Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.34455

The dominance of feedback mechanisms for perception in the central visual field: the flip tilt illusion by backward masking and the Feedforward-Feedback-Verify-reWeight algorithm

Li Zhaoping
View original source ↗Read source document ↗Export metadata ↓

Abstract

The flip tilt illusion is the misperception of the orientation of the alignment between a pair of dots by 90 degrees. It occurs when the dot-pair contains one black dot and one white dot, is typically visible only in the peripheral visual field. It is predicted by the central-peripheral dichotomy (CPD) theory. Along the visual pathway from retina to downstream stages, our brain admits only a tiny fraction of visual input information for recognition due to a processing bottleneck that starts from the output of the primary visual cortex (V1) along the visual pathway. Accordingly, perception downstream from V1 relies on impoverished information, selected largely by gaze shifts to center the selected content at fovea and additionally by the capacity of the bottleneck even in the central visual field, likely making perceptual hypotheses about the visual scene ambiguous or illusory. To overcome, downstream areas send feedback to upstream areas such as V1 to query for additional information, thereby re-assessing the weights of the initial hypotheses. This perceptual algorithm is termed Feedforward-Feedback-Verify-reWeight (FFVW). To save resources, the feedback query is limited largely to the central visual field. The peripheral visual field is thus vulnerable to perceptual ambiguities and illusions. The CPD theory further predicts that impairing the feedback renders the flip tilt illusion visible in the central visual field. This paper reports a confirmation of the prediction. The feedback is disrupted by masking the visual inputs after a short viewing duration t. Furthermore, the FFVW algorithm is studied by a computational model of the perceptual data as a function of t. It reveals that the initial feedforward sweep of visual information explains the flip tilt illusion for t shorter than 20 ms, whereas the feedback mechanism dominates perception for longer t.

Explore related subjects

q-bio.NC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Li Zhaoping. 2026-10-25. The dominance of feedback mechanisms for perception in the central visual field: the flip tilt illusion by backward masking and the Feedforward-Feedback-Verify-reWeight algorithm. https://arxiv.org/abs/2610.34455

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

KEEP EXPLORING

Related papers

arXiv2026

Retrieval-Augmented Parkinson's Disease Classification from Resting-State Functional Connectivity

Altered resting-state functional connectivity (FC) is a candidate marker for the diagnosis of Parkinson's disease (PD). Publicly available PD datasets, however, are small and fragmented across sites, scanners and clinical criteria. Transfer learning mitigates data scarcity by reusing a model trained on larger cohorts, but represents their knowledge primarily through a single set of weights shared by all individuals. Motivated by precision neuroimaging, we propose a retrieval-augmented classification (RAC) framework that additionally places each target individual in the context of similar individuals from external cohorts. A graph convolutional autoencoder (GAE), trained on external PD and control cohorts, encodes these cohorts into a neuro-knowledge base. Each target individual is encoded using the pretrained GAE, and its nearest neighbors are retrieved from the external knowledge base. Their embeddings are combined with the target embedding using mean aggregation or attention for classification. A systematic comparison on the TaoWu cohort separated the contributions of encoder pretraining, retrieval, attention and partial fine-tuning. Pretraining improved mean classification accuracy from 60.5% to 66.0%. However, retrieval and attention did not consistently improve performance over the pretrained-only variants. Several retrieval-based configurations nevertheless showed modestly higher ROC-AUC than the randomly initialized baseline. Varying the number of retrieved neighbors and partially fine-tuning the encoder provided no consistent additional gains. Retrieval-neighborhood analysis further showed diagnostically mixed neighborhoods. These findings provide initial evidence that population-learned representations can be complemented by individual-specific retrieval in neuroimaging, although larger datasets are needed to determine when such contextual information improves classification.

q-bio.NC↗
arXiv2026

Phase response approach to networked neural activity models with delays

Networks of delay-coupled neural mass models alternate between synchrony and anti-phase locking as the delay between brain regions increases. The origin of these delay-dependent bands has remained unclear. Here, we show that a single function, the phase interaction function $H$ of an isolated node, predicts where the bands occur and explains why they repeat. For a Wilson--Cowan model with intra-nodal delay $τ$ and inter-nodal delay $ρ$, we compute the limit cycle and phase response, and hence $H$, by harmonic balance. Each band boundary is a zero of $H'$ shifted by the phase lag $Ωρ$, so the bands repeat in $ρ$ with the oscillation period. Simulations of the full delay-differential networks confirm these predictions, from two nodes, where predicted frequencies agree to second order in the coupling strength, to rings, where the theory also identifies coexisting stable twisted states that random initial conditions rarely reach. At weak coupling, the delays at which synchrony gains or loses stability are set by the node dynamics alone, whereas stable anti-phase states require a bipartite network. For a macaque connectome, metastability peaks at the edges of the synchrony bands, where the stability margin of phase-locking vanishes. The reduction is accurate for coupling strengths well below the slowest Floquet exponent of the node, beyond which a phase--amplitude description is needed. More broadly, vanishing phase-locking stability provides a simple route to metastability in delay-coupled oscillator networks and a candidate mechanism for the flexible coordination of brain dynamics.

q-bio.NC↗
arXiv2026

On the stability of learning

Learning algorithms are usually analyzed under the assumption that informative gradients are continuously available. But this is not always true---learning signals in biological neural networks, for example, may be sparse, local, and only intermittently available at any given synapse. In this work, we show that if gradient information is noisy and only intermittently available, learning may be unstable. In particular, even if average learning dynamics converge to the correct optimum, variance in learning may be asymptotically infinite. We explore this point in a number of analytically tractable settings, including single-layer and multi-layer linear networks, and derive explicit conditions under which learning is stable. These conditions complement, but are conceptually distinct from, existing conditions related to the stability of stochastic gradient descent. Our work demonstrates that unbiased credit assignment is not enough for reliable learning; sufficiently frequent and informative feedback is also required, which yields an additional constraint on models of biologically plausible learning.

q-bio.NC↗