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arxiv: 2604.04439 · v1 · submitted 2026-04-06 · 💻 cs.LG · cs.CV

Estimating Central, Peripheral, and Temporal Visual Contributions to Human Decision Making in Atari Games

Henrik Krauss , Takehisa Yairi This is my paper

Pith reviewed 2026-05-10 19:23 UTC · model grok-4.3

classification 💻 cs.LG cs.CV
keywords Atari gameshuman decision makingperipheral visioneye trackingaction predictionablation studyvisual contributions
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The pith

Peripheral visual information accounts for most of the predictive power in modeling human actions during Atari gameplay, far more than gaze focus or recent past states.

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

The paper tests how much different visual and temporal cues shape human choices in fast-paced games by training action-prediction networks on eye-tracked Atari data under controlled ablations. Removing peripheral vision causes the largest accuracy losses, around 35 to 44 percent median drop, while removing explicit gaze maps or past frames produces much smaller effects. Clustering game states by the models' output probabilities reveals distinct regimes where decisions lean on focus, periphery, or context. The approach shows that people draw on wide-field information well beyond the current point of gaze when acting in dynamic visual settings.

Core claim

Across twenty Atari games, ablation of peripheral information produces median prediction-accuracy drops of 35.27-43.90 percent, exceeding the 2.11-2.76 percent drops from gaze-map removal and the 1.52-15.51 percent range from past-state removal, indicating that human decision making depends strongly on information outside the current gaze location.

What carries the argument

The six-setting ablation framework that selectively includes or excludes peripheral vision, gaze maps, and past-state frames when training action-prediction networks on synchronized eye-tracking and gameplay data.

If this is right

  • Human players in dynamic visual tasks rely predominantly on peripheral cues rather than foveal gaze or short-term memory of recent frames.
  • Game states can be grouped into coarse behavioral regimes such as focus-dominated, periphery-dominated, and context-heavy decisions.
  • The ablation method provides a behavioral route to quantify information-source contributions without direct neural measurements.
  • Models of human action in games should weight wide-field visual processing more heavily than narrow gaze or temporal history alone.

Where Pith is reading between the lines

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

  • Similar ablation designs could be applied to other continuous visual decision tasks such as driving or sports to test whether peripheral dominance is general.
  • The modest effect of gaze maps suggests that explicit eye-position channels add limited value once peripheral context is available.
  • If the upper end of the past-state range reflects reduced peripheral leakage, longer temporal context may matter more in some game types than others.

Load-bearing premise

That differences in action-prediction accuracy across the ablation settings directly reflect how much the human player relies on each information source, without the model architecture or training process introducing systematic bias.

What would settle it

Training an alternative architecture or using substantially more data without peripheral input yet recovering accuracy levels close to the full model would indicate that the observed accuracy drops do not measure information reliance.

Figures

Figures reproduced from arXiv: 2604.04439 by Henrik Krauss, Takehisa Yairi.

Figure 1
Figure 1. Figure 1: The controlled ablation framework performed in this study: Human [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of the human action prediction network with three options of including (I) peripheral information, (II) gaze information, and (III) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Validation action-prediction accuracies across games (left) and median relative performance drops with respect to model A, normalized by the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Mean silhouette scores per game and cluster, with an additional [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Cluster composition across games (upper), mean true-action [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: t-SNE visualization of the clustered six-model response space for [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example states from the two games with the highest silhouette score for each cluster. Focus region of six visual degrees is shaded in blue. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Single-subject comparison for DemonAttack and SpaceInvaders. [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
read the original abstract

We study how different visual information sources contribute to human decision making in dynamic visual environments. Using Atari-HEAD, a large-scale Atari gameplay dataset with synchronized eye-tracking, we introduce a controlled ablation framework as a means to reverse-engineer the contribution of peripheral visual information, explicit gaze information in form of gaze maps, and past-state information from human behavior. We train action-prediction networks under six settings that selectively include or exclude these information sources. Across 20 games, peripheral information shows by far the strongest contribution, with median prediction-accuracy drops in the range of 35.27-43.90% when removed. Gaze information yields smaller drops of 2.11-2.76%, while past-state information shows a broader range of 1.52-15.51%, with the upper end likely more informative due to reduced peripheral-information leakage. To complement aggregate accuracies, we cluster states by true-action probabilities assigned by the different model configurations. This analysis identifies coarse behavioral regimes, including focus-dominated, periphery-dominated, and more contextual decision situations. These results suggest that human decision making in Atari depends strongly on information beyond the current focus of gaze, while the proposed framework provides a way to estimate such information-source contributions from behavior.

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

1 major / 2 minor

Summary. The manuscript introduces a controlled ablation framework using the Atari-HEAD dataset to quantify the contributions of peripheral visual information, explicit gaze maps, and past-state information to human action selection in 20 Atari games. Action-prediction networks are trained in six configurations, revealing that peripheral ablation leads to the largest median accuracy drops (35.27-43.90%), with smaller effects for gaze (2.11-2.76%) and temporal information (1.52-15.51%). State clustering by action-probability vectors identifies regimes such as periphery-dominated and focus-dominated decisions.

Significance. If the ablation successfully isolates human information use, the work provides quantitative evidence that peripheral vision dominates human decision-making in dynamic Atari environments, beyond foveal gaze. The framework offers a reproducible empirical method for estimating source contributions from eye-tracked behavioral data, with the public dataset and post-hoc clustering strengthening interpretability and potential applicability to visual attention modeling.

major comments (1)
  1. [Methods section (ablation framework)] Methods section (ablation framework): The interpretation that accuracy drops (e.g., 35.27-43.90% median for peripheral removal) directly measure human reliance on each information source assumes the six separately trained models are comparable probes. However, each configuration alters the input tensor (full frames vs. masked periphery vs. gaze overlay vs. history concatenation), changing pixel statistics, spatial support, and optimization landscapes. Without explicit regularization, domain-adaptation steps, or architecture-level invariance to these shifts, lower test accuracy on ablated inputs may reflect model convergence issues rather than the absence of that source in human play. This assumption is load-bearing for the abstract claims and the clustering-based regime identification.
minor comments (2)
  1. [Methods] The manuscript would benefit from explicit reporting of model architectures (CNN/transformer details), training hyperparameters, per-game data splits, and exact gaze-map integration procedure to allow verification that the six settings are trained under equivalent conditions.
  2. [Clustering Analysis] Clustering analysis: Additional details on the clustering algorithm, choice of number of clusters, and robustness checks (e.g., silhouette scores or sensitivity to probability-vector normalization) would strengthen the identification of behavioral regimes.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading and for identifying a key methodological assumption in our ablation framework. We address the concern point by point below.

read point-by-point responses

  1. Referee: Methods section (ablation framework): The interpretation that accuracy drops (e.g., 35.27-43.90% median for peripheral removal) directly measure human reliance on each information source assumes the six separately trained models are comparable probes. However, each configuration alters the input tensor (full frames vs. masked periphery vs. gaze overlay vs. history concatenation), changing pixel statistics, spatial support, and optimization landscapes. Without explicit regularization, domain-adaptation steps, or architecture-level invariance to these shifts, lower test accuracy on ablated inputs may reflect model convergence issues rather than the absence of that source in human play. This assumption is load-bearing for the abstract claims and the clustering-based regime identification.

    Authors: We appreciate the referee highlighting this important consideration regarding input distribution shifts. All six model variants use an identical convolutional architecture, the same cross-entropy loss, Adam optimizer, learning-rate schedule, and batch size, and are trained on the same human gameplay trajectories from Atari-HEAD. The full-input baseline achieves high test accuracy across games, indicating that the training procedure converges successfully when the complete visual input is available. The peripheral-ablation condition produces markedly larger and more consistent accuracy drops (median 35.27-43.90%) than the gaze or temporal ablations, an outcome that would be unlikely if optimization difficulties alone were responsible. Nevertheless, we agree that the assumption is load-bearing and that explicit discussion of potential domain-shift effects is warranted. In the revised manuscript we will expand the Methods section to detail input preprocessing and normalization steps and add a paragraph in the Discussion section acknowledging that input-statistic changes could contribute to performance differences while arguing that the relative ordering and magnitude of effects across 20 games still support differential information-source contributions. The clustering analysis operates on softmax probability vectors over the identical action space and therefore remains comparable across configurations. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical ablation on external dataset

full rationale

The paper conducts an empirical study by training separate action-prediction networks on six ablation variants of the Atari-HEAD dataset (full frames, masked periphery, gaze maps, history) and reports accuracy drops as measures of information-source contribution. No equations, derivations, or fitted parameters are defined in terms of the target quantities; the accuracy differences are computed directly from held-out test performance on the public dataset. No self-citation chains, uniqueness theorems, or ansatzes are invoked to justify the central claims. The framework is self-contained against external benchmarks and does not reduce any reported result to a renaming or self-definition of its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that neural-network ablation faithfully isolates human information use; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Differences in model prediction accuracy under selective ablation directly reflect the human player's dependence on the removed information source.
    Invoked when interpreting accuracy drops as contribution estimates.

pith-pipeline@v0.9.0 · 5523 in / 1156 out tokens · 54257 ms · 2026-05-10T19:23:00.532401+00:00 · methodology

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Reference graph

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