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ID
int64
ExperimentalCondition
large_string
PresentationOrder
int64
StimID
large_string
TestSampleIdx
int64
CorrectAnswer
int64
ModelAnswer
int64
TimeTabWasLeft
int64
TimeSpent
float64
TimesAnswerChanged
int64
ParticipantAnswer
int64
Confidence
int64
Feature1_GT
int64
Feature2_GT
int64
Feature3_GT
int64
Feature4_GT
int64
Feature5_GT
int64
Feature6_GT
int64
Feature1_Detected
float64
Feature2_Detected
float64
Feature3_Detected
float64
Feature4_Detected
float64
Feature5_Detected
float64
Feature6_Detected
float64
Feature1_Clicks
float64
Feature2_Clicks
float64
Feature3_Clicks
float64
Feature4_Clicks
float64
Feature5_Clicks
float64
Feature6_Clicks
float64
Interactions
int64
ParticipantAnswerCorrect
int64
ModelAnswerCorrect
int64
Confidence_Adjusted
int64
1
InteractiveConcepts
1
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1
1_003521_Final2.png
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NonInteractiveConcepts
2
1_003630_Final2.png
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NonInteractiveConcepts
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NonInteractiveConcepts
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NonInteractiveConcepts
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0_003537_Final2.png
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NonInteractiveConcepts
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InteractiveConcepts
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InteractiveConcepts
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1_003633_Final2.png
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1_003531_Final2.png
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0_003608_Final2.png
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6
End of preview. Expand in Data Studio

NWeak/CBM-user-study-cub

Participant responses from the CUB concept-bottleneck user study (Le Conte's Sparrow vs. Savannah Sparrow), 10 stimuli each plus attention checks and a trust questionnaire. Three configs:

  • full (568 participants, one row each): every participant who started the study, as recorded.
  • clean_wide (342 participants, one row each): full filtered to remove participants who were (a) blank/incomplete, (b) failed AttentionCheck1/AttentionCheck2, or (c) switched away from the browser tab more than 3 times total across the 10 stimuli (a tab-switching risk heuristic, e.g. possible use of an external tool to answer).
  • clean_long (3420 rows, one row per participant-stimulus): the same 342 participants as clean_wide, melted to long format. TimeSpent is in minutes; Confidence_Adjusted = abs(Confidence - 7) folds the 1-13 confidence scale into a direction-independent 0-6 measure.

The paper analyses the clean configs. Together with NWeak/CBM-user-study-emails's clean_wide/clean_long they give the reported totals of 705 participants and 6,959 observations.

Columns

  • ExperimentalCondition: NoSupport, LabelOnly, NonInteractiveConcepts, or InteractiveConcepts (the same four arms, under the same names, as the emails study).
  • StimID / StimX_StimID (X = 1..10): the stimulus filename shown.
  • TestSampleIdx / StimX_TestSampleIdx: the sample_idx into the CUB test split. Join against NWeak/cub-mirror's test split to recover the image, concepts, and label behind any stimulus. -1 means no stimulus was shown at that position.
  • FeatureN_GT / FeatureN_Detected / FeatureN_Clicks: per-concept ground truth, model activation, and how many times the participant clicked it.
from datasets import load_dataset

full  = load_dataset("NWeak/CBM-user-study-cub", "full",       split="test")
wide  = load_dataset("NWeak/CBM-user-study-cub", "clean_wide", split="test")
long_ = load_dataset("NWeak/CBM-user-study-cub", "clean_long", split="test")

Participant IDs are sequential integers; no identifying information is present.

Ownership and licence

This dataset is our own work, (c) 2026 the authors, released under CC-BY-4.0. That covers every participant response, our model's outputs, the experimental design, and the concept ground truth.

The six concepts are not CUB's official attributes. We defined them for this study (warm-coloured eyebrow, warm-coloured chest, plain sides, crested head, white throat, striped chest) and annotated the stimuli ourselves; FeatureN_GT reproduces CUB's own 112 attributes in 0 of 3420 rows, because it is not derived from them.

CUB-200-2011 contributes only two things here: the stimulus images, which are referenced by StimID/TestSampleIdx but not included, and the species label in CorrectAnswer. For the images and CUB's official annotations see NWeak/cub-mirror, which stays under CUB's non-commercial research-use terms (Wah et al., 2011) because we do not own them.

Citation

Produced for Are Concept Bottleneck Models Effective as Decision-Support Systems? (arXiv:2608.25581) -- Bogani, Debole, Marconato, Pugnana, Tentori, Passerini. Code: github.com/debryu/user-study-CBMs.

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Paper for NWeak/CBM-user-study-cub