Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Authors: Chen Wei
Chi Zhang
Jiachen Zou
Haotian Deng
Dietmar Heinke
Quanying Liu
Venue: ICML 2025 poster (2025)
Keywords: Perceptual variability
Object Recognition
Behavior Manipulation
Behavioral Alignment
Primary area: applications->neuroscience_cognitive_science
TL;DR: By sampling along neural network perceptual boundaries, we generated images that induce high variability in human decision and can predict and manipulate individual behavior on these samples.

Abstract: Human decision-making in cognitive tasks and daily life exhibits considerable variability, shaped by factors such as task difficulty, individual preferences, and personal experiences. 
Understanding this variability across individuals is essential for uncovering the perceptual and decision-making mechanisms that humans rely on when faced with uncertainty and ambiguity. 
We propose a systematic Boundary Alignment Manipulation (BAM) framework for studying human perceptual variability through image generation. BAM combines perceptual boundary sampling in ANNs and human behavioral experiments to systematically investigate this phenomenon. 
Our perceptual boundary sampling algorithm generates stimuli along ANN perceptual boundaries that intrinsically induce significant perceptual variability. 
The efficacy of these stimuli is empirically validated through large-scale behavioral experiments involving 246 participants across 116,715 trials, culminating in the variMNIST dataset containing 19,943 systematically annotated images.
Through personalized model alignment and adversarial generation, we establish a reliable method for simultaneously predicting and manipulating the divergent perceptual decisions of pairs of participants.
This work bridges the gap between computational models and human individual difference research, providing new tools for personalized perception analysis. Code and data for this work are publicly available.

Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Chen Wei 1 Chi Zhang" !
Jiachen Zou
Haotian Deng'
Dietmar Heinke 2 Quanying Liu
Abstract
3
Cat
Human decision-making in cognitive tasks and daily life exhibits considerable variability, shaped by factors such as task difficulty, individual pref- erences, and personal experiences. Understand- ing this variability across individuals essen- tial for uncovering the perceptual and decision- making mechanisms that humans rely when faced with uncertainty and ambiguity. We pro- pose systematic Boundary Alignment Manip- ulation (BAM) framework for studying human perceptual variability through image generation. BAM combines perceptual boundary sampling in ANNs and human behavioral experiments to sys- tematically investigate this phenomenon. Our per- ceptual boundary sampling algorithm generates stimuli along ANN perceptual boundaries that intrinsically induce significant perceptual variabil- ity. The efficacy of these stimuli is empirically validated through large-scale behavioral experi- ments involving 246 participants across 116,715 trials, culminating in the variMNIST dataset con- taining 19,943 systematically annotated images. Through personalized model alignment and adver- sarial generation, we establish a reliable method for simultaneously predicting and manipulating the divergent perceptual decisions pairs of par- ticipants. This work bridges the gap between computational models and human individual dif- ference research, providing tools for person- alized perception analysis. Code and data for this work are publicly available.
Bird
Figure 1. Human perceptual variability. For the same set of stimuli, individuals often exhibit varied responses, highlighting differences in their visual pereeption. For instance, the digit on the left may be interpreted as a "3" by some and as a "5" by others. Similarly, the image the right might be initially perceived as cat by some individuals, while others may perceive as a bird.
1. Introduction
A core goal of cognitive science is establish models that reflect the relationship between external stimuli and human internal experiences. The development of ANNs has sig- nificantly contributed to this goal, particularly through the latent representations ANNs that have shown a strong correlation with human psychological representations ((Wei et al., 2024a;b; Muttenthaler et al., 2022a; Mahner et al., 2024; Zheng al., 2019; Hebart et al., 2020; Muttenthaler et al., 2022b)). Current studies focus on the phenomenon that even with identical physical stimuli, individuals may ex- hibit significant differences in their perceptual experiences. While such perceptual variability has been extensively doc- umented in complex cognitive tasks (e.g., aesthetic or moral judgments), studies on simple visual decision tasks (e.g.. handwritten digit classification) often neglect such inter- individual differences. As shown in Figure 1, the stimulus the left may be recognized as either a "3" or a "5", This high perceptual variability in human perception has been inadequately explored current research into human vi- sual perception. Inspired by the similarity between ANNs and humans, we hypothesize that the perceptual bound- aries of ANNs are also related inter-individual variabil- ity in human perception and that images generated along these boundaries can evoke divergent perceptual experiences across individuals.
'Equal contribution 'Department of Biomedical Engineer- ing, Southern University of Science and Technology, Shen- zhen, China DDpartmment of Psychology. University of Birming- ham, Birmingham, UK. Correspondence Quanying Liu <li- uqy @ sus.ech.cdu.cn>.
Recent advances in using ANNs as perceptual models have revealed novel relationships between stimuli and human experiences. (Veerabadran al., 2023; Zhou & Firestone,
Proceedings of the 42nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
Perceptual boundary sampling
1. Labeling
2. Aligning
3. Manipulating
Model A recognize
Subject
recognize
B
4
2
0 a
2
S
5
Madel recognize
2
8
9
2
7
21 3 of
7
0
3
q
8
4
8
56 1
66
Q
b 6
Q
6
3
7
9
9 8
/
7
9
5
7
?
3
4
Figure 2. Overview of BAM. We sample images from ANN perceptual boundaries using the perceptual boundary sampling algorithm for subsequent human evaluations. Our approach consists of three main components: 1. Labeling: The images generated by perceptual boundary sampling labeled by human experiments. thus constructing the variMNIST dataset. In this process, single image will be presented to multiple participants: 2. Aligning: Finetuning models with human behavioral data to align them with human perceptual variability the group and individual levels, enhancing behavior prediction accuracy and aligning the models with humans; 3. Manipulating: Employing two individually aligned models, each corresponding to a specific individual, to generate images designed to elicit divergent responses between them, which are then validated through these two human participants.
2019; Elsayed et al., 2018) demonstrated that ANNs not only exhibit vulnerability to small changes but also that these changes can systematically influence human perceptual de- cisions in controlled environments. Similarly, (Gaziv al., 2024) leveraged robustified ANNs to identify low-norm perturbation patterns that significantly disrupt human classi- fication behavior. Methodologicallly, (Feather et al., 2023; 2019) proposed model metamers constructnnnnn stimuli with identical ANN latent representations but dis- tinct appearances-whicl revealed systematic discrepancies between model metamers and human perception from the perspective of representational invariance. Additionally, (Golan et al., 2020; 2023) developed controversial stimuli that provoke clearly distinct responses among two or more models, further exposing their misalignment with human perception. Building on these frameworks, we extend the concept of controversial stimuli from model-to-model com- parisons to human perception, generating stimuli that evoke perceptual divergences among human participants.
validation of their behavioral manipulation efficacy (Sec. 6).
Our principal contributions are:
(I) We develop image sampling method along ANN perceptual boundaries, constructing the variMNIST dataset through human behavioral labeling. Experimental results demonstrate successful induction of high perceptual vari- ability.
(2) We achieve precise alignment between ANN and human perceptual variability using limited behavioral data, proving the computational feasibility of individual differences.
(3) We design a dual-subject controversial generation frame- work that synthesizes stimuli inducing targeted divergent decisions. Human experiments confirm these stimuli signifi- cantly surpass random baselines in behavioral manipulation.
2. Related Works
Researchers have extensively used synthetic images gener- ated by ANNS to study human perceptual space, uncovering differences between model and human perception while re- fining generation techniques to enhance their influence on human cognition. For instance, (Golan et al., 2020; 2023) utilized controversial stimuli to highlight classification dis- crepancies between neural networks. Similarly, (Veerabad- ran et al., 2023) demonstrated that adversarial perturbations could simultancously influence ANN classifications and human perceptual choices, revealing shared sensitivities. However, (Gaziv et al., 2024) found that while standard ANN perturbations fail to impact human perception, robusti- fied ANN models can generate low-norm perturbations that significantly disrupt human percepts.
We propose a systematic framework BAM (Boundary Aligmment & Manipulation framework) for studying hu- man perceptual variability through image generation. As illustrated in Figure 2, BAM builds upon the Perceptual Boundary Sampling (Sec. 3) algorithm and comprises three interconnected steps: 1. Labeling: We sample images from ANN perceptual boundaries and construct the variMNIST dataset through human behavioral experiments, system- atically capturing inter-individual perceptual differences (Sec. 4). 2. Aligning: By fine-tuning ANN models with human behavioral data, we establish computational models of perceptual variability at both group and individual levels (Sec. 5). 3. Manipulating: Using individualized models as adversarial generators, we synthesize controversial stim- uli that amplify perceptual differences, with experimental
Other studies have approached this problem from different
2

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
Models disagree
b
Pa(3
variMNIST
Optional models
Pa(7
(3kx)
Target guidance:
Pretrained Classifier
Pa(3)x)
Pa (a(3)x + Po(7
VIT
Py(3)x)
3 7 +
3
3
(xl2))
VGG
Pa(7
3
CORNet
P=(7[x)
3
7
7
7
LRM
MLP
7
3
Aligned model on each individual
Controversial samples
Pa(3lx)
Diffusion Pri)r:p(x)
IndivNet N
Diffusion model
trained on
PA(7
P3(3))
MNIST
J 7
Models disagree
Figure 3. Sampling on perceptual boundaries. (a) The sample space can be partitioned into four distinct regions based on two classification axes. Taking the digit pair (3.7) as an example, our objective is to generate samples that induce disagreement between models A and B, as illustrated in the figure. The upper region consists of stimuli classified as "7" by model B and "3" by model A, whereas the lower region contains stimuli classified as "3" by model A and "7" by model B. The left region includes stimuli that both models classify as "3," while the right region contains stimuli that both models classify as "7." (b) Utilizing targeted controversial guidance, we constructed the variMNIST dataset. This approach employs classifier guidance on the diffusion model, directing model A toward "3" and model B toward "7," thereby constraining the generated samples to lic perceptual pereeptual boundaries while preserving the diffusion prior. Following model alignment with human perception, this method was further applied to generate controversial samples designed to modulate human perceptual decisions, shown in the lower section of the right panel.
angles. For example, works like (Feather al., 2023; 2019; Nanda al., 2022: 2023) investigated model metamers, revealing fundamental mismatches between model activa- tions and human recognition. Extending beyond perceptual discrepancies, (Fu et al., 2023) introduced DreamSim, a perceptual metric leveraging synthetic data and human ex- perimental data to better reflect human similarity judgments and address shortcomings in conventional perceptual met- rics. Building on such synthetic data and behavioral insights, recent efforts have sought align vision models with hu- man perceptual representations by incorporating human-like conceptual structures, resulting in improved alignment and enhanced performance across diverse tasks (Muttenthaler et al., 2024; Sundaram et al., 2024).
3. Generating Images by Sampling from the Perceptual Boundary of ANNS
The image perturbations that significantly affect ANN per- ception also influence human perception (Gaziv et al., 2024; Veerabadran et al., 2023; Wei et al., 2024a; Muttenthaler et al., 2022a), suggesting that ANNs and humans may share similar perceptual boundaries. Based on this, we hypothe- size that samples on these boundaries (which exhibit high perceptual variability for ANNs) may also lead to ambigu- ous perception in humans, resulting in different internal ex- periences for the same stimuli. You can find the schematic diagram in Figure 3.
We adopted two guidance strategies: uncertainty guidance and controversial guidance. Uncertainty guidance aims to generate images that lie near the perceptual boundaries of classifiers. Its loss function is defined as:
To study the variability of human perception, it is essen- tial that generated images significantly influence human cognition. Given that we sample from the perceptual bound- aries of ANNs, which often contain high noise levels, better methods are needed to ensure that the generated images appear natural. Recently, the fields of adversarial examples and counterfactual explanations in machine learning have adopted effective techniques to help deal with this problem, such as (Jeanneret et al., 2023), (Wei al., 2024b). (Chen et al., 2023), (Jeanneret et al., 2022), (Vaeth et al., 2023), and (Atakan Bedel & Çukur, 2023). These studies use dif- fusion models with training- muining-free guidance Yu et al., 2023; Ma et al., 2023; Yang et al., 2024) as regularizers to intro- duce prior distributions, thereby enhancing the naturalness of generated images and their impact on human perception.
L = H (pi (3)z), g1
where (p, q) the cross-entropy function that measures the discrepancy between the predicted distribution p(y) and the target distribution q(y). The target distribution ensures equal probabilities for two categories (e.g., "3" and "7"), re- sulting in high-uncertainty images. Controversial guidance generates images that cause conflicting predictions between two classifiers. Its loss function is defined as:
L = H (p (v)æ), (v)) H (p2(v)x), g2(w)), where Pı (v/2)) and p2(y)22) (z) are the predicted probability dis- tributions of classifiers and and gı (v) and g2(y) are their
3

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
we employ a classifier-guided diffusion model for image generation. This method produces images that are closer to the true distribution of handwritten digits, thereby signif- icantly enhancing their impact on human perception (see Appendix A.I).
Success
Bias
J
Failure
9
4. Collecting Human Perceptual Variability by Recognition Experiment
10-
Guidance Outcome
Success
4.1. Digit Recognition Experiment
0.8
Bias
Direty
0.6
Dlaraity
Fallure
We used the image dataset generated through uncertainty or controversial guidance and digit judgment surrogate guid- ance(section B.1.3) as experimental samples to measure human behavior in a digit recognition task. An illustration of the experiment procedure can be found Figure 2. For each test image, participants were asked, "What number is this image?" with responses restricted to one of the digits from 0 to 9. We collected the probability distributions of hu- man responses and calculated the average response time and entropy distribution for all test images (Figure A.6). The experiment collected behavioral data from 400 participants, each completing 500 trials, resulting in a total of 200,000 trials across 20,000 stimuli. During data preprocessing. 154 participants were excluded based on Sentinel trials, leaving data from 246 participants (116,715 trials and 19,943 valid stimuli). Using this cleaned dataset, we constructed a high perceptual variability dataset, variMNIST, which serves as foundation for subsequent analysis and modeling.
0.4
A1
0.0
0.5
1.0
1.5
2.0
2.5
Dntmpy
0.0
Demiy
0.4
11.
0.5
1.0
1.5
2.0
2.5
Entropy
Figure 4. Controversial guidance influence human perception. (a) Examples of three types of guidance outcome: success, bias, and failure.
(b) We present the proportion and entropy distribution of our generated datasets based on handwritten digits and natural images. The upper section displays the results for handwritten digits, while the lower section corresponds to natural images. As observed, cliciting human perceptual variability is more challeng- ing for handwritten digits, as human observers tend exhibit high agreement on such straightforward classification task.
4.2. Quantitative Analysis variMNIST
Evaluation metries. To comprehensively evaluate the guiding effectiveness of the generation method, we define three types of guidance outcome, as illustrated in Figure 4a: success, bias, and failure. For the guidance targets or and O2, let p1 and p2 represent the probabilities of participants choosing 01 and 02. respectively.A result is considered suc- cess if p1 + P2 2 80% and min(p1, P2) 2 10%, indicating the generated stimuli guide participants to make a balanced choice between the two targets. result labeled as bias if Pi P2 2 80% but min(p1, P2) 10%, indicating a strong bias toward one target. A result is classified failure if Pi + P2 < 80%, meaning the stimuli fail to guide partici- pants effectively. These definitions allow us to evaluate and compare the performance of different guidance strategies and classifiers.
corresponding target distributions. The target distributions ensure that classifier 1 predicts one category (e.g.. "3") with high confidence, while classifier 2 predicts another category (e.g.. "7") with high confidence, generating controversial images. Figure A.1 illustrates the guidance methods.
Details of additional analyses and comparisons of guidance methods can be found in Appendix A.2. Previous studies have shown that when using generated images to investigate models and human perception (c.g., (Golan et al., 2020: Gaziv et al., 2024; Veerabadran et al., 2023; Feather et al., 2023)), a common issue is the lack of naturalness in the generated images. This often makes the images difficult for participants to recognize, thereby weakening their impact on human cognition (see Figure A.2). Recent research has demonstrated that diffusion models, when used as regulariz- ers, can introduce prior information and help generate more natural images (Jeanneret et al., 2023; Wei et al., 2024b; Chen et al., 2023: Jeanneret et al., 2022; Vaeth et al., 2023; Atakan Bedel & Çukur, 2023). Building on these findings,
ANN variability can arouse human variability. To eval- uate whether the images generated by sampling on the per- ceptual boundaries of ANNs can arouse human perceptual variability, we first calculated the entropy of participants' choice probabilities in the digit recognition experiment. As shown Figure. A.6 (bottom left), the entropy values for
4

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
more than half of the generated images were significantly greater than zero, indicating substantial variability in human choices. This suggests that the generated images success- fully elicited human perceptual variability. Furthermore, as illustrated in Figure. 4b, the average sum of success rate and bias rate across all generated images was close to 80%. This indicates that, in the majority of cases, human choices aligned with either both or one of the guidance targets. This demonstrates that the generation method effectively guided human digit recognition behavior. To further analyze the effects of different models and different guidance methods on the outcome, we compared the outcome results, shown in Figure A.15.
Different classifiers exhibit inconsistent performance. Figure 5b and A.8 compares the fine-tuning performance of five classifiers. On the MNIST dataset, group fine- tuning improved the prediction accuracy of VIT (Dosovit- skiy et al., 2021) and VGG (Simonyan & Zisserman, 2015), while CORNet (Kubilius et al., 2019) and MLP(Rumelhart et al., 1986) remained unchanged, and LRM (Seber & Lee, 2003) showed a significant decrease in accuracy. On the variMNIST dataset, all classifiers exhibited improvements, with VIT and MLP achieving the largest gains and LRM the smallest. Individual fine-tuning further improved all classi- fiers, with VIT and MLP showing the greatest adaptability to fine-tuning, while LRM demonstrated weaker generaliza- tion ability. These results highlight that both group-level and individual-level fine-tuning can significantly enhance classi- fier performance, but the degree of improvement depends the classifier architecture.
5. Predicting Human Perceptual Variability
5.1. Model Fine-tuning for Human Alignment
To align the base models pretrained on MNIST with the performance of both group and individual levels, we adopted a mixed fine-tuning approach with an 80:20 split for training and validation. For individual-level datasets (variMNIST-i), which are subsets of variMNIST corresponding to specific individuals, the validation set was designed to avoid overlap with the group validation set. For group-level fine-tuning, we combined the MNIST and variMNIST datasets in 1:1 ratio, ensuring performance on MNIST while fine-tuning for perceptual variability. For individual-level fine-tuning, we mixed variMNISTTi, variMNIST, and MNIST datasets in 2:1:1 ratio, ensuring the models performed effectively on individual-specifie, group, and original datasets. For each individual, a customized individual model was trained. For the whole procedure, the group model (GroupNett was finetuned from the base model (BaseNet), and the individual model (IndivNet) was finetuned from the group model. See Appendix C.2 for more details.
Human variability can be predicted by models. To eval- uate the alignment between model and human perceptual variability, we analyzed the correlation between model and human entropy, as shown in Figure 5c and A.16. Taking VGG as an example, group fine-tuning increased the Spear- man rank correlation between model and human entropy from p = 0.08 to p = 0.74. This significant improvement indicates that fine-tuning enables the model to better capture human uncertainty, aligning model predictions more closely with human perceptual behavior.
Performance of behavior prediction across images with varying entropy levels. Image entropy reflects task diffi- culty, with higher entropy indicating more challenging sam- ples. To examine the impact of entropy levels on prediction accuracy, we analyzed model performance across varying entropy levels, as shown in Figure 5d and A.17. Both Group- Net and IndivNet outperform BaseNet across all entropy levels, demonstrating that fine-tuning enhances prediction accuracy regardless of task difficulty. Notably, IndivNet's s performance gains over GroupNet are most pronounced for high- high-entropy images, suggesting that individual fine-tuning primarily improves prediction accuracy for difficult samples. These findings highlight the ability of fine- munettuedd models to better handle challenging stimuli, capturing subtle variations in human perceptual behavior more effectively.
5.2. Alignment Analysis on Validation Datasets
Fine-tuning improves both group-level and individual- level prediction performance. As shown in Figure 5a, BaseNet, GroupNet, and IndivNet achieve nearly identical prediction accuracy on the MNIST dataset, indicating no sig- nificant loss of baseline performance after fine- tne-tuning. On the variMNIST dataset, both GroupNet and IndivNet outper- form BaseNet by ~20%. Furthermore, IndivNet achieves additional 55% accuracy improvement over GroupNet the variMNIST-i dataset, demonstrating its superior adapt- ability to individual differences. After individual fine-tuning, accuracy improved for 241 participants, while only par- ticipants experienced a slight decrease, highlighting the effectiveness of individual fine-tuning in adapting to unique participant behavior and capturing human perceptual vari- ability more accurately.
Extended validation on ImageNet. To demonstrate the generalizability of this method, we conducted the same experiments on natural images, instead of only on digits. Following the generation methods outlined in (Gaziv et al., 2024; Wei et al., 2024a;b), we generated and evaluated data based on the ImageNet dataset, with detailed method de- scription provided in the Appendix B.4. It can be observed that these results are consistent with those based on the dig- its. Examples and further analysis of the results are provided
5

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
GroupNet- BaseNet
IndivNet- Grouppet C
p == 0.08
p 0.74
1.0
0.8
Performance Difference
0.3
0.05
1.5
Accuracy
0.2
0.04
Model entropy
0.6
0.03
1.0
0.4
0.1
1.0
0.2
0.01
0.5
0.00
0.0
0.0
MNIST
variMNIST variMNIST-i
MNIST
variMNIST
variMNIST-i
2
Human entropy
Human entropy
d
Legend
0.9
B
d
4
2
1.0
Model
Base madel
0.8
GroupNet
Accuracy
0.7
BaseNet
4
2
Accuracy
0.6
Classifier
IndivNet
0.6
VIT
GroupNet
8
4
8
VGG
0.5
0.4
CORNet
0.4
IndivNet
8
9
8
MLP
low
medium
high
LRM
model
GroupNet
IndivNet
Entropy
Figure 5. Human alignment results. Accuracy of BaseNet, GroupNet, and IndivNet on MNIST, variMNIST, and variMNISTTi. All models performed similarly on MNIST. On variMNIST, GroupNet and IndivNet improved accuracy by ~00 over BaseNet, with IndivNet outperforming GroupNet by ~5% on variMNIST-i. Accuracy improved for 241 participants and decreased for 5 after inividual fine-tuning. (b) Fine-tuning results for five classifiers. On MNIST, group fine-tuning improved VIT and VGG, while others remained unchanged or declined. On variMNIST, all classifiers improved, with VIT and MLP showing the largest gains and LRM the smallest. Individual fine-tuning further improved all classifiers with the same trend. (c) For VGG, Spearman rank correlation between model and human entropy increased from p = 0.08 to = 0.74 after group fine-tuning. (d) Performance BaseNet, GroupNet, and IndivNet of varying entropy levels. The choices from selected subject for the example images are 8, 6, 9, 6, with increasing entropy levels. Here, the gray background indicates that the model's choice is inconsistent with the subject. GroupNet and IndivNet improved over BaseNet on all entropy levels, while IndivNet's gains over GroupNet were focused high-entropy images.
in Appendix B.4.
completing trials designed to test whether the fine-tuned models could effectively guide their decisions in opposite directions. The goal was to evaluate whether the generated stimuli amplified perceptual differences and aligned partici- pants' responses with their respective guidance targets. For each subject pair, approximately 180 controversial samples were generated, ensuring the sample distribution remained as balanced as possible. A total of 18 participants were re- cruited for in-lab experiments, grouped into six sets of three participants each. Within each group, participants were paired in all possible combinations, resulting in three pairs per group and 18 pairs overall. Each participant completed 500 trials in the first round and approximately 360 trials (180 per pair, across two pairs) in the second round.
6. Manipulating Human Perceptual Variablity
6.1. Experimental Paradigm
Building on variMNIST and alignment experiments, we designed paradigm to test whether individually fine- tuned models can amplify perceptual differences and guide decision-making (Figure 6a). This experiment evaluates the ability of targeted stimuli to reveal individual variability and achieve precise manipulation of perceptual outcomes, highlighting the potential of personalized modeling in un- derstanding human perception. For the first round of experi- ments, we initially selected around 500 balanced samples from the variMNIST dataset as stimuli. After collecting be- havioral data from pairs of participants, we fine-tuned their individual models using the method described in Section 5.1. Controversial stimuli were then generated using the updated models, aiming to elicit distinct choices between the two participants, with each choosing their respective guidance targets.
6.2. Manipulating Results
Evaluation metrics. To analyze the effects of individual manipulation, we employed two key metrics. The first met- ric, referred as the guidance outcome (Figure 6b), was adapted from Section 4.2. categorizes outcomes for two participants, 81 and 82, with respective guidance targets on and 02, and choices e₁ and c2. A result is labeled as success if both participants' choices fall within their respec-
In the second round of experiments, these controversial stim- uli were presented to participants in pairs, with each pair
6

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
Subject
recognize
Positive
2
9
Y1
3
5
6
8
Success
Negative
2
-2
3 5h
-
~
5
Subjeet
Y/2
4
2
1
2
7 1
7
0 8
2
9
7 q
Bias
5
5
5
0
8
C
1
9 1
7
8
9
8
9
9
Failure
7
4
?
2
8
9
4
9
b
Guidance Success Rates
Targeted ratios
Guidance
23%
26%
Outcome
50%
0.7
62%
Success
Success Rate
Bias
Failure
45%
46%
0.25
0.6
Directions
50%
Positive
32%
28%
38%
0.15
0.5
Negative
varMNIST
varMNIST
Customized
Customized
varMNIST
Customized
varMNIST Customized
Figure Manipulation analysis. (a) We first utilizee behavioral data from the experiments to fine-tune the models, resulting in aligned models, as depicted in the leftmost section with green and blue regions. Subsequently, controversial guidance is applied, directing one model toward output yn and other toward yz. The generated images are then presented to participants Based on the behavioral responses, the images categorized into three groups: Success, Bias, and Failure. To further analyze the effects the manipulation, the Success category is subdivided into Positive and Negative cases. Representative examples Positive stimuli are displayed in the rightmost section. (b) The middle two bars show the guidance outcomes for variMNIST and the individually customized dataset, which is generated under the guidance of finetuned individual models to better evoke variability and bias participants toward certain directions, with the individually customized datasct achieving higher success rate. The left and right bars further analyze the successful samples, where the dark blue indicates the participant's 's choices aligned with the guidance direction, and the light blue indicates the opposite. Compared variMNIST, IndivNets also improves the directionality of guiding perceptual changes. The left panel shows the guidance success rates for the first-round stimuli and the second-round stimuli generated by the finetuned models, with an improvement of <33% (p < 0.001). The right panel shows the targeted ratios (i.c., the proportion of participant choices aligned with the guidance direction) for these two groups of stimuli, with an increase of ~ 122 % (p < 0.001).
follow-up experiment.
tive guidance targets and are distinct, i.e., C1, {01, and C1 * C2. If both choices are biased toward the same target, such as 61 = = or 02, it categorized as bias. Finally, if at least one choice is outside the tar- gets (cI, 02 & {01,02) 02}), the outcome is labeled as fail- ure. The second metric, called the fargeted ratio (Fig- 6c), quantifies the directionality of successful guidance. Within successful trials, participant choices are classified as either positive, where
Improvement in guidance outcome. We first analyzed the improvements in the guidance outcome achieved through individual manipulation. As shown in Figure. 6, A.27, A.28, A.29, compared to variMNIST, the success rate in the indi- vidually customized dataset increased by 3%, the bias rate increased by 1%, and the failure rate decreased by 4%. Con- sidering that each participant completed only around 200 samples in the experiment, compared to 20,000 samples in variMNIST, this represents very small sample size. There- fore, these results indicate that even with limited sample size, we successfully captured the perceptual differences among individual participants. These findings validate the feasibility and effectiveness of individual manipulation us- ing small, customized datasets, demonstrating that precise modeling and manipulation of human perceptual behavior can be achieved even at low cost.
and c2 02. meaning both choices align with their respective targets, or negative, where c = 02 and C2 = 01, indicating swapped choices. The targeted ratio is defined as the proportion positive trials among all success trials, providing a measure of the effectiveness of directional guidance. We present examples of stimuli demonstrating various guidance outcomes and directions in Figure A.

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
Improvement in guiding directionality. We further eval- uated the guiding directionality in successful trials. As shown in Figure. 6, A.27, A.28, A.29, compared to variMNIST, the target ratio of IndivNets improved by 12%, indicating a significant enhancement in the directional guid- ance achieved with individually customized datasets. This result suggests that individual fine-tuning not only improves the model's guiding capability but also enables more precise directional guidance, leading participants make choices aligned with the intended targets. This finding further vali- dates the effectiveness of the individual manipulation exper- iment, demonstrating that small, customized datasets can achieve more efficient and precise human behavior manipu- lation.
tion, our use of diffusion models with prior distributions allows for broader applicability across various ANN models and perturbation methods. This also expands the range of image sampling, enabling sampling from high-noise areas like perceptual boundaries. Moreover, the incorporation of prior distributions ensures that our generated images more closely resemble natural images, enhancing their effective- ness in influencing human perception. With this significant improvement in the usability and flexibility of generated images, we successfully explored individual differences in human perception and opened the door for personalized manipulation, increasing the efficiency and scope of human perception studies.
From the perspective of computer science methodology, we have made significant improvements upon existing meth- ods, opening new avenues for fields of AI for science and Al-human alignment. Drawing on the controversial stimuli from (Golan et al., 2020) and adversarial perturbations from (Veerabadran et al., 2023), we integrated these concepts with diffusion model priors to create two new loss- guiding meth- ods: controversial guidance and uncertainty guidance. This enhancement increases the naturalness of the generated im- ages and their influence on human perception. Additionally, inspired by works such as (Jeanneret et al., 2023), (Wei et al., 2024a), (Wei et al., 2024b), (Chen et al., 2023), (Jeanneret et al., 2022), (Vaeth et al., 2023), (Atakan Bedel & Çukur, 2023). we introduced counterfactual methodolo- gies into the study of human perceptual variability, allowing us to explore this relatively reser-rese area in greater depth. Our experiments demonstrate that the variMNIST dataset we generated significantly evokes human percep- tual variability, providing a novel approach for aligning Al and human by harmonizing their perceptual variabilities. Furthermore, variMNIST can reveal individual differences among humans, enabling the generation of customized im- ages that reflect these differences through ANNs aligned with individual participants.
7. Discussion
Human decision-making in cognitive tasks and daily life exhibits considerable variability, shaped by factors such as task difficulty, individual preferences, and personal experi- ences. Understanding this variability across individuals is essential for uncovering the perceptual and ppeiiiinnmmakng mechanisms that humans rely on when faced with uncer- tainty and ambiguity. We present computational frame- work that combines perceptual boundary sampling with behavioral manipulation to systematically investigate this phenomenon. By generating stimuli along ANN perceptual boundaries through large-scale behavioral experiments, we construct the variMNIST dataset demonstrating significantly enhanced perceptual variability. Through subject-specific fine-tuning of ANN models using behavioral data, we de- velop predictive models that capture individual perceptual patterns with high fidelity. We implement adversarial gen- eration strategies to synthesize stimuli that systematically shift perceptual boundaries between individuals, enabling targeted manipulation of perceptual judgments. This work bridges the gap between computational models and human individual difference research, providing new tools for per- sonalized perception analysis.
Despite our progress in exploring human perceptual vari- ability, several limitations remain. Our datasets, generated by sampling along ANN perceptual boundaries, cannot fully capture human variability, especially influences like culture, as some ANNs are trained on data from specific groups. To address this, we plan include participants from diverse cul- tural backgrounds for more comprehensive understanding. Furthermore, the dataset is focused on object recognition tasks, while effective for evoking perceptual variability, lim- its the exploration of broader visual phenomena. Expanding beyond object recognition to tasks like similarity judgments, emotion recognition, visual attention, and scene memory could offer deeper insights. However, exploring such com- plex tasks remains challenging given the limited number of trials available in individual behavioral experiments.
From the perspectives of cognitive science and neuroscience, our method significantly enhances the utility and flexibil- ity of generated images in the study of human perception. Unlike the methods employed by (Golan et al., 2020) and (Feather et al., 2023), which reveal the disparities between model and human perception by generating images that strongly affect ANNs while having minimal impact on hu- man cognition, our method is capable of influencing both models and human perception simultaneously. This dual im- pact allows for a nuanced counterfactual examination of the subtle differences in perceptual variability between the two. In contrast to the approaches taken by (Veerabadran et al., 2023) and (Gaziv et al., 2024), which focus on improving ANNS to produce images that can influence human percep-
8

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
In terms of aligning AI with humans, although ANNs fine- tuned with individual behavioral data showed notable im- provement in predicting perceptual variability, there remains significant gap when compared to their performance in standard classification tasks. This indicates that perceptual variability is a promising but underexplored method for Al- human alignment, with ample room for improvement. To address this, we propose incorporating optimal experimental design (Rainforth al., 2024; Foster et al., 2019; 2021) into human experiments, using ANNs finetuned with individual behavioral data to generate customized images that maxi- mize individual variability. These new behavioral data could then be fed back into the training of ANNs, dramatically improving Al-human alignment with fewer experimental trials. This approach would significantly increase the effi- ciency of human behavior data collection, reduce the cost of Al-human alignment, and accelerate the advancement of both cognitive science and artificial intelligence.
References
Atakan Bedel, H. and Çukur, T. Dreamr: Diffusion-driven counterfactual explanation for functional mri. arXiv prints, pp. 23077 2023.
Bansal, A., Chu, H.-M., Schwarzschild, A., Sengupta, S.,
Goldblum, M., Geiping, J., and Goldstein, T. Universal guidance for diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 843-852, 2023.
Chen, X., Gao, X., Zhao, J.. Ye, K., and Xu, C.-Z. Ad- vdiffuser: Natural adversarial example synthesis with diffusion models. In Proceedings of the IEEC/CVF Inter- national Conference on Computer Vision, pp. 4562-4572, 2023.
Dosovitskiy, A., Beyer, I.., Kolesnikov, A., Weissenbom,
D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.1 19299, 2021.
Code and Data Availability
Please see our website https://eaeemmmaaaorrttuubiiooia for a description of our work. Code and data for this paper released htts://gttubb.co/nccaaa- unsteh/FuumaaPerppuuulariia
Elsayed, G., Shankar, S., Cheung, B., Papernot, N., Kurakin, A., Goodfellow, I., and Sohl-Dickstein, J. Adversarial examples that fool both computer vision and time-limited humans. Advances in neural information processing sys- lems, 31, 2018
Ethics
In this study, All participants signed an informed consent form before the experiments. This study was approved by the local ethics committee of Southern University of Science and Technology (Resolution Number: 20240326).
Engstrom, L., Ilyas, A., S., H., S., S., and T., D. Ro- bustness (python library). https: / wiiithub.coo com/ Madrylabbrobustnnee 2019. Accessed: 2025-01- 25.
Acknowledgements
Feather, J., Durango, A., Gonzalez, R., and McDermott, J. Metamers of neural networks reveal divergence from hu- man perceptual systems. Advances in Neural Information Processing Systems, 32, 2019.
This work was funded in part by the National Natural Science Foundation of China (62472206), Shenzhen Sci- ence and Technology Innovation Committee (2022410129, KJZD202309231 15221044) Guangdong Provincial Key Laboratory of Advanced Biomaterials (2022B 12 12010003), and the Center for Computational Science and Engineering at Southern University of Science and Technology.
Feather, J., Leclere, G., Madry, A., and McDermott, J. H. Model metamers reveal divergent invariances between biological and artificial neural networks. Nature Neuro- science, 26 26(11):0017-204, 2023.
Impact Statement
Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., Teh, Y. W., Rainforth, T., and Goodman, N. Variational bayesian optimal experimental design. Advances in Neu- ral Information Processing Systems, 32, 2019.
This work advances the integration of machine learning methods within cognitive science, opening new avenues for modeling human perception. Our algorithms and datasets are intended solely for scholarly research in cognitive sci- ence. We emphasize that they should not be repurposed for deceptive or potential misleading manipulative applications under any circumstances.
Foster, A., Ivanova, D. R., Malik, I., and Rainforth, Deep adaptive design: Amortizing sequential bayesian experi- mental design. In International conference on machine learning, pp. 3384-3395, PMLR, 2021.
Fu, S., Tamir, N., Sundaram, S., Chai, L., Zhang, R., Dekel, T., and Isola, Dreamsim: Learning new dimensions

---

Synthesizing Images Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
of human visual similarity using synthetic data, arXiv preprint arXiv:2306.099 09344, 2023.
Ma, J., Hu, T., Wang, W., and Sun, J. Elucidating the design space of classifier-guided diffusion generation. arXiv preprint arXiv:2310.11311, 2023.
Gaziv, G., Lee, M., and DiCarlo, J. Strong and precise modulation of human percepts via robustified anns. Ad- vances in Neural Information Processing Systems, 36, 2024.
Mahner, F. P., Muttenthaler, L., Güglüü, U., and Hebart, M. Dimensions underlying the representational alignment of deep neural networks with humans. arXiv preprint arXiv:2406. 19087, 2024.
Golan, T., Raju, P. C., and Kriegeskorte, N. Controversial stimuli: Pitting neural networks against each other as models of human cognition. Proceedings of the National Academy Sciences, 11(47))29330-299 29337, 2020.
Muttenthaler, L., Dippel, J., Linhardt, L., Vandermeulen, R. A., and Korbblith, S. Human alignment of neural net- work representations. arXiv preprint 1rX1v:2211.01201, 2022a.
Golan, T., Siegelman, M., Kriegeskorte, N., and Baldassano, C. Testing the limits of natural language models for predicting human language judgements. Nature Machine Intelligence, 5(9):552-944, 2023.
Muttenthaler, L., Zheng, C. Y., McClure, P., Vandermeulen, R. A., Hebart, M. N., and Pereira, F. Vice: Variational interpretable concept embeddings. Advances in Neural In- formation Processing Systems, 35:33661-33673 2022b.
Hebart, M. N., Zheng, C. Y., Pereira, F., and Baker, C. I. Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nature human behaviour, 4(1 1):1 173-1185, 2020.
Muttenthaler, L.,, Greff, K., Born, F., Spitzer, B., Kornblith, S., Mozer, M. C., Müller, K.RR,, Unterthiner, T., and Lampinen, A. K. Aligning machine and human visual representations across abstraction levels. arXiv preprint arXiv:2409.06509 2024.
Ho, J., Jain, A., and Abbeel, P. Denoising diffusion proba- bilistic models. Advances in neural information process- ing systems, 33:6840-666 6851, 2020.
Jeanneret. G., Simon, L., and Jurie, F. Diffusion models for counterfactual explanations. In Proceedings of the Asian Conference on Computer Vision, pp. 858-876, 2022.
Nanda, V., Speicher, T., Kolling, C., Dickerson, J. P., Gum- madi, K., and Weller, A. Mcasuring representational robustness neural networks through shared invariances. International Conference on Machine Learning, pp. 16368-16382. PMLR, 2022.
Jeanneret, G., Simon, I.,, and Jurie, F. Adversarial coun- terfactual visual explanations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16425-16435, 2023.
Nanda, V., Majumdar, A., Kolling, C., Dickerson, J. P., Gum- madi, K. P., Love, B. C., and Weller, A. Do invariances in deep neural networks align with human perception? In Proceedings of the AAAI Conference Artificial Intelli- gence, volume 37, pp. 9277-9285, 2023.
Karras, T., Aittala, M., Aila, T., and Laine, S. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35: 26565 2657,,667,2221 2022.
Peebles, W. and Xie, S. Scalable diffusion models with transformers. In Proceedings the IEEE/CVF Interna- tional Conference on Computer Vision, pp. $195-4208 2023.
Kubilius, J., Schrimpf, M., Kar, K., Rajalingham, R., Hong, H., Majaj, N., Issa, E., Bashivan, P., Prescott-Roy. J., Schmidt, K., Nayebi, A., Bear, D., Yamins, D. L., and DiCarlo, J. J. Brain-like object recognition with high-performing shallow recurrent anns. In Wallach, H., Larochelle, H., Beygelzimer, A., d' Alché-Buc, F., Fox, E., and Garnett, R. (eds.), Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL https://proceediing neuriinnnsnnnnnnnnnno.eee.m cc cc/paper__fllessppeerr//o/ 7813d1 72l.aal1500222277772255555553333 pdf.
Rainforth, T., Foster, A., Ivanova, D. R., and Bickford Smith, F. Modern bayesian experimental design. Statistical Science, 39(1):100- 110,, 14, 2024.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Learn- ing representations by back pack-propagating errors. Nature, 323(6088) 1986.
Seber, G. A. and Lee, A. J. Linear regression analysis. Wiley-Interscience,, 2003.
Liang, Y., He, J., Li, G., Li, P., Klimovskiy, A., Carolan, N., Sun, J., Pont-Tuset, J., Young, S., Yang, F., et al. Rich human feedback for text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19401-19411, 2024.
Simonyan, K. and Zisserman, A. Very deep convolu- tional networks for large-scale image recognition. arXiv preprint arXiv: 1556, 2015.
10

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Er- mon, S., and Poole, B. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations, 2020.
time [0. 7], while the reverse phase reconstructs the image from noise by reversing this process. At any time t, the state 2 is defined as:
Sundaram, S., Fu, S., Muttenthaler, L.,, Tamir, N. Y., Chai, L., Kornblith, S., Darrell, T., and Isola, When does per- ceptual alignment benefit vision representations? arXiv preprint arXiv:2410.1055 10817, 2024.
3t = o4T0 + byct,
(1)
where de Vai, b, V at increases with t, and c No0,, 1). A neural network is trained to predict the added noise:
Vaeth, P., Fruehwald, A. M., Paassen, B., and Gregorova, M. Diffusion- visual counterfactual explanations- towards systematic quantitative evaluation. arXiv preprint arXiv:2308. 06100, 2023.
min Bayger [Ileg(tr, () cel12]
(2)
where the loss depends on the noise and the probability distribution (rre). The reverse process follows an ordinary differential equation (ODE):
Veerabadran, V., Goldman, J., Shankar, S., Cheung. B., Papernot, N., Kurakin, A., Goodfellow, I., Shlens, J., Sohl-Dickstein, J., Mozer, M. C.,, et al. Subtle adversarial image manipulations influence both human and machine perception, Nature Communications, 14(1):4933, 2023.
dx. f(1),, (2 dt
V log))
Wei, C., Zou, J., Heinke, D., and Liu, Q Cocog: Control- lable visual stimuli generation based on human concept representations. In Proceedings of the Thirty-Thire In- ternational Joint Conference on Artificial Inteligence, IJCAI-24, 2024a.
with f(t) = d'loga and g²(1) db? clon This dt
ODE enables the reconstruction of the image by reversing the noise-adding process.
The specific steps for both phases are determined by the sampling algorithm. We use the DDPM algorithm (Ho et al., 2020), where the forward and reverse steps are represented as:
Wei. C., Zou, J., Heinke, D., and Liu, Q. Cocog- 2: Controllable generation of visual stimuli for under- standing human concept representation. arXiv preprint arXiv:2407. 14949, 2024b.
= DDPM (x1-1) and Tz-1 Tt-1= DDPM (Ir).
Yang. L., Ding, S., Cai, Y., Yu, J., Wang, J., and Shi, Y. Guidance with spherical gaussian constraint for condi- tional diffusion. arXiv preprint arXiv:2402.03201, 2024.
A.1.2. CLASSIFIER GUIDANCE
Classifier guidance is also known as Training-free guidance. Using diffusion model and the conditional information y. we define the conditional probability of the generative process as:
Yu, J., Wang, Y., Zhao, C., Ghanem, B., and Zhang, J. Free- dom: Training free energy guided conditional diffusion model. Proceedings of the IEFF/CVF International Conference on Computer Vision, pp. 23174-23184, 2023.
meery--guided
p(x,ly) p(ylar))p(z))
Zheng, C. Y., Pereira, F., Baker, C. I., and Hebart, M. N. Re- vealing interpretable object representations from human behavior. arXiv preprint arXiv:
p(y)
where x, is the generated stimuli at time step
2019.
The gradient of this probability is calculated as follows:
Zhou, Z. and Firestone, C. Humans can decipher adversarial images. Nature communications, 10(1):1334, 2019.
Vn, log pa (z, v) V ve log):(xz)) + V log Pz
In the training-free approach, we utilize network So and define loss function ((fo(z:)), v) for conditional genera- tion. Thus, we obtain:
A. Details of perceptual boundary sampling
A.1. Classifier guidance diffusion model
logp:(y V., ((o(())())))))
1.1.1. DIFFUSION MODEL.S.
In the reverse sampling process, we introduce a correction step:
Diffusion models (Song et al., 2020; Karras et al., 2022) consist of two main phases: forward and reverse. The for- ward phase transforms an image into Gaussian noise over
Tt-1 DDPM (v,)

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
XT
a
Vf 3) + V 7)
Uncertainty Stimuli
Stimuli
1
3 or 7?
Diffusion
XT
Forward
b
Backward
VH f
),3) + VH 222
0,
Controversial
Stimuli
fi
Stimuli
3?
1
f2
Diffusion
7?
Figure I. Guidance methods. (a) The uncertainty guidance. It utilizes the classification uncertainty of the generated images from model to guide diffusion model in generating stimuli toward specific directions. Model f ensures the image a digit. (b) The controversial guidance. It employs the classification differences between the generated images from model and model fa to guide the diffusion model generating stimuli toward specific directions.
Therefore we can generate certain stimuli by designing the loss function To obtain stimuli that can disrupt human perception, we explored two potentially suitable approaches: uncertainty sampling and controversial sampling. High uncertainty sampling aims generate stimuli that challenge the model's judgment, while controversial sampling seeks to produce stimuli that maximize the difference in probability distributions between two models.
multiples of 100 for each targeted guidance. This ensures that stimuli corresponding to positions from to 9 x to (i.e., covering all guidance directions) are included, thereby maximizing sampling uniformity. In the generation the guidance scale is set to 0.1, resampling steps is set 5, and the inference steps is set to 50.
When generating stimuli using this guidance strategy, en- sure that each term in the loss function is effectively utilized. While this approach guarantees category category-balaneee balanced sampling during the generation process, the final retained stimuli may not necessarily exhibit category balance. For the stimuli intended for human digit recognition experiments, we apply additional filtering the generated images. Specifically, for uncertainty sampling, we require that the top two p-values exceed 0.4 and the digit surrogate score is above 0.5. For controversial sampling, we ensure that the classification out- puts of both classifiers correspond the intended guidance direction, with the highest p- p-value exceeding 0.9 and the digit surrogate score above 0.5. A detailed analysis of the fil- tered dataset derived from uniform sampling was performed, and the distribution of category counts is presented in Figure
A.2. Details of guidance algorithms
1. DETAILS OF TARGETED GUIDANCE
In targeted guidance, specify directions for the guidance. For instance, at position (3, 5), uncertainty guidance directs towards both categories 3 and 5. For controversial guidance, classifier directed towards category 3, while classifier is directed towards category 5. Similarly, position (5, 3), uncertainty guidance directs towards both categories 5 and 3. For controversial guidance directs classifier 1 to- wards category 5 and classifier 2 towards category 3. To ensure balanced targeted guidance, we generate samples in
12

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
A.13. By comparing this distribution with the cognitive data shown in Figure A.14, 44, a correlation can be observed.
where DKL represents the KL divergence between two dis- tributions. We conducted experiments on five models. For uncertainty sampling, we generated stimuli for each model, resulting in five groups of stimuli. In the controversial sampling experiments, we pitted the models against each other in pairs, creating ten groups of stimuli. However, this approach can be perceived as manipulating one class into another, which is similar to our goal of sampling along the perceptual boundaries of ANNs, but not exactly the same. In generation, the guidance scale is set to 0, resampling steps is set to 5, the inference steps is set to 50, and a is set to 50.
A.2.2. THE ROLE OF DIFFUSION PRIOR
In previous studies that employed generated images to in- vestigate model and human perception (Golan et al., 2020; Gaziv et al., 2024; Veerabadran et al., 2023; Feather et al., 2023)), a common issue was that the generated images lacked sufficient naturalness and failed to significantly in- fluence human perception. This issue is particularly cru- cial within the context of our research objectives. Using previous methods often resulted in images that were unrec- ognizable to human participants, leading to nearly random classification results (see Figure. A.2). Recent advances in adversarial examples and counterfactual explanations in machine learning (Jeanneret et al., 2023; Wei et al.. 2024b; Chen et al., 2023; Jeanneret et al., 2022; Vaeth al., 2023; Atakan Bedel & Çukur, 2023)) have addressed this issue by employing diffusion models as regularizers to introduce prior information. This technique allows for the generation of natural images capable of influencing human perception.
A. 2.4. TARGETED GUIDANCE VS. UNTARGETED GUIDANCE
Untargeted guidance focuses solely on increasing the vari- ability of the generated images, without considering the overall distribution of the images. We adopted an untargeted guidance method to generate stimuli for the digit recogni- tion experiment. To sample at the perceptual boundary of the model, we drew on previous research and proposed two guidance methods: uncertainty guidance and controversial guidance. Uncertainty guidance ensures that the generated images are close as possible to the model's perceptual boundary by maximizing the entropy of the classification probability distribution of single ANN model for the gener- ated images, thereby obtaining images with high perceptual variability for the model. For uncertainty guidance, this can represented as:
Inspired by these advances, we utilize a classifier-free dif- fusion model as the core of image generation process. By sampling noise from the target dataset (MNIST) distribution and feeding it into the diffusion model for denoising, we effectively incorporate prior information. This approach enhances the naturalness of the generated images, making them more reflective of the real distribution of handwrit- ten digits and thereby increasing their impact on human perception, as shown in Figure. A.2.
e((x)) v) = Hy)
A.2.3. EDITING EXISTING DATASETS VS. GENERATING DATA FROM SCRATCH
The process of adding MSE loss to the loss function can be seen as editing existing datasets. The MSE loss is used to constrain the pixel space of the stimuli. Without the MSE loss, the model is more likely to sample from distributions of stimuli that are very similar in pixel space within a certain class. We aim to enforce constraint in the pixel space that encourages the stimuli to be closer to the original distribu- tion of randomly sampled samples from the MNIST dataset.
Where H is the entropy, y is the output probability of the neural network. Controversial guidance, on the other hand, utilizes two different ANN models and generates images that maximize the KL divergence between their classification probability distributions, thereby maximizing perceptual differences between the models. For controversial guidance, this can be represented as:
This approach is intended to enhance the diversity of the generated stimuli. We define a as the pixel-level restraint
((f(rz)))) = Dww(p(mnla))
scale.
For uncertainty guidance with MSE constraint, we have:
Where DEL is the KL divergence, p(y is the output probability of the first neural network, p(y2 is the output probability of the second neural network. In generation the guidance scale is set to 0.1, resampling steps is set to 5, and the inference steps is set to 50. Targeted and untargeted guidances are compared in Figure.A.11 Losses with and without MSE are also compared in Figure, A.10.
{(f(z)). v) H (ylk,) + aall,, 1
where H represents the entropy, represents the strength of the MSE loss. For controversial guidance with MSE constraint, we have:
((f(x)) v) =
+ allare - 1
13

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Guidance without prior
Guidance with prior
VAE
0
0
5
0
0
0
0
0
5
6
6
6
6
6
6
/
1
J
1
8
1
]
,
5
5 6
9
6
6
6
6
6
6
2
2
2
2
à
2
2
2 3 8 5 6 Y ?
2
5 5
5
6
6
6
6
J
3
3
J
5
6
3
3
5 5 5
5
8
2
2
2
2
2
U / 6
1
4
4
u
1
4
8 8
5
5
3
3
2
2
2 7 5
2 8 9 9
5
\
5
5
5
6
9
5 6
8 8
8
8
3
3
5
8
0 6
C 7
b
D
6
6
8
8
8
8
8
8
5
5
17 7
1
4
7
7
8
2
7
7
7
7
3
8
4
0
8
8
8
0
5
?
8 8
7
7
7
7
4
4
4
4
9
9
9
f
9
9
4
9
C,
9
9
9
7
7
4 4
4
9 9 9
Figure A.2. Comparison of method with other approaches. Images generated by diffusion model without prior distribution exhibit severe noise. Images produced by Variational Autoencoder (VAF) show minimal differences and are generally blurry. Our method (with prior), however, yields images that only clear and noise-free but also exhibit substantial diversity.
B. Details of collecting human perceptual variability
process is conducted using a single GPU (NVIDIA GeForce RTX 4090) with the Adam optimizer.
B.1. Model configuraration and training
The data loaded into the training pipeline using PyTorch DataLoader with batch size of 128, andthe number of worker threads for data loading is set to 128. The model is trained for 150 epochs, with learning rate of 1e - 4 and an unconditional training rate of 0.1, and the weight decay is not applied. Dropout is applied the class embedding with a probability of 0.1, while the model does not learn the variance (sigma).
B.I.I. DIFFUSION MODEL
Configuration of DiT. The Diffusion Transformer (DiT) (Peebles & Xie, 2023)) is Transformer- -basodd diffusion model tailored for generative tasks. In our configuration, the model processes 28 28 grayscale images using patch size of 2 x 2, resulting in patch embeddings transformed into sequences of hidden size 128, with 1 input channel and 10 output classes. The architecture includes Transformer layers with 8 attention heads per layer and an MLP ratio of 4.0.
1.1.2. CLASSIFIERS
The classifier models were trained on the MNIST dataset using 28 x 28 grayscale images, normalized with the "ToTen- sor' transformation. Training and testing sets were loaded with a batch size of 100, and the models were implemented with 5 different configurations (VIT, VGG, MLP, CORNET, LRM, see Table. A. map input images 10 output classes. Training was performed
DIT incorporates Patch Embedding, Timestep Embedding, and Label Embedding modules. These embeddings are com- bined with fixed sinusoidal positional encodings to provide spatial and temporal context. AdaLN (Adaptive Layer Nor- malization) layers condition the model on timestep and label embeddings, with zero-initialized manipulation for training stability.
NVIDIA GPU us- ing the Adam optimizer (Ir = 1 x 10 for 16 epochs, and CrossEntropyl.oom function was used to compute the classification loss.
The model outputs spatial predictions through final linear layer followed by an unpatching operation, restoring the input image dimensions. Classifier-free guidance is sup- ported by computing conditional and unconditional outputs, enabling control over generated samples.
B.1.3. DIGIT JUDGMENT SURROGATE
Digit judgment experiment. We used the synthetic im- ages as experimental stimuli to measure human behavior in digit judgment task. The purpose of this experiment was to collect human judgments on whether given image qualifies as a digit, thereby establishing a human criterion for handwritten digit. For each image, participants were asked the question, "Is this image a digit?" with responses limited to "True" or "False." More experimental details can
Training of Diffusion Model. For prior diffusion model, we use the MNIST dataset as the training dataset. The dataset consists of grayscale images of size 28 x 28, which are directly used without further resizing. The training
14

---

Synthesizing Images on Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
Table A. Configurations and MNIST Accuracy of Classifiers
Model Name
Model Type
MNIST Accuracy (%)
VIT
Vision Transformer
97.2
VGG
Small VGG
98.2
CORNet
CORnet-Z
98.9
MLP
Multi-Layer Perceptron
98.3
LRM
Logistic Regression Model
92.7
be found in Appendix B.2.1.
examples reveal that as the predicted score increases, the images progressively resemble digits more closely. These results demonstrate that the digit judgment surrogate effec- tively simulates human digit judgment behavior.
The experiment collected behavioral data from 400 partici- pants, comprising 200,000 trials and 20,000 stimuli. During the data cleaning process, 124 participants were excluded based on Sentinel trials, leaving data from 276 participants (138,000 trials and 19.878 valid stimuli). This dataset pro- vides robust foundation for analyzing perceptual standards for handwritten digits.
Guiding generative process by digit judgment surrogate. For any given image, we use the frequency of participants responding "True" as the probability of the image being digit. The initial image dataset, along with the corre- sponding probabilities, was used to train a digit judgment surrogate. As the previous works of image generation by human preferences ((Liang et al., 2024; Bansal et al., 2023)), this surrogate, functioning as a image quality predictor, was then employed to guide the image generation process (see Appendix B.1.3). The guidance formula can be expressed as:
Training of digit judgment surrogate. For the training of the digit judgment surrogate model, we constructed a dataset based on the results of the human digit judgment experiment. Specifically, for any given image, the frequency of participants responding "True" was taken as the proba- bility of the image being judged as digit. These images and their corresponding probabilities were then used to train the digit judgment surrogate. The dataset was split into a training set and a test set in a ratio 8:2.
Caurr C (1ax((1-aaaa (x)) 0.5)
In this formula, Laur represents the total loss. foure (x) represents the probability give by the digit judge model. The probability of the digit judge is combined to the formula to ensure the generated image is considered as digit by humans. The max function used so that when the score is above certain threshold, the gradient of the digit judge will not effect generation.
The surrogate model is based on the SmalIVGG architec- ture, with final output layer designed for regression tasks. The model was trained using the AdamW optimizer with learning rate of 0.001, and the mean squared error (MSE) was used as the loss function. The training process lasted for 8 epochs with a batch size of 128. After each epoch, the validation loss was monitored, and the model with the best validation performance was saved for further evaluation.
B.2. Online human behaviroal measurement
Performance of digit judgment surrogate. To ensure the
B.2.1. DIGIT JUDGMENT
validity of the digit judgment surrogate's 's predictions, we computed the correlation between the predicted scores and human scores. For any given image, the human score was defined as the frequency of participants responding "True," indicating the image is a digit, while the predicted score was the probability assigned by the model classifying the image as a digit.
We use the initial synthetic dataset experimental stimuli measure human behavior in the judgment task. The purpose of the experiment is to collect human judgments on whether any given image test is a digit, in order to filter out images that do not meet the standards of handwritten digits.
Task paradigm Before the formal experiment, partici- pants will first complete a pre-experiment. Each round of the pre-experiment consists of two stages. (1) Selection Stage: A test image appears at the center of the screen, with two buttons labeled "True" and "False" displayed below it. Participants are required to judge whether the image rep- resents a number. (2) Feedback Stage: After making their choice, participants will receive feedback below the image
As shown in Figure. A.3a, the Spearman rank correlation coefficient between the predicted scores and human scores is 0.8035. This indicates that the model's digit judgment is highly consistent with human. Additionally, Figure. A.3b presents image examples corresponding to different pre- dicted scores. For scores of 0010, 0.25, 0.50, 0.75, and 0.90, eight samples were randomly selected for each score. The
15

---

Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
a
b
/
3
0.10
1.0
Predicted score
0.25
/
2
8
7
7
2
7
0.5
0.50
0
2
T
I
6
0.0
0.75
8
B
4
8
Q
9
0.0
0.5
1.0
0.90
g
5
S
6
2
8
8
9
Human score
Figure A.3. Performance digit judgment surrogate. (a) The predicted scores and human scores show a strong correlation. For any given image, the human score is defined as the frequency of participants answering "True" for the image being digit, while the predicted score is the model's probability of classifying the image as a digit. The Spearman rank correlation coefficient between the two scores 0.8035. (b) Examples of images with different scores. For predicted scores of 0.10, 0.25, 0.50, 0.75, and 0.90, samples randomly displayed for each score. As the score increases, the images increasingly resemble digits.
indicating whether it is number. The pre-experiment in- cludes a total of 10 rounds, after which participants will proceed to the formal experiment. In formal experiment, participants performed multiple rounds of choice task (see Figure. A.4). Each trial consisted of two phases: (1) Fix- ation Phase: A black cross was displayed the center of the screen for 300 ms to direct participants' attention to the center. (2) Selection Phase: A test image appears at the center of the screen, with two buttons labeled "True" and "False" displayed below it. The positions of the buttons were fixed and remained unchanged throughout the trials. Participants were asked judge whether the image repre- sents figure by selecting the corresponding button with the mouse pressing the key the keyboard (A represents True and D represents False). There was no time limit for responding. Each session of formal experiment comprised 500 trials, divided into two types: (1) Sentinel trials (n = 10), in which participants are shown set of pre-selected MNIST images, i.e., the correct response should be True. We screened participants based their accuracy the sen- tinel trials to ensure high-quality responses. (2) Random Trials (n=490), where images were randomly selected from the dataset, excluding the fixed images. The two trial types were presented in a random alternating order. No feedback was provided after participants made their selection. The experiment was programmed using JSPsych, with stimuli presented via the JuPsych--sychophysicee component.
(N=400) and collected data through the NAODAO platform. Prior to the experiment, participants read an informed con- sent form detailing any potential risks associated with par- ticipation. Participants were allowed to withdraw from the experiment at any time. No personal identification informa- tion was collected. We only included data from participants with sentinel trial accuracy greater than 70%, resulting in data from 276 participants and 135240 trials involved in the following analyses.
B.2.2. DIGIT RECOGNITION EXPERIMENT
We used the filtered synthetic dataset as experimental stim- uli measure human behavior in digit recognition task. The goal of the experiment tribution of human choices for any given test image. In this task, participants were presented with ten possible choices, represented by the digits 0 to 9.
to collect the probability dis-
Task paradigm Participants performed multiple rounds of a category comparison task. Each trial consisted two phases (see Figure, A.5): (1) Fixation Phase: A black cross was displayed the center the screen for 300 ms direct participants' attention the center. (2) Selection Phase: test image appeared the center of the screen, accompanied by ten labeled buttons below it, with labels ranging from O to 9. The positions of the buttons were fixed and remained unchanged throughout the trials. Participants were asked to identify the digit the image by selecting the corresponding button with the mouse or pressing the number key on the
Human data collection The experiment got ethics ap- proval from the local University, We recruited participants
16

---

Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
a
b
9
True
Febe
+
Make choice
Click choose press choose True and press lo choose false
300ms
Figure A.4. Human digit judgment experiment procedure. In each trial, participants first observe fixation cross ("+") for 300 milliseconds. Following the fixation, stimulus image is presented along with clickable buttons labeled "True" and "False". Participants are instructed to judge whether image represents digit and either click the corresponding button or pressing the key keyboard (A represents True and D represents False). The images shown to participants are generated by our model. After each selection, no feedback provided, the next trial begins immediately. Each participant first performed rounds of pre-experiments feedback, followed by 500 formal trials without feedback, including 10 sentinel trials and 490 random trials.
keyboard. There was time limit for responding.
B.3. additional dataset details
Each session comprised 500 trials, where images were ran- domly selected from the dataset. No feedback was provided after participants made their selection. The experiment was programmed using JSPsych, with stimuli presented via the JSPsych-Psychople component.
B. EVALUATION METRICS
Judgment distribution. As shown in Figure. A.6 (top left), evaluated the distribution of human judgments across the ten digit classes (0-9). The results indicate that the probabilities are relatively uniform, with all categories exhibiting values close to 0.1. Notably, digits 0, 6, and 9 were judged with slightly higher probabilities (around 0.15) compared to other digits, while digits through demonstrated lower probabilities (around 0.06).
Human data collection The experiment got ethics ap- proval from the local University. The experiment collected behavioral data from 400 participants through the NAO- DAO platform, comprising 200,000 trials and 20,000 stim- uli. Prior to the experiment, participants read an informed consent form detailing any potential risks associated with participation. Participants were allowed to withdraw from the experiment at any time. No personal identification in- formation was collected. During data preprocessing, 154 participants were excluded based on Sentinel trials (accu- racy 0.7), leaving data from 246 participants (123,000 trials and 19,952 valid stimuli). Table A.2 and Table A.3 shows the stimuli distribution across guidance strategies and classifier. Using this cleaned dataset, we constructed a high perceptual variability dataset, varIMNIST, which serves as a foundation for subsequent analysis and modeling.
RT and entropy. We further examined the relationship between response time (RT) and entropy gain insights into the cognitive process underlying human judgments. RTs were predominantly distributed between 500 and 1500 ms, following long-tail distribution, indicating that most decisions were made quickly, with a few requiring signifi- cantly more time (Figure. A.6 (top right)). The entropy of human judgments primarily concentrated near 0, reflecting high confidence about half of the trials. Values between 0.5 and 2 also appeared, indicating uncertainty guity (Figure. A.6 (bottom left)). A positive correlation (Spearman rank correlation coefficient = 0.55) was observed between entropy and RT, suggesting that higher uncertainty in judgment often corresponds to longer decision times (Fig- ure. A.6 (bottom right)).
ambi-
17

---

Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
b
3
2
300 ms
1
5
make a choice
+
choose the most probable digit
300 ms
Figure A.5. Human digit recognition experiment procedure. In each trial, participants first observe a fixation cross ("+") for 300 milliseconds. Following the fixation, stimulus image is presented along with 10 clickable buttons representing the digits 0 9. Participants are instructed to identify the most likely digit represented by the image and either click the corresponding button or press the corresponding number the keyboard. The images shown participants are generated by our model. After each selection, no feedback is provided, and the next trial begins immediately. Each participant completes a total of 500 trials, consisting of 10 sentinel trials and 490 random trials.
Classifier configurations influence the guidance outcome. We evaluated how different classifier configurations affected the guidance outcome under controversial guidance condi- tions. The overall guidance success was determined by measuring the probability that participants selected digit x when the model guided the judgment toward x. As shown in Figure. A.7 (left), the results show that CORNet and VGG achieved the highest success rates, both nearing 0.6, indi- cating their strong ability to influence human judgments. VIT and MIP followed with moderate success rates of ap- proximately 0.3, while LRM had the lowest success rate at around 0.2, reflecting its weaker guidance capability.
targets. Target pairs such as (1, 7), (1, 2), and (4, 9) demon- strated the highest success rates, each exceeding 0.35. This suggests that these pairs may align better with human per- ceptual biases or model representations, leading to more effective guidance. Conversely, pairs such as (1, 8), (2, 9), and (7, exhibited the lowest success rates, with values below 0.03, indicating greater difficulty in guiding these pairs. These findings highlight the importance of selecting appropriate guidance targets to maximize the effectiveness of the generated stimuli.
B.3.2. ADDITIONAL VISUALIZATION RESULTS
Further analysis compared the guidance outcome differ- ences between classifiers when used adversarial pairs in controversial guidance (see Figure. A.7 (right)). CORNet and VGG consistently outperformed other classifiers, show- ing significantly higher success rates. In contrast, LRM exhibited the lowest success rates compared to other clas- sifiers. These findings suggest that the choice of classifiers significantly impacts the effectiveness of controversial guid- ance, with certain architectures like CORNet and VGG being more effective at aligning human responses with their intended guidance.
We generated 900 images using both targeted and untar- geted approaches under the guidance of uncertainty and controversial methods. A t-SNE analysis conducted the targeted and untargeted methods for both the con- troversial and uncertainty approaches. To ensure fairness, the t-SNE analysis was performed directly on the raw pixel space for dimensionality reduction. The results are shown in Figure 12. can observed that the distribution more uniform when targeted guidances are adopted.
B.4. Details of validation on ImageNet
1.4.1. METHOD
Guidance targets influence the guidance outcome. We analyzed how different guidance targets influenced the guid- ance outcome, defined as the proportion of successful stim- uli generated for each target pair. As shown Figure. 1.1, the results revealed significant variability across guidance
Similar to handwritten digits, we constructed high percep- tual variability dataset consisting of nine classes based on the ImageNet dataset. Consistent with (Gaziv et al., 2024).
18

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
0.25
0.15
0.20
Probability
Probability
0.15
0.10
0.10
0.05
0.05
0.00
0.00
1
3
5
7
9
2000
4000
Human Judgment
Response Time (ms)
P 0.55
0.4
Response Time (ms)
3000
Probability
0.3
0.2
2000
0.1
1000
0.0
0
1
2
2
Entropy
Entropy
Figure A.6. Behavioral results of the Digit recognition task. Top left: In the digit recognition task on the variMNIST dataset, human judgment probabilities are relatively uniform, with values close to 0.1 for each category. Among these, digits O, higher probabilities, while digits to 5 have lower prohabilities. Top right: Human response times for the digit recognition task are concentrated between 500 and 1500 ms, showing long-tail distrihution. Bottom left: The entropy human judgment results primarily distributed around 0, with additional values observed between 0.5 and 2. Bottom right: Entropy and response time exhibit positive correlation, with a Spearman rank correlation coefficient of 0.55.
have relatively
we utilize the restricted ImageNet dataset that contains nine classes. Meanwhile, we refer to the generation schemes presented (Wei al., 2024a) and (Wei et al., 2024b). The CLIP latent was first generated with controversial guidance before, and then fed into the second stage diffusion model to generate images. The classifier models were constructed by adding additional linear regression layer to the origi- nal CLIP model's 's image encoder. The finetune process was only conducted on the additional layer, with the original parameters CLIP unchanged. Following the procedure of collecting human perceptual variability based on the MNIST dataset, the models were first finetuned on the group level, then finetuned the individual level based the group level model.
B.4.2. RESULTS
We show some example stimuli in Figure A.20. After gen- erating the stimuli using controversial guidance, an online experiment was conducted and we show the result of our analysis in Figure A.22. Our guidance successfully achieved the goal of sampling on the perceptual boundary. Then grouplevel finetuning and individual level finetuning conducted, and the results are shown in Figure A.24. An increase between the group-level finetuned model and the individual-level finetuned model is observed, showing that individual differences also exist in the task of classifying natural images. After individual finetuning, we also tested controversial generation the individual-finetunee models, with the results shown in Figure A.25. can be observed
19

---

Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
0.8
Model 1 LRM MLPCORNeMGG VIT
-0.34
-0.41
-0.02
0.22
0.4
0.6
0.34
-0.09
0.46
0.53
0.2
Difference
0.41
0.09
0.46
0.52
0.0
0.4
0.02
-0.46
-0.46
0.20
-0.2
0.2
-0.22
-0.53
-0.52
-0.20
-0.4
0.0
VIT
VGG CORNet MLP Model 2
LRM
Noise CORNet VGG
VIT
MLP
LRM
Ceiling
Figure A.7. Guidance outcome under different classifiers configurations. Left: Overall controversial guidance outcomes for different classifiers. Under controversial guidance conditions, the success rate is measured the probability that participants chose digit when the model guided the judgment to x. CORNet and VGG achieved the highest success rates, nearing 0.6, followed by VIT and MLP with success rates of approximately 0.3. LRM had the lowest success classifiers during controversial guidance (when using two classifiers as adversarial classifiers, the difference in the guidance outcome of one classifier and the other). CORNet and VGG exhibited significantly higher success rates compared other classifiers, while L.RM showed notably lower success rates than the rest.
around 0.2. Right: Differences in guidance outcomes among
that images at some positions cause variability and have the potential to manipulate human perceptual variability.
tuning. For classifiers other than LRM, individual fine-tuned models achieved greater accuracy improvements on high- entropy images compared to group-tuned models, indicating that individual fine-tuning is particularly effective for chal- lenging stimuli.
C. Additional Results of predicting human perceptual variability
C.1. Effects of Fine-Tuning Across Classifiers
Estimation of the effects of finetuning different datasets. As illustrated in Figure A.26, increasing the vol- ume of training data yields diminishing returns for Group- Net accuracy, whereas IndivNet accuracy continues to im- prove more substantially. This suggests that the available data are largely sufficient for GroupNet, but remain insuffi- cient for IndivNet. Our dataset was deliberately designed to capture perceptual variability, resulting in many samples that GroupNet struggles to predict reliably. To better un- derstand these effects, accuracy can be decomposed into three sources of uncertainty: group epistemic uncertainty, individual epistemic uncertainty, and aleatoric uncertainty. Here, individual epistemic uncertainty reflects variability arising from individual differences, while aleatoric uncer- tainty corresponds intra-subject variability. However, be- cause repeated presentations of the same stimulus introduce trial-to-trial interference, quantifying the precise impact of aleatoric uncertainty is challenging. Therefore, we estimate that individual epistemic uncertainty accounts for at least a
Prediction accuracy. As shown in Figure. A.8, on MNIST, group/individual fine-tuning resulted in slight accu- racy improvements for VIT and VGG, while CORNet and MI.P showed no significant changes. LRM''s accuracy de- creased after fine-tuning, indicating limited generalization. On variMNIST, all classifiers exhibited significant accuracy gains after fine-tuning, highlighting the benefits of group and individual fine-tuning for datasets with high perceptual variability.
Model and human entropy. Figure. A. 16 highlights the entropy and
changes in correlation between human behavioral entropy before and after fine-tuning. A positive correlation was observed across all baseline clas- sifiers, indicating that even in the baseline condition, mod- els capture human perceptual variability. Fine-tuning on variMNIST significantly enhanced this correlation, demon- strating improved alignment with human perceptual vari- ability.
C.2. Model Fine- Tine-Tuning
Impact of image difficulty. As shown Figure. A.17, fine-tuned models outperformed baseline models across all entropy levels, confirming the general effectiveness of fine-
For group-level fine-tuning, The original classifier models were trained on the mixed (ratio 1:1) MNIST, variMNIST datasets using x 28 grayscale images, normalized with
20

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
a
b
C
Model
1.0
1.0
1.0
Base model
GroupNet
0.8
0.8
IndivNet
Accuracy
Accurae
0.6
Accuracy
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
CORNet MLP
LRM
VGG CORNet MLP
LRM
CORNet MLP
LRM
MNIST
varMNIST
varMNIST-
Figure A.8. Correlation between model entropy and human behavior. (a) Prediction accuracy on MNIST before and after fine-tuning for different classifiers. VIT and VGG show slight improvements in accuracy after group/individual fine-tuning, CORNet and MLP exhibit no significant changes, while LRM experiences decrease accuracy post-finettnning (b) Prediction accuracy on variMNIST before after fine-tuning. All five classifiers demonstrate substantial improvements in accuracy after group/individual fine-tuning. (c) Prediction accuracy on variMNIST-i before and after fine-tuning. All classifiers show moderate improvements in accuracy after group/individual fine-tuning.
the 'ToTensor' transformation. For individual-level fine- tuning, the dataset is mixture of variMNIST-i, variMNIST and MNIST at ratio of 2:1:1 and the initial model is the group model. Training and testing sets were loaded with batch size of 128, and the models were implemented with different configurations (see Table. A.1) to map input images to output classes. Training was performed on an NVIDIA GPU using the AdamW optimizer (Ir x 10-3) for 16 epochs, and CrossEntropyLoss function used compute the classification loss.
D. Details of manipulating human perceptual variability
We present the result of further analysis(Predictinn accuracy, success rate and targeted ratio) on each subject pair in Figure 18. To illustrate the examples on different positions in the perceptual space and demonstrate the manipulation results, we present some examples the manipulation stimuli in Figure A.27, A.28 and A.29.
C.3. Clustering analysis
There is large variability in the subject's digit recogni- tion behaviors, since participants differ in high-level factors such culture, ethnicity, educational background, regional customs, and psychological states. We hypothesize that participants could be grouped into several clusters, with participants within the same cluster likely to exhibit similar perceptual variability. To test this hypothesis, we used each participant's subject-finetuned model to predict the behavior of all participants, and calculated inter-subject similarity matrix based on the prediction results. The better the pre- diction performance, the higher the inter-subject similarity. As shown in Figure. A. 19a, the similarity matrix between participants revealed the existence eight distinct clusters. Furthermore, we observed that the subject-inntuned models performed better in predicting the behavior of participants within the same cluster (in-cluster) compared those out- side the cluster (out-cluster), as shown in Figure. 19b... Our results indicate that the clustering is valid and that there are indeed high-level percept differences between participants.
21

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Table A.2. Stimuli Counts before Experiment
Guidance Strategy
Classifier
Stimuli Count
Controversial
CORNet LRM
1000
CORNet MLP
1000
1000
VGG.CORNet
1000
VGG_LRM
1000
VGG MLP
1000
ViT
1000
VIT LRM
1000
ViT MI.P
1000
VIT VGG
1000
Uncertainty
CORNet
2000
LRM
2000
MLP
2000
VGG
2000
ViT
2000
Sum
20000
Table A.3. Stimuli and Trial Counts after Experiment
Guidance Strategy
Classifier
Stimuli Count
Trial Count
Controversial
CORNeLLRRM
997
5766
CORNet MLPP
996
5688
997
5684
VGG.CORNet
995
5806
VGG_LRM
994
5767
VGG_MLP
999
5823
VIT CORNet
997
5865
VIT_LRM
995
5949
VIT MLP
999
5811
VIT_ VIT_VGG VGG
999
5881
Uncertainty
CORNet
1994
11631
LRM
1992
11668
MLP
1997
11849
VGG
1996
11710
VIT
1996
11817
Sum
19943
116715
22

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
a
Perceptual boundary
1. Uncertainty
2. Controversial
3
5
0.5
Diffusion model
T
b
Digit judgment experiment
this picture digit?
Surrogate
Diffusion
a cligit
7
True
variMNIST
Digit recognition experiment
6
4
2
What digit this picture?
19,943 images
8
2
3
4 5 2
246 subjects
7 0
2 2
7
1
116,715 trials
7
0
9
Figure A.9. Generating images clicit human perceptual variability. (a) The example illustrates two guidance methods sampling from the perceptual boundary between "3" and "5" in ANN: uncertainty guidance and controversial guidance. Specifically, Uncertainry guidance aims to make the ANN model f assign equal probabilities to "3" and "5," while controversial guidance generates images classified "3" by fi but as "5" by fz. One of these guidance methods is incorporated into the image generation process. (b) The synthetic images were used in a digit judgment experiment where participants answered, "Is this picture a digit?" We trained digit judgment surrogate based human responses and used it classifier to guide the image generation process. (c) We used the images synthesized using the two guidance methods, ANN perceptual boundary sampling and digit judgment surrogate, for the digit recognition human experiment. Participants asked "What digit is this picture?" total of 19,952 images were used, with 123,000 trials conducted across 246 participants, resulting in the high perceptual variability dataset variMNIST.
23

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Uncertainty without MSE
Uncertainty with MSE
3
4
<
c
0
9
3
1
4
1
9
7
7
2
5
2
0
b
7
1
y
5
2
4
3
3
1
/
1
Y
3
8
/
1 6 1 1 6661 8 ? 8
9
9
1
7
6
6
9
9
}
4
9
/
5
/
0
7
5
5
5
0
2
0
9
3
7
6
1
7
3
3
9
Y
7
4
8
6
5
8
4
0
8
1
7
5
1
5
1
4
L
3
7
9
9
9
1
(
9
5
0
9
9
1
$
h
0
3
4
5
8
5
3
0 /
9
3
9
1
9
0
4
6
6
0
4
6
1
8
9
9
8
O
9
8
9
3
Controversial without MSE
Controversial with MSE
1
5
&
7
4
3
S
3
9
/
6
7
5
7
4
1
4
9
4
0
0
4
1
5
P
8
b
8
}
7
3
1
/
1
9
9
1
5
9
5
3
Y
7
9
0
/
9
// 1/ 7
9
4
7
0
2
1
6
9
7
5
7
4
e
0
9
9
9
1
5
7 0
0
4
8
8
1
6
9
5
0
7
f
9
1
8
1/ 3
9 /
O
4
9
9
6
9
6
1
8
9
G
4 4 9
1
f
9
4
0
0
4
1
1
3
9
9 1
9
5
?
7
7
e
9
J
I
/
/
9
6
6
/
0
1
3
/
Figure A.10. Comparison of generated images with/withon MSE loss. The losses here are all untargeted.
24

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Untargeted uncertainty
Targeted uncertainty
3
2
6
9
0
7
0
0
1
0
7
1
A
8
1
0
/ )
9
3
/
p
d
3
A
_
0
7
a
9
3
) d 5< 6 0
7
1
0
9
5
9
1 {
6
9
J
9
d
\
d
7
0
}
6
5
L
Y
r
4
7
/
5
0
6
8
S
b
1
6
J
1 1
0
7
d
1
,
y
1
6
4
6
9
0
7
7
1
{
{
-
6
1
0 0 6
9
7
9
0
8
7
8
f
5
b
1 9
0
9
)
1
9
2
8
5
(
9
d
4
0
J
6
Untargeted controversial
Targeted controversial
?
8
8
7
9
7
0
3
0
5
7
0
9
0
6
3
7
4
7
2
J
4
6
2
[
9
4
2
6
0 }
Y
6
9
9 5
7 1
5
5
6
R
8
7
0
1 1 3 w 1 7 8 9
2
0
8
5
?
4
9
T
1
9
0
/
6
y
9
?
9 9
J / 7 5 P
7
S 4 7
{
9
5
J 3 1 8 1
1
/
6
8
9
0
2
1
1
1
}
f
d
9
7 8 6 9 0
9
0
/
3
L
3
/
/
1
2
8
6 7
1
/
A
9
4
f
d
9
1
2
9
4
5
0
7
/
0
a
U
0
b
0
0
Figure A.II. Examples of generated stimuli.
Untargeted guidance
Targeted guidance
Controversial
Uncertainty
Figure A.12. t-SNE analysis the pixel space for the generated images of different guidance methods, From the figure it is obvious stimuli from targeted sampling are distributed more uniformly.
25

---

Synthesizing Images Perceptual Boundaries of ANNS for Uncovering and Manipulating Human Perceptual Variability
Uncertainty sampling
Controversial sampling
samplis
10.0
3:3
803
314
705
C50
373
635
504
940
2:32
125
364
562
693
723
Co4
820
707
21.3
221
271
121
307
1180
133
222
5/5
201
583
1004
334
221
881
357
949
691
522
912
2003
-500
546
452
336
454
502
629
E0:
921
96L
206
141
605
542
567
595
520
13.05
564
201
624
624
574
664
400
879
640
601
543
71.
721
605
629
574
202
605
52.5
333
11:0
300
223
350
413
377
743
500
624
380
673
300
444
433
202
713
50.5
364
506
210
608
504
743
1000
22:
530
801
921
811
Figure 1.13. Category distribution in uncertainty sampling and controversial sampling. Certain target pairs yield a higher number of stimuli that successfully pass the filtering criteria. instance, target pairs such as (4, 9) and (8, 9) consistently produce more valid stimuli under both uncertainty and controversial sampling. In contrast, pairs like (1. 5) and (1. 3) result in significantly fewer stimuli meeting the filtering requirements.
0.06
0.09
0.08
0.15
0.05
0.25
0.31
0.09
0.19
0.35
0.06
0.36
0.21
0.22
0.10
0.10
0.37
0.01
0.08
0.30
2
0.09
0.36
0.06
0.12
0.08
0.08
0.23
0.05
0.02
0.25
3
0.08
0.21
0.06
0.15
0.19
0.06
0.31
0.05
0.09
Success Rate
4
0.15
0.22
0.12
0.15
0.24
0.08
0.23
0.04
0.35
0.20
5
0.05
0.10
0.08
0.19
0.24
0.15
0.18
0.09
0.29
0.15
0.25
0.10
0.08
0.06
0.08
0.15
0.00
0.19
0.04
0.10
0.31
0.37
0.23
0.31
0.23
0.18
0.00
0.03
0.17
0.09
0.01
0.05
0.05
0.04
0.09
0.19
0.03
0.12
0.05
0.19
0.08
0.02
0.09
0.35
0.29
0.04
0.17
0.12
0.00
0
1
3
4
7 8
Figure A.14, The success rate (proportion of successful stimuli) varies significantly across different guidance targets. Pairs such as (1. 7). (1, 2), and (4, 9) achieve the highest success rates, exceeding 0.35. In contrast, pairs such (1, 8), (2, 9), and (7, 8) have the lowest success rates, falling below 0.03.
26

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
b
a
Success
1.0
0.8
4
0.6
0.4
Bias
0.0
All
Uncertainty Controversial
0.10
Legend
Guidance Outcome
0.08
Success
Bias
Failure
0.06
Failure
0.04
Classifier
0
VIT
J
0.02
VGG
CORNet
0.00
MLP
LRM
Figure A.15. Quantitative analysis of variMNIST. (a) Examples of three types of guidance outcome: success, bias, and failure. (b) Guidance outcomes across strategies and classifiers. The average of overall success and bias rates approaches 80%. Controversial guidance achieves higher success rate than uncertainty guidance. with similar bias rates. CORNet performs best in uncertainty guidance, while LRM performs worst. In controversial guidance, combinations of VGG and CORNet achieve the highest success rates and lowest bias rates, but exhibit relatively high bias rates when paired with other classifiers.
VGG
Model entropy
2.0
P-0.05
Model entropy
1.5
R=0.08
Model entropy
1.5
P-0.16
Model entropy
1.5
P-0.05
entropy
2.0
p=
0.04
1.0
1.0
1.0
1.0
0.5
0.5
0.5
0.0
0.0
0.0
0.0
0.0
2
2
Behavioral entropy
entropy
entropy
Behavioral entropy
entropy
VIT
VGG
CORNet
MLP
LRM
2.0
2.0
2.0
2.0
Model entropy
Model entropy
Model entropy
Model entropy
entropy
1.5
1.5
1.5
1.5
1.0
1.0
1.0
0.5
0.5
0.0
p-0.67
0.0
p-0.74
0.0
p-0.72
0.0
P-0.72
0.0
0-0.13
Behavioral entropy
Behavioral entropy
Behavioral entropy
Behavioral entropy
Behavioral entropy
Figure A.16. Correlation between model entropy and human behavior. (a) Positive correlation between the entropy caleulated from participants' hchavior and the entropy predicted by the model for visual stimuli across five models. Fach blue dot represents an image stimulus, and the red line shows the fitted result. (b) Significant improvement the correlation between behavioral entropy and model-predicted entropy after fine-tuning on variMNIST, across five models.
27

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
VI
VGG
CORNet
MLP
LRM
Model
1.0
1.0
1.0
Grouphiet
0.8
IndivNet
0.6
0.0
0.4
bw
medium
Nigh
bw
medium
high
low
medium
high
bow
medium
high
low
medium
high
Figure A.17. Correlation between model entropy of different classifiers and human behavior. Prediction accuracy of different classifiers on images with varying entropy levels before and after fine-tuning, For all five classifiers, fine-tuned models show significant improvements in accuracy across all entropy levels compared to the bascline models. For the four classifiers other than LRM, the improvements of individual fine-tuned models over group-fine-tuned models are primarily observed on high-entropy images.
a
b
C
Prediction Accuracy
0.7
Success Rate
0.30
Target Ratio
0.6
0.25
0.5
0.20
0.15
varMNIST
Customized
varMNIST
Customized varMNIST
Customized
Figure A.18. Detailed analysis of each subject pair. (a) Prediction accuracy of individual models trained on in-lab participants. showing consistent improvement fine-tuning. The original models are group models fine-tuned on variMNIST. (b) Comparison of guidance success rates between variMNIST and customized stimuli, indicating notable improvement for the majority of subject pairs. (c) Target ratio comparisons on variMNIST and customized stimuli, demonstrating an increase across nearly all subject pairs.
b
0.9
0.8
Accuracy
0.7
0.7
0.6
0.6
0.5
0.5
0.4
Out-cluster
In-cluster
In-subject
Figure A. Subject clustering analysis. (a) Subject similarity matrix and clustering results. The subject-finese finetuned model was used predict the entire variMNIST dataset, and similarity between subjects computed based on their prediction results. The left axis and gray boxes indicate subjects belonging to the same cluster, with a total of cight clusters. (b) Performance of the subject- finetuned model in predicting data from different groups: out-cluster, in-cluster, and in-subject correspond to different clusters, the cluster, and the subject itself, respectively. Each point represents the average prediction performance a subject on data from the corresponding group, and The black line represents the average of all subjects.
28

---

Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Monkey
Turtle
Insect
Dog
Cat
Frog
Bird
Fish
Crab
Dog
Cat
Frog
Turtle
Bird
Monkey
Fish
Crab
Insect
Figure A.20, Examples of stimuli for the natural image based experiment. We only use the categories from the Restricted ImageNet (Engstrom al., 2019).
29

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Examples of label pairs with the highest Success rate
Bird Monkey
Crab Insect
Frog Turtle
Figure A.2I. Examples of stimuli for the natural image based experiment that have the highest value of uncertainty. We only the categories from the Restricted ImageNet (Engstrom et al., 2019).
0.10
2500
Response Time (ms)
0.08
Probabiltyy
0.15
Probability
2000
0.06
0.10
1500
0.04
0.05
1000
0.02
0.00
0.00
500
2000
4000
2
1
2
Response Time (ms)
Entropy
Entropy
Figure A.22, Further analysis on the samples. show the entropy distribution of the samples. On the right, show the correlation between the entropy and the response time. can be observed that the entropy and the response time are correlated.
left we show the distribution the response time of samples, the middle we
1.0
0.8
Legend
Density
0.6
Density
Guidance Outcome
Success
0.4
Bias
0.2
Failure
0.0
All
0.0
0.5
1.0
1.5
2.0
2.5
Entropy
Figure A.23. Guidance outcome of the Imagenet based experiment. The left part shows the proportion the Success, Bias and Failure The right part shows the entropy distribution of the samples.
30

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Performance on the
Performance on the
Group-Level Validation Set
Individual-Level Validation Set
0.65
0.65
0.60
0.60
Accuracy
Accuracy
0.55
0.55
0.50
0.50
Base model
GroupNet
GroupNet
IndivNet
model
model
Figure A.24, Performance of models on different validation sets. The accuracy of the group model on the group-level validation set increased by about percent and the individual-Ievel finetune inereased the accuracy by about percent, which aligns with the results MNIST.
31

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
Individual 1
Monkey
Dog
Cat
Frog
Turtle
Bird
Fish
Crab
Insect
Dog
Cat
Frog
Turtle
Individual 2
Bird
Monkey
Fish
Crab
Insect
Figure A.25. Controversial generation results on individually finetuned models.
32

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
1.00
0.95
Relative accuracy
0.90
0.85
0.80
vit
vit group
0.75
vgg
vgg group
cornet
0.70
cornet group
mip
0.65
mip group
-1.0
-0.8
-0
-0,4
-0.2
0.0
Log Current Ratio
Figure A.26. Relative accuracies (improvements at reduced data volume//mprovemeet at full data volume) v.s. data volume. can he observed that increasing data volume has diminishing returns GroupNet accuracy, whereas IndivNet accuracy continues to improve significantly.
33

---

Synthesizing Images Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
212/7(7) (313/5(5) (12/414)
(9/33 (2224((4
(7)73333 (212/8(8) (212/8(8) (22/9 (333/9(9)
2
5
X
5
2
7
0
8
Q
3
(717/9(9)
(4)4/8(8) (11//7(7) (0)0/9(9) (3134((4 ()555(5) (818/6(6
(4)4/((7)
2222/7(1) (4)4/7(7)
9
7
S
8
7
2
4
Guidance
(9)9/8(8) (3)3/9(9) (2)2/9(9) 3310/333
(44/9(99 ())6/
7 (010/919)
(8 (899 (7)7/9(9)
Outcome
3
9
Q
3
4
S
3
a
p
9
Success
Blas
Failure
32
(818/((2) (1)1/7(7)
Directions
2
1
Positive
Negative
(1331133) (2)1/2(1) (518/5(8) (217/2(7) (019/0/9) (210/2(0) (016/0r6) (0)3/013)
(0)7/0(7) (719/719)
1
2
9
45
13
(15511(5)
1
50
(8188(33) (8)5/5(5)
(014/4(4) (0)4/4(4) (4)7/7(7) (813/3(3) (414/4(0) (5)5/5(8)
8
5
5
3
4
A
4
3
S
(116/6(8) (5)2/5(4) (5)4/5(7) (315/8/9) (618/6(3) (212/5(3) (11//((4
(818/610)
(511/2(2) (2)/6610
57
055820
S
5
5
1
0
K
V
Figure A.27. Examples of manipulation stimuli for subject 1 and subject The left part of the figure shows the actual numbers of each category stimuli. The real stimuli used to manipulate the subjects are shown on the right. The choices of the subjects are in the middle, with the guidance label marked in parentheses. All positive and negative examples presented, along with 10 typical bias and failure cases.
34

---

Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
(21//000 (2)2/2(5) (8)8/1
( ()/ (8)8/6(6) (6)6/2(2 (717/3(3 (666/5(5) (313/1(1
3
S
9
8
3
5
(13/S(S) (7)7/0(0) (4)4/1(1)
(818/0(O) (919/5(5) (7)7/1(1) (717/0(0) (212/1(1) (3)3/4(4) (8)8/4(4)
3
7
Y
5
7
2
4
4
Guidance
(818/1(1) (112/1(3) (8)8/616)
(77/4444) (414/9(9)
(6)6/((1) (12/0((0 (18/0(0) (919/D(0) (8)8/2(2)
Outcome
Y
2
6
7
4
/
0
Success
Bias
32
(6)6/2(2)
Failure
2
Directions
Positive
Negative
(812/8(2) (1)7/1(7) (911/9(1) (910/9(0) (819/8(9) (8)4/8(4) (810/8(0) (419/4(9) (310/3(0) (8)7/8(7)
0
7
9
9
9
3
7
51
19
(810/8(0) (816/866)
(719/(99 (4))/4(1) (710/70) (4)0/4(0) (412/4(2) (615/6(5) (7)1/7(1
0
6
9
Y
5
1
56
(666/6(9)
(0)8/8(8) (716/6(6)
(914/4(4)
(1)5/5(5)
(010/018) (919/9(1) (4444(33 (711/1(1) (716/6/6)
6
8
C
4
S
0
9
4
1
O
917/2(7) (219/((9) (712/1(2) (112/1(4) (112/0(0) (2)7/0(7) (618/6(3)
(1 (144/(9) (5)9/5(7) (6)4/1(4)
51
S
7
6
C
?
3
4
S
4
Figure A.28. Examples of manipulation stimuli for subject and subject The structure the same Figure A.27,
35

---

Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering and Manipulating Human Perceptual Variability
(77/211) (717/4(4) 099/0 (6)6/2(2) (6)6/5(5) (7)
(55/9199 (5)5/4(4) (3)3/1(1 (818/4(4)
7
7
Q
2
6
7
(717/0(0) 313/0
88800
919/0/01
(1) (33/00
666/0
()//1( (7)7/010) (515/2(3)
2
5
6
Q
9
J
6
5
Guidance
Outcome Success
(818/9(9) (3335555 (3131((1) (7)7/4(44 (6662((2 (9)9/((7) (6)6/1(1) (616/(
28
0
3
3
7
6
9
6
Failure
Directions
Positive
Negative
(810/8(0) (618/618) (9)4//(4) (7)0/710) (3)0/((0) (610/6(0) (4)2/412) (910/9/0) (5)4/5(4) (819/8(9)
6
q
4
45
17
(37/3377
(113//13) ()4//964) (6/0/8/)) (4)3/4(3) (814/8/4) (B)4/8/4)
7
4
e
K
K
54
())
(15/5())
(33/377) (4)4/4(6) (3)5/5(5) (014/4/4) (715/5(5)
(6)8/8/8)
2
R
4
5
B
4
(4)7/4(0) (214/4(0)
(9)9/6(3) (2)3/0(5)
(215/2(4) (2)1/2(5) (315/4(4)
(3)5/2(5) (3)6/2(2)
50
5
H
4
3
5
<
4
S
2
Figure A.29. Examples of manipulation stimuli for subject 2 and subject 3. The structure the Figure A.27.
36


Reviews:
[{"review_id": "mUj8w0F4ZM", "reviewer": "Reviewer_62LP", "summary": "This paper studies the human perceptual judgements by generating controversial stimuli that lie close to the boundary between different classes. The experiments first show that finetuning vision networks on data collected from human judgements enables these models to better capture the human behavior. They then show that using these models they can generate new stimuli that can sometimes specifically bias individuals towards particular choices.\n\n\n\n## update after rebuttal \nSeveral issues were clarified. The discussion helped with understanding the practical issues in scaling the approach to natural images. I increased my score by 1 as a result. The authors agreed to make changes to address the issues and unclarities.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to reviewer 62LP\n\nWe sincerely thank the reviewer for the positive feedback. Below we address the reviewer’s concerns and questions:\n\n1. **Comment:** The success rate in selective manipulation of human behavior is relatively low, showing limited success in using the proposed approach. This is especially true for handwritten digits with ~20% success rate. Surprisingly, while the initial experiment using natural images seem to be much more successful, none of the following experiments were conducted using natural stimuli.  \n   **Response:** Please refer to point 1 of the rebuttal to Reviewer YcpF.\n\n2. **Comment:** Section 5.2: The IndivNet only improves the behavioral prediction accuracy by 5% over GroupNet, yet it is claimed that this approach captures individual differences in perceptual judgments. The result seems to suggest that the model training mostly captures the group effect in this behavior.  \n   **Response:** We believe the relatively small improvement is due to the similarity between the individual perceptual boundary and the group-level perceptual boundary. We hope the reviewer agree with us that it is reasonable to assume that individuals, in general, are close to the group, resulting in a relatively small improvement. \n3. **Response to typos:**  \nWe thank the reviewer for pointing out these mistakes. We will reorganize the appendix and revise the text in a future version.\n\n4. **Question:** Were subjects given extended time or was there a time limit in labeling the images?  \n   **Response:** In the major experiments, all subjects were given extended time to reduce random effects that could impact the experiment, such as arbitrary gaze positions or unexpected distractions.\n\n5. **Question:** (Line 232) The varMNIST-i dataset is not defined. Is that a subset of samples labeled by one individual? If so, was one model trained per individual? The dataset and procedure should be better explained.  \n   **Response:** The varMNIST dataset consists of images with multiple labels (labeled by different participants or trials, as described in the paper). Each participant performed around 500 trials on different images. The dataset corresponding to each participant is referred to as varMNIST-i, and one model is trained per individual. We appreciate your valuable suggestions and will clarify the dataset and procedure in future revisions to the main text.\n\n6. **Comment:** (Line 236) The description of model training is confusing. The first part suggests that the network was jointly trained on two datasets, but the latter part and the term \"finetuning\" suggest first training on MNIST and then on varMNIST.  \n   **Response:** The training process is conducted as follows:\n   - **Base Model:** First, we train a model using only the MNIST dataset.\n   - **Group Model:** Next, we finetune the base model to align it with the human group level. The finetuning dataset is constructed by mixing MNIST data into our varMNIST data, a common technique to prevent overfitting and model forgetting. This process yields the group model.\n   - **Individual Model:** Finally, based on the group model, we perform another finetuning at the individual level to align the model with each individual. This finetuning dataset is created by mixing MNIST, varMNIST, and varMNIST-i together. The training and evaluation sets are carefully divided so that even if there is an overlap between varMNIST and varMNIST-i, nothing in the evaluation set appears in the training set. Parameter details can be found in Appendix C.2.\n\n7. **Question:** Were there shared images across different individuals? How were the labels for repeated images from different individuals combined?  \n   **Response:** Yes, there are shared images across different individuals. Each image-label pair is treated as a trial and used in the finetuning process.\n\n8. **Question:** It is unclear whether the networks used in the study (VGG, ViT, CORnet) were trained from scratch or if a pretrained network was used.  \n   **Response:** The base models are trained from scratch. The group models are finetuned from the base models, and the individual models are finetuned from the group models, as explained in point 6.\n\n9. **Comment:** There are no references to the models used in the study.  \n   **Response:** We sincerely appreciate your suggestion and will add references for these models in future revisions.\n\n10. **Comment:** Figure 6: It is unclear what the \"individually customized dataset\" is.  \n    **Response:** The individually customized dataset is generated under the guidance of finetuned individual models to better evoke variability and bias participants toward certain directions.\n\n11. **Ethics**  \n    **Response:** We mentioned the ethics approval in Appendix B.2.2. Regarding human data collection, we can provide ethics documents and participant agreements upon request. We will incorporate these details into the main text in future revisions.", "claims_and_evidence": "- The success rate in selective manipulation of human behavior is relatively low, showing limited success in using the proposed approach. This is especially true for handwritten digits with ~20% success rate. Surprisingly, while the initial experiment using natural images seem to be much more successful, none of the following experiments were conducted using natural stimuli. \n\n- section 5.2: the IndivNet only improves the behavioral prediction accuracy by 5% over groupNet, yet it is claimed that this approach captures individual differences in perceptual judgments. The result seem to suggest that the model training mostly captures the group effect in this behavior.", "methods_and_evaluation_criteria": "The methods are appropriate.", "theoretical_claims": "No new theory was proposed.", "experimental_designs_or_analyses": "They are generally appropriately designed although I have several questions about the details of how they are conducted.", "relation_to_broader_scientific_literature": "The introduction and discussion sections puts the paper into the perspective of the prior literature. Specially the work by Golan and Kriegeskorte is very closely related to the current work.", "essential_references_not_discussed": "All relevant work were cited", "other_strengths_and_weaknesses": "Strengths: \n- the idea of capturing individual differences in human subjects by collecting personal data and training neural networks on them is interesting. \n\n- the paper was well structured, mostly well-written with clear figures\n\nWeaknesses:\n\n- the dataset is almost too easy and restrictive \n\n- despite being well written many details were missing\n\n- overall, the method does appear to be very effective, especially in the context of capturing individual differences and successfully biasing judgements in a selective way", "other_comments_or_suggestions": "typos: line 161 additional parentheses \nline 173: incorrect figure reference\nfig A11 caption: incorrect subplot indicators", "questions_for_authors": "- Were subjects given extended time or was there a time limit in labeling the images? This is important because, some of the individual differences may be due to arbitrary position of gaze that may have influenced the declared labels.\n\n- line232: varMINST-i dataset is not defined. Is that a subset of samples labeled by one individual? If so, was one model trained per individual? The dataset and procedure should be better explained\n\n-line 236: the description of model training is confusing. The first part of the description suggests that the network was jointly trained on two datasets but the latter part and the term \"finetuning\" suggests first training on mnist then on vamnist. \n\n- were there shared images across different individuals? how were the labels for repeated images from different individuals combined?\n\n- It is unclear whether the networks used in the study VGG, VIT, CORnet were trained from scratch or a pretrained network was used. The text gives me the impression that they were trained from scratch but the number of parameters in these networks and the relatively small dataset size makes me doubt that. \n\n- there are no references to the models used in the study \n\n- figure 6: it's unclear what the \"individually customized dataset\" is. Not explained.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_flag": "Flag this paper for an ethics review.", "ethics_expertise_needed": ["Responsible Research Practice (e.g., IRB, documentation, research ethics, participant consent)"], "ethical_review_concerns": "Human behavior experiments require IRB or similar approvals but they were not mentioned in the text."}}, {"review_id": "81rW1Cmbym", "reviewer": "Reviewer_YcpF", "summary": "The functional alignment between artificial neural networks (ANNs) and the human visual system has been a major hot topic in recent years. In this study, the authors generated images that lie on the perceptual boundaries of various ANNs and examined their relationship with individual differences in human perception. Experimental results using MNIST demonstrated that the images generated by this method effectively explain individual variability in human category judgments and can be used to manipulate perceptual variability.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to reviewer YcpF\n\nWe sincerely thank the reviewer for the support of our work. Below we address the reviewer’s concerns and questions:\n\n1. **Comment:**  \n    The MNIST dataset is not suitable for examining human perceptual variability, as evident from Figure 4b, where participants' judgments appear to be highly consistent. This suggests that MNIST lacks the complexity necessary to capture meaningful individual differences in perception.  \n   **Response:**  \n   We agree with the reviewer that in the MNIST dataset participants' judgments appear to be highly consistent and are less complex than natural images. We conduct the major experiments using handwritten digits for two main reasons:\n   - **Efficiency:** The models are much easier to train and require less time, which is very important for a large-scale behavioral experiment like ours.\n   - **Perceptual Variability:** As mentioned by Reviewer 62LP and Reviewer YcpF, perceptual variability is harder to evoke on handwritten digits. If we can evoke perceptual variability even on a highly consistent dataset such as MNIST, then it could be applied to more complex datasets where human perceptual variability is more diverse. Based on this assumption, we evaluated our framework on natural images and observed that perceptual variability is indeed larger on natural images than on handwritten digits. Although further experiments on natural images were not conducted, it is reasonable to assume that individual perceptual boundaries are more complex on natural image datasets. This complexity might require additional rounds of experiments or a parameter-efficient method for finetuning(e.g., LoRA) as the models would become bigger and more complex. We plan to conduct more experiments and analysis on natural images in future work.\n   We hope the reviewer can agree with our considerations.\n\n2.  **Comment:**  \n   In addition to reporting the results, the authors should provide further analysis to explain why fine-tuning performance varies across different model architectures. A deeper investigation into the underlying factors driving these differences would strengthen the study's findings.  \n   **Response:**  \n   We appreciate the reviewer's suggestion, as further analysis of the differences in fine-tuning performance across various model architectures could indeed provide valuable insights. However, the primary focus of this paper is to demonstrate the feasibility of aligning humans and AI using our method and to explore the potential for manipulating perceptual variability within our framework. Given the limited space available, we believe that an in-depth analysis of architectural differences would be better suited for a separate, more focused study.\n\n3.   **Comment:**  \n   The use of technical terms in the paper lacks consistency and deviates from standard terminology in cognitive science. For example, a numerical category classification task is not typically referred to as \"decision making.\" Additionally, the paper alternates between terms like \"decision boundaries of classifiers\" and \"ANN perception boundary,\" suggesting that the authors may conflate perception and decision-making processes. The terminology should be clarified and used consistently to avoid confusion.  \n   **Response:**  \n   Thank you for pointing out this inconsistency. We will pay more attention to the use of terms and revise the text to ensure consistency with standard cognitive science terminology.\n\n4.   **Comment:**  \n   In Figure 4, the colors in the legend do not match those in the bar graph. The authors should ensure consistency between the legend and the figure for clarity.  \n   **Response:**  \n   We thank the reviewer for highlighting this issue. We will revise the figure to ensure that the legend colors match those used in the bar graph.\n\n5.   **Question:**  \n   Are there specific reasons showing the large error bars in the customized dataset for guidance success rates in Figure 6c?  \n   **Response:**  \n   We admit that the customized manipulation experiment can be difficult to conduct, and the effect of manipulation is largely influenced by the individual status of the participants due to the need for precise alignment with each participant. Under these circumstances, large error bars can occur. Despite the large error bars and relatively low improvement, our statistical analysis confirmed that the results are statistically significant (p < 0.001).", "claims_and_evidence": "The study comprehensively presents its claims and supporting experiments. However, the organization of the paper is not well-structured.\n\nIn general, it is difficult to derive generalizable conclusions only from the MNIST dataset due to low diversity, and analysis using natural images is necessary. The Introduction and Methods sections describe the paper as if this study only used MNIST. However, in Figure 4b, natural image data is introduced without any prior explanation, and the figure result of perceptual variability suggests that natural images are more appropriate for the analysis. It is only in Section 5.2 that the use of natural images is explicitly mentioned, but the results are only in the Appendix.\n\nInstead, the authors should restructure the paper and frame while including the results obtained from natural images, which are more relevant for discussing perceptual variability.", "methods_and_evaluation_criteria": "The MNIST dataset is not suitable for examining human perceptual variability, as evident from Figure 4b, where participants' judgments appear to be highly consistent. This suggests that MNIST lacks the complexity necessary to capture meaningful individual differences in perception.", "theoretical_claims": "N/A. Theoretical (mathematical) proof was not conducted in this manuscript.", "experimental_designs_or_analyses": "In addition to reporting the results, the authors should provide further analysis to explain why fine-tuning performance varies across different model architectures. A deeper investigation into the underlying factors driving these differences would strengthen the study's findings.", "relation_to_broader_scientific_literature": "This study may provide broader insights into understanding personalized models, which take into account individual differences across populations, including individuals with mental disorders or neurodevelopmental conditions. Such models are needed for appropriate diagnosis of these individuals.", "essential_references_not_discussed": "Appropriate references are discussed in this manuscript.", "other_strengths_and_weaknesses": "Other weakness:\n\nThe use of technical terms in the paper lacks consistency and deviates from standard terminology in cognitive science. For example, a numerical category classification task is not typically referred to as \"decision making.\"\n\nAdditionally, the paper alternates between terms like \"decision boundaries of classifiers\" and \"ANN perception boundary,\" suggesting that the authors may conflate perception and decision-making processes. The terminology should be clarified and used consistently to avoid confusion.", "other_comments_or_suggestions": "In Figure 4, the colors in the legend do not match those in the bar graph. The authors should ensure consistency between the legend and the figure for clarity.", "questions_for_authors": "Are there specific reasons showing the large error bars in the customized dataset for guidance success rates in Figure 6c?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "s7t6KFOxJG", "reviewer": "Reviewer_Aam6", "summary": "This paper studies individual perceptual variability by generating controversial stimuli—images perceived differently by various individuals. To do so the authors 1) sample images on the perceptual boundary of ANNs, 2) collect subject-specific labels through psychophysics experiments on the previously generated images, 3) train individually aligned neural network classifiers and 4) generate images eliciting divergent responses between individuals. The authors demonstrate the validity of their method with human experiments.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "# Response to reviewer Aam6\n\nWe sincerely thank the reviewer for the support of our work. Below we address the reviewer’s concerns and questions:\n\n1. **Question:**  \n    How exactly are the uncertainty or controversial guidance signals integrated into the diffusion process?  \n   **Response:**  \n   The uncertainty and controversial guidance signals are integrated into the diffusion process using a classifier guidance method. More details can be found in Appendix A.2.\n\n2. **Question:**  \n   Are you using classifier-free guidance diffusion models, and if so, how are the dual-label conditions handled?  \n   **Response:**  \n   We adopted classifier guidance methods for all of our experiments. Although classifier-free guidance diffusion models may offer interesting insights, they were not used in our work. In our approach, all guidance is introduced by the classifiers, and the dual-label conditions are handled by our proposed controversial guidance and uncertainty guidance.\n\n3. **Question:**  \n   Which classifiers (architectures and specifics) were tested for generating controversial stimuli?  \n   **Response:**  \n   For generating controversial stimuli using the base models (trained from scratch on the MNIST dataset), all five models are paired with each other. The guidance outcome results are presented in Figure A.14.\n\n4. **Question:**  \n   Could you clarify precisely how fine-tuning is conducted (GroupNet, IndivNet, BaseNet), and what are the exact targets for population-level fine-tuning?  \n   **Response:**  \n   We apologize for any confusion. For detailed information on the fine-tuning process, please refer to point 6 of our rebuttal to Reviewer 62LP. The goal of the population-level fine-tuning is to align the models with the population-level characteristics. Starting from GroupNet for the fine-tuning of IndivNet provides the model with a human group-level prior and helps prevent overfitting, particularly since the varMNIST-i dataset contains only a small amount of data.\n\n5. **Question:**  \n   What prevents applying the full analysis presented for MNIST to more complex datasets like ImageNet?  \n   **Response:**  \n   Please refer to point 1 of our rebuttal to Reviewer YcpF.\n\n6. **Question:**  \n   Can you discuss how the generative model’s biases impact the validity of perceptual variability experiments (versus GAN-based or other types of generative models)?  \n   **Response:** We chose classifier-guided diffusion models for their superior performance, stability, and flexibility. While we explored alternatives (e.g., VAEs and prior-free guidance[1], as shown in Figure A.3), none achieved the quality or precise individual alignment that our approach provides.\n   We acknowledge that though a comprehensive comparison of generative model biases would indeed provide more insights, it would require developing new methodologies based on various backbone models, which falls outside the scope of this work.\n\n7.  **Question:**  \n   The authors assert that their diffusion-based method produces more \"natural\" controversial stimuli compared to existing approaches. This claim, however, is insufficiently supported, especially given the comparison is largely limited to MNIST rather than photorealistic domains.  \n   **Response:**  \n   We apologize for any confusion in our writing. By \"natural,\" we mean stimuli that are closer to the original distribution of the dataset. For example, in Figure A.3, stimuli generated by methods without a prior would not be recognized as digits by human participants, thereby classifying them as out-of-distribution or \"unnatural\" in our terms. We deliberately chose this terminology to distinguish our approach from others, such as the one proposed by Golan [1]. Although Golan’s method can generate an image x that causes classifier f1 to label it as y1 and classifier f2 as y2, it fails to produce images that human participants recognize as digits—a shortcoming that our method effectively overcomes.\n\n**References:**\n1. T. Golan, P. C. Raju, and N. Kriegeskorte, \"Controversial stimuli: Pitting neural networks against each other as models of human cognition,\" *Proceedings of the National Academy of Sciences*, vol. 117, no. 47, pp. 29330–29337, 2020.", "claims_and_evidence": "The authors claim their computational framework can effectively produce controversial stimuli that uncover human perceptual variability. Evidence includes i) human experimental validation, demonstrating that synthesized images indeed elicit varying perceptual judgments and ii) various quantitive analyses (entropy and accuracy analysis).", "methods_and_evaluation_criteria": "The methods combine i) perceptual boundary sampling from ANNs, 2) individually aligned and 3) generation of images that elicit divergent responses between individuals. While the article is very clear and well written in general, I found section 3, describing the perceptual sampling method, not clear enough. It would have been useful to detail more how the uncertainty and controversial guidance integrate into the decision process.\n\nThe evaluation process relies on human experiments, which makes sense for this article. In general, I found the method clear (except section 3), and the evaluation criteria are relevant.", "theoretical_claims": "This is not a theoretical claim, but the authors assert that their diffusion-based method produces more \"natural\" controversial stimuli compared to existing approaches. This claim, however, is insufficiently supported, especially given the comparison is largely limited to MNIST rather than photorealistic domains.", "experimental_designs_or_analyses": "Experiments predominantly focus on MNIST. The effectiveness of the method on ImageNet is briefly demonstrated but not thoroughly analyzed. Detailed analyses demonstrating perceptual boundary manipulations on complex datasets would substantially strengthen the experimental claims.", "relation_to_broader_scientific_literature": "The paper aligns well with existing literature, addressing perceptual variability and generative modeling approaches.", "essential_references_not_discussed": "I have not noticed any important references that were not discussed", "other_strengths_and_weaknesses": "Strengths:\n* Well motivated and clearly written.\n* Successfully demonstrates the possibility of manipulating perceptual variability.\n\nWeaknesses:\n* Insufficient clarity and detail regarding the diffusion guidance mechanism.\n* Limited analysis beyond MNIST dataset.\n* Lack of clarity regarding classifier architecture and fine-tuning specifics", "other_comments_or_suggestions": "* Clearly label axes in Figure 3a to improve readability and comprehension (e.g., akin to Figure 1 from the Golan et al. article).\n* Correct the referencing error: Figure A.2 should be in the supplementary material, not the main article.", "questions_for_authors": "* How exactly are the uncertainty or controversial guidance signals integrated into the diffusion process?\n* Are you using classifier-free guidance diffusion models, and if so, how are the dual-label conditions handled?\n* Which classifiers (architectures and specifics) were tested for generating controversial stimuli?\n* Could you clarify precisely how fine-tuning is conducted (GroupNet, IndivNet, BaseNet), and what are the exact targets for population-level fine-tuning?\n* What prevents applying the full analysis presented for MNIST to more complex datasets like ImageNet?\n* Can you discuss how the generative model’s biases impact the validity of perceptual variability experiments (versus GAN-based or other type of generative models)?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_flag": "Flag this paper for an ethics review."}}]

Author rebuttal:

Meta-review: All reviewers initially shared a positive view on this paper. The rebuttal was helpful to resolve some misunderstanding and the authors have to update the paper accordingly.

In particular the reviewers identified the following strengths : 
- capturing individual differences in human subjects by collecting personal data and training neural networks on them is interesting,
- paper was well structured, mostly well-written with clear figures,
- successfully demonstrates the possibility of manipulating perceptual variability.
- experimental results using MNIST demonstrated that the images generated by this method effectively explain individual variability in human category judgments and can be used to manipulate perceptual variability,
- this study may provide broader insights into understanding personalized models, which take into account individual differences across populations, including individuals with mental disorders or neurodevelopmental conditions.

The authors should move the statement about the ethical approval of their experiment into the main text. Because they only received an approval from their university and they don't have an IRB number, they should provide the proof that they receive this approval and add it in appendix.
Decision: Accept (poster)