Summary

This study was a preregistered replication of a prior CogSci study, with book covers removed and statements in randomized order.

We replicated our prior findings where hearing generic statements about a novel social group increased the inferred inductive potential of the group (relative to baseline), while hearing specific statements decreased inductive potential.

Methods

Participants

n_collect attn_check AI check_task n n_excl excl_rate
452 6 4 1 444 8 0.01769912

Data was collected from 452 adults via Prolific on Monday 4/21/26 - Tuesday 4/22/2026. Participants required to be in the United States, fluent in English, and having not participated in prior studies under this protocol. Participants were paid $2.00 for an estimated 8 minute task.

condition n
generic 147
baseline 152
specific 145

Exclusion criteria

Of the recruited participants, 8 (1.8%) were excluded for meeting at least 1 of the following exclusion criteria:

  • failing the attention check (i.e., did not select 100% on slider when asked to during induction task) (n = 6 participants)

  • admitting to use of AI after being explicitly informed use was prohibited (n = 4 participants)

  • failing the task check (n = 1 participants)

Participants who failed the sound check were included, since a few participants mentioned technical difficulties with the Qualtrics automatically progressing past that video.

Demographics

We used the Prolific representative sample feature to recruit a sample representative on sex, age, and ethnicity (simplified US Census categories).

Age

age
mean sd n
45.42 16.06 444
  • The sample skewed young in age.

Gender

gender n prop
Female 225 50.7%
Male 209 47.1%
Non-binary 6 1.4%
Prefer not to specify 2 0.5%
Genderqueer 1 0.2%
really sick of scoail construct of gender. I am who i am and like what i like. i refuse to participate for any team... 1 0.2%
  • The sample reflected the diversity of the gender identities in the US.

Race

race n prop
White, Caucasian, or European American 271 61.0%
Black or African American 51 11.5%
Hispanic or Latino/a 35 7.9%
East Asian 13 2.9%
South or Southeast Asian 12 2.7%
White, Caucasian, or European American,Hispanic or Latino/a 7 1.6%
White, Caucasian, or European American,Native American, American Indian, or Alaska Native 7 1.6%
Middle Eastern or North African 5 1.1%
Native American, American Indian, or Alaska Native 5 1.1%
White, Caucasian, or European American,South or Southeast Asian 5 1.1%
Prefer not to specify 4 0.9%
White, Caucasian, or European American,Black or African American 3 0.7%
South or Southeast Asian,East Asian 2 0.5%
White, Caucasian, or European American,Black or African American,Native American, American Indian, or Alaska Native 2 0.5%
White, Caucasian, or European American,East Asian 2 0.5%
White, Caucasian, or European American,Hispanic or Latino/a,Black or African American 2 0.5%
White, Caucasian, or European American,Hispanic or Latino/a,Native American, American Indian, or Alaska Native 2 0.5%
White, Caucasian, or European American,Middle Eastern or North African 2 0.5%
Black or African American,Native American, American Indian, or Alaska Native 1 0.2%
Cape Verdean 1 0.2%
European American 1 0.2%
HUMAN RACE 1 0.2%
Hispanic or Latino/a,Black or African American 1 0.2%
Hispanic or Latino/a,Black or African American,Native American, American Indian, or Alaska Native 1 0.2%
Hispanic or Latino/a,East Asian 1 0.2%
Mixed 1 0.2%
Multi-race 1 0.2%
Native Hawaiian or other Pacific Islander 1 0.2%
White, Caucasian, or European American,Black or African American,East Asian 1 0.2%
White, Caucasian, or European American,Native American, American Indian, or Alaska Native,East Asian 1 0.2%
White, Caucasian, or European American,Native Hawaiian or other Pacific Islander 1 0.2%
mixed race 1 0.2%
  • The sample was also racially diverse, with White Americans slightly overrepresented and Hispanic Americans undererepresented.

Education

education n prop
Less than high school 4 0.9%
High school/GED 62 14.0%
Some college 114 25.7%
Bachelor's (B.A., B.S.) 180 40.5%
Master's (M.A., M.S.) 62 14.0%
Doctoral (Ph.D., J.D., M.D.) 18 4.1%
Prefer not to specify 4 0.9%
  • A slight majority of the sample had completed at least a college education.

Behavioral analyses

Large circles with bars indicate means with 95% confidence intervals. Small dots indicate individual trial-level responses.

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: prevalence
##            Chisq Df            Pr(>Chisq)    
## condition 81.918  2 < 0.00000000000000022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##  contrast            estimate    SE  df z.ratio p.value
##  generic - baseline     0.621 0.113 Inf   5.481 <0.0001
##  generic - specific     1.031 0.115 Inf   8.981 <0.0001
##  baseline - specific    0.410 0.114 Inf   3.602  0.0003
## 
## Results are given on the log odds ratio (not the response) scale.

As predicted, participants gave different prevalence estimates in the different conditions (Chisq(2) = 81.92, p < .001).

Specifically, participants in the generic condition inferred higher prevalence of test features than participants in the baseline condition (z = 5.48, p < .001), while participants in the specific condition inferred lower prevalence of test features than participants in the baseline condition (z = 3.6, p < .001).

Model comparison

Footnotes

## R version 4.5.2 (2025-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Tahoe 26.5.2
## 
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] emmeans_2.0.1   car_3.1-3       carData_3.0-5   glmmTMB_1.1.14 
##  [5] lubridate_1.9.4 forcats_1.0.1   stringr_1.6.0   dplyr_1.1.4    
##  [9] purrr_1.2.1     readr_2.1.6     tidyr_1.3.2     tibble_3.3.1   
## [13] ggplot2_4.0.3   tidyverse_2.0.0 gt_1.3.0        scales_1.4.0   
## [17] janitor_2.2.1   here_1.0.2     
## 
## loaded via a namespace (and not attached):
##  [1] Rdpack_2.6.5        gridExtra_2.3       sandwich_3.1-1     
##  [4] rlang_1.1.7         magrittr_2.0.4      multcomp_1.4-29    
##  [7] snakecase_0.11.1    otel_0.2.0          compiler_4.5.2     
## [10] mgcv_1.9-4          systemfonts_1.3.1   vctrs_0.7.1        
## [13] pkgconfig_2.0.3     crayon_1.5.3        fastmap_1.2.0      
## [16] backports_1.5.0     labeling_0.4.3      rmarkdown_2.30     
## [19] tzdb_0.5.0          nloptr_2.2.1        ragg_1.5.0         
## [22] bit_4.6.0           xfun_0.56           cachem_1.1.0       
## [25] jsonlite_2.0.0      parallel_4.5.2      cluster_2.1.8.1    
## [28] R6_2.6.1            bslib_0.10.0        stringi_1.8.7      
## [31] RColorBrewer_1.1-3  boot_1.3-32         rpart_4.1.24       
## [34] jquerylib_0.1.4     numDeriv_2016.8-1.1 estimability_1.5.1 
## [37] Rcpp_1.1.2          knitr_1.51          zoo_1.8-15         
## [40] base64enc_0.1-3     Matrix_1.7-6        splines_4.5.2      
## [43] nnet_7.3-20         timechange_0.3.0    tidyselect_1.2.1   
## [46] rstudioapi_0.18.0   abind_1.4-8         yaml_2.3.12        
## [49] TMB_1.9.19          codetools_0.2-20    lattice_0.22-7     
## [52] withr_3.0.2         S7_0.2.1            coda_0.19-4.1      
## [55] evaluate_1.0.5      foreign_0.8-90      survival_3.8-6     
## [58] xml2_1.5.2          pillar_1.11.1       checkmate_2.3.3    
## [61] reformulas_0.4.3.1  generics_0.1.4      vroom_1.6.7        
## [64] rprojroot_2.1.1     hms_1.1.4           minqa_1.2.8        
## [67] xtable_1.8-4        glue_1.8.0          Hmisc_5.2-5        
## [70] tools_4.5.2         data.table_1.18.0   lme4_2.0-6         
## [73] fs_1.6.6            mvtnorm_1.3-3       rbibutils_2.4.1    
## [76] colorspace_2.1-2    nlme_3.1-168        htmlTable_2.4.3    
## [79] Formula_1.2-5       cli_3.6.5           textshaping_1.0.4  
## [82] ggthemes_5.2.0      gtable_0.3.6        sass_0.4.10        
## [85] digest_0.6.39       TH.data_1.1-5       htmlwidgets_1.6.4  
## [88] farver_2.1.2        htmltools_0.5.9     lifecycle_1.0.5    
## [91] bit64_4.6.0-1       MASS_7.3-65