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REVIEW 3 major objections 5 minor 147 references

Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A reinforcement-learning agent reproduces human gaze and click decisions in hybrid visual foraging without any training on human data.

desk verdict A valuable new dataset and a credible RL model for hybrid visual foraging, but the behavioral-alignment claims are weakened by a shuffled-versus-static comparison, a tuned saccade parameter, and a mislabeled p-value. read the letter →

arxiv 2411.09176 v3 pith:F6VZDHNH submitted 2024-11-14 cs.AI cs.CV

Bo Wang , Dingwei Tan , Yen-Ling Kuo , Zhaowei Sun , Jeremy M. Wolfe , Tat-Jen Cham , Mengmi Zhang This is my paper · ORCID
classification cs.AIcs.CV
keywords hybridvisualforagingeyemovementsreinforcementlearningvalue-baseddecisionmakingfoveatedvisionsearchtransformerscanpathprediction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

In hybrid visual foraging—searching a cluttered display for multiple instances of several target types that carry different rewards—humans behave as efficient reward foragers: their fixations land in high-reward regions, they linger longer on more valuable targets, and their cumulative rewards approach an optimal upper bound. This paper asks whether an artificial agent can acquire the same gaze-and-click policy purely from the reward structure of the task, without any supervised training on human eye or mouse data. The authors introduce Visual Forager (VF), a transformer-based reinforcement-learning agent with foveated vision, and report that it outperforms all tested baselines, achieves rewards comparable to human participants, and approximates human click biases and saccade-size distributions. If the claim holds, VF is the first model to reproduce human hybrid-foraging behavior—including where the eyes go—from reward maximization alone, and it offers a computational tool for probing the link between oculomotor decisions and value-based choice.

What carries the argument

The load-bearing mechanism is the Visual Forager's value-modulated, foveated policy network. A VGG16 backbone with eccentricity-dependent pooling (replicated from macaque neurophysiology) converts the search image into fixation-dependent feature maps; target features are matched to those maps by convolution to form similarity maps; a learnable value encoder's embedding is added at every spatial location, so that the value of each target type directly reshapes the priority map; and a vision transformer over these value-modulated maps outputs a softmax fixation map plus a click probability, trained jointly by PPO with a two-stage curriculum (first fixations with full resolution and infinite inhibition of return, then clicks with eccentricity-dependent vision and a decaying memory of visited locations). This design lets value and visual similarity interact inside the policy rather than being combined heuristically.

What would settle it

Run VF in a foraging environment where item positions are reshuffled every three seconds, exactly as in the human experiment, keeping all reward and time-cost parameters equal. If VF's normalized scores, click-bias ratios, or saccade-size distributions no longer track the human data under this shuffling, the central claim that VF approximates human foraging behavior without human data is falsified.

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Extended reading notes

Core claim

The central claim is that a value-guided reinforcement learner, with no human behavioral data, can converge on the same foraging policy that humans use when target values and prevalence vary. Specifically, VF reads in a search image, the target templates, and their monetary values; a foveated feature extractor, with eccentricity-dependent pooling modelled on the primate visual system, produces fixation-dependent similarity maps; a value encoder adds target values into those maps; and a transformer actor-critic decides both where to fixate next and whether to click the currently fixated item. Trained with PPO under a curriculum, VF is reported to reach 72.6%, 67.1%, and 81.6% normalized scores in the three in-domain conditions (humans: 87.4%, 84.1%, 93.1%), to share the sign of human click-bias ratios for high- and low-valued targets, and to produce a mean saccade size of 4.06 degrees versus 4.05 degrees for humans, despite zero training on human eye movements. The paper interprets this as evidence that eye-movement policies in hybrid foraging are largely learned from reward structure, and that fixation decisions are a reliable lens on value-based decision-making.

Load-bearing premise

The match between humans and VF assumes that the simplified grid world with fixed item positions and constant per-click and per-fixation time costs faithfully captures human foraging strategy, even though the human experiment shuffled item positions every three seconds and the AI environments did not.

Editorial extensions

If this is right

  • VF achieves human-comparable cumulative rewards and closer-to-human scanpaths than all baselines in time-limited hybrid foraging, suggesting that reward maximization alone can induce human-like search priorities.
  • VF reproduces the human overpicking of high-value targets and underpicking of low-value targets, so value-driven click biases need not be trained from human examples.
  • The saccade-size match implies that human-like fixation step lengths emerge partly from foveated processing rather than from explicit oculomotor constraints.
  • VF generalizes to out-of-distribution conditions—unseen target objects, unseen value ranges, different item counts and set sizes—better than the strongest baseline, indicating that the learned value-modulation policy is transferable.
  • Eye fixation locations and durations carry measurable signatures of reward, giving a non-invasive readout of subjective value during search.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct test of the model's causal claim would be to remove target values from the input: the paper's analysis predicts fixations and click biases should then flatten toward chance, offering a falsifiable dissociation between feature-driven and value-driven guidance.
  • The human experiment shuffled item positions every three seconds while the AI environments did not, so adding the same periodic shuffle to VF's environment would test whether the reported human–AI alignment persists under working-memory load; this is an extension the paper does not run.
  • The VF architecture effectively produces a dynamic priority map that is updated by value after each fixation; that map could be repurposed as a generative model of scanpaths in other multi-target tasks, or as a synthetic data source for training models of attention.
  • The paper's finding that fixation durations scale with target value suggests a natural extension: an augmented VF that predicts fixation durations, rather than only locations, would let the model speak directly to the time-cost trade-offs humans face in foraging.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces a hybrid visual foraging task with varying target values and prevalence, reports a human eye-tracking experiment, and proposes Visual Forager (VF), a transformer-based reinforcement-learning agent that sequentially produces fixation locations and click decisions. The authors claim that VF outperforms all baselines, achieves cumulative rewards comparable to humans, approximates human eye-movement statistics and click biases without supervised training on human data, and generalizes to out-of-distribution conditions. The supplementary material contains implementation details, ablations, and additional analyses.

Significance. If the central claims hold, the paper makes a valuable contribution: it provides a large human eye-tracking dataset for hybrid visual foraging and demonstrates that an RL agent, not trained on human gaze data, can reproduce several qualitative features of human foraging, including value-guided click biases and approximate saccade-size distributions. The central Norm.Score results are clearly presented, VF consistently outperforms the considered baselines, and the authors provide code, data, and models. The generalization tests to unseen targets, values, set sizes, and item numbers are a useful stress test. However, three load-bearing issues—the shuffled versus static environment mismatch, the unsupported fixation-duration significance claim, and the calibrated saccade-size comparison—need to be resolved before the behavioral-alignment claim is fully established.

major comments (3)
  1. [§3.2, Appendix S1.1, Appendix S3.9] The human and AI foraging environments differ in a way that confounds the quantitative human–VF comparisons. Appendix S1.1 states that item positions in human trials are shuffled every 3 seconds, while the AI environments described in §3.2 and Appendix S1.2 do not include any shuffling. The paper restricts its qualitative scanpath example to pre-shuffle data only in Appendix S3.9, but Figures 4 and 5, Table S4, and Figures S4 and S5 report full-trial aggregated metrics such as Norm.Score, CBR, saccade size, and fixation-area rewards. After a shuffle, humans must re-localize targets and may change both fixation and click policies, whereas VF never faces this change. Unless all quantitative human–VF comparisons are also restricted to the interval before the first shuffle, or the AI environment is modified to include shuffling, the reported behavioral alignment may reflect two different tasks rather than similar foraging policies. This issue is load-bearing because the central claim is precisely that VF approximates human gaze-and-click behavior.
  2. [Appendix S3.3, §5.2, Abstract] The fixation-duration claim is not supported by the reported statistics. Appendix S3.3 reports p = 0.13 for UnValEqPre and p = 0.12 for UnValUnPre, yet the text states that "fixation durations are significantly different for targets with different values" in those conditions. With conventional thresholds, p > 0.05 indicates no significant difference. The Abstract and §5.2 rely on this claim when stating that fixation durations are longer on more valuable targets. The authors should either report a properly powered and correctly specified statistical test that supports the claim, or reframe the finding as a descriptive trend and remove it from the paper's central contributions.
  3. [Appendix S1.4, Table S1, §5.3] The saccade-size match is partly calibrated, so the claim that VF "approximates the saccade size distributions of humans, without training on human eye movements" needs qualification. Table S1 shows that the eccentricity-dependent pooling scaling factor β was varied and set to β = 1 because it yields a mean saccade size of 4.06 dva, closely matching the human value of 4.05 dva. Because β was selected on the basis of human saccade data, the favorable saccade-size comparison is not an emergent property of the RL-trained policy alone. The authors should acknowledge this parameter selection explicitly, report results across β values, or provide a principled a priori justification for β = 1.
minor comments (5)
  1. [§3.2 vs Appendix S1.2] There is an inconsistency in the target values used for training: §3.2 states that values are set at 2, 4, 8, and 16, while Appendix S1.2 says values are consistently set at 2, 4, 8, and 12. Please reconcile these statements.
  2. [§5.2 heading] The heading "Eye movements are effected by target values" contains a typo; it should be "affected."
  3. [Appendix S3.3] The p-values in Figure S3 are reported without specification of the statistical test used; please state the test (e.g., ANOVA, Kruskal-Wallis, mixed-effects model) and the associated sample sizes or degrees of freedom.
  4. [Appendix S1.3] The description of OOD - USetSize says "the set size of target objects was manipulated to include either one or two," but the name and surrounding text are ambiguous about whether this refers to the number of target types or the number of instances per type. Please clarify.
  5. [§4.4 and Appendix S3.7] The intrinsic blank-click penalty is introduced with a single value of -0.01, and Table S5 shows that the Norm.Score with this penalty is 72.6 while the model with no penalty achieves 79.9. The text says the penalty "has no significant effect," but the difference of 7.3 percentage points is not negligible; please provide a significance test or a discussion of why this difference is considered non-significant.

Circularity Check

1 steps flagged · score 6.0 of 10

The reported human-like saccade size is selected by tuning the pooling coefficient beta to match humans, so that specific eye-movement 'prediction' is a fitted input; the RL-trained foraging and click-bias results remain independent.

  1. fitted input called prediction [Sec. 5.3 (saccade-size claim); Appendix S1.4 and Table S1 (beta selection)]
    "To examine how the layer-specific scaling factor γl affects search efficiency and average saccade amplitudes, we varied γl by a coefficient β. Tab. S1 shows that increasing β reduces saccade size and cumulative rewards in UnValEqPre. VF with β = 1 best matches human saccades and achieves comparable rewards. ... Despite lacking prior training on human eye movements, VF yields a mean saccade size of 4.06 degree, closely approximating the mean saccade size of 4.05 degree for humans."

    The saccade-size match is not an emergent prediction: the model hyperparameter β in the eccentricity-dependent pooling was swept (β=1,2,4) and β=1 was selected explicitly because it 'best matches human saccades' (human 4.05 dva; β=1 gives 4.06 dva; β=2 gives 2.26 dva, Table S1). Reporting this selected value as 'approximates human saccade size' is a fitted input renamed as a prediction. The circularity is partial: CBR click biases, cumulative-reward ranking against baselines, and fixation-area reward analyses are not fitted to human gaze/click data and retain independent evidential value.

full rationale

The core VF derivation is not circular: fixation and click policies are trained with PPO on procedurally generated foraging environments without supervised human gaze or click labels, and the comparisons against Chance, FeatOnly, MaxVal, AvgVal, DQN, UpperBound, and OOD conditions are independent of the human data. The single clear circular step is the saccade-size 'prediction': Appendix S1.4/Table S1 show the eccentricity-pooling coefficient β was varied and β=1 was chosen because it best matches human saccades, and Sec. 5.3 then reports the 4.06 vs 4.05 dva agreement as if it were an unconstrained approximation. That specific eye-movement claim reduces to model selection. Two non-circular caveats should be weighed in the overall verdict. First, the 776 ms/click and 336 ms/fixation time costs are obtained by linear regression on human response data (Sec. 3.2, Figs. S1-S2) and imposed on the AI, so the 'cumulative rewards comparable to humans' result is partly constrained by imported human timing rather than being a pure prediction. Second, human trials shuffle item positions every 3 s (Appendix S1.1) while the AI environments do not shuffle, and Appendix S3.9 restricts its qualitative scanpath example to pre-shuffle data; the full-trial aggregate metrics in Figs. 4-5 and S3.4 are not similarly restricted, leaving a task-mismatch confound in the quantitative human-VF alignment. These are benchmark-validity concerns, not equation-level circularity, but they reinforce that the strongest human-likeness evidence should be read with caution. Overall, the central RL result has independent content, so the paper is partially circular rather than definitionally forced.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim rests on several hand-set parameters (beta, time costs, IOR decay) and domain assumptions about grid-world validity, foveated-vision fidelity, reward alignment, and feature sufficiency. No new physical entities are introduced.

free parameters (5)
  • beta (eccentricity-dependent pooling scaling multiplier) = 1
    In Tab S1, beta is varied and beta=1 is selected because it best matches human saccade size; the subsequent saccade-size match is therefore not fully independent.
  • time cost per mouse click = 776 ms
    Derived from linear regression on human response times (Fig S1); used to limit the AI's number of clicks per trial.
  • time cost per fixation = 336 ms
    Derived from linear regression on human fixation times (Fig S2); used to limit the AI's number of fixations per trial.
  • IOR decay constant eta = 0.8
    Chosen by hand for finite inhibition-of-return in stage 2; the paper does not report a sweep for this value.
  • intrinsic blank-click penalty = -0.01
    Ad hoc penalty for clicking blank cells; the ablation in Tab S5 shows only a small effect, so it is not load-bearing.
assumptions (5)
  • domain assumption Object search on a 16x16 grid with items from [14] captures the essential structure of hybrid visual foraging
    The paper generalizes from this simplified grid world to conclusions about human foraging; real-world occlusions and scene context are acknowledged as limitations in Appendix S4.
  • domain assumption Eccentricity-dependent pooling from [51] approximates human foveated vision
    VF's foveal processing is borrowed from [51], and beta is tuned on human data, but the pooling structure itself is taken as valid.
  • domain assumption The reward function (target value for a click, -1 for a distractor) matches human utility
    The same reward is applied to humans and models; if humans weight time or errors differently, the comparison is affected.
  • domain assumption Eye movements reflect decision-making processes
    The entire 'eye movements as a lens' argument rests on this standard assumption from the cited literature; it is not independently established here.
  • domain assumption ImageNet-pretrained VGG16 features provide sufficient semantic features for target recognition
    Frozen ImageNet weights are used; the model cannot acquire task-specific features, which may limit the relevance of the human comparison.

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Pith. "Pith review of Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging." pith.science (2026). https://pith.science/paper/F6VZDHNH

@misc{pith2026241109176,
  author       = {Pith},
  title        = {Pith review of: Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F6VZDHNH}},
  note         = {Machine review of arXiv:2411.09176}
}
abstract

Imagine searching a collection of coins for quarters ($0.25$), dimes ($0.10$), nickels ($0.05$), and pennies ($0.01$)-a hybrid foraging task where observers look for multiple instances of multiple target types. In such tasks, how do target values and their prevalence influence foraging and eye movement behaviors (e.g., should you prioritize rare quarters or common nickels)? To explore this, we conducted human psychophysics experiments, revealing that humans are proficient reward foragers. Their eye fixations are drawn to regions with higher average rewards, fixation durations are longer on more valuable targets, and their cumulative rewards exceed chance, approaching the upper bound of optimal foragers. To probe these decision-making processes of humans, we developed a transformer-based Visual Forager (VF) model trained via reinforcement learning. Our VF model takes a series of targets, their corresponding values, and the search image as inputs, processes the images using foveated vision, and produces a sequence of eye movements along with decisions on whether to collect each fixated item. Our model outperforms all baselines, achieves cumulative rewards comparable to those of humans, and approximates human foraging behavior in eye movements and foraging biases within time-limited environments. Furthermore, stress tests on out-of-distribution tasks with novel targets, unseen values, and varying set sizes demonstrate the VF model's effective generalization. Our work offers valuable insights into the relationship between eye movements and decision-making, with our model serving as a powerful tool for further exploration of this connection. All data, code, and models are available at https://github.com/ZhangLab-DeepNeuroCogLab/visual-forager.

Figures

Figures reproduced from arXiv: 2411.09176 by the authors.

Figure 1
Figure 1. Illustrative example of eye movements and decision-making in a hybrid visual foraging task. The image depicts a real-world scenario where the goal is to search piles of coins for multiple instances of target coins with varying monetary values in order to maximize the accumulative monetary reward, within a time-limited environment. Yellow dots and arrows represent the locations and order of eye movements during the s… view at source ↗
Figure 2
Figure 2. Schematic of the hybrid visual foraging experiment. Each foraging trial starts with a 2-second center fixation (omitted here for simplicity), followed by the presentation of target images and their associated values (e.g., a plant valued at 4). To ensure human participants memorize the targets and their values, they must pass a recognition test by selecting all targets among distractors and correctly matching their … view at source ↗
Figure 3
Figure 3. Architecture overview of our Visual Forager. VF consists of three modules elaborated in Sec. 4: visual feature modulation from target images with foveated vision mimicking eccentricity-dependent sampling in human vision (Sec. 4.1), modulation from various values of different targets (Sec. 4.2), and decision-making process with an actor-critic transformer architecture, outputting next fixation locations from predicte… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Humans and AI models are reward-seeking agents. We report the normalized scores (Norm. Score) as a function of click numbers for humans (red), our VF model (blue), and other baseline models (varying gray). Chance is in black. Three experimental conditions of foraging t…
Figure 5
Figure 5. Figure 5: (A) Our VF model has consistent clicking biases with humans. Humans (red) and our VF models (blue) share the same signs of CBR for most targets under UnValEqPre (a) and UnValUnPre (b). Chance (gray) has no preferences over target objects; hence, a CBR of 0. (B) Our VF …

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.