arXiv subjects

Carter Sale

Publications and source records attributed to Carter Sale.

3 recordsLinked to original sources

arXiv2026

Persistent homology broadens the controllable subspace in human structural connectomes

Network control theory applied to structural connectomes typically ranks brain regions as candidate driver nodes by their structural connectivity strength, and evaluates performance through scalar control energy. We test whether this framing captures the most relevant information about how driver-node selection shapes brain network control. We introduce an alternative criterion based on the persistent topological cycles in which each node participates---a measure of mesoscale integration that captures features beyond local connectivity---and compare it to standard degree-based selection across 70 human structural connectomes at three parcellation scales. Topology- and degree-informed driver sets achieve nearly identical scalar control energy, differing by approximately 0.2%. The geometry of the controllable subspace, however, differs substantially: topology-informed sets distribute controllability across more dimensions of state space and produce better-conditioned controllability matrices. This geometric advantage is preserved when high-degree hub nodes are removed, and it carries a functional signature: because the two criteria place driver nodes in different cortical territory, each most efficiently reaches a different class of target state. The choice of node-ranking criterion therefore shapes which brain-state transitions are energetically favored even when average control cost is unchanged. The results reveal a dissociation between control cost and control geometry, and demonstrate that persistent topology captures information about brain network control that scalar energy summaries miss.

q-bio.NC↗

arXiv2026

Nonlinear Methods for Analyzing Pose in Behavioral Research

Advances in markerless pose estimation have made it possible to capture detailed human movement in naturalistic settings using standard video, enabling new forms of behavioral analysis at scale. However, the high dimensionality, noise, and temporal complexity of pose data raise significant challenges for extracting meaningful patterns of coordination and behavioral change. This paper presents a general-purpose analysis pipeline for human pose data, designed to support both linear and nonlinear characterizations of movement across diverse experimental contexts. The pipeline combines principled preprocessing, dimensionality reduction, and recurrence-based time series analysis to quantify the temporal structure of movement dynamics. To illustrate the pipeline's flexibility, we present three case studies spanning facial and full-body movement, 2D and 3D data, and individual versus multi-agent behavior. Together, these examples demonstrate how the same analytic workflow can be adapted to extract theoretically meaningful insights from complex pose time series.

cs.CV↗

arXiv2026

Facial Movement Dynamics Reveal Workload During Complex Multitasking

Real-time cognitive workload monitoring is crucial in safety-critical environments, yet established measures are intrusive, expensive, or lack temporal resolution. We tested whether facial movement dynamics from a standard webcam could provide a low-cost alternative. Seventy-two participants completed a multitasking simulation (OpenMATB) under varied load while facial keypoints were tracked via OpenPose. Linear kinematics (velocity, acceleration, displacement) and recurrence quantification features were extracted. Increasing load altered dynamics across timescales: movement magnitudes rose, temporal organisation fragmented then reorganised into complex patterns, and eye-head coordination weakened. Random forest classifiers trained on pose kinematics outperformed task performance metrics (85% vs. 55% accuracy) but generalised poorly across participants (43% vs. 33% chance). Participant-specific models reached 50% accuracy with minimal calibration (2 minutes per condition), improving continuously to 73% without plateau. Facial movement dynamics sensitively track workload with brief calibration, enabling adaptive interfaces using commodity cameras, though individual differences limit cross-participant generalisation.

cs.HC↗