bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/153668

Alpha power reflects suppression of Pavlovian bias during social reinforcement learning

Thompson, J. C.·Westwater, M. L.
View original source ↗Export metadata ↓

Abstract

Socially appropriate behavior involves learning actions that are valued by others and those that have a social cost. Facial expressions are one way that others can signal the social value of our actions. The rewarding or aversive properties of signals such as smiles or frowns also evoke automatic approach or avoidance behaviors in receivers, and a Pavlovian system learns cues that predict rewarding or aversive outcomes. In this study, we examined the computational and neural mechanisms underlying interactions between Pavlovian and Instrumental systems during social reinforcement learning. We found that Pavlovian biases to approach cues predicting social reward and avoid cues predicting social punishment interfered with Instrumental learning from social feedback. While the computations underlying Pavlovian and Instrumental interactions remained the same as when learning from monetary feedback, Pavlovian biases from social outcomes to approach or withdraw were not significantly correlated with biases from money. Trial-by-trial measures of alpha (8-14Hz) EEG power was associated with suppression of Pavlovian bias to social outcomes, while suppression of bias from money was associated with theta (4-7Hz) EEG power. Our findings demonstrate how emotional reactions to feedback from others are balanced with the instrumental value of that feedback to guide social behavior.\n\nSignificance statementA smile from another can be a signal to continue what we are doing, while an angry scowl is a sure sign to stop. Feedback from others such as this plays an important role in shapeing social behavior. The rewarding nature of a smile (or the aversive nature of a scowl) can also lead to automatic tendencies to approach (or avoid), and we can learn situations that predict positive or negative social outcomes. In this study, we examined the brain mechanisms that come into play when the instrumental demands of a situation are in conflict with our automatic biases to approach or withdraw, such as when we have to approach someone who is scowling at us or withdraw from someone who is smiling.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thompson, J. C., Westwater, M. L.. 2017-06-21. Alpha power reflects suppression of Pavlovian bias during social reinforcement learning. https://doi.org/10.1101/153668

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Why probe registration is crucial for infant fNIRS and HD-DOT? An evaluation across 49 newborns

Source-detector probe registration, i.e., the mapping of optode positions on the scalp to their true anatomical locations in head/brain space, is crucial for brain imaging with fNIRS or HD-DOT. While individual, MRI-based registration is desirable, it is not always feasible, motivating the development of alternative registration approaches. This study uses the STORM-Net registration protocol and quantifies the variation of cap alignment in a cohort of 49 newborns to evaluate how this affects reconstructed images using HD-DOT. Relative to a centrally aligned template, the cohort showed a median probe-position error of 10.12 mm, while the least and most misaligned subjects exhibited mean errors of 4.41 mm and 20.60 mm, respectively. The results demonstrate that the level of cap misalignment typically observed in newborn imaging can affect the spatial accuracy of reconstructed functional images, particularly in cases with larger probe displacements. These findings provide a quantitative basis for adopting more conservative interpretations of group-level functional brain activity and functional connectivity results, and strongly support the routine use of registration methods in infant HD-DOT studies.

neuroscience↗
bioRxiv2026

Donor Aware Reanalysis of Neuronal Nuclei Supports STMN2 Depletion Associated with Nuclear TDP 43 loss in FTD and ALS

Background. Nuclear TDP-43 depletion is a central pathological feature of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), but donor-to-donor variation can obscure transcriptomic associations in small postmortem cohorts. We reanalyzed public RNA-seq data from FACS-isolated NeuN-positive frontal-cortex neuronal nuclei to quantify gene-expression changes associated with nuclear TDP-43 loss while explicitly accounting for donor structure. Methods. The supplied GSE126542-derived matrix contained 62,754 genes across 12 libraries from seven donors, including five complete matched pairs. Source labels TDP-43 positive and TDP-43 negative were harmonized as nuclear TDP-43 retained and lost, respectively. After validation against featureCounts-assigned read counts, 44,225 genes with at least 10 total counts were analyzed using PyDESeq2. The primary model was ~ donor + nuclear_status; status-only and diagnosis-adjusted models were sensitivity analyses. The contrast was TDP-43 loss minus retained. Results. The donor-aware model identified 3,442 genes at false discovery rate (FDR) < 0.05: 1,865 lower and 1,577 higher in TDP-43-loss nuclei. STMN2 was consistently depleted (log2 fold change = -1.813, 95% Wald interval -2.229 to -1.396; FDR = 6.22e-15), corresponding to approximately 71.5% lower expression. Normalized STMN2 counts were lower in TDP-43-loss nuclei in all five complete donor pairs. TARDBP expression was also lower (log2 fold change = -0.606; FDR = 4.98e-6). In contrast, UNC13A whole-gene expression was modestly higher in the donor-aware model (log2 fold change = 0.372; FDR = 0.00622) and was not significant in either sensitivity model. Conclusions. Donor-aware analysis supports a robust association between nuclear TDP-43 loss and STMN2 depletion in this neuronal-nuclei cohort, alongside extensive but model-dependent transcriptomic variation. Whole-gene UNC13A expression cannot be used to infer UNC13A cryptic-exon inclusion or functional UNC13A depletion. No splice-junction, percent-spliced-in, isoform, or protein measurements were available; mechanistic claims therefore require junction-level and experimental validation.

neuroscience↗
bioRxiv2026

Photoentrainment as Bayesian Inference: How the central circadian clock may weight and integrate the intensity and wavelength of natural light to estimate astronomical time

Synchronising with the Earth's 24-hour rotation using natural light as a timing cue presents a problem for the mammalian brain's central circadian clock. Natural light is highly variable and can take a range of values at any given time of day. For example, it fluctuates due to differences in weather conditions and across seasons. Therefore, an absolute value of light provides an uncertain indication of the time of day. Despite this uncertainty, the central clock maintains close alignment with astronomical time, and here we describe how this may be achieved computationally. We present a Bayesian model in which the clock's endogenous oscillation is used to predict the probability of observing features of light, including intensity and spectral composition, and their co-occurrence given the time of day. These predictions are drawn from a distribution of values typically experienced. When observed light falls outside the predicted range, a prediction error provides evidence that the clock may be misaligned with astronomical time. How much the clock adjusts its phase depends on the relative uncertainty associated with its internal prediction and observed light. These are combined as a weighted average according to their relative reliability, drawing the clock's updated phase towards whichever provides the more reliable estimate of astronomical time. Using natural light measurements collected over one week from 59 human observers and 100 days of environmental spectral measurements, we characterize how the variability of natural light systematically changes across the day and show how the clock may exploit this structure for circadian timekeeping. The model explains how the canonical shape of the phase response curve emerges from the relationship between light distributions and time of day, and how properties of light, including intensity and spectral composition, can be integrated according to their reliability. The central clock's synchrony to light may represent a specific instance of a general computational strategy employed by the mammalian nervous system, whereby sensory information is interpreted relative to an internally generated prediction.

neuroscience↗