An Exploration of Internal States in Collaborative Problem Solving

Anne M. Cleary; Mariah Bradford; Nathaniel Blanchard; Sifatul Anindho; Videep Venkatesha

REVIEW 3 major objections 6 minor 41 references

An Exploration of Internal States in Collaborative Problem Solving

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Retrospective video-recall monologues reveal a twelve-label map of internal states during collaborative problem solving.

desk verdict Honest, useful exploratory dataset, but the 61.4% positive-prevalence headline is not yet supported: the label mapping is post hoc, overlapping, and unreported. read the letter →

arxiv 2507.02229 v1 pith:NSLUGSQG submitted 2025-07-03 cs.HC

Sifatul Anindho , Videep Venkatesha , Mariah Bradford , Anne M. Cleary , Nathaniel Blanchard This is my paper · ORCID

classification cs.HC

keywords collaborativeproblemsolvinginternalstatesemotionlabelsretrospectiveself-reportstimulatedrecalllinguisticanalysissemanticsimilarityn-gramfrequency

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that the internal emotional states people experience while collaborating can be recovered from their own spoken reflections, collected immediately after the task as they watch a video replay of the team working. Analyzing 29 participants' monologues from a four-person Lego building task, the authors identify frequent words and phrases, group them into twelve emotion labels, and report the distribution of those labels. Three positive labels—Optimistic, Engaged, and Satisfied—account for 333 (61.4%) of emotion-label word occurrences, while Disengaged, Reserved, and Frustrated together make up only 24 (4.4%). The value of the claim, if it holds, is a cheap and scalable way to study individual internal states in collaborative settings without interrupting the team's work.

What carries the argument

The load-bearing mechanism is the stimulated-recall protocol: each participant, alone after the task, watches a video of their team and narrates their thoughts and feelings moment-to-moment, with the video allowing those states to be mapped back to task time. On top of that transcript, the analysis builds a small emotion taxonomy by hand: 29 frequent keywords and phrases are assigned to twelve labels, and the labels are validated by within-category and between-category cosine similarities computed from pretrained transformer word embeddings. The taxonomy is what turns free-form monologue into a quantitative distribution of internal states.

What would settle it

Collect concurrent in-task measures of emotion, such as physiological arousal or button-press emotion ratings, during the collaborative task and compare them with the retrospective label distribution from the video-recall monologues; if the two show little or no correspondence, the claim that retrospective speech reveals actual internal states would be falsified.

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Extended reading notes

Core claim

The central claim is that internal states during collaborative problem solving leave reliable traces in retrospective speech, and that a hand-derived set of twelve emotion labels—Engaged, Disengaged, Conflicted, Confident, Reserved, Frustrated, Optimistic, Anxious, Disappointed, Satisfied, Confused, Surprised—captures the distribution of those states. The authors show that frequent n-grams are mostly filler, so they manually select 29 content-bearing keywords and phrases, assign them to labels using discrete-emotion literature, and then use cosine similarity of word embeddings to show within-label words are semantically close and labels are reasonably distinct. The resulting frequency distribution is skewed positive: engagement, optimism, and satisfaction dominate, while frustration, disengagement, and reservedness are rare.

Load-bearing premise

The entire dataset is retrospective speech, so the paper must assume that what participants say while watching the video accurately reconstructs what they actually felt during the task; if memory or social desirability distorts those recollections, the emotion-label frequencies describe the retelling rather than the experience.

Editorial extensions

If this is right

Future collaborative problem solving studies can use prompted video recall to collect internal-state data at scale without interrupting collaboration.
The twelve labels and their associated keywords give later researchers a starting vocabulary for automatic emotion annotation.
The distribution suggests positive states dominate in this kind of cooperative construction task, so interventions aimed at reducing frustration may target a minority of experience.
Because confusion is relatively frequent and known to accompany learning, its presence can be treated as a sign of engagement rather than failure.
The within-category and between-category semantic similarity method offers a template for checking that hand-built emotion categories are coherent.

Reading between the lines

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

Inference: if the retrospective protocol is validated against concurrent measures, the same method could be extended to role-level analysis, since directors and builders may show different emotion profiles.
Inference: the label set is likely task- and context-specific; the Lego construction task may induce more positive states than competitive or high-stakes collaboration, so the 61% positive figure should not be generalized without replication.
Inference: the semantic-similarity validation could be turned into a testable prediction that automatic classifiers trained on the twelve labels should predict task-relevant events, such as a builder making an error or a director giving confusing instructions, better than chance.
Inference: one could test whether the act of narrating changes the experience by comparing first-phase monologues with second-phase task behavior, a check for reactivity of the method.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a genuinely useful exploratory dataset wrapped in a too-strong interpretive frame. I agree with the conditional verdict, and the stress-test note on label circularity lands.

What's new: a new CPS task with 8 four-person teams, 29 usable retrospective monologues collected by having participants watch the GoPro video of the first phase and speak their moment-to-moment thoughts. The raw n-gram tables are plausible, and the twelve-category emotion label set (Engaged, Optimistic, Satisfied, Confused, etc.) is more granular than what the cited CPS literature uses. The authors are transparent about the obvious limits: small sample, self-report bias, excluding three builder recordings, and no role-level analysis. That transparency makes the paper easy to read and build on.

The soft spots are the same ones the stress-test flags. The central prevalence claim — three positive labels account for 61.4% of emotion words — depends on a post hoc label-to-keyword mapping (Table 8) that is never fully specified. Several surface forms appear under multiple labels: 'figure out' in Engaged and Optimistic, 'easi' in Optimistic and Satisfied, 'vagu' in Conflicted and Confused, 'mistak' in Anxious and Disappointed, 'crazi' in Frustrated and Anxious. The paper does not say how each occurrence was assigned to one label, or whether occurrences were counted under every matching label. Without that, the frequency distribution is not a measured prevalence; it is an artifact of the mapping decisions. The same issue shadows the BERT similarity 'validation': words were placed together because they are semantically close, so high within-category cosine similarity is expected and proves little. No random baseline and no inter-rater agreement are reported.

This is fixable. The authors should release the transcripts, add a second annotator, report Cohen's kappa, resolve overlapping keywords with a written rule, and re-frame Section 4.4 as descriptive rather than confirmatory. They should also either include the remaining builder recordings or explicitly state the role imbalance as a scope limit — they do mention it, but the exclusion should be justified more carefully.

Who should read it: people designing emotion annotation schemes for CPS or studying self-report methodology. It is not a definitive empirical result, but it is a legitimate foundation. I would send it to review, with the expectation of major revision. The data collection design is reproducible and the research question is worth pursuing.

Referee Report

3 major / 6 minor

Summary. This paper reports an exploratory linguistic analysis of retrospective self-reports collected from 29 participants immediately after a four-person Lego collaborative problem-solving task. Participants watched a video replay of the task and narrated their moment-to-moment internal states; the authors transcribed these monologues, applied frequency analyses of unigrams, bigrams, and trigrams, hand-selected 29 keywords, grouped them into twelve emotion labels, and report that "Optimistic," "Engaged," and "Satisfied" account for 333 (61.4%) of emotion-label word occurrences. A BERT-based semantic-similarity analysis (within- and between-category cosine similarities) is presented as validation that the labels are coherent and distinct.

Significance. The study addresses a real gap: internal states during CPS are usually inferred from observable behavior, and retrospective replay is a relatively underexplored method for accessing individual experience. The paper is commendable for making its full keyword tables and label mappings visible, for transparently reporting demographic and exclusion information, and for acknowledging the retrospective-recall limitation in Section 6. If the label-based prevalence claims were supported by a reproducible matching rule and independent validation, the dataset would be a useful descriptive baseline for future CPS emotion research. As it stands, the exploratory n-gram findings are credible, but the headline distributional claim is not yet supported.

major comments (3)

[Section 4.3, Table 8, Figure 2] The 333-occurrence/61.4% figure is not well-defined because Table 8 lists the same surface keywords under multiple labels without a stated counting rule. For instance, "figure out" appears under Engaged and Optimistic; "easier"/"easi" under Optimistic and Satisfied; "vagu" under Conflicted and Confused; "assum" under Conflicted and Reserved; "mistak" under Anxious and Disappointed; and "crazi" under Frustrated and Anxious. If each occurrence is counted once per label, positive labels with duplicated keywords are systematically inflated; if occurrences are assigned to one label only, the adjudication rule is missing. Table 8 also contains words not in Table 7 (e.g., "zoning out," "conflict," "sure," "stress," "worri," "fault," "lost," "surpris"), so the source of the occurrence counts in Figure 2 is unclear. Please report a complete, single-label mapping from each keyword/phrase occurrence to one label, or a precise multi-label counting rule, and provide a sensitivity analysis of the 61.4% figure under alternative assignments.
[Section 4.4, Table 9] The semantic-similarity validation is circular. The labels and their member words were constructed post hoc from the same keyword-frequency data by the same authors, so high within-category BERT cosine similarity is expected and does not independently confirm that the categories are coherent. No random baseline, shuffled-label comparison, or inter-rater reliability is reported. Please add a baseline such as average similarity of random word sets of matched size, a second coder re-applying the codebook to transcripts, or a hold-out coding exercise, so that Table 9 can be interpreted as evidence for label coherence.
[Sections 3.1 and 6] The entire dataset consists of retrospective verbal reports produced during video replay, and the paper itself states in Section 6 that "participants may forget details or not accurately report their emotional experiences after the task is completed." Because the paper's central claims concern internal states during the task, the absence of any concurrent or independent check on the fidelity of recall leaves the construct validity of the frequency tables open. Either provide evidence for the fidelity of the replay procedure (e.g., alignment with known task events, or a subset with in-task measures), or revise the wording throughout Sections 4 and 5 so that the results are explicitly framed as characterizations of retrospective accounts rather than directly measured in-task states.

minor comments (6)

[Section 3.3] "When mapping, we accounted for the the context used by participants" contains a duplicated "the."
[Section 3.3] The frequency cutoffs for unigrams (>=30), bigrams (>=6), and key terms (>=5) are introduced without a rationale; a sentence on why these thresholds were chosen would help.
[Section 3.1, Table 1] The text says the age range was 20 to 32, while the table groups ages as 18-24, 25-31, and 32+; please reconcile these descriptions.
[Section 4.1] The trigram analysis reports phrases with frequencies between 3 and 5, but no trigram cutoff is specified in Section 3.3; please state the criterion used.
[Section 4.4, Figure 4] The discussion of highest and lowest between-category similarities would be easier to check if the specific numeric values or a full similarity matrix were provided in the text.
[General] The paper does not include a data or code availability statement; if the de-identified transcripts can be shared, adding such a statement would substantially improve reproducibility and would directly help reviewers evaluate the label-mapping concerns.

Circularity Check

2 steps flagged · score 6.0 of 10

The 61.4% positive-prevalence claim and the within-category semantic-similarity 'validation' reduce to the same hand-built keyword-to-label mapping used to define the labels, making the central quantitative result circular.

fitted input called prediction [Section 3.3 (Data Analysis), Section 4.3 (Emotion Labels), Table 8]
"We developed a set of emotion labels based on the distribution of frequent key words and phrases, drawing on work on discrete emotions and their corresponding emotional expressions [12]. Words and phrases were then mapped to these labels."

The reported prevalence in Section 4.3 — "Optimistic", "Engaged", and "Satisfied" labels were the most prevalent, collectively accounting for 333 (61.4%) of the total word occurrences — is the sum of frequencies of the keywords assigned to each label in Table 8. Because the labels were constructed from that same keyword-frequency distribution in Section 3.3, the prevalence is the input frequency relabeled, not an independent measurement. The mapping is non-exclusive: "figure out" appears under Engaged and Optimistic, "easier"/"easi" under Optimistic and Satisfied, "vagu" under Conflicted and Confused, "assum" under Conflicted and Reserved, and "mistak" under Anxious and Disappointed.
self definitional [Section 4.4 (Semantic Similarity), Table 9]
"The within-category represents average semantic similarity between word-pairs that we associate with the same label."

This is presented as a validation: "We used semantic similarity to validate that the words within each label were appropriately similar." But the labels in Table 8 were created by the authors choosing keywords that are semantically related (e.g., "frustrat", "stupid", "stress" for Frustrated). Computing BERT cosine similarity within each such hand-grouped set and reporting high scores is therefore expected by construction; no random baseline, inter-rater reliability, or held-out coding is provided. The high within-category similarity confirms only that the labels were defined using similar words, not that the labels independently capture the participants' internal states.

full rationale

The paper's raw n-gram and keyword-frequency analyses are self-contained and not circular: Table 5, Table 6, and Table 7 report counts directly from transcripts. The circularity enters when these same frequencies are used to construct the twelve emotion labels (Section 3.3), after which Section 4.3 presents the label distribution (including the headline 61.4% positive prevalence) as a finding about internal states. Since each label is defined by the very keywords whose frequencies are then counted, the distribution is forced by the construction. The Section 4.4 semantic-similarity check is similarly self-confirming because it measures average similarity within word sets that were grouped on the basis of semantic relatedness; there is no independent validation that a second coder would assign the same labels or that the overlapping keywords would resolve to the same categories. Self-citations to the authors' prior multimodal-CPS work appear only in related work and are not load-bearing. The acknowledged retrospective-recall limitation is a construct-validity concern rather than a circularity concern. Overall, the exploratory n-gram observations remain credible, but the central quantitative prevalence claim reduces in part to the hand-built mapping, so a score of 6 is appropriate.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rest on the retrospective self-report assumption, hand-chosen thresholds, and a manually constructed emotion taxonomy. No code or data are provided, so the analysis cannot be independently reproduced. The BERT model is pretrained and standard, so it counts as a domain assumption rather than a free parameter, but the choice of model affects similarity values.

free parameters (3)

Frequency cutoffs (unigram >=30, bigram >=6, keyword >=5) = 30/6/5
Thresholds for inclusion in frequency tables (Sections 3.3, 4.1, 4.2) are chosen post-hoc and affect which words enter the label construction.
Emotion label-to-keyword mapping (Table 8) = 12 labels, 69 keyword entries
Hand-assigned mapping from frequent words/phrases to emotion labels (Section 4.3). No inter-rater reliability; directly determines all prevalence claims.
Pretrained BERT (bert-base-uncased) embeddings = 768-dimensional vectors
Used for semantic similarity (Section 3.3). While not fit to this dataset, the choice of model and the cosine threshold implicitly define the similarity interpretation.

assumptions (4)

domain assumption Retrospective stimulated recall accurately reproduces in-task internal states
Entire data collection is based on post-task video-cued narration (Section 3.1); acknowledged as a limitation in Section 6.
domain assumption Frequency of emotion words reflects prevalence of internal states
The study equates word occurrence counts with the distribution of internal states (Sections 4.2, 4.3).
domain assumption Cosine similarity between BERT embeddings is a valid measure of semantic relatedness for emotion categories
Used to validate that labels are coherent and distinct (Section 4.4).
domain assumption The first phase of the task is representative of the second phase
Only first-phase video was shown to participants (Section 3.1), citing [3] without empirical support in this dataset.

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Cite this review

Pith. "Pith review of An Exploration of Internal States in Collaborative Problem Solving." pith.science (2026). https://pith.science/paper/NSLUGSQG

@misc{pith2026250702229,
  author       = {Pith},
  title        = {Pith review of: An Exploration of Internal States in Collaborative Problem Solving},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NSLUGSQG}},
  note         = {Machine review of arXiv:2507.02229}
}

read the original abstract

Collaborative problem solving (CPS) is a complex cognitive, social, and emotional process that is increasingly prevalent in educational and professional settings. This study investigates the emotional states of individuals during CPS using a mixed-methods approach. Teams of four first completed a novel CPS task. Immediately after, each individual was placed in an isolated room where they reviewed the video of their group performing the task and self-reported their internal experiences throughout the task. We performed a linguistic analysis of these internal monologues, providing insights into the range of emotions individuals experience during CPS. Our analysis showed distinct patterns in language use, including characteristic unigrams and bigrams, key words and phrases, emotion labels, and semantic similarity between emotion-related words.

Figures

Figures reproduced from arXiv: 2507.02229 by the authors.

**Figure 2.** The Total Occurrences of Words Associated with each Emotion Theme Label [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** The Distribution of the Most Frequent Key Words Categorized by their Respective Emotion Theme Label. some patterns in the semantic relationships between emotion labels. The highest similarities between categories was observed between "Frustrated" and "Surprised", "Frustrated" and "Conflicted", and "Surprised" and "Conflicted". The lowest were found between "Disengaged" and "Frustrated", "Disengaged" and "Surprise… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Heat-map of the Between-Category Similarity Scores Between Each Pair of Categories [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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