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arxiv: 2607.02317 · v1 · pith:3UXV3QZ7new · submitted 2026-07-02 · 💻 cs.CV

NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity

Yingtian Tang , Sogand Salehi , Ming Zhou , Amir Zamir , Leyla Isik , Martin Schrimpf This is my paper

Pith reviewed 2026-07-03 15:27 UTC · model grok-4.3

classification 💻 cs.CV
keywords video synthesisneural encoding modelsevolutionary optimizationvisual selectivitybrain regionsdynamic stimulifMRI localizers
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The pith

Evolutionary search over video prompts finds stimuli that hyper-activate target brain regions more than handcrafted localizers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a framework that uses evolutionary algorithms to search a space of video prompts for stimuli that maximize predicted activity in chosen brain regions. It relies on a model that forecasts voxel responses to dynamic video inputs to steer the search. The resulting videos activate the target areas more strongly than traditional localizer videos and recover established functional selectivities in ventral, dorsal, and lateral visual pathways. They also expose consistent differences in how these pathways respond to temporal features in the stimuli. This supplies a computational route to generating new probes for studying dynamic visual processing.

Core claim

By performing evolutionary search over a structured prompt space guided by a dynamic encoding model that predicts voxel-level responses to video inputs, the framework generates stimuli that maximize predicted activity for a target ROI, consistently surpassing handcrafted localizer videos while recovering known selectivities across ventral, dorsal, and lateral pathways and revealing systematic differences in sensitivity to temporal dynamics.

What carries the argument

Evolutionary search over structured prompt space guided by dynamic encoding model predictions to maximize target ROI activity

If this is right

  • Synthesized videos recover known selectivities across ventral, dorsal, and lateral pathways.
  • Systematic differences appear in sensitivity to temporal dynamics.
  • Searchlight analysis shows progression toward complex social-dynamic features along the lateral stream.
  • The framework supplies new predictions for in vivo experiments on dynamic visual selectivity.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same search procedure could be used to generate candidate stimuli for testing specific hypotheses about brain function before running actual scans.
  • Applying the method to abstract non-naturalistic videos may isolate the minimal features sufficient to drive selectivity.
  • Extending the framework beyond visual cortex could identify optimized stimuli for other sensory or cognitive regions.

Load-bearing premise

The dynamic encoding model must accurately predict voxel responses to the arbitrary videos produced during search.

What would settle it

Run fMRI on human subjects viewing both the synthesized videos and the handcrafted localizers and compare measured activation in the target ROI; equal or lower activation for the synthesized videos would falsify the claim.

Figures

Figures reproduced from arXiv: 2607.02317 by Amir Zamir, Leyla Isik, Martin Schrimpf, Ming Zhou, Sogand Salehi, Yingtian Tang.

Figure 1
Figure 1. Figure 1: A. Video synthesis framework. We propose NEvo, a framework that iteratively optimizes video-generation prompts to maximize predicted activation in a target brain region. B. Overview of results. By combining dynamic brain encoding with video synthesis, NEvo generates highly activating stimuli, highlights visual dynamics, and enables probing along the lateral visual pathway. More recently, deep neural networ… view at source ↗
Figure 2
Figure 2. Figure 2: A. Evolutionary prompting. Visual attributes are encoded as interpretable genes, decoded into text prompts, and rendered into stimuli by the generation model. Predicted ROI activations from the brain model then guide selection, crossover, and mutation across generations. B. Two-stage search. We identify the best-scoring image through image prompt search (Tab. S1), then use it to initialize image-to-video s… view at source ↗
Figure 3
Figure 3. Figure 3: A. Example synthetic videos from NEvo. Two-second videos optimized to maximize predicted activation in ventral and dorsal/lateral regions. B. Two-stage search over iterations. Optimization trajectories across genetic-search evaluations for each ROI, spanning both image and video search phases. Faded lines denote individual seeds; the solid line denotes the mean. C. Comparison of ROI activations. Predicted … view at source ↗
Figure 4
Figure 4. Figure 4: A. Two-stage search over time. Optimization trajectories across search evaluations and wall-clock time, aggregated across ROIs. B. Dynamic stimuli improve activation. For each ROI, we compare predicted activation between the synthetic video and its first frame presented as a static video. C. Ablation of two-stage search. The two-stage strategy outperforms single-stage search baselines in two representative… view at source ↗
Figure 5
Figure 5. Figure 5: A. Searchlight synthesis. Top-synthesis stimuli are shown alongside word clouds from Gemini-annotated descriptions for example searchlight patch along the lateral stream trajectory (V1 to aSTS). Patch indices are marked on the cortical surface. B. Visual property–activation correlations. Left: Representative correlations between annotated visual-property scores and predicted activations for two example cas… view at source ↗
Figure 6
Figure 6. Figure 6: Controlled synthesis from a non-naturalistic anchor. Starting from a fixed first frame of two stacked plasticine discs, we run video-stage NEvo to maximize predicted pSTS or MT activation. For each ROI, examples show the highest and lowest activation boosts across seeds, measured relative to the static first-frame baseline. pSTS optimization introduces face-like features and coordinated interactions, where… view at source ↗
read the original abstract

The human brain processes dynamic visual input through hierarchically organized, functionally specialized regions. While recent in silico brain encoding models can synthesize optimal stimuli to probe selectivity in different brain regions, prior work has been largely limited to static images, leaving dynamic visual processing underexplored. We introduce a novel neural-guided video synthesis framework that generates stimuli optimized for target brain regions across visual cortex. Our method performs evolutionary search over a structured prompt space, guided by a dynamic encoding model that predicts voxel-level responses to video inputs. By maximizing predicted activity for a target ROI, the framework efficiently discovers hyper-activating dynamic stimuli that consistently surpass handcrafted localizer videos. The synthesized videos recover known selectivities across ventral, dorsal, and lateral pathways, and further reveal systematic differences in sensitivity to temporal dynamics. A searchlight analysis provides new insight into the progression toward increasingly complex social-dynamic features along the lateral stream, further supported by probing with synthesized abstract, non-naturalistic stimuli. Taken together, our framework enables in silico exploration of dynamic visual selectivity, with new predictions for in vivo experiments

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces NEvo, a neural-guided evolutionary video synthesis framework that performs evolutionary search over a structured prompt space, guided by a dynamic encoding model predicting voxel-level responses to videos. By maximizing predicted activity in target ROIs, it claims to discover hyper-activating dynamic stimuli that surpass handcrafted localizer videos, recover known selectivities across ventral/dorsal/lateral pathways, reveal differences in temporal dynamics sensitivity, and provide searchlight insights into progression toward complex social-dynamic features along the lateral stream, supported by abstract non-naturalistic stimuli probes.

Significance. If the encoding model generalizes accurately, the framework offers a scalable in silico method for generating novel dynamic stimuli to probe functional selectivity in visual cortex, extending prior static-image work and generating testable predictions for in vivo experiments.

major comments (2)
  1. [Abstract] Abstract: The central claim that evolved videos 'consistently surpass handcrafted localizer videos' and recover known selectivities requires the dynamic encoding model to accurately predict responses to the novel, complex temporal dynamics and abstract features it generates; no held-out correlation, noise-ceiling, or OOD validation metrics are reported for these stimuli.
  2. [Abstract] Abstract: The searchlight analysis claiming 'new insight into the progression toward increasingly complex social-dynamic features along the lateral stream' is presented without quantitative details on the analysis procedure, statistical thresholds, or how the synthesized abstract stimuli specifically support this progression.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on the validation of the dynamic encoding model and the searchlight analysis. We address each point below and will incorporate clarifications and additional analyses in the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that evolved videos 'consistently surpass handcrafted localizer videos' and recover known selectivities requires the dynamic encoding model to accurately predict responses to the novel, complex temporal dynamics and abstract features it generates; no held-out correlation, noise-ceiling, or OOD validation metrics are reported for these stimuli.

    Authors: We acknowledge the importance of OOD validation for the generated stimuli. The dynamic encoding model was trained and cross-validated on large-scale video datasets with standard metrics, but we agree that explicit evaluation on stimuli with complex temporal dynamics would strengthen the claims. In the revision, we will add held-out correlation, noise-ceiling, and OOD performance metrics for the encoding model on a held-out video set that includes abstract and dynamic features similar to NEvo outputs. revision: yes

  2. Referee: [Abstract] Abstract: The searchlight analysis claiming 'new insight into the progression toward increasingly complex social-dynamic features along the lateral stream' is presented without quantitative details on the analysis procedure, statistical thresholds, or how the synthesized abstract stimuli specifically support this progression.

    Authors: We will revise the methods and results sections to provide full quantitative details on the searchlight procedure (including voxel selection, radius, and cross-validation), statistical thresholds (e.g., cluster-corrected p-values), and specific metrics (such as feature complexity scores or activation gradients) demonstrating how the abstract non-naturalistic stimuli support the observed progression along the lateral stream. Additional supplementary figures will illustrate these quantitative results. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on external encoding model

full rationale

The paper introduces a neural-guided evolutionary search framework that uses a pre-existing dynamic encoding model to predict voxel responses and optimize video stimuli. No equations, parameter fitting steps, or derivations appear in the abstract or described method. The encoding model is treated as an independent input whose accuracy is an external assumption, not derived or fitted within the paper itself. The central claim of discovering hyper-activating stimuli therefore does not reduce to any self-referential construction or self-citation chain. This is the common case of a method whose validity hinges on an external benchmark rather than internal circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; full manuscript required to audit modeling assumptions such as the structure of the prompt space or the encoding model's training regime.

pith-pipeline@v0.9.1-grok · 5732 in / 1134 out tokens · 23758 ms · 2026-07-03T15:27:28.198284+00:00 · methodology

discussion (0)

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