implicit, real-time lexico-semantic activation, were modulated by item-level difficulty for individuals with weaker knowledge but to a much lesser degree for those with stronger knowledge. These findings demonstrate that domain-specific knowledge shapes not only what information can be implicitly accessed in the moment, but also whether explicit memory mechanisms are immediately engaged. Consistent with the memory literature, we propose that explicit (possibly intentional) memory processes support deeper encoding when input is "desirably difficult" based on an individual's knowledge base. Because these effects appear only in knowledgeable individuals, we suggest they reflect a strengthening of relational memory between in-the-moment linguistic input and extant knowledge networks.

  PublisherDOI

• Investigating the Dynamics of Ambiguity Processing in Multi-Referent Scenarios

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Uncovering Patterns of Brain Activity from EEG Data Consistently Associated with Cybersickness Using Neural Network Interpretability Maps

  ArXiv.org · 2025-11-03

  articleOpen accessSenior author

  Cybersickness poses a serious challenge for users of virtual reality (VR) technology. Consequently, there has been significant effort to track its occurrence during VR use with passive measures like brain activity recorded through electroencephalogram (EEG). To classify cybersickness accurately, including in real time, machine learning algorithms which can extract meaningful signals from the rest of the brain data will be required. However, EEG datasets are typically very small and very high in variability between participants, which makes building effective models extremely challenging. To address these concerns, we first introduce a framework for neural networks which has subject-adaptive training with calibration and interpretation for classification given limited and imbalanced EEG data. Which features the models determine are most useful can be visualized by plotting interpretability maps from integrated gradients and class activation. The framework is demonstrated here with convolutional neural networks and transformer models. Using a set of brain data recorded with EEG while participants viewed a stimulus in VR designed to elicit cybersickness, we show which spatio-temporal EEG features (from electrodes and time steps) were most important for discomfort classification. Across 12 runs of our framework with three different neural networks over multiple random seeds, the models consistently pointed to the same scalp locations as having patterns of brain data that were the most helpful in determining whether or not a sample of EEG data belonged to someone who was experiencing cybersickness. These results help clarify a hidden pattern in other related research and can be used as tagged features for better real-time cybersickness classification with EEG in the future. We provide our code at [anonymized] to enable feature interpretation across different neural network architectures.

  PublisherOA PDF

• Diffindo! Precise language comprehension in older adulthood revealed by event-related brain potential studies of domain knowledge

  Cognition · 2025-06-10 · 1 citations

  articleSenior author
  PublisherDOI

• Uncovering patterns of semantic predictability in sentence processing

  Journal of Memory and Language · 2025-05-23

  articleSenior author
  PublisherDOI

Recent grants

• Hemispheric differences in memory and language

  NIH · $4.5M · 2005–2025

Frequent coauthors

• Brennan R. Payne

  University of Utah

  41 shared

• Marta Kutas

  University of California, San Diego

  34 shared

• Neal J. Cohen

  Icahn School of Medicine at Mount Sinai

  20 shared

• Monica Fabiani

  University of Illinois Urbana-Champaign

  18 shared

• Nils Corson

  Sinai Health System

  16 shared

• Stuart J. Johnstone

  16 shared

• Colette M. Smart

  University of Victoria

  16 shared

• Benjamin Zimmerman

  National University of Natural Medicine

  16 shared

Labs

• Cognition and Brain LabPI

  The Cognition and Brain Lab conducts research on human capacities for learning, memory, and language.

Awards & honors

• Award for Distinguished Early Career Contributions to Psycho…
• Cognitive Neuroscience Society Young Investigator Award (201…
• University Scholar (2012)
• College of Liberal Arts and Sciences Centennial Scholar (201…
• Fellow of the Association for Psychological Science