Heading perception and the structure of the optic acceleration field

Charlie S. Burlingham; Mengjian Hua; Oliver Xu; Kathryn Bonnen; David J. Heeger

Home/Archive/arXiv

arXiv · 2204.12607

Heading perception and the structure of the optic acceleration field

Charlie S. Burlingham·Mengjian Hua·Oliver Xu·Kathryn Bonnen·David J. Heeger

View original source ↗Read source document ↗Export metadata ↓

Abstract

Visual estimation of heading in the human brain is widely believed to be based on instantaneous optic flow, the velocity of retinal image motion. However, we previously found that humans are unable to use instantaneous optic flow to accurately estimate heading and require time-varying optic flow (Burlingham and Heeger, 2020). We proposed the hypothesis that heading perception is computed from optic acceleration, the temporal derivative of optic flow, based on the observation that heading is aligned perfectly with a point on the retina with zero optic acceleration. However, this result was derived for a specific scenario used in our experiments, when retinal heading and rotational velocity are constant over time. We previously speculated that as the change over time in heading or rotation increases, the bias of the estimator would increase proportionally, based on the idea that our derived case would approximate what happens in a small interval of time (when heading and rotation are nearly constant). In this technical report, we characterize the properties of the optic acceleration field and derive the bias of this estimator for the more common case of a fixating observer, i.e., one that moves while counter-rotating their eyes to stabilize an object on the fovea. For a fixating observer tracking a point on a fronto-parallel plane, there are in fact two singularities of optic acceleration: one that is always at fixation (due to image stabilization) and a second whose bias scales inversely with heading, inconsistent with human behavior. For movement parallel to a ground plane, there is only one singularity of optic acceleration (at the fixation point), which is uninformative about heading. We conclude that the singularity of optic acceleration is not an accurate estimator of heading under natural conditions.

Explore related subjects

q-bio.NC

Keep this discovery

BibTeX RIS

Charlie S. Burlingham, Mengjian Hua, Oliver Xu, Kathryn Bonnen, David J. Heeger. 2022-04-26. Heading perception and the structure of the optic acceleration field. https://arxiv.org/abs/2204.12607

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

KEEP EXPLORING

Related papers

arXiv2026

The Platonic brain bridge hypothesis: human brain networks as an architectural prior for omni models

We propose the Platonic brain bridge hypothesis: omni models, which process video, audio and text jointly like the brain, converge on brain-like representations, and the correspondence is bidirectional. From model to brain, brain-likeness of seven omni models is stable across participants, and our encoding models on their internal hidden states rank first on the Algonauts 2025 out-of-distribution leaderboard. From brain to model, three contributions follow. Brain-MoE gives seven cortical networks one brain-pretrained expert each and raises held-out accuracy in all 15 model-benchmark pairs by 6.42 percentage points on average. Brain-AVQA builds questions from video clips labelled by the most responsive brain network; the real network-to-expert map exceeds shuffled maps in-domain on all three models. Brain-Scope uses sparse autoencoders to localize the correspondence to a small subset whose removal weakens brain prediction in all three bases tested. Human brain networks are therefore a usable architectural prior for omni models.

q-bio.NC↗

arXiv2026

Degeneracy along the sensorimotor hierarchy: motor control within a framework larger than redundancy

Motor control has described the surplus of solutions available to the nervous system as redundancy, a term that names duplication: interchangeable elements, robust to loss but incapable of differential adaptation. Biology has had a second term for twenty-five years. Degeneracy names elements that are not interchangeable and are nonetheless isofunctional with respect to a given output, and it supports adaptability, since non-identical elements necessarily diverge in some context. Circuit neuroscience has relabeled its own results accordingly, while motor control has kept the older vocabulary. Neuromechanical models, by placing a spinal circuit in the loop with a musculoskeletal apparatus, bring the two traditions onto the same class of objects. We restate the Edelman and Tononi distinction for sensorimotor systems and derive an operational requirement: not the existence of multiple solutions, but their divergence in contexts they were not selected for. Three influential studies each meet part of that requirement and none meets all. We then argue that degeneracy and redundancy coexist along the sensorimotor hierarchy in a proportion that varies continuously, and that this proportion is measurable: computing degeneracy twice for the same configuration, once with muscle activation as the output and once with the movement produced, isolates what the musculoskeletal apparatus contributes. Five predictions follow, with the single outcome that would refute the proposal. We set out the adaptations the measurement requires in a nonlinear, non-stationary, closed-loop system, and what changes for motor control once solutions are no longer assumed equivalent: the question shifts from which rule selects a command to what the repertoire of the system still allows.

q-bio.NC↗

arXiv2026

pyAvalanches: A Python Package for Analyzing Spatiotemporal Propagation in Neuronal Avalanches

The analysis of neuronal avalanches offers insights into brain dynamics utilizing the framework of criticality, but the reproducibility and comparability of studies are limited by the use of fragmented, lab-specific scripts. To address this issue, we introduce pyAvalanches, an open-source Python package providing a standardized, end-to-end pipeline for avalanche analysis from electrophysiological recordings (e.g., electroencephalography-EEG). Starting from the detection of neuronal avalanches the package provides their core statistical characterization, including size and duration distributions. Beyond this, the main aim of pyAvalanches is to characterize the spatiotemporal organization of activity propagation during avalanches. To this end, the core innovation of pyAvalanches is the compuation of Avalanche Transition Matrices (ATMs) to map spatiotemporal propagation patterns. Building on this, the package derives network-based metrics from the ATMs, bridging the study of the topology and organization of the underlying dynamical interactions with network neuroscience adopting the framework of neuronal avalanches. The entire workflow is encapsulated in a modular and scikit-learn compatible architecture. We demonstrate the utility of pyAvalanches through an illustrative group-level analysis on a public resting-state EEG dataset, comparing propagation patterns across different clinical populations. By providing a user-friendly, tested, and extensible tool, pyAvalanches facilitates reproducible research, enables the development of novel avalanche-based biomarkers, and makes complex avalanche analysis accessible to a broader scientific community. The package is fully documented and distributed via the Python Package Index (PyPI).

q-bio.NC↗