Dear Dr. Wong,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Sihua Xu

Academic Editor

PLOS ONE

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Additional Editor Comments:

Dear Dr. Wong,

Thank you for submitting your manuscript entitled “Beyond Traditional Stimuli: Establishing AI-Generated Images as Valid Tools for Emotion and Affect Research” (Manuscript ID: PONE-D-25-25040) to PLOS ONE. Your work addresses a timely and novel question regarding the validity of AI-generated images as affective stimuli. Reviewers recognized several strengths, including the originality of the topic, appropriate sample sizes and statistical analyses, high inter-rater reliability, and transparent reporting of unexpected findings.

At the same time, all reviewers identified substantial issues that need to be resolved before the manuscript can be considered further. Key concerns include: the need for extensive English language editing and clearer presentation; missing or incomplete supplementary materials; insufficient articulation of the study’s purpose, hypotheses, novelty, and relation to prior literature; and the unclear necessity of Study 1 given habituation confounds. Reviewers also emphasized that the complete omission of positive stimuli substantially limits the study’s claims, and that issues of ecological validity, participant awareness of AI-generated images, and the reproducibility of stimuli (including disclosure of prompts and model parameters) require fuller justification and discussion.

In light of these points, the editorial decision is Major Revision. We invite you to submit a thoroughly revised manuscript that addresses the reviewers’ comments in detail. Careful attention to these issues will be essential to strengthen the contribution and ensure the manuscript meets the standards of PLOS ONE.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Recommendations for Manuscript ID PONE-D-25-25040 Title: “ Beyond Traditional Stimuli: Establishing AI-Generated Images as Valid Tools for Emotion and Affect Research” for the Plos One Journal.

General Comments

From my point of view, it is a very interesting topic and simultaneously it seems that to the best of my knowledge is an empirical research aims to explored the feasibility of using generative AI, specifically text-to-image generators, to create tailored affective stimuli. Across two studies, participants rated the valence and arousal of 160 and 200 AI-generated images (negative and neutral). Our findings revealed that AI-generated images exhibit the typical valence-arousal patterns observed in standardized affective databases, demonstrating moderate to strong associations between these two emotional dimensions. These results highlight the potential of generative AI as a valuable methodological tool for creating customized affective stimuli aligned with distinct research objectives and experimental designs.

The paper contains the following sections: Introduction, Current uses of artificial intelligence in stimuli development, Present study, Study 1, Study 2, Discussion, .

However, I find some recommendations:

1. The Manuscript needs careful English proofreading because there are some shortcomings. For instance, the article “the” is sometimes missing in front of nouns, the message in some paragraphs is not clear enough. It looks like the first part was written by one author with a greater command of the English language, and the rest of the paper was written by someone else. The numerous grammar errors made this a difficult paper to read. It was strange to see the authors refer to tables that were not submitted. I was unable to find any supplementary material to the submission, so I think this was truly omitted by the authors. Please read the manuscript carefully.

2. The abstract must contain the main purpose of the paper, the research method used in the research and the main contributions.

3. It would be very useful to add in the "Introduction" section the purpose, objectives and hypothesis of the research. I consider that a weak point of the paper is that the authors did not show the novelty of the paper compared to other works. That is why, I consider that the introduction should specify the novelty of the paper compared to other papers published in this area.

4. The Literature Review cannot be missing from the paper.

5. The research is well based on science and the results are in agreement with the theoretical part.

6. I believe that the authors should also include other indicators from Descriptive Statistics.

7. I think that the literature needs to be improved with other recent works, refers to the companies listed on the economic growth. That is why I recommend the authors to refer to other recent works indexed in Web of Science, Scopus, Emerald and Cambrige Journals. We suggest that the authors cite papers published in Web of Science Journals such as:

a. https://doi.org/10.3390/cancers15030843

b. https://doi.org/10.3390/s23052398

c. https://doi.org/10.3390/math11102305

d. https://doi.org/10.3390/fractalfract8100604

In conclusion, the article should be improve. It should also be enhanced with a review of the literature adequate to the subject and a broader interpretation and commentary of the research results.

Reviewer #2: Thank you for this interesting manuscript. Through my reading, I found several strengths of this study as well as its key limitations. In what follows, I will discuss the strengths first and then move on to the key limitations.

The most salient strength of this study pertains to its topic. The authors address a truly novel topic: whether AI-generated images can be valid stimuli for emotion and affect research. I believe that this topic is timely and worthwhile, given the increasing use of AI in academic research.

Another strength I’d like to mention is that the manuscript is well-written and effectively organized. The introduction and literature review sections are smoothly connected. The previous studies mentioned in this early part are highly relevant to the current study and effectively justify why this study is necessary. One important virtue of the manuscript is its clarity. The section “Research gap” is a case in point; the section title is straightforward, and the corresponding content is also easy to follow.

The last strength I appreciate in this manuscript is the authors’ transparency. They openly share even some information that can appear to be against the study’s credibility. For instance, the authors did not hide the fact that neutral images received higher-than-expected valence ratings. While this could have given some doubt to the validity of the stimuli, the authors smartly explained their best guess about the reason, which sounds reasonable and well thought out. I believe that the authors show a good practice of transparency throughout the manuscript.

Despite these strengths, I also found some areas for improvement, which might diminish the study’s value, as follows:

First, it is not clear why the authors have two sub-studies. Study 1 and Study 2 overlap, and it seems obvious that Study 2 is more rigorous than Study 1. The authors stated that the participants in Study 1 saw the stimulus images before Study 1 as part of another study. This is a very significant limitation, which I think the authors cannot get away with. As mentioned in the manuscript as well, habituation effects likely happened. Since the authors did not explain what kind of study the previous study was and in what context the images were displayed, it is difficult to guess how seriously habituation effects occurred there. But, one clear thing is that the previous experience must have harmed the quality of Study 1 to a certain extent. My best guess about why the authors included Study 1 is probably that the consistent findings across the two sub-studies can demonstrate the reliability of their findings. However, I would say that is not the case here, due to the critical limitation of Study 1. Therefore, I suggest the authors retain only Study 2.

Another key limitation of this study is that the study did not test positive images. Although the authors clearly admit this as their limitation in the Discussion section, I do not think this is a simple limitation that they can admit and move on. In fact, not examining positive images makes this study’s findings look half-baked. The true utility of AI-generated images as tools for valence/emotion research can be only confirmed after testing positive images (and positive valences). If there was an unavoidable reason behind this critical exclusion, the authors should clarify it in the manuscript. Without that, the current version leaves some doubt about the completeness of the study.

The two areas for improvement I mentioned above may give an impression in common that the study was designed based on the authors’ convenience rather than scientific criteria. I think it would be ideal if the authors could resolve this potential issue through their revision. I truly hope my comments help and wish the authors all the best in their research. Thank you.

Reviewer #3: The study aims to validate AI-generated images (created via text-to-image prompts) as viable stimuli in emotion research. It is, to the authors' knowledge, the first to systematically assess whether such images can elicit valence–arousal patterns comparable to those found in standardized affective databases such as IAPS. This represents a meaningful methodological contribution to affective psychology, particularly given the increasing interest in customizable and scalable stimulus creation using AI.

The two studies are well-designed, with adequate sample sizes, strong inter-rater reliability (ICC > .95), and the use of validated tools such as the Self-Assessment Manikin (SAM). The statistical analyses—t-tests, correlations, and regression models—are appropriate and competently executed. The results confirm that AI-generated images, at least for negative and neutral content, follow the classic U-shaped valence–arousal curve observed in traditional databases. This finding suggests that such images may offer a flexible alternative for generating emotionally calibrated stimuli in experimental paradigms.

Despite these strengths, the study has several serious limitations that must be addressed before it can be considered for publication.Most notably, the study excludes positive stimuli entirely, undermining the central claim that AI-generated images can replicate the full range of affective responses typically captured in standardized image sets. Without positive images, the U-shaped valence–arousal distribution is only partially validated, which significantly limits the generalizability of the findings. The authors must clearly justify this omission and discuss its implications. Second, no data were collected on whether participants recognized the images as AI-generated. This omission is problematic, as prior work suggests that perceived artificiality can modulate emotional responses. Furthermore, the authors report that images with visual distortions or unrealistic elements were intentionally retained. While this decision may reflect practical constraints, it raises concerns about ecological validity and the applicability of findings to real-world emotional processing.

While the authors briefly acknowledge the absence of positive images and the choice to include AI-distorted outputs, these decisions require more detailed justification and discussion. Specifically, the complete omission of positive stimuli significantly limits the ability to validate the full valence–arousal spectrum and undermines the claim that AI-generated images replicate the distributional patterns observed in standardized databases like IAPS. The rationale for this exclusion is not sufficiently developed. Additionally, although the authors note that realism was not a primary goal, they do not assess whether participants were aware that the images were AI-generated or whether such awareness might have influenced their emotional ratings. Given growing evidence that perceived authenticity can modulate affective responses, this represents a potential confound that should be directly addressed. Both points—the exclusion of positive stimuli and the lack of participant awareness checks—require clearer theoretical and methodological justification.

The paper provides only a few examples of the generated images and their corresponding prompts, which raises concerns about transparency and reproducibility. Given that the study’s main contribution lies in the use of generative AI for flexible stimulus creation, it is essential to disclose a representative and sufficiently detailed sample of prompts, along with generated outputs. Without this information, it is difficult to assess the thematic accuracy, variability, or potential biases in the generation process. Most critically from a methodological standpoint, the authors do not address the inherent stochasticity of text-to-image generation models. Most generative AI systems sample from a probability distribution, meaning that identical prompts can yield different images across runs—especially on platforms like Adobe Firefly and DALL·E, which do not allow for control over random seed values or generation parameters. This severely compromises the reproducibility of the stimuli. The authors’ proposal that AI-generated images could replace standardized image databases is therefore premature unless procedures are put in place to ensure deterministic, shareable outputs. At a minimum, the authors should document the AI model configuration and any image metadata. If the same prompt was used in both Study 1 and Study 2, it would be valuable to examine and report whether the emotional ratings of the resulting images differed—highlighting the variability introduced by prompt reuse without seed control.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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Dear Dr. Wong,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jan 15 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Sandra Carvalho, Ph.D.

Academic Editor

PLOS ONE

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2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

Reviewer #1: The authors have responded to all of the reviewer's recommendations. I agree with the acceptance of the paper for publication.

Reviewer #2: Thank you for the revision incorporating my comments. The revised manuscript looks significantly more rigorous than the original one, which I appreciate. Of the two key limitations I mentioned, I think the issue of not testing positive images, which was also raised by another reviewer, has now been addressed as far as it can be taken at this point. That said, it seems that the first limitation—the potentially problematic inclusion of Study 1—has not been fully resolved.

In fact, the following response from the authors has crystallized that Study 1 has a significant methodological issue: "However, this design introduced a potential confound of habituation, as the ratings were collected upon the participants' second exposure to the images." Given this potential issue, and considering the nature of this research, retaining only Study 2 appears to be a more reasonable and scientifically sound choice than including a study that may be fundamentally compromised (i.e., Study 1). As a fellow researcher, I fully understand the desire to share as much work as possible, but I would like to encourage the authors to prioritize scientific rigor over the scale of their manuscript. In the end, I believe what we must prioritize as researchers is sharing the most reliable results/findings, thereby serving as a safe stepping stone for future research. if the authors would still like to stick to the dual-study approach, I suggest that they conduct an additional study to accompany Study 2 rather than retaining the current Study 1.

I sincerely hope my comments are helpful, and I wish the authors all the best in their continued work. Thank you.

Reviewer #3:  Point 1

Lines 140-151:

The rationale provided remains somewhat unconvincing. Specifically, the theoretical and clinical arguments (lines 140-145) explain why negative images are valuable, but do not justify excluding positive images. These are distinct questions.

The statement about sourcing negative images from databases being "more challenging" is confusing in this context. Since you are using AI to generate images rather than sourcing from existing databases, this point does not logically support your methodological choice.

Your strongest justification is participants' burden and task feasibility but it is buried at the end of this paragraph. The practical constraint should be the primary rationale presented.

I recommend restructuring this paragraph to lead with the practical constraints (task duration, fatigue, habituation), followed by the clinical relevance of negative stimuli. Either remove or significantly revise the database sourcing statement, as it undermines rather than strengthens your argument.

Lines 569-574:

The limitation is now acknowledged, which is good. However, the phrase "opposite end of the observed U-shaped distribution" is technically imprecise, as you have observed only the left portion of what may be a U-shaped distribution. Please revise to state that you cannot determine whether positive images would "complete" or "extend the observed pattern to form" the U-shaped distribution, and explicitly note that this limits claims about fully replicating distributional patterns from standardized databases.

As a general concern, please review your manuscript to ensure that claims about replicating the "full range" or "complete spectrum" of affective responses are modified to accurately reflect your study's scope (negative-to-neutral range only).

Point 2

Lines 574-586

You mention that you informally collected data on participant perceptions and have approximate numbers (11 in Study 1, 27 in Study 2). This represents a significant missed opportunity. If possible, include these informal observations in more detail, even if with appropriate caveats about the unsystematic collection method. A simple descriptive comparison of ratings from participants who noticed vs. didn't notice AI generation would strengthen the paper.

Regarding Zhou & Kawabata, 2023 and Bilucaglia et al., 2025, my understanding is that these studies actually present mixed findings: awareness affects some measures but not others. Their mixed evidence supports my original concern that awareness represents a potential confound that should have been measured and controlled. Please clarify which of your measures are not confounded by awareness with respect to the two papers' measures.

Point 3

Ok. However, I recommend that your negative and neutral AI stimuli be made publicly available on platforms such as OSF.io or similar repositories.

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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Beyond Traditional Stimuli: Validating AI-Generated Images on the Negative-Neutral Spectrum for Affective Research

PONE-D-25-25040R2

Dear Dr. Wong,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Sandra Carvalho, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Thank you for your careful revisions. The manuscript now clearly demonstrates the methodological validity of AI-generated affective stimuli, supported by robust analyses across two independent studies. The work is transparent, appropriately cautious in its claims, and well aligned with the journal’s scope. I am pleased to inform you that the manuscript is accepted for publication.

Reviewers' comments: