The claim accurately summarizes the published findings of a 2022 randomized online experiment, including the effect-size labels the study authors used in their own abstract.
What Was Claimed?
The claim says that listening to six minutes of birdsong made people feel less anxious by a "medium" amount, and that listening to six minutes of city traffic noise made people feel more depressed by the same "medium" amount. This is the kind of plain-language summary that travels through press releases and headlines about how the soundscape around us affects mental state. It is worth checking because soundscape findings get repeated in popular coverage, and the claim could quietly drift away from what the original researchers actually reported.
What Did We Find?
The claim traces back to a single study: Stobbe and colleagues, "Birdsongs alleviate anxiety and paranoia in healthy participants," published in Scientific Reports in October 2022. The study assigned 295 online participants at random to listen to one of four six-minute soundscapes — low-diversity birdsong, high-diversity birdsong, low-diversity traffic, or high-diversity traffic — and measured their anxiety and depression with standard questionnaires before and after.
For the birdsong conditions, anxiety dropped significantly in both. The published statistics show Cohen's d values of about 0.77 and 0.70, which the authors describe in their own abstract as "medium effect sizes." That phrase in the claim is not a journalist's gloss — it is the researchers' own label.
For the traffic-noise conditions, depression rose significantly in both. The Cohen's d was 0.29 in the low-diversity condition and 0.59 in the high-diversity condition. The authors describe this as a "small effect size in low, medium effect size in high diversity condition." So the "medium" label the claim uses for the traffic effect matches the high-diversity arm of the study.
Both findings — the birdsong effect on anxiety and the high-diversity traffic effect on depression — were verified verbatim against two independent sources: the full text of the paper hosted on Nature, and the abstract archived on PubMed by the U.S. National Library of Medicine. The numbers and the magnitude labels agree.
The claim's phrase "with the same effect size" describes a category, not a decimal. The two findings are not numerically identical — anxiety effects are slightly larger in absolute terms — but both sit inside the same "medium" bracket as classified by the study authors. Under the standard reading of plain-language summaries of psychological research, they are described as the same size.
What Should You Keep In Mind?
A few caveats matter. The claim simplifies the traffic finding: depression also rose in the low-diversity traffic condition, but only with a small effect size (d = 0.29), not a medium one. The "medium" label only applies to the high-diversity traffic condition. The low-diversity finding does not contradict the claim, but the claim glosses over it.
The study measured immediate, short-term changes after a single six-minute exposure under online laboratory conditions. It does not say anything about whether long-term exposure to birdsong produces lasting anxiety relief, whether traffic noise causes clinical depression in real-world settings, or whether these effects matter for people with existing mental-health conditions. The descriptive claim — what this study reported — is supported. Broader claims that this proof does not address would need different evidence.
Cohen's d values of 0.70 and 0.77 are sometimes called "large" rather than "medium" by reviewers using strict cutoffs (0.5 medium, 0.8 large with midpoint thresholds at 0.65). The proof follows the study authors' own classification, which is what the claim mirrors. A reader who insists on strict cutoffs could reasonably classify the birdsong-anxiety effects as large, but that would not contradict the existence of the effect.
How Was This Verified?
Five quotes from the original publication and its NLM-archived abstract were fetched and matched character-by-character against the live source pages. The reasoning chain is laid out in the structured proof report, with adversarial checks and credibility details available in the full verification audit. To check this yourself, you can re-run the proof yourself.
What could challenge this verdict?
Four adversarial checks were conducted.
Did secondary coverage mischaracterize the findings? The full Nature paper was fetched directly and the within-group t-test results located in the Results section. The numeric d values (−0.77, −0.70 for anxiety; 0.29, 0.59 for depression) and the abstract's "medium effect size" labels were both verified verbatim on nature.com and on PubMed. The press-release-level summary faithfully reflects the authors' own abstract.
Does "the same effect size" break the claim because the d values differ numerically? The d values are not identical (anxiety |d| in [0.70, 0.77]; high-diversity depression d = 0.59), but both fall within Cohen's medium bracket per the authors' own labels. Operationalizing "same effect size" as "same magnitude category" is the standard reading for plain-language summaries of psychological findings.
Does the low-diversity traffic condition (d = 0.29, small effect) contradict the claim? The low-diversity finding is not a contradiction — depression still increased, just less. The claim is faithful to the high-diversity finding (which IS medium); it omits the diversity conditioning, but the medium effect it asserts genuinely exists in the study.
Does the study have methodological problems that would undermine the "study reported X" claim? The study is online-administered and uses self-report measures (STAI-S, PHQ-D, R-GPTS). It reports immediate pre/post effects only — no clinical, ecological, or long-term outcomes. These are limitations on what the finding GENERALIZES to, not on whether the study reported these specific d values. The descriptive claim about what the study reported is unaffected; broader causal/clinical generalizations are out of scope.
The claim accurately summarizes the published findings of a 2022 randomized online experiment, including the effect-size labels the study authors used in their own abstract.
What Was Claimed?
The claim says that listening to six minutes of birdsong made people feel less anxious by a "medium" amount, and that listening to six minutes of city traffic noise made people feel more depressed by the same "medium" amount. This is the kind of plain-language summary that travels through press releases and headlines about how the soundscape around us affects mental state. It is worth checking because soundscape findings get repeated in popular coverage, and the claim could quietly drift away from what the original researchers actually reported.
What Did We Find?
The claim traces back to a single study: Stobbe and colleagues, "Birdsongs alleviate anxiety and paranoia in healthy participants," published in Scientific Reports in October 2022. The study assigned 295 online participants at random to listen to one of four six-minute soundscapes — low-diversity birdsong, high-diversity birdsong, low-diversity traffic, or high-diversity traffic — and measured their anxiety and depression with standard questionnaires before and after.
For the birdsong conditions, anxiety dropped significantly in both. The published statistics show Cohen's d values of about 0.77 and 0.70, which the authors describe in their own abstract as "medium effect sizes." That phrase in the claim is not a journalist's gloss — it is the researchers' own label.
For the traffic-noise conditions, depression rose significantly in both. The Cohen's d was 0.29 in the low-diversity condition and 0.59 in the high-diversity condition. The authors describe this as a "small effect size in low, medium effect size in high diversity condition." So the "medium" label the claim uses for the traffic effect matches the high-diversity arm of the study.
Both findings — the birdsong effect on anxiety and the high-diversity traffic effect on depression — were verified verbatim against two independent sources: the full text of the paper hosted on Nature, and the abstract archived on PubMed by the U.S. National Library of Medicine. The numbers and the magnitude labels agree.
The claim's phrase "with the same effect size" describes a category, not a decimal. The two findings are not numerically identical — anxiety effects are slightly larger in absolute terms — but both sit inside the same "medium" bracket as classified by the study authors. Under the standard reading of plain-language summaries of psychological research, they are described as the same size.
What Should You Keep In Mind?
A few caveats matter. The claim simplifies the traffic finding: depression also rose in the low-diversity traffic condition, but only with a small effect size (d = 0.29), not a medium one. The "medium" label only applies to the high-diversity traffic condition. The low-diversity finding does not contradict the claim, but the claim glosses over it.
The study measured immediate, short-term changes after a single six-minute exposure under online laboratory conditions. It does not say anything about whether long-term exposure to birdsong produces lasting anxiety relief, whether traffic noise causes clinical depression in real-world settings, or whether these effects matter for people with existing mental-health conditions. The descriptive claim — what this study reported — is supported. Broader claims that this proof does not address would need different evidence.
Cohen's d values of 0.70 and 0.77 are sometimes called "large" rather than "medium" by reviewers using strict cutoffs (0.5 medium, 0.8 large with midpoint thresholds at 0.65). The proof follows the study authors' own classification, which is what the claim mirrors. A reader who insists on strict cutoffs could reasonably classify the birdsong-anxiety effects as large, but that would not contradict the existence of the effect.
How Was This Verified?
Five quotes from the original publication and its NLM-archived abstract were fetched and matched character-by-character against the live source pages. The reasoning chain is laid out in the structured proof report, with adversarial checks and credibility details available in the full verification audit. To check this yourself, you can re-run the proof yourself.
Before any verdict ships, the engine runs adversarial searches for evidence that could break the proof. 4 were run here.
| subject | Stobbe et al. 2022 randomized online experiment on soundscape exposure |
|---|---|
| threshold | |
| note | PRIMARY-SOURCE CLAIM. This is a claim about what one specific study reported. Both sub-claims trace to Stobbe et al. (2022) Scientific Reports 12:16414. Threshold of 2 is justified by domain scarcity: there is exactly one study with this design, so 'independent sources' are the full paper text and the PubMed-mirrored abstract, which agree on numeric d values and on the authors' magnitude labels. FORMALIZATION SCOPE NOTE: The natural-language phrase 'with the same effect size' is operationalized as 'both effects are characterized as medium by the study authors' (Cohen's d magnitude category), not as 'numerically identical d values'. Birdsong-anxiety has |d| in [0.70, 0.77] (authors: 'medium effect sizes'). Traffic-depression has d = 0.59 in the high-diversity condition (authors: 'medium effect size'). Both fall within Cohen's medium magnitude bracket per the authors' own classification. CAVEAT NOT CAPTURED IN THE CLAIM: The traffic-depression effect was 'medium' only in the high-diversity condition; the low-diversity traffic condition produced a small effect (d = 0.29). The compound claim oversimplifies by omitting this conditioning, but the medium effect the claim asserts does exist in the study. SC2 is satisfied by the high-diversity finding. The claim is NOT a causal claim about long-term mental health outcomes — it describes immediate pre/post questionnaire changes after a 6-minute laboratory exposure. It is also NOT a claim that traffic noise causes clinical depression. |
| sub-claims | SC1 |
| SC2 |
SC1: birdsong reduces anxiety, medium effect: 2 >= 2 = True
SC2: traffic noise raises depression, medium effect (high-diversity): 2 >= 2 = True
compound: all sub-claims hold: 2 == 2 = True
Source: proof.py inline output (execution trace)
Four adversarial checks were conducted.
Did secondary coverage mischaracterize the findings? The full Nature paper was fetched directly and the within-group t-test results located in the Results section. The numeric d values (−0.77, −0.70 for anxiety; 0.29, 0.59 for depression) and the abstract's "medium effect size" labels were both verified verbatim on nature.com and on PubMed. The press-release-level summary faithfully reflects the authors' own abstract.
Does "the same effect size" break the claim because the d values differ numerically? The d values are not identical (anxiety |d| in [0.70, 0.77]; high-diversity depression d = 0.59), but both fall within Cohen's medium bracket per the authors' own labels. Operationalizing "same effect size" as "same magnitude category" is the standard reading for plain-language summaries of psychological findings.
Does the low-diversity traffic condition (d = 0.29, small effect) contradict the claim? The low-diversity finding is not a contradiction — depression still increased, just less. The claim is faithful to the high-diversity finding (which IS medium); it omits the diversity conditioning, but the medium effect it asserts genuinely exists in the study.
Does the study have methodological problems that would undermine the "study reported X" claim? The study is online-administered and uses self-report measures (STAI-S, PHQ-D, R-GPTS). It reports immediate pre/post effects only — no clinical, ecological, or long-term outcomes. These are limitations on what the finding GENERALIZES to, not on whether the study reported these specific d values. The descriptive claim about what the study reported is unaffected; broader causal/clinical generalizations are out of scope.
audit trail · Detailed Evidence
All 5 citations verified.
Original audit log
B1 — sc1_nature_paper - Status: verified - Method: full_quote - Fetch mode: live - Verbatim status: verbatim (exact substring of Nature paper Results section)
B2 — sc1_pubmed_abstract - Status: verified - Method: full_quote - Fetch mode: live - Verbatim status: verbatim (exact substring of PubMed abstract page)
B3 — sc2_nature_paper - Status: verified - Method: full_quote - Fetch mode: live - Verbatim status: verbatim (exact substring of Nature paper Results section)
B4 — sc2_pubmed_abstract - Status: verified - Method: full_quote - Fetch mode: live - Verbatim status: verbatim (exact substring of PubMed abstract page)
B5 — duration_nature_paper - Status: verified - Method: full_quote - Fetch mode: live - Verbatim status: verbatim (exact substring of Nature paper Methods section)
Source: proof.py JSON summary
| Field | Value |
|---|---|
| Subject | Stobbe et al. 2022 randomized online experiment on soundscape exposure |
| SC1 property | 6-minute birdsong → significant anxiety decrease, medium effect (d in [-0.77, -0.70]) |
| SC1 operator / threshold | ≥ / 2 sources |
| SC2 property | 6-minute traffic noise → significant depression increase, medium effect (d = 0.59 high-diversity) |
| SC2 operator / threshold | ≥ / 2 sources |
| Compound operator | AND |
Source: proof.py JSON summary
The natural-language claim is:
"Six minutes of birdsong reduced anxiety with a medium effect size, while six minutes of traffic noise raised depression with the same effect size."
This is a primary-source descriptive claim about findings from Stobbe, Sundermann, Foerster & Kühn (2022), "Birdsongs alleviate anxiety and paranoia in healthy participants," Scientific Reports 12:16414. The claim is decomposed into two sub-claims joined by AND.
- SC1: 6-minute exposure to birdsong soundscapes produced a significant decrease in self-reported anxiety, characterized by the authors as a medium effect size (Cohen's d in [-0.77, -0.70]).
- SC2: 6-minute exposure to traffic noise soundscapes produced a significant increase in self-reported depression, with a medium effect size in the high-diversity condition (Cohen's d = 0.59).
The compound operator is AND — both must hold.
Formalization scope. The natural-language phrase "with the same effect size" is operationalized as "both effects are characterized as medium by the study authors" (Cohen's d magnitude category), not as "numerically identical d values." Birdsong-anxiety has |d| in [0.70, 0.77] (authors: "medium effect sizes"). Traffic-depression has d = 0.59 in the high-diversity condition (authors: "medium effect size"). Both fall within Cohen's medium bracket per the authors' own classification. The traffic-depression effect was "medium" only in the high-diversity condition; the low-diversity traffic condition produced a small effect (d = 0.29). The compound claim oversimplifies by omitting this conditioning, but the medium effect the claim asserts genuinely exists in the study; SC2 is satisfied by the high-diversity finding. The claim is not a causal claim about long-term mental health outcomes — it describes immediate pre/post questionnaire changes after a 6-minute laboratory exposure.
| Fact ID | Domain | Type | Note |
|---|---|---|---|
| B1, B3, B5 | nature.com | Academic | Springer Nature peer-reviewed publication; tier 4 |
| B2, B4 | pubmed.ncbi.nlm.nih.gov | Government | U.S. National Library of Medicine, NIH; tier 5 |
Source: proof.py JSON summary
SC1: birdsong reduces anxiety, medium effect: 2 >= 2 = True
SC2: traffic noise raises depression, medium effect (high-diversity): 2 >= 2 = True
compound: all sub-claims hold: 2 == 2 = True
Source: proof.py inline output (execution trace)
SC1 cross-check. The birdsong-anxiety finding is supported by two independent quotations of the published record: B1 (Nature paper full text, raw Cohen's d values d = -0.77 and d = -0.70) and B2 (PubMed-indexed abstract with the authors' qualitative label "medium effect sizes"). Nature and PubMed are separate hosted instances of the published paper (Nature serves the publisher's full text; NLM PubMed archives the abstract independently). They agree on both the numeric d values and the authors' magnitude label. No COI flags identified — both are canonical archival channels for peer-reviewed research, and neither has a stake in the substantive finding.
SC2 cross-check. The traffic-noise depression finding is supported by two independent quotations: B3 (Nature paper full text, raw Cohen's d = 0.29 low-diversity, d = 0.59 high-diversity) and B4 (PubMed abstract with the authors' qualitative labels "small effect size in low, medium effect size in high diversity condition"). Same independence note as SC1. No COI flags identified.
Source: proof.py JSON summary
- Rule 1 (Never hand-type values): N/A — citation-counting proof, no numeric values extracted via parsers.
- Rule 2 (Verify citations by fetching): All five citations fetched and verified via
verify_all_citations()with live HTTP, statusverifiedfor all. - Rule 3 (Anchor to system time): N/A — claim is not time-sensitive (describes a published study's reported numbers; result does not depend on today's date).
- Rule 4 (Explicit claim interpretation):
CLAIM_FORMALdeclares two sub-claims with explicit operator, threshold, and operator_note. Compoundoperator_notedocuments formalization scope (medium-as-category) and the low-diversity caveat. - Rule 5 (Independent adversarial check): 4 adversarial checks documented, covering accuracy of secondary coverage, the "same effect size" qualifier, the low-diversity small-effect counterpoint, and study methodology limits.
- Rule 6 (Independent cross-checks): 2 sources per sub-claim from independent hosting (Nature publisher full text + NLM PubMed abstract). COI flags assessed and empty.
- Rule 7 (Never hard-code constants/formulas): All verdict computation flows through
compare()andapply_verdict_qualifier()fromscripts/computations.py. No hard-coded*_holds = True/Falseliterals. - validate_proof.py result: PASS — 20/20 checks, 0 issues, 0 warnings.
Source: proof.py JSON summary; author analysis for rule applicability narrative
For qualitative/citation-counting proofs, extraction records track citation verification status per source.
| Fact ID | Verification Status | Found in Source | Quote Snippet |
|---|---|---|---|
| B1 | verified | Yes | low diversity: T(1, 62) = − 6.13, p < 0.001, d = − 0.77; high di |
| B2 | verified | Yes | Anxiety and paranoia significantly decreased in both birdsong conditions |
| B3 | verified | Yes | depressive symptoms significantly increased within both the low div |
| B4 | verified | Yes | the traffic noise soundscapes were associated with a significant in |
| B5 | verified | Yes | N = 295 participants were exposed to one out of four conditions for 6 |
Source: proof.py JSON summary
| ID | Fact | Verified |
|---|---|---|
| B1 | SC1: Nature paper (Stobbe et al. 2022) — verbatim Cohen's d values for birdsong-anxiety (d = -0.77 low diversity, d = -0.70 high diversity) | Yes |
| B2 | SC1: PubMed-indexed abstract — author characterization 'Anxiety and paranoia significantly decreased in both birdsong conditions (medium effect sizes)' | Yes |
| B3 | SC2: Nature paper (Stobbe et al. 2022) — verbatim Cohen's d values for traffic-depression (d = 0.29 low, d = 0.59 high diversity) | Yes |
| B4 | SC2: PubMed-indexed abstract — author characterization 'increase in depression (small effect size in low, medium effect size in high diversity condition)' | Yes |
| B5 | Both SCs: Nature paper confirms 6-minute exposure duration | Yes |
| A1 | SC1 verified source count | Computed: 2 independent sources confirmed birdsong-anxiety finding |
| A2 | SC2 verified source count | Computed: 2 independent sources confirmed traffic-depression finding |
Cite this proof
Proof Engine. (2026). Claim Verification: “Six minutes of birdsong reduced anxiety with a medium effect size, while six minutes of traffic noise raised depression with the same effect size.” — Proved. https://proofengine.info/proofs/birdsong-traffic-noise-anxiety-depression/
Proof Engine. "Claim Verification: “Six minutes of birdsong reduced anxiety with a medium effect size, while six minutes of traffic noise raised depression with the same effect size.” — Proved." 2026. https://proofengine.info/proofs/birdsong-traffic-noise-anxiety-depression/.
@misc{proofengine_birdsong_traffic_noise_anxiety_depression,
title = {Claim Verification: “Six minutes of birdsong reduced anxiety with a medium effect size, while six minutes of traffic noise raised depression with the same effect size.” — Proved},
author = {{Proof Engine}},
year = {2026},
url = {https://proofengine.info/proofs/birdsong-traffic-noise-anxiety-depression/},
note = {Verdict: PROVED. Generated by proof-engine v1.33.2},
}TY - DATA TI - Claim Verification: “Six minutes of birdsong reduced anxiety with a medium effect size, while six minutes of traffic noise raised depression with the same effect size.” — Proved AU - Proof Engine PY - 2026 UR - https://proofengine.info/proofs/birdsong-traffic-noise-anxiety-depression/ N1 - Verdict: PROVED. Generated by proof-engine v1.33.2 ER -
View proof source
This is the proof.py that produced the verdict above. Every fact traces to code below. (This proof has not yet been minted to Zenodo; the source here is the working copy from this repository.)
"""
Proof: Six minutes of birdsong reduced anxiety with a medium effect size,
while six minutes of traffic noise raised depression with the same effect size.
Generated: 2026-04-28
This is a primary-source compound claim about the findings of one specific
randomized online experiment (Stobbe, Sundermann, Foerster & Kuühn, 2022,
Scientific Reports). The proof verifies via verbatim quotes from the original
peer-reviewed paper and its PubMed-indexed abstract.
"""
import os
import sys
PROOF_ENGINE_ROOT = os.environ.get("PROOF_ENGINE_ROOT")
if not PROOF_ENGINE_ROOT:
_d = os.path.dirname(os.path.abspath(__file__))
while _d != os.path.dirname(_d):
if os.path.isdir(os.path.join(_d, "proof-engine", "skills", "proof-engine", "scripts")):
PROOF_ENGINE_ROOT = os.path.join(_d, "proof-engine", "skills", "proof-engine")
break
_d = os.path.dirname(_d)
if not PROOF_ENGINE_ROOT:
raise RuntimeError("PROOF_ENGINE_ROOT not set and skill dir not found via walk-up from proof.py")
sys.path.insert(0, PROOF_ENGINE_ROOT)
from scripts.verify_citations import verify_all_citations
from scripts.computations import compare, apply_verdict_qualifier
from scripts.proof_summary import ProofSummaryBuilder
# ---
# 1. CLAIM INTERPRETATION (Rule 4)
# ---
CLAIM_NATURAL = (
"Six minutes of birdsong reduced anxiety with a medium effect size, "
"while six minutes of traffic noise raised depression with the same effect size."
)
CLAIM_FORMAL = {
"subject": "Stobbe et al. 2022 randomized online experiment on soundscape exposure",
"sub_claims": [
{
"id": "SC1",
"property": (
"In Stobbe et al. (2022), 6-minute exposure to birdsong soundscapes "
"produced a significant decrease in self-reported anxiety, characterized "
"by the authors as a medium effect size (Cohen's d in [-0.77, -0.70])."
),
"operator": ">=",
"threshold": 2,
"operator_note": (
"SC1 verifies what the study reports about birdsong and anxiety. "
"Two sources confirm: (a) the full Nature paper gives the raw "
"Cohen's d values d = -0.77 (low diversity) and d = -0.70 (high diversity); "
"(b) the PubMed-indexed abstract characterizes these as 'medium effect sizes'."
),
},
{
"id": "SC2",
"property": (
"In Stobbe et al. (2022), 6-minute exposure to traffic noise soundscapes "
"produced a significant increase in self-reported depression, with a "
"medium effect size in the high-diversity condition (Cohen's d = 0.59)."
),
"operator": ">=",
"threshold": 2,
"operator_note": (
"SC2 verifies what the study reports about traffic noise and depression. "
"Two sources confirm: (a) the full Nature paper gives Cohen's d = 0.59 "
"(high diversity, characterized as medium) and d = 0.29 (low diversity, "
"characterized as small); (b) the PubMed-indexed abstract characterizes "
"the high-diversity result as a 'medium effect size'. The claim's match "
"for SC2 is to the high-diversity finding, not the low-diversity finding."
),
},
],
"compound_operator": "AND",
"operator_note": (
"PRIMARY-SOURCE CLAIM. This is a claim about what one specific study reported. "
"Both sub-claims trace to Stobbe et al. (2022) Scientific Reports 12:16414. "
"Threshold of 2 is justified by domain scarcity: there is exactly one study "
"with this design, so 'independent sources' are the full paper text and the "
"PubMed-mirrored abstract, which agree on numeric d values and on the authors' "
"magnitude labels. \n\n"
"FORMALIZATION SCOPE NOTE: The natural-language phrase 'with the same effect size' "
"is operationalized as 'both effects are characterized as medium by the study "
"authors' (Cohen's d magnitude category), not as 'numerically identical d values'. "
"Birdsong-anxiety has |d| in [0.70, 0.77] (authors: 'medium effect sizes'). "
"Traffic-depression has d = 0.59 in the high-diversity condition (authors: "
"'medium effect size'). Both fall within Cohen's medium magnitude bracket per "
"the authors' own classification. \n\n"
"CAVEAT NOT CAPTURED IN THE CLAIM: The traffic-depression effect was 'medium' "
"only in the high-diversity condition; the low-diversity traffic condition "
"produced a small effect (d = 0.29). The compound claim oversimplifies by "
"omitting this conditioning, but the medium effect the claim asserts does "
"exist in the study. SC2 is satisfied by the high-diversity finding. \n\n"
"The claim is NOT a causal claim about long-term mental health outcomes — it "
"describes immediate pre/post questionnaire changes after a 6-minute laboratory "
"exposure. It is also NOT a claim that traffic noise causes clinical depression."
),
}
# ---
# 2. FACT REGISTRY
# ---
FACT_REGISTRY = {
"B1": {
"key": "sc1_nature_paper",
"label": (
"SC1: Nature paper (Stobbe et al. 2022) — verbatim Cohen's d values for "
"birdsong-anxiety (d = -0.77 low diversity, d = -0.70 high diversity)"
),
},
"B2": {
"key": "sc1_pubmed_abstract",
"label": (
"SC1: PubMed-indexed abstract — author characterization 'Anxiety and "
"paranoia significantly decreased in both birdsong conditions (medium effect sizes)'"
),
},
"B3": {
"key": "sc2_nature_paper",
"label": (
"SC2: Nature paper (Stobbe et al. 2022) — verbatim Cohen's d values for "
"traffic-depression (d = 0.29 low, d = 0.59 high diversity)"
),
},
"B4": {
"key": "sc2_pubmed_abstract",
"label": (
"SC2: PubMed-indexed abstract — author characterization 'increase in depression "
"(small effect size in low, medium effect size in high diversity condition)'"
),
},
"B5": {
"key": "duration_nature_paper",
"label": "Both SCs: Nature paper confirms 6-minute exposure duration",
},
"A1": {"label": "SC1 verified source count", "method": None, "result": None},
"A2": {"label": "SC2 verified source count", "method": None, "result": None},
}
# ---
# 3. EMPIRICAL FACTS — grouped by sub-claim
# ---
empirical_facts = {
# SC1: birdsong reduces anxiety with medium effect size
"sc1_nature_paper": {
"quote": (
"low diversity: T(1, 62) = − 6.13, p < 0.001, d = − 0.77; "
"high diversity: T(1, 60) = − 6.32, p < 0.001, d = − 0.70"
),
"url": "https://www.nature.com/articles/s41598-022-20841-0",
"source_name": (
"Stobbe E, Sundermann J, Foerster L, Kühn S (2022). Birdsongs alleviate "
"anxiety and paranoia in healthy participants. Scientific Reports 12:16414."
),
},
"sc1_pubmed_abstract": {
"quote": (
"Anxiety and paranoia significantly decreased in both birdsong conditions "
"(medium effect sizes)."
),
"url": "https://pubmed.ncbi.nlm.nih.gov/36229489/",
"source_name": (
"PubMed-indexed abstract of Stobbe et al. 2022 (PMID 36229489), "
"U.S. National Library of Medicine."
),
},
# SC2: traffic noise raises depression with medium effect size (high diversity)
"sc2_nature_paper": {
"quote": (
"depressive symptoms significantly increased within both the low diversity "
"urban soundscape (T(1, 82) = 2.64, p = 0.010, d = 0.29) and high diversity "
"urban condition (T(1, 68) = 4.88, p < 0.001, d = 0.59)"
),
"url": "https://www.nature.com/articles/s41598-022-20841-0",
"source_name": (
"Stobbe E, Sundermann J, Foerster L, Kühn S (2022). Birdsongs alleviate "
"anxiety and paranoia in healthy participants. Scientific Reports 12:16414."
),
},
"sc2_pubmed_abstract": {
"quote": (
"the traffic noise soundscapes were associated with a significant increase "
"in depression (small effect size in low, medium effect size in high diversity "
"condition)."
),
"url": "https://pubmed.ncbi.nlm.nih.gov/36229489/",
"source_name": (
"PubMed-indexed abstract of Stobbe et al. 2022 (PMID 36229489), "
"U.S. National Library of Medicine."
),
},
# Duration anchor — confirms 'six minutes' part of the claim
"duration_nature_paper": {
"quote": (
"N = 295 participants were exposed to one out of four conditions for 6 min: "
"traffic noise low, traffic noise high, birdsong low, and birdsong high "
"diversity soundscapes."
),
"url": "https://www.nature.com/articles/s41598-022-20841-0",
"source_name": (
"Stobbe E, Sundermann J, Foerster L, Kühn S (2022). Birdsongs alleviate "
"anxiety and paranoia in healthy participants. Scientific Reports 12:16414."
),
},
}
# ---
# 4. CITATION VERIFICATION (Rule 2)
# ---
citation_results = verify_all_citations(empirical_facts, wayback_fallback=True)
# ---
# 5. COUNT VERIFIED SOURCES PER SUB-CLAIM
# ---
COUNTABLE_STATUSES = ("verified", "partial")
sc1_keys = [k for k in empirical_facts if k.startswith("sc1_")]
sc2_keys = [k for k in empirical_facts if k.startswith("sc2_")]
n_sc1 = sum(1 for k in sc1_keys if citation_results[k]["status"] in COUNTABLE_STATUSES)
n_sc2 = sum(1 for k in sc2_keys if citation_results[k]["status"] in COUNTABLE_STATUSES)
# ---
# 6. PER-SUB-CLAIM EVALUATION (Rule 7 — never hardcode holds values)
# ---
sc1_holds = compare(n_sc1, ">=", CLAIM_FORMAL["sub_claims"][0]["threshold"],
label="SC1: birdsong reduces anxiety, medium effect")
sc2_holds = compare(n_sc2, ">=", CLAIM_FORMAL["sub_claims"][1]["threshold"],
label="SC2: traffic noise raises depression, medium effect (high-diversity)")
# ---
# 7. COMPOUND EVALUATION
# ---
n_holding = sum([sc1_holds, sc2_holds])
n_total = len(CLAIM_FORMAL["sub_claims"])
claim_holds = compare(n_holding, "==", n_total, label="compound: all sub-claims hold")
# ---
# 8. COI FLAGS
# ---
sc1_coi_flags = [] # Primary-source verification; PubMed independently archives the abstract.
sc2_coi_flags = []
# ---
# 9. ADVERSARIAL CHECKS (Rule 5)
# ---
adversarial_checks = [
{
"question": (
"Does the original paper actually report the effect sizes described, or did "
"secondary coverage (press release, news articles) mischaracterize the findings?"
),
"verification_performed": (
"Fetched the full Nature paper at https://www.nature.com/articles/s41598-022-20841-0 "
"and located the within-group t-test results in the Results section. Confirmed: "
"anxiety decreased in both birdsong conditions (d = -0.77, d = -0.70) and "
"depression increased in both traffic-noise conditions (d = 0.29, d = 0.59). "
"Cross-checked the abstract verbatim on both nature.com and pubmed.ncbi.nlm.nih.gov "
"to confirm the authors' own 'medium effect size' characterization for both finding."
),
"finding": (
"The paper itself supports both sub-claims. The abstract explicitly labels both "
"the birdsong-anxiety effects and the high-diversity traffic-depression effect as "
"'medium effect sizes'. The claim's language faithfully reflects the authors' "
"own summary."
),
"breaks_proof": False,
},
{
"question": (
"Does the qualifier 'the same effect size' break the claim? The d values are not "
"numerically identical (anxiety |d| in [0.70, 0.77]; depression d = 0.59 high diversity)."
),
"verification_performed": (
"Reviewed Cohen's (1988) conventional d magnitude thresholds: 0.2 small, 0.5 "
"medium, 0.8 large. Examined the authors' own labels: birdsong-anxiety = 'medium', "
"traffic-depression high diversity = 'medium'. Considered the standard "
"interpretation that 'effect size' in plain-language summaries refers to Cohen's "
"magnitude category rather than to identical decimal values."
),
"finding": (
"Both effects share the 'medium' magnitude category as labeled by the study "
"authors. The d values differ (0.70-0.77 vs 0.59) but both lie within Cohen's "
"medium bracket per the authors' classification. Operationalizing 'same effect "
"size' as 'same magnitude category' is the standard reading for non-technical "
"summaries of psychological findings, and this matches the abstract phrasing."
),
"breaks_proof": False,
},
{
"question": (
"Does the low-diversity traffic condition (d = 0.29, small effect) contradict "
"or break the claim?"
),
"verification_performed": (
"Re-read the abstract: 'small effect size in low, medium effect size in high "
"diversity condition'. Considered whether the claim's omission of the "
"low-diversity small-effect finding is a fatal oversimplification."
),
"finding": (
"The low-diversity finding is not a contradiction — depression still increased, "
"just less. The claim is faithful to the high-diversity finding (which IS medium) "
"and to the abstract-level summary; it omits the diversity conditioning. SC2's "
"operator_note documents this scope. Operationally, the claim is supported by "
"the high-diversity arm and is not refuted by the low-diversity arm."
),
"breaks_proof": False,
},
{
"question": (
"Does the study have methodological problems (sample, design, replication) that "
"would undermine even the descriptive 'this study reported X' claim?"
),
"verification_performed": (
"Searched for replications, criticism, and retractions of Stobbe et al. 2022. "
"Reviewed the published methods: N = 295, randomized online experiment, four "
"between-subjects conditions, validated questionnaires (STAI-S for state anxiety, "
"PHQ-D for depression, R-GPTS for paranoia), 6-minute soundscape exposure. The "
"paper has been cited extensively in mental-health and environmental-psychology "
"literature without retraction or formal correction."
),
"finding": (
"The study is online-administered and has the usual limits of self-report and "
"short-duration laboratory designs (immediate effects only, no ecological "
"validity for long-term outcomes, no health-outcomes follow-up). These are "
"limitations of what the finding GENERALIZES to, not of whether the study "
"reported the effect sizes shown. The descriptive claim about what the study "
"reported holds; broader causal/clinical generalizations are out of scope."
),
"breaks_proof": False,
},
]
# ---
# 11. VERDICT
# ---
if __name__ == "__main__":
any_unverified = any(
cr["status"] != "verified" for cr in citation_results.values()
)
any_breaks = any(ac.get("breaks_proof") for ac in adversarial_checks)
# Per-sub-claim COI gate (Rule 6) — primary-source claim, no COI flagged.
sc1_confirmed_keys = {k for k in sc1_keys
if citation_results[k]["status"] in COUNTABLE_STATUSES}
sc1_coi_favorable = {f["source_key"] for f in sc1_coi_flags
if f["direction"] == "favorable_to_subject"
and f["source_key"] in sc1_confirmed_keys}
sc1_coi_unfavorable = {f["source_key"] for f in sc1_coi_flags
if f["direction"] == "unfavorable_to_subject"
and f["source_key"] in sc1_confirmed_keys}
sc1_coi_majority = max(len(sc1_coi_favorable), len(sc1_coi_unfavorable)) if sc1_coi_flags else 0
sc1_threshold = CLAIM_FORMAL["sub_claims"][0]["threshold"]
sc1_coi_override = n_sc1 >= sc1_threshold and sc1_coi_majority > n_sc1 / 2
sc2_confirmed_keys = {k for k in sc2_keys
if citation_results[k]["status"] in COUNTABLE_STATUSES}
sc2_coi_favorable = {f["source_key"] for f in sc2_coi_flags
if f["direction"] == "favorable_to_subject"
and f["source_key"] in sc2_confirmed_keys}
sc2_coi_unfavorable = {f["source_key"] for f in sc2_coi_flags
if f["direction"] == "unfavorable_to_subject"
and f["source_key"] in sc2_confirmed_keys}
sc2_coi_majority = max(len(sc2_coi_favorable), len(sc2_coi_unfavorable)) if sc2_coi_flags else 0
sc2_threshold = CLAIM_FORMAL["sub_claims"][1]["threshold"]
sc2_coi_override = n_sc2 >= sc2_threshold and sc2_coi_majority > n_sc2 / 2
any_coi_override = sc1_coi_override or sc2_coi_override
is_contested_qualifier = "qualifier" in CLAIM_FORMAL.get("operator_note", "").lower()
if any_breaks:
base_verdict = "UNDETERMINED"
elif any_coi_override:
base_verdict = "UNDETERMINED"
elif is_contested_qualifier and sc1_holds and not sc2_holds:
base_verdict = "DISPROVED"
elif not claim_holds and n_holding > 0:
base_verdict = "PARTIALLY VERIFIED"
elif claim_holds:
base_verdict = "PROVED"
elif not claim_holds and n_holding == 0:
base_verdict = "UNDETERMINED"
else:
base_verdict = "UNDETERMINED"
verdict = apply_verdict_qualifier(base_verdict, any_unverified)
print(f"\n{'='*60}")
print(f"VERDICT: {verdict}")
print(f"{'='*60}")
print(f"SC1 (birdsong reduces anxiety, medium effect): n={n_sc1}, holds={sc1_holds}")
print(f"SC2 (traffic noise raises depression, medium effect): n={n_sc2}, holds={sc2_holds}")
print(f"Compound: {n_holding}/{n_total} sub-claims hold")
print(f"Any unverified citations: {any_unverified}")
print()
# --- Build JSON summary ---
builder = ProofSummaryBuilder(CLAIM_NATURAL, CLAIM_FORMAL)
# Map each B-fact to its sub-claim. B5 (duration) supports both — assign to SC1 for indexing.
fact_to_sc = {
"B1": "SC1", "B2": "SC1",
"B3": "SC2", "B4": "SC2",
"B5": "SC1", # duration anchor; both sub-claims depend on it
}
for fid, info in FACT_REGISTRY.items():
if not fid.startswith("B"):
continue
ef_key = info["key"]
ef = empirical_facts[ef_key]
cr = citation_results.get(ef_key, {})
sub_claim = fact_to_sc[fid]
builder.add_empirical_fact(
fid,
label=info["label"],
source_name=ef["source_name"],
source_url=ef["url"],
source_quote=ef["quote"],
sub_claim=sub_claim,
)
builder.set_verification(
fid,
status=cr.get("status", "unknown"),
method=cr.get("method", "full_quote"),
coverage_pct=cr.get("coverage_pct"),
fetch_mode=cr.get("fetch_mode", "live"),
credibility=cr.get("credibility", {}),
)
builder.set_extraction(
fid,
value=cr.get("status", "unknown"),
value_in_quote=cr.get("status") in COUNTABLE_STATUSES,
quote_snippet=ef["quote"][:80],
)
sc1_fact_ids = [fid for fid, info in FACT_REGISTRY.items()
if fid.startswith("B") and info["key"] in sc1_keys]
sc2_fact_ids = [fid for fid, info in FACT_REGISTRY.items()
if fid.startswith("B") and info["key"] in sc2_keys]
builder.add_computed_fact(
"A1",
label="SC1 verified source count",
method=f"count(verified sc1 citations) = {n_sc1}",
result=n_sc1,
depends_on=sc1_fact_ids,
sub_claim="SC1",
)
builder.add_computed_fact(
"A2",
label="SC2 verified source count",
method=f"count(verified sc2 citations) = {n_sc2}",
result=n_sc2,
depends_on=sc2_fact_ids,
sub_claim="SC2",
)
builder.add_cross_check(
description=(
"SC1: independent quotation of birdsong-anxiety finding from primary paper "
"(raw d values) and PubMed-indexed abstract (author 'medium effect size' label)."
),
fact_ids=sc1_fact_ids,
n_sources_consulted=len(sc1_keys),
n_sources_verified=n_sc1,
sources={k: citation_results[k]["status"] for k in sc1_keys},
independence_note=(
"Nature paper full text and NLM PubMed abstract are separate hosted instances "
"of the published record. They confirm both the numeric d values and the "
"authors' qualitative label."
),
coi_flags=sc1_coi_flags,
agreement=sc1_holds,
)
builder.add_cross_check(
description=(
"SC2: independent quotation of traffic-noise depression finding from primary "
"paper (raw d values) and PubMed-indexed abstract (author 'medium effect size' "
"label for high-diversity condition)."
),
fact_ids=sc2_fact_ids,
n_sources_consulted=len(sc2_keys),
n_sources_verified=n_sc2,
sources={k: citation_results[k]["status"] for k in sc2_keys},
independence_note=(
"Same as SC1 — Nature and PubMed mirror the published record and are "
"the canonical archival sources for the abstract and full text."
),
coi_flags=sc2_coi_flags,
agreement=sc2_holds,
)
builder.add_sub_claim_result(
id="SC1", n_confirming=n_sc1,
threshold=CLAIM_FORMAL["sub_claims"][0]["threshold"], holds=sc1_holds,
)
builder.add_sub_claim_result(
id="SC2", n_confirming=n_sc2,
threshold=CLAIM_FORMAL["sub_claims"][1]["threshold"], holds=sc2_holds,
)
for ac in adversarial_checks:
builder.add_adversarial_check(
question=ac["question"],
verification_performed=ac["verification_performed"],
finding=ac["finding"],
breaks_proof=ac["breaks_proof"],
)
builder.set_verdict(base_verdict, any_unverified=any_unverified)
builder.set_key_results(
n_holding=n_holding,
n_total=n_total,
claim_holds=claim_holds,
sc1_d_low=-0.77,
sc1_d_high=-0.70,
sc2_d_low=0.29,
sc2_d_high=0.59,
exposure_minutes=6,
sample_size=295,
)
builder.emit()
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