Biology subjects

Jangraw, D. C.

Publications and source records attributed to Jangraw, D. C..

5 recordsLinked to original sources

bioRxiv2026

Brain-Language Alignment During Naturalistic Reading and Its Disruption by Mind-Wandering

Encoding models offer a principled framework for linking computational representations of language to neural activity, but most electroencephalography (EEG) evidence for brain-language alignment comes from tightly controlled, word-by-word reading paradigms. Whether such alignment is detectable during naturalistic reading, and how it is affected by lapses in attention, remains unclear. We addressed these questions using ROAMM, a multimodal dataset containing simultaneous EEG and eye-tracking recordings with time-resolved mind-wandering (MW) annotations from 44 participants reading naturalistic texts. Ridge regression encoding models were trained to predict fixation-aligned EEG spectral power and fixation-related potentials (FRPs) from five word-embedding models (GloVe, word2vec, BERT, GPT-2, and Llama 3). Using permutation testing with false discovery rate correction, we found statistically reliable brain-language alignment across both feature types, with contextual embeddings outperforming static embeddings. Spectral alignment was strongest in the alpha and low-beta bands over parietal electrodes, while FRP-based alignment peaked 200-300 ms after fixation onset over central and parietal-occipital regions. Leveraging ROAMMs span-level MW annotations, we further show that brain-language alignment is systematically reduced during MW, an effect that was substantially larger for oscillatory (PSD) than for event-related (FRP) features. These findings demonstrate that modern language-model representations are reflected in EEG activity during naturalistic reading despite the modalitys inherent noise, and that fluctuations in attention constitute an underappreciated source of variability in brain-language encoding studies.

neuroscience↗

bioRxiv2026

ReMind: A Retrospective Self-Report Paradigm for Studying Mind-Wandering Onset During Reading

Mind-wandering (MW) is a frequent and pervasive phenomenon, yet it is commonly assessed using self-reports or probe-based methods that offer limited temporal precision regarding its onset. In this study, we introduce a novel paradigm, ReMind, that estimates the onset and duration of MW episodes during natural reading by combining retrospective self-reports with eye-tracking. Participants indicated the words where they believed their mind started and stopped wandering, and these reports were aligned with gaze timestamps to estimate MW onset. Using data from 44 participants, we examined whether knowledge of MW onset improves the detection of MW from eye-tracking signals. To evaluate relevance for both self-report and thought-probe paradigms, we additionally simulated thought probes by randomly sampling time points during reading. Logistic regression classifiers trained on eye-tracking features extracted from time windows anchored to MW onset achieved AUROC scores of 0.659 and 0.621 under the self-report and simulated thought-probe paradigms, respectively, using leave-one-subject-out cross-validation. In both cases, onset-aligned windows outperformed classifiers trained using arbitrary MW windows. Sliding-window analyses further revealed systematic temporal changes around MW onset, with classification performance peaking at approximately 3 seconds after onset. Feature-level analyses showed reduced fixation rate and fixation dispersion, along with increased pupil size following MW onset. Together, these findings characterize the temporal progression from on-task reading to MW. Overall, ReMind provides a useful framework for studying the temporal dynamics of MW during naturalistic reading.

bioengineering↗

bioRxiv2026

Dynamic emotional updating as a computational marker of well-being

Mood shapes behavior, physiology, and well-being. While mood continuously fluctuates in response to external events, it can also remain surprisingly stable, raising a fundamental question: what shapes emotional well-being, mood stability or variability? Quantifying mood along these two dimensions is challenging. We combined a closed-loop mood modulating task with computational modeling to derive quantitative markers of emotional stability and variability and map them onto the continuum from well-being to depressive symptoms. Participants (n=209) experienced adaptive reward-based mood modulation and modeling revealed that higher well-being associates with mood variability and stronger weighting of recent events, whereas depressive symptoms were associated with greater mood stability and stronger anchoring to earlier events. Results replicated in an independent test-retest dataset, which also showed that these mood updating parameters are reliable across days. Our findings identify adaptive responsiveness to recent experiences as a quantitative marker of well-being and unravel the temporal mood dynamics in health and depression.

neuroscience↗

bioRxiv2018

Layer-dependent activity in human prefrontal cortex during working memory

Working memory involves a series of functions: encoding a stimulus, maintaining or manipulating its representation over a delay, and finally making a behavioral response. While working memory engages dorsolateral prefrontal cortex (dlPFC), few studies have investigated whether these subfunctions are localized to different cortical depths in this region, and none have done so in humans. Here, we use high-resolution functional MRI to interrogate the layer specificity of neural activity during different epochs of a working memory task in dlPFC. We detect activity timecourses that follow the hypothesized patterns: superficial layers are preferentially active during the delay period, while deeper layers are preferentially active during the response. Results demonstrate that layer-specific fMRI can be used in higher-order brain regions to non-invasively map cognitive information processing along cortical circuitry in humans.

neuroscience↗

bioRxiv2018

Spatio-temporally specific transcranial magnetic stimulation to test the locus of perceptual decision making in the human brain

Previous research modeling EEG, fMRI and behavioral data has identified three spatially distributed brain networks that activate in temporal sequence, and are thought to enable perceptual decision-making during face-versus-car categorization. These studies have linked late activation (>300ms post stimulus onset) in the lateral occipital cortex (LOC) to object discrimination processes. We applied paired-pulse transcranial magnetic stimulation (ppTMS) to LOC at different temporal latencies with the specific prediction, based on these studies, that ppTMS beginning at 400ms after stimulus onset would slow reaction time (RT) performance. Thirteen healthy adults performed a two-alternative forced choice task selecting whether a car or face was present on each trial amidst visual noise pre-titrated to approximate 79% accuracy. ppTMS, with pulses separated by 50ms, was applied at one of five stimulus onset asynchronies: -200, 200, 400, 450, or 500ms, and a sixth no-stimulation condition. As predicted, TMS at 400ms resulted in significant slowing of RTs, providing causal evidence in support of LOC contribution to perceptual decision processing. In addition, TMS delivered at -200ms resulted in faster RTs, indicating early stimulation may result in performance enhancement. These findings build upon correlational EEG and fMRI observations and demonstrate the use of TMS in predictive validation of psychophysiological models.

neuroscience↗