Face Stereotypes from Insufficient Statistical Learning Article Swipe
Xuechunzi Bai
,
Stefan Uddenberg
,
Brandon P. Labbree
,
Alexander Todorov
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.31219/osf.io/s9y8n
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.31219/osf.io/s9y8n
Social stereotypes are prevalent and consequential, yet sometimes inaccurate. How do people learn these inaccurate beliefs in the first place and why do these beliefs persist in the face of counter evidence? Building on past research on cognitive limitations and environmental sample biases, we propose an integrative perspective: Insufficient statistical learning (Insta-learn). Instalearn posits that humans are active learners of the environment. Starting from a small sample, people are able to extract statistical patterns within the sample accurately and quickly. However, people do not continue sampling sufficiently. If they decide not to collect more samples once they are (prematurely) satisfied, inaccurate stereotypes can emerge even when more data would show otherwise. We investigated this hypothesis across six online experiments (N = 1565), using novel pairs of computer-generated faces and social behaviors. Fixing the population level statistics of face-behavior associations to zero and varying the initial sample statistics, we found that participants quickly learned the initial sample statistics (from as few as three examples) and persisted in using such spurious associations in their final decisions. Granting the sampling power to participants — samples were endogenously generated by participants and not defined by the experimenters — we found insufficient sampling caused spurious associations to persist. Insta-learn provides a domain-general framework for a mechanistic explanation of the emergence and persistence of social stereotypes.
Related Topics
Concepts
Spurious relationship
Sample (material)
Psychology
Perspective (graphical)
Face (sociological concept)
Population
Sample size determination
Sampling (signal processing)
Statistics
Social psychology
Cognitive psychology
Computer science
Artificial intelligence
Mathematics
Sociology
Demography
Chemistry
Social science
Filter (signal processing)
Computer vision
Chromatography
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31219/osf.io/s9y8n
- https://osf.io/s9y8n/download
- OA Status
- gold
- Cited By
- 1
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4210705188
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4210705188Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.31219/osf.io/s9y8nDigital Object Identifier
- Title
-
Face Stereotypes from Insufficient Statistical LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-25Full publication date if available
- Authors
-
Xuechunzi Bai, Stefan Uddenberg, Brandon P. Labbree, Alexander TodorovList of authors in order
- Landing page
-
https://doi.org/10.31219/osf.io/s9y8nPublisher landing page
- PDF URL
-
https://osf.io/s9y8n/downloadDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://osf.io/s9y8n/downloadDirect OA link when available
- Concepts
-
Spurious relationship, Sample (material), Psychology, Perspective (graphical), Face (sociological concept), Population, Sample size determination, Sampling (signal processing), Statistics, Social psychology, Cognitive psychology, Computer science, Artificial intelligence, Mathematics, Sociology, Demography, Chemistry, Social science, Filter (signal processing), Computer vision, ChromatographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 1Per-year citation counts (last 5 years)
- References (count)
-
62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4210705188 |
|---|---|
| doi | https://doi.org/10.31219/osf.io/s9y8n |
| ids.doi | https://doi.org/10.31219/osf.io/s9y8n |
| ids.openalex | https://openalex.org/W4210705188 |
| fwci | 0.13427806 |
| type | preprint |
| title | Face Stereotypes from Insufficient Statistical Learning |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12520 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.8550999760627747 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | Psychology of Moral and Emotional Judgment |
| topics[1].id | https://openalex.org/T10315 |
| topics[1].field.id | https://openalex.org/fields/18 |
| topics[1].field.display_name | Decision Sciences |
| topics[1].score | 0.7615000009536743 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1800 |
| topics[1].subfield.display_name | General Decision Sciences |
| topics[1].display_name | Decision-Making and Behavioral Economics |
| topics[2].id | https://openalex.org/T11875 |
| topics[2].field.id | https://openalex.org/fields/26 |
| topics[2].field.display_name | Mathematics |
| topics[2].score | 0.7609000205993652 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2613 |
| topics[2].subfield.display_name | Statistics and Probability |
| topics[2].display_name | Statistics Education and Methodologies |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C97256817 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8905538320541382 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1462316 |
| concepts[0].display_name | Spurious relationship |
| concepts[1].id | https://openalex.org/C198531522 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6897039413452148 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q485146 |
| concepts[1].display_name | Sample (material) |
| concepts[2].id | https://openalex.org/C15744967 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6095266342163086 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[2].display_name | Psychology |
| concepts[3].id | https://openalex.org/C12713177 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6029630899429321 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1900281 |
| concepts[3].display_name | Perspective (graphical) |
| concepts[4].id | https://openalex.org/C2779304628 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5580155849456787 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3503480 |
| concepts[4].display_name | Face (sociological concept) |
| concepts[5].id | https://openalex.org/C2908647359 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5359196662902832 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2625603 |
| concepts[5].display_name | Population |
| concepts[6].id | https://openalex.org/C129848803 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5247043371200562 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2564360 |
| concepts[6].display_name | Sample size determination |
| concepts[7].id | https://openalex.org/C140779682 |
| concepts[7].level | 3 |
| concepts[7].score | 0.46341270208358765 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q210868 |
| concepts[7].display_name | Sampling (signal processing) |
| concepts[8].id | https://openalex.org/C105795698 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4427200257778168 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[8].display_name | Statistics |
| concepts[9].id | https://openalex.org/C77805123 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4059484004974365 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q161272 |
| concepts[9].display_name | Social psychology |
| concepts[10].id | https://openalex.org/C180747234 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3333396017551422 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q23373 |
| concepts[10].display_name | Cognitive psychology |
| concepts[11].id | https://openalex.org/C41008148 |
| concepts[11].level | 0 |
| concepts[11].score | 0.3210490942001343 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[11].display_name | Computer science |
| concepts[12].id | https://openalex.org/C154945302 |
| concepts[12].level | 1 |
| concepts[12].score | 0.28890460729599 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[12].display_name | Artificial intelligence |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.22887644171714783 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C144024400 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0978974997997284 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q21201 |
| concepts[14].display_name | Sociology |
| concepts[15].id | https://openalex.org/C149923435 |
| concepts[15].level | 1 |
| concepts[15].score | 0.08518919348716736 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q37732 |
| concepts[15].display_name | Demography |
| concepts[16].id | https://openalex.org/C185592680 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[16].display_name | Chemistry |
| concepts[17].id | https://openalex.org/C36289849 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q34749 |
| concepts[17].display_name | Social science |
| concepts[18].id | https://openalex.org/C106131492 |
| concepts[18].level | 2 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q3072260 |
| concepts[18].display_name | Filter (signal processing) |
| concepts[19].id | https://openalex.org/C31972630 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[19].display_name | Computer vision |
| concepts[20].id | https://openalex.org/C43617362 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q170050 |
| concepts[20].display_name | Chromatography |
| keywords[0].id | https://openalex.org/keywords/spurious-relationship |
| keywords[0].score | 0.8905538320541382 |
| keywords[0].display_name | Spurious relationship |
| keywords[1].id | https://openalex.org/keywords/sample |
| keywords[1].score | 0.6897039413452148 |
| keywords[1].display_name | Sample (material) |
| keywords[2].id | https://openalex.org/keywords/psychology |
| keywords[2].score | 0.6095266342163086 |
| keywords[2].display_name | Psychology |
| keywords[3].id | https://openalex.org/keywords/perspective |
| keywords[3].score | 0.6029630899429321 |
| keywords[3].display_name | Perspective (graphical) |
| keywords[4].id | https://openalex.org/keywords/face |
| keywords[4].score | 0.5580155849456787 |
| keywords[4].display_name | Face (sociological concept) |
| keywords[5].id | https://openalex.org/keywords/population |
| keywords[5].score | 0.5359196662902832 |
| keywords[5].display_name | Population |
| keywords[6].id | https://openalex.org/keywords/sample-size-determination |
| keywords[6].score | 0.5247043371200562 |
| keywords[6].display_name | Sample size determination |
| keywords[7].id | https://openalex.org/keywords/sampling |
| keywords[7].score | 0.46341270208358765 |
| keywords[7].display_name | Sampling (signal processing) |
| keywords[8].id | https://openalex.org/keywords/statistics |
| keywords[8].score | 0.4427200257778168 |
| keywords[8].display_name | Statistics |
| keywords[9].id | https://openalex.org/keywords/social-psychology |
| keywords[9].score | 0.4059484004974365 |
| keywords[9].display_name | Social psychology |
| keywords[10].id | https://openalex.org/keywords/cognitive-psychology |
| keywords[10].score | 0.3333396017551422 |
| keywords[10].display_name | Cognitive psychology |
| keywords[11].id | https://openalex.org/keywords/computer-science |
| keywords[11].score | 0.3210490942001343 |
| keywords[11].display_name | Computer science |
| keywords[12].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[12].score | 0.28890460729599 |
| keywords[12].display_name | Artificial intelligence |
| keywords[13].id | https://openalex.org/keywords/mathematics |
| keywords[13].score | 0.22887644171714783 |
| keywords[13].display_name | Mathematics |
| keywords[14].id | https://openalex.org/keywords/sociology |
| keywords[14].score | 0.0978974997997284 |
| keywords[14].display_name | Sociology |
| keywords[15].id | https://openalex.org/keywords/demography |
| keywords[15].score | 0.08518919348716736 |
| keywords[15].display_name | Demography |
| language | en |
| locations[0].id | doi:10.31219/osf.io/s9y8n |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://osf.io/s9y8n/download |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.31219/osf.io/s9y8n |
| locations[1].id | pmh:oai:share.osf.io:E0064-8D4-EEA |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306400047 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | Arabixiv (OSF Preprints) |
| locations[1].source.host_organization | https://openalex.org/I2799848540 |
| locations[1].source.host_organization_name | Center for Open Science |
| locations[1].source.host_organization_lineage | https://openalex.org/I2799848540 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | preprint |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://doi.org/10.31219/OSF.IO/S9Y8N |
| locations[2].id | pmh:oai:share.osf.io:d76f1880-dbe3-4500-bd1d-29b700f7339f |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400047 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | Arabixiv (OSF Preprints) |
| locations[2].source.host_organization | https://openalex.org/I2799848540 |
| locations[2].source.host_organization_name | Center for Open Science |
| locations[2].source.host_organization_lineage | https://openalex.org/I2799848540 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Preprint |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://osf.io/s9y8n |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5014394213 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2277-5451 |
| authorships[0].author.display_name | Xuechunzi Bai |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I20089843 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Psychology, Princeton University |
| authorships[0].institutions[0].id | https://openalex.org/I20089843 |
| authorships[0].institutions[0].ror | https://ror.org/00hx57361 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I20089843 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Princeton University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xuechunzi Bai |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Psychology, Princeton University |
| authorships[1].author.id | https://openalex.org/A5032708228 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2689-3906 |
| authorships[1].author.display_name | Stefan Uddenberg |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I40347166 |
| authorships[1].affiliations[0].raw_affiliation_string | The University of Chicago Booth School of Business |
| authorships[1].institutions[0].id | https://openalex.org/I40347166 |
| authorships[1].institutions[0].ror | https://ror.org/024mw5h28 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I40347166 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of Chicago |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Stefan Uddenberg |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | The University of Chicago Booth School of Business |
| authorships[2].author.id | https://openalex.org/A5020358012 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5893-4469 |
| authorships[2].author.display_name | Brandon P. Labbree |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I20089843 |
| authorships[2].affiliations[0].raw_affiliation_string | Princeton University |
| authorships[2].institutions[0].id | https://openalex.org/I20089843 |
| authorships[2].institutions[0].ror | https://ror.org/00hx57361 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I20089843 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Princeton University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Brandon P. Labbree |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Princeton University |
| authorships[3].author.id | https://openalex.org/A5028626457 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-1271-6113 |
| authorships[3].author.display_name | Alexander Todorov |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I40347166 |
| authorships[3].affiliations[0].raw_affiliation_string | The University of Chicago Booth School of Business |
| authorships[3].institutions[0].id | https://openalex.org/I40347166 |
| authorships[3].institutions[0].ror | https://ror.org/024mw5h28 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I40347166 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of Chicago |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Alexander Todorov |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | The University of Chicago Booth School of Business |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://osf.io/s9y8n/download |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2022-02-08T00:00:00 |
| display_name | Face Stereotypes from Insufficient Statistical Learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12520 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.8550999760627747 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | Psychology of Moral and Emotional Judgment |
| related_works | https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W2028917246, https://openalex.org/W2057598446, https://openalex.org/W4308354915, https://openalex.org/W2290081135, https://openalex.org/W1993731342, https://openalex.org/W3126132007, https://openalex.org/W4310475903, https://openalex.org/W104619080 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2021 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.31219/osf.io/s9y8n |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://osf.io/s9y8n/download |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.31219/osf.io/s9y8n |
| primary_location.id | doi:10.31219/osf.io/s9y8n |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://osf.io/s9y8n/download |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.31219/osf.io/s9y8n |
| publication_date | 2021-08-25 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W6658933747, https://openalex.org/W3026440911, https://openalex.org/W6661895114, https://openalex.org/W2518907272, https://openalex.org/W6634990112, https://openalex.org/W6761257993, https://openalex.org/W6729482482, https://openalex.org/W3178844725, https://openalex.org/W1974236782, https://openalex.org/W2030364704, https://openalex.org/W7074221964, https://openalex.org/W2163796799, https://openalex.org/W2064226402, https://openalex.org/W6677199439, https://openalex.org/W6616379665, https://openalex.org/W2979265655, https://openalex.org/W2109453861, https://openalex.org/W1520494989, https://openalex.org/W6813186812, https://openalex.org/W2019737291, https://openalex.org/W6816130630, https://openalex.org/W6639310012, https://openalex.org/W2079988037, https://openalex.org/W2903701459, https://openalex.org/W2146233270, https://openalex.org/W6643323099, https://openalex.org/W2824348558, https://openalex.org/W2738717410, https://openalex.org/W2342281051, https://openalex.org/W6645640999, https://openalex.org/W2081357278, https://openalex.org/W1988653852, https://openalex.org/W90970949, https://openalex.org/W2333508197, https://openalex.org/W1991107651, https://openalex.org/W2151695699, https://openalex.org/W6654421034, https://openalex.org/W3136535094, https://openalex.org/W2018071688, https://openalex.org/W2120209240, https://openalex.org/W2551120122, https://openalex.org/W4235686293, https://openalex.org/W2161418887, https://openalex.org/W1971687303, https://openalex.org/W4210705188, https://openalex.org/W4234249859, https://openalex.org/W4245010546, https://openalex.org/W3125360567, https://openalex.org/W4237326452, https://openalex.org/W2016429292, https://openalex.org/W2045633722, https://openalex.org/W4298246334, https://openalex.org/W2928191607, https://openalex.org/W1980862600, https://openalex.org/W4295294118, https://openalex.org/W3123814086, https://openalex.org/W4292157289, https://openalex.org/W574024287, https://openalex.org/W4255416805, https://openalex.org/W1584506349, https://openalex.org/W2123713131, https://openalex.org/W4240979024 |
| referenced_works_count | 62 |
| abstract_inverted_index.= | 120 |
| abstract_inverted_index.a | 64, 205, 209 |
| abstract_inverted_index.(N | 119 |
| abstract_inverted_index.If | 87 |
| abstract_inverted_index.We | 111 |
| abstract_inverted_index.an | 45 |
| abstract_inverted_index.as | 158, 160 |
| abstract_inverted_index.by | 185, 190 |
| abstract_inverted_index.do | 10, 22, 82 |
| abstract_inverted_index.in | 16, 26, 165, 170 |
| abstract_inverted_index.of | 29, 59, 125, 136, 212, 217 |
| abstract_inverted_index.on | 33, 36 |
| abstract_inverted_index.to | 70, 91, 139, 178, 201 |
| abstract_inverted_index.we | 43, 147, 194 |
| abstract_inverted_index.How | 9 |
| abstract_inverted_index.and | 4, 20, 39, 78, 128, 141, 163, 187, 215 |
| abstract_inverted_index.are | 2, 56, 68, 97 |
| abstract_inverted_index.can | 102 |
| abstract_inverted_index.few | 159 |
| abstract_inverted_index.for | 208 |
| abstract_inverted_index.not | 83, 90, 188 |
| abstract_inverted_index.six | 116 |
| abstract_inverted_index.the | 17, 27, 60, 75, 132, 143, 153, 175, 191, 213 |
| abstract_inverted_index.why | 21 |
| abstract_inverted_index.yet | 6 |
| abstract_inverted_index.— | 180, 193 |
| abstract_inverted_index.able | 69 |
| abstract_inverted_index.data | 107 |
| abstract_inverted_index.even | 104 |
| abstract_inverted_index.face | 28 |
| abstract_inverted_index.from | 63 |
| abstract_inverted_index.more | 93, 106 |
| abstract_inverted_index.once | 95 |
| abstract_inverted_index.past | 34 |
| abstract_inverted_index.show | 109 |
| abstract_inverted_index.such | 167 |
| abstract_inverted_index.that | 54, 149 |
| abstract_inverted_index.they | 88, 96 |
| abstract_inverted_index.this | 113 |
| abstract_inverted_index.were | 182 |
| abstract_inverted_index.when | 105 |
| abstract_inverted_index.zero | 140 |
| abstract_inverted_index.(from | 157 |
| abstract_inverted_index.faces | 127 |
| abstract_inverted_index.final | 172 |
| abstract_inverted_index.first | 18 |
| abstract_inverted_index.found | 148, 195 |
| abstract_inverted_index.learn | 12 |
| abstract_inverted_index.level | 134 |
| abstract_inverted_index.novel | 123 |
| abstract_inverted_index.pairs | 124 |
| abstract_inverted_index.place | 19 |
| abstract_inverted_index.power | 177 |
| abstract_inverted_index.small | 65 |
| abstract_inverted_index.their | 171 |
| abstract_inverted_index.these | 13, 23 |
| abstract_inverted_index.three | 161 |
| abstract_inverted_index.using | 122, 166 |
| abstract_inverted_index.would | 108 |
| abstract_inverted_index.1565), | 121 |
| abstract_inverted_index.Fixing | 131 |
| abstract_inverted_index.Social | 0 |
| abstract_inverted_index.across | 115 |
| abstract_inverted_index.active | 57 |
| abstract_inverted_index.caused | 198 |
| abstract_inverted_index.decide | 89 |
| abstract_inverted_index.emerge | 103 |
| abstract_inverted_index.humans | 55 |
| abstract_inverted_index.online | 117 |
| abstract_inverted_index.people | 11, 67, 81 |
| abstract_inverted_index.posits | 53 |
| abstract_inverted_index.sample | 41, 76, 145, 155 |
| abstract_inverted_index.social | 129, 218 |
| abstract_inverted_index.within | 74 |
| abstract_inverted_index.beliefs | 15, 24 |
| abstract_inverted_index.biases, | 42 |
| abstract_inverted_index.collect | 92 |
| abstract_inverted_index.counter | 30 |
| abstract_inverted_index.defined | 189 |
| abstract_inverted_index.extract | 71 |
| abstract_inverted_index.initial | 144, 154 |
| abstract_inverted_index.learned | 152 |
| abstract_inverted_index.persist | 25 |
| abstract_inverted_index.propose | 44 |
| abstract_inverted_index.quickly | 151 |
| abstract_inverted_index.sample, | 66 |
| abstract_inverted_index.samples | 94, 181 |
| abstract_inverted_index.varying | 142 |
| abstract_inverted_index.Building | 32 |
| abstract_inverted_index.Granting | 174 |
| abstract_inverted_index.However, | 80 |
| abstract_inverted_index.Starting | 62 |
| abstract_inverted_index.continue | 84 |
| abstract_inverted_index.learners | 58 |
| abstract_inverted_index.learning | 50 |
| abstract_inverted_index.patterns | 73 |
| abstract_inverted_index.persist. | 202 |
| abstract_inverted_index.provides | 204 |
| abstract_inverted_index.quickly. | 79 |
| abstract_inverted_index.research | 35 |
| abstract_inverted_index.sampling | 85, 176, 197 |
| abstract_inverted_index.spurious | 168, 199 |
| abstract_inverted_index.cognitive | 37 |
| abstract_inverted_index.emergence | 214 |
| abstract_inverted_index.evidence? | 31 |
| abstract_inverted_index.examples) | 162 |
| abstract_inverted_index.framework | 207 |
| abstract_inverted_index.generated | 184 |
| abstract_inverted_index.persisted | 164 |
| abstract_inverted_index.prevalent | 3 |
| abstract_inverted_index.sometimes | 7 |
| abstract_inverted_index.Instalearn | 52 |
| abstract_inverted_index.accurately | 77 |
| abstract_inverted_index.behaviors. | 130 |
| abstract_inverted_index.decisions. | 173 |
| abstract_inverted_index.hypothesis | 114 |
| abstract_inverted_index.inaccurate | 14, 100 |
| abstract_inverted_index.otherwise. | 110 |
| abstract_inverted_index.population | 133 |
| abstract_inverted_index.satisfied, | 99 |
| abstract_inverted_index.statistics | 135, 156 |
| abstract_inverted_index.Insta-learn | 203 |
| abstract_inverted_index.experiments | 118 |
| abstract_inverted_index.explanation | 211 |
| abstract_inverted_index.inaccurate. | 8 |
| abstract_inverted_index.integrative | 46 |
| abstract_inverted_index.limitations | 38 |
| abstract_inverted_index.mechanistic | 210 |
| abstract_inverted_index.persistence | 216 |
| abstract_inverted_index.statistical | 49, 72 |
| abstract_inverted_index.statistics, | 146 |
| abstract_inverted_index.stereotypes | 1, 101 |
| abstract_inverted_index.Insufficient | 48 |
| abstract_inverted_index.associations | 138, 169, 200 |
| abstract_inverted_index.endogenously | 183 |
| abstract_inverted_index.environment. | 61 |
| abstract_inverted_index.insufficient | 196 |
| abstract_inverted_index.investigated | 112 |
| abstract_inverted_index.participants | 150, 179, 186 |
| abstract_inverted_index.perspective: | 47 |
| abstract_inverted_index.stereotypes. | 219 |
| abstract_inverted_index.(prematurely) | 98 |
| abstract_inverted_index.environmental | 40 |
| abstract_inverted_index.experimenters | 192 |
| abstract_inverted_index.face-behavior | 137 |
| abstract_inverted_index.sufficiently. | 86 |
| abstract_inverted_index.(Insta-learn). | 51 |
| abstract_inverted_index.consequential, | 5 |
| abstract_inverted_index.domain-general | 206 |
| abstract_inverted_index.computer-generated | 126 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.45563061 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |