FakeArt - Data Analysis

Data Preparation

library(tidyverse)
library(easystats)
library(patchwork)
library(ggside)
library(ggdist)

df <- read.csv("../data/data_participants.csv")
dftask <- read.csv("../data/data_task.csv") |> 
  mutate(Condition = fct_relevel(Condition, "Human Original", "Human Forgery", "AI-Generated")) 

cols <- c(
  # Condition
  "Human Original" = "#F51D56",
  "Human Forgery" = "#F5A41D",
  "AI-Generated" = "#1D9AF5",
  "AI Original" = "#1D9AF5",
  "AI Copy" = "#4CAF50",
  "Not recognized" = "#607D8B",
  # Style
  "Abstract and Avant-garde" = "#E41A1C", 
  "Classical" = "#377EB8", 
  "Impressionist and Expressionist" = "#4DAF4A", 
  "Romantic and Realism" = "#FF7F00"
)

Stimuli Selection

fig_stimselection <- png::readPNG("../experiment/stimuli/stimuli_selection/stimuli_selection_files/figure-html/unnamed-chunk-10-1.png") |>
  grid::rasterGrob(interpolate=TRUE) |> 
  patchwork::wrap_elements()
fig_stimselection

Effect of Condition

Beauty

make_plot  <- function(p, outcome = "Beauty", x_title = NULL, alpha_title=NULL) {
  p <- p + theme_minimal() 
  if (outcome %in%  c("Beauty", "Beauty2")) {
    if(!is.null(x_title) && x_title %in% c("Self-Relevance", "Image Complexity (Norms)", "Image Familiarity (Norms)")) {
      ybreaks <- c(0.4, 0.5, 0.6)
    } else if(!is.null(alpha_title) && alpha_title == "Emotion") {
      ybreaks <- c(0.3, 0.5, 0.7)
      alpha_title <- NULL
    } else {
      ybreaks <- c(0.45, 0.5, 0.55)
    }
    by <- diff(ybreaks)[1]
    title <- "Beauty"
    if (outcome == "Beauty2") title <- "Beauty (Follow-up)"
    p <- p +
      geom_hline(yintercept = 0.5, linetype = "dashed", color = "gray") +
      labs(title = title, y = "This artwork is...", x = x_title, alpha = alpha_title) +
      scale_y_continuous(
        breaks = c(min(ybreaks) - by, ybreaks, max(ybreaks) + by), 
        labels = c("Ugly", format_percent(ybreaks, digits=0), "Beautiful")
      ) +
      coord_cartesian(ylim = c(min(ybreaks) - by, max(ybreaks) + by)) +
      theme(axis.title.y = element_text(face = "italic"), plot.title = element_text(face = "bold"))
  } else if (outcome ==  "Valence") {
    if(!is.null(x_title) && x_title %in% c("Self-Relevance", "Image Complexity (Norms)", "Image Familiarity (Norms)")) {
      ybreaks <- c(0.4, 0.5, 0.6)
    } else if(!is.null(alpha_title) && alpha_title == "Emotion") {
      ybreaks <- c(0.3, 0.5, 0.7)
      alpha_title <- NULL
    } else {
      ybreaks <- c(0.45, 0.5, 0.55)
    }
    by <- diff(ybreaks)[1]
    p <- p +
      geom_hline(yintercept = 0.5, linetype = "dashed", color = "gray") +
      labs(title = "Valence", y = "This artwork made me feel...", x = x_title, alpha = alpha_title) +
      scale_y_continuous(
        breaks = c(min(ybreaks) - by, ybreaks, max(ybreaks) + by), 
        labels = c("Negative", format_percent(ybreaks, digits=0), "Positive")
      ) +
      coord_cartesian(ylim = c(min(ybreaks) - by, max(ybreaks) + by)) +
      theme(axis.title.y = element_text(face = "italic"), plot.title = element_text(face = "bold"))
  } else if (outcome ==  "Meaning") {
    if(!is.null(x_title) && x_title %in% c("Self-Relevance", "Image Complexity (Norms)", "Image Familiarity (Norms)")) {
      ybreaks <- c(0.35, 0.45, 0.55)
    } else if(!is.null(alpha_title) && alpha_title == "Emotion") {
      ybreaks <- c(0.3, 0.4, 0.5)
      alpha_title <- NULL
    } else {
      ybreaks <- c(0.3, 0.4, 0.5)
    }
    by <- diff(ybreaks)[1]
    p <- p +
      labs(title = "Meaning", y = "This artwork expresses something meaningful and deep...", x = x_title, alpha = alpha_title) +
      scale_y_continuous(
        breaks = c(min(ybreaks) - by, ybreaks, max(ybreaks) + by), 
        labels = c("Not at all", format_percent(ybreaks, digits=0), "Very much")
      ) +
      coord_cartesian(ylim = c(min(ybreaks) - by, max(ybreaks) + by)) +
      theme(axis.title.y = element_text(face = "italic"), plot.title = element_text(face = "bold"))
  } else if (outcome ==  "Worth") {
    ylim <- c(0, 3/5)
    p <- p +
      labs(title = "Worth", y = "To own this artwork, I'd be willing to pay...", x = x_title, alpha = alpha_title) +
      scale_y_continuous(
        breaks = c(0, 1/5, 2/5, 3/5, 4/5, 1), 
        labels = c("$ 0", "$ 10", "$ 100", "$ 1,000", "$ 10,000", "$ 100,000")
      ) +
      coord_cartesian(ylim = ylim) +
      theme(axis.title.y = element_text(face = "italic"), plot.title = element_text(face = "bold"))
  } else if (outcome ==  "Reality") {
    if(!is.null(x_title) && x_title %in% c("Self-Relevance", "Image Complexity (Norms)", "Image Familiarity (Norms)")) {
      ybreaks <- c(0.4, 0.5, 0.6)
    } else if(!is.null(alpha_title) && alpha_title == "Emotion") {
      ybreaks <- c(0.4, 0.5, 0.6)
      alpha_title <- NULL
    } else {
      ybreaks <- c(0.425, 0.5, 0.575)
    }
    by <- diff(ybreaks)[1]
    p <- p +
      geom_hline(yintercept = 0.5, linetype = "dashed", color = "gray") +
      labs(title = "Syntheticness", y = "This artwork is...", x = x_title, alpha = alpha_title) +
      scale_y_continuous(
        breaks = c(min(ybreaks) - by, ybreaks, max(ybreaks) + by), 
        labels = c("AI-Generated", format_percent(ybreaks, digits=0), "Human Creation")
      ) +
      coord_cartesian(ylim = c(min(ybreaks) - by, max(ybreaks) + by)) +
      theme(axis.title.y = element_text(face = "italic"), plot.title = element_text(face = "bold"))
  } else if (outcome ==  "Authenticity") {
    if(!is.null(x_title) && x_title %in% c("Self-Relevance", "Image Complexity (Norms)", "Image Familiarity (Norms)")) {
      ybreaks <- c(0.5, 0.6, 0.7)
    } else if(!is.null(alpha_title) && alpha_title == "Emotion") {
      ybreaks <- c(0.5, 0.6, 0.7)
      alpha_title <- NULL
    } else {
      ybreaks <- c(0.55, 0.6, 0.65)
    }
    by <- diff(ybreaks)[1]
    p <- p +
      geom_hline(yintercept = 0.5, linetype = "dashed", color = "gray") +
      labs(title = "Authenticity", y = "This artwork is...", x = x_title, alpha = alpha_title) +
      scale_y_continuous(
        breaks = c(min(ybreaks) - by, ybreaks, max(ybreaks) + by), 
        labels = c("Copy / Forgery", format_percent(ybreaks, digits=0), "Original Creation")
      ) +
      coord_cartesian(ylim = c(min(ybreaks) - by, max(ybreaks) + by)) +
      theme(axis.title.y = element_text(face = "italic"), plot.title = element_text(face = "bold"))
  }  else if (outcome ==  "SelfRelevance") {
    ybreaks <- c(0.2, 0.3, 0.4)
    by <- diff(ybreaks)[1]
    p <- p +
      geom_hline(yintercept = 0.5, linetype = "dashed", color = "gray") +
      labs(title = "Self-Relevance", y = "This painting is personally relevant..", x = x_title, alpha = alpha_title) +
      scale_y_continuous(
        breaks = c(min(ybreaks) - by, ybreaks, max(ybreaks) + by), 
        labels = c("Not at all", format_percent(ybreaks, digits=0), "Very much")
      ) +
      coord_cartesian(ylim = c(min(ybreaks) - by, max(ybreaks) + by)) +
      theme(axis.title.y = element_text(face = "italic"), plot.title = element_text(face = "bold"))
  } 

  p
}

make_table <- function(rez, outcome = "Beauty", subtitle = NULL) {
  rez <- datawizard::data_select(rez, exclude = "SE")
  t <- format_table(rez, zap_small = TRUE)

  if("Difference" %in% names(rez)) {
    effect_col <- "Difference"
  } else {
    effect_col <- "Slope"
  }
  t$effectsize <- rez[[effect_col]]
  t$sig <- rez$p
  
  gt::gt(t) |> 
    gt::data_color(columns = "effectsize", target_columns = effect_col, method = "numeric",
palette = c("red", "red", "white", "green", "green"), domain = c(-0.6, 0.6)) |> 
    gt::data_color(columns = "sig", target_columns = "p", fn = \(x) {
      ifelse(x < 0.001, "#FFC107", ifelse(x < 0.01, "#FFEB3B", ifelse(x < 0.05, "#FFF59D", "white")))
    }) |> 
    gt::cols_hide(c("effectsize", "sig")) |> 
    gt::tab_header(title = outcome, subtitle = subtitle)

}

make_analysis <- function(dftask, outcome = "Beauty") {
  
  # Descriptive
  bins <- sort(c(unique(dftask[[outcome]]) - 0.05, unique(dftask[[outcome]]) + 0.05))
  if(length(bins) > 100) bins <- seq(-0.01, 1.01, by = 0.02)
  p_desc <- ggplot(dftask, aes(x = Condition, y = .data[[outcome]])) +
    ggdist::stat_histinterval(aes(fill = Condition), point_interval = "mean_qi", side="right",  breaks = bins, align = "center") +
    scale_fill_manual(values = cols) +
    scale_y_continuous(labels = scales::percent_format()) +
    theme_minimal() +
    labs(title = paste0("Distribution of ", outcome), y = outcome)

  # Model
  formula <- as.formula(paste0(outcome, " ~ Style * Condition * ManipulationDistrust + (1|Participant) + (1|Item)"))
  model <- lme4::lmer(formula, data = dftask)

  # Performance
  # TODO.

  # Marginal effect of Condition
  t_cond <- estimate_contrasts(model, contrast = "Condition", by = NULL, backend = "marginaleffects") |> 
    arrange(Difference) |> 
    make_table(outcome=outcome, subtitle = "Marginal Contrasts")
  
  p_condition <- estimate_means(model, by = c("Condition"), backend = "marginaleffects") |> 
    ggplot(aes(x = Condition, y = Mean)) 
  p_condition <- make_plot(p_condition, outcome = outcome) +
    geom_line(aes(group=1)) +
    geom_pointrange(aes(ymin = CI_low, ymax = CI_high, color = Condition), 
                    linewidth = 1.5, size = 1.5,
                    key_glyph=draw_key_point) +
    scale_color_manual(values = cols) +
    guides(color=guide_legend(override.aes = list(size = 5)))
  

  # By Style
  t_style <- estimate_contrasts(model, contrast = "Condition", by = "Style", backend = "marginaleffects") |> 
    arrange(Style, Difference) |> 
    make_table(outcome=outcome, subtitle = "Marginal Contrasts by Style")

  p_style <- estimate_means(model, by = c("Condition", "Style"), backend = "marginaleffects") |> 
    ggplot(aes(x = Condition, y = Mean)) 
  p_style <- make_plot(p_style, outcome = outcome) +
    geom_line(aes(group = Style, color=Style), position = position_dodge(width = 0.15),
              show.legend  = FALSE) +
    geom_pointrange(aes(ymin = CI_low, ymax = CI_high, group = Style, color = Style), 
                    position = position_dodge(width = 0.15), linewidth = 1, size = 1,
                    key_glyph=draw_key_point) +
    scale_color_manual(values = cols)  +
    guides(color=guide_legend(override.aes = list(size = 5)))

  # Manipulation Mistrust
  t_trust <- estimate_contrasts(model, contrast = "Condition", by = "ManipulationDistrust = c(-5, 0, 5)", backend = "marginaleffects") |> 
    arrange(ManipulationDistrust, Difference) |> 
    make_table(outcome=outcome, subtitle = "Manipulation Distrust")

  p_trust <- estimate_means(model, by = c("Condition", "ManipulationDistrust = c(-5, 0, 5)"), backend = "marginaleffects") |> 
    mutate(ManipulationDistrust = as.factor(ManipulationDistrust)) |>
    ggplot(aes(x = Condition, y = Mean)) 
  p_trust <- make_plot(p_trust, outcome = outcome) +
    geom_line(aes(group = ManipulationDistrust, color=ManipulationDistrust), 
              position = position_dodge(width = 0.1), linewidth = 1) +
    labs(color = "Doubts about the cover story") +
    scale_color_manual(breaks = c(-5, 0, 5), labels = c("All images are 'AI'", "No doubts about the manipulation", "All images are 'Real'"), values = c("#2196F3", "black", "#E91E63")) +
    ggnewscale::new_scale_color() +
    geom_pointrange(aes(ymin = CI_low, ymax = CI_high, group = ManipulationDistrust, color = Condition), 
                    position = position_dodge(width = 0.1), linewidth = 1, size = 1,
                    key_glyph=draw_key_point) +
    scale_color_manual(values = cols)
  
  # By Emotion
  formula <- as.formula(paste0(outcome, " ~ Condition * Emotion + (1|Participant) + (1|Item)"))
  model <- lme4::lmer(formula, data = dftask)
  
  t_emotion <- estimate_contrasts(model, contrast = "Condition", by = "Emotion", backend = "marginaleffects") |> 
    arrange(Emotion, Difference) |> 
    make_table(outcome=outcome, subtitle = "Marginal Contrasts by Emotion")
  
  p_emotion <- estimate_means(model, by = c("Condition", "Emotion"), backend = "marginaleffects") |> 
    mutate(Emotion = fct_rev(Emotion)) |> 
    ggplot(aes(x = Condition, y = Mean))
  p_emotion <- make_plot(p_emotion, outcome = outcome, alpha_title = "Emotion") +
    geom_line(aes(group = Emotion, color=Emotion), 
              position = position_dodge(width = 0.1), linewidth = 1) +
    labs(color = "Emotion") +
    scale_color_manual(values = c("#C5E1A5", "#4CAF50", "#CE93D8", "#9C27B0")) +
    ggnewscale::new_scale_color() +
    geom_pointrange(aes(ymin = CI_low, ymax = CI_high, group = Emotion, color = Condition), 
                    position = position_dodge(width = 0.1), linewidth = 1, size = 1,
                    key_glyph=draw_key_point) +
    scale_color_manual(values = cols) 
  
  
  # Complexity
  formula <- as.formula(paste0(outcome, " ~ Condition * poly(Norms_Complexity, 2) + poly(Norms_Familiarity, 2) + (1|Participant) + (1|Item)"))
  model <- lme4::lmer(formula, data = dftask)

  p_complexity <- estimate_relation(model, by = c("Condition", "Norms_Complexity"), length=20, backend = "marginaleffects") |> 
    ggplot(aes(x = Norms_Complexity, y = Predicted)) 
  p_complexity <- make_plot(p_complexity, outcome = outcome, x_title = "Image Complexity (Norms)") +
    geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = Condition), alpha = 0.2, show.legend  = FALSE) +
    geom_line(aes(group = Condition, color = Condition), linewidth = 1) +
    scale_color_manual(values = cols) +
    scale_fill_manual(values = cols) +
    scale_x_continuous(labels = scales::percent_format()) 
  p_complexity2 <- estimate_relation(model, by = c("Condition", "Norms_Familiarity"), length=20, backend = "marginaleffects") |> 
    ggplot(aes(x = Norms_Familiarity, y = Predicted)) 
  p_complexity2 <- make_plot(p_complexity2, outcome = outcome, x_title = "Image Familiarity (Norms)") +
    geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = Condition), alpha = 0.2, show.legend  = FALSE) +
    geom_line(aes(group = Condition, color = Condition), linewidth = 1) +
    scale_color_manual(values = cols) +
    scale_fill_manual(values = cols) +
    scale_x_continuous(labels = scales::percent_format()) +
    theme(axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(),
          plot.title = element_blank(), plot.subtitle = element_blank())
  p_complexity <- (p_complexity | p_complexity2) + plot_layout(guides = "collect")
  
  # Self-Relevance
  if(outcome != "SelfRelevance") {
    formula <- as.formula(paste0(outcome, " ~ Condition * poly(SelfRelevance, 2) + (1|Participant) + (1|Item)"))
    model <- lme4::lmer(formula, data = filter(dftask, !is.na(SelfRelevance)))
  
    vals <- paste0("SelfRelevance = c(", paste0(as.character(sort(c(0, round(mean(dftask$SelfRelevance, na.rm=TRUE), 2), 0.5, 1))), collapse = ", "), ")")
    t_selfrelevance <- estimate_contrasts(model, contrast = "Condition", by = vals, backend = "marginaleffects") |> 
      arrange(SelfRelevance, Difference) |> 
      make_table(outcome=outcome, subtitle = "Self-Relevance (Marginal Contrasts)")
    t_selfrelevance2 <- estimate_slopes(model, trend = "SelfRelevance", by = c("Condition", "SelfRelevance = c(0, 0.25, 0.5, 0.75, 1)"), backend = "marginaleffects") |> 
      arrange(Condition, SelfRelevance) |> 
      make_table(outcome=outcome, subtitle = "Self-Relevance (Marginal Slopes)")
  
    p_selfrelevance <- estimate_relation(model, by = c("Condition", "SelfRelevance"), length=20, backend = "marginaleffects") |> 
      ggplot(aes(x = SelfRelevance, y = Predicted)) 
    p_selfrelevance <- make_plot(p_selfrelevance, outcome = outcome, x_title = "Self-Relevance") +
      geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = Condition), alpha = 0.2, show.legend  = FALSE) +
      geom_line(aes(group = Condition, color = Condition), linewidth = 1) +
      scale_color_manual(values = cols) +
      scale_fill_manual(values = cols) +
      scale_x_continuous(labels = scales::percent_format()) 
  } else{
    t_selfrelevance <- "N/A"
    t_selfrelevance2 <- "N/A"
    p_selfrelevance <- "N/A"
  }
  

  # Beauty (Follow-up)
  if(outcome != "Beauty2") {
    formula <- as.formula(paste0("Beauty2 ~ Condition * poly(", outcome, ", 2) + (1|Participant) + (1|Item)"))
    model <- lme4::lmer(formula, data = filter(dftask, !is.na(Beauty2)))
  
    vals <- paste0(outcome, " = c(", paste0(as.character(c(0, 0.25, 0.5, 0.75, 1)), collapse = ", "), ")")
    t_beauty <- estimate_contrasts(model, contrast = "Condition", by = vals, backend = "marginaleffects") |>
      make_table(outcome=outcome, subtitle = "Predicting Follow-up Beauty (Contrasts)")
    t_beauty2 <- estimate_slopes(model, trend = outcome, by = c("Condition", paste0(outcome, "= c(0, 0.25, 0.5, 0.75, 1)")), backend = "marginaleffects") |> 
      arrange(Condition) |> 
      make_table(outcome=outcome, subtitle = "Predicting Follow-up Beauty (Slopes)")
  
    p_beauty <- estimate_relation(model, by = c("Condition", outcome), length=60, backend = "marginaleffects") |>
      ggplot(aes(x = .data[[outcome]], y = Predicted)) +
      geom_hline(yintercept = 0.5, linetype = "dashed", color = "gray")

    if(outcome %in% c("Beauty", "Valence")) p_beauty <- p_beauty + geom_vline(xintercept = 0.5, linetype = "dashed", color = "gray")
    p_beauty <- p_beauty +
      geom_ribbon(aes(ymin = CI_low, ymax = CI_high, fill = Condition), alpha = 0.2, show.legend  = FALSE) +
      geom_line(aes(group = Condition, color = Condition), linewidth = 1) +
      scale_color_manual(values = cols) +
      scale_fill_manual(values = cols) +
      scale_x_continuous(labels = scales::percent_format()) +
      scale_y_continuous(
        breaks = c(0.2, 0.35, 0.5, 0.65, 0.8),
        labels = c("Ugly", "35%", "50%", "65%", "Beautiful")
      ) +
      labs(y = "Beauty (Follow-up)", title = "Beauty (Follow-up)", 
           subtitle = "After being reminded that all artworks were Human originals") +
      coord_cartesian(ylim = c(0.2, 0.8)) +
      theme_minimal() +
      theme(plot.subtitle = element_text(face = "italic"), plot.title = element_text(face = "bold"))
  } else {
    t_beauty <- "N/A"
    t_beauty2 <- "N/A"
    p_beauty <- "N/A"
  }

  list(p_desc=p_desc, 
       t_condition=t_cond, p_condition=p_condition, 
       t_style=t_style, p_style=p_style, 
       t_emotion=t_emotion, p_emotion=p_emotion,
       p_complexity = p_complexity,
       t_trust=t_trust, p_trust=p_trust,
       t_selfrelevance=t_selfrelevance, t_selfrelevance2=t_selfrelevance2, p_selfrelevance=p_selfrelevance,
       t_beauty=t_beauty, t_beauty2=t_beauty2, p_beauty=p_beauty
  )
}

rez_beauty <- make_analysis(dftask, outcome="Beauty")
rez_beauty$p_desc

Condition

rez_beauty$p_condition

rez_beauty$t_condition

Beauty
Marginal Contrasts
Level1	Level2	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	-0.06	[-0.07, -0.05]	-16.62	< .001
Human Forgery	Human Original	-0.03	[-0.04, -0.03]	-8.99	< .001
AI-Generated	Human Forgery	-0.03	[-0.03, -0.02]	-7.63	< .001

Style

rez_beauty$p_style

rez_beauty$t_style

Beauty
Marginal Contrasts by Style
Level1	Level2	Style	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	Abstract and Avant-garde	-0.05	[-0.07, -0.04]	-7.08	< .001
Human Forgery	Human Original	Abstract and Avant-garde	-0.03	[-0.05, -0.02]	-4.33	< .001
AI-Generated	Human Forgery	Abstract and Avant-garde	-0.02	[-0.03, -0.01]	-2.79	0.005
AI-Generated	Human Original	Classical	-0.06	[-0.08, -0.05]	-8.90	< .001
AI-Generated	Human Forgery	Classical	-0.03	[-0.05, -0.02]	-4.72	< .001
Human Forgery	Human Original	Classical	-0.03	[-0.04, -0.02]	-4.17	< .001
AI-Generated	Human Original	Impressionist and Expressionist	-0.07	[-0.08, -0.05]	-9.29	< .001
Human Forgery	Human Original	Impressionist and Expressionist	-0.04	[-0.05, -0.03]	-5.57	< .001
AI-Generated	Human Forgery	Impressionist and Expressionist	-0.03	[-0.04, -0.01]	-3.70	< .001
AI-Generated	Human Original	Romantic and Realism	-0.06	[-0.07, -0.04]	-7.98	< .001
AI-Generated	Human Forgery	Romantic and Realism	-0.03	[-0.04, -0.02]	-4.07	< .001
Human Forgery	Human Original	Romantic and Realism	-0.03	[-0.04, -0.01]	-3.92	< .001

Emotion

rez_beauty$p_emotion

rez_beauty$t_emotion

Beauty
Marginal Contrasts by Emotion
Level1	Level2	Emotion	Difference	95% CI	t(14625)	p
AI-Generated	Human Original	Negative - High intensity	-0.05	[-0.07, -0.04]	-7.29	< .001
AI-Generated	Human Forgery	Negative - High intensity	-0.04	[-0.05, -0.02]	-5.11	< .001
Human Forgery	Human Original	Negative - High intensity	-0.02	[-0.03, 0.00]	-2.20	0.028
AI-Generated	Human Original	Negative - Low intensity	-0.07	[-0.08, -0.06]	-9.66	< .001
Human Forgery	Human Original	Negative - Low intensity	-0.04	[-0.05, -0.02]	-5.12	< .001
AI-Generated	Human Forgery	Negative - Low intensity	-0.03	[-0.05, -0.02]	-4.43	< .001
AI-Generated	Human Original	Positive - High intensity	-0.07	[-0.08, -0.05]	-9.00	< .001
Human Forgery	Human Original	Positive - High intensity	-0.04	[-0.05, -0.02]	-5.32	< .001
AI-Generated	Human Forgery	Positive - High intensity	-0.03	[-0.04, -0.01]	-3.74	< .001
AI-Generated	Human Original	Positive - Low intensity	-0.05	[-0.07, -0.04]	-7.05	< .001
Human Forgery	Human Original	Positive - Low intensity	-0.04	[-0.05, -0.02]	-5.22	< .001
AI-Generated	Human Forgery	Positive - Low intensity	-0.01	[-0.03, 0.00]	-1.83	0.067

Complexity and Familiarity

rez_beauty$p_complexity

Manipulation Belief

rez_beauty$p_trust

rez_beauty$t_trust

Beauty
Manipulation Distrust
Level1	Level2	ManipulationDistrust	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	-5	-0.09	[-0.12, -0.07]	-8.17	< .001
Human Forgery	Human Original	-5	-0.05	[-0.08, -0.03]	-4.56	< .001
AI-Generated	Human Forgery	-5	-0.04	[-0.06, -0.02]	-3.62	< .001
AI-Generated	Human Original	0	-0.06	[-0.07, -0.05]	-16.90	< .001
Human Forgery	Human Original	0	-0.03	[-0.04, -0.03]	-9.17	< .001
AI-Generated	Human Forgery	0	-0.03	[-0.04, -0.02]	-7.74	< .001
AI-Generated	Human Original	5	-0.03	[-0.05, -0.01]	-2.83	0.005
AI-Generated	Human Forgery	5	-0.02	[-0.04, 0.01]	-1.44	0.150
Human Forgery	Human Original	5	-0.01	[-0.04, 0.01]	-1.40	0.162

Self-Relevance

rez_beauty$p_selfrelevance

rez_beauty$t_selfrelevance

Beauty
Self-Relevance (Marginal Contrasts)
Level1	Level2	SelfRelevance	Difference	95% CI	t(10548)	p
AI-Generated	Human Original	0.00	-0.05	[-0.06, -0.04]	-8.11	< .001
Human Forgery	Human Original	0.00	-0.03	[-0.04, -0.01]	-4.03	< .001
AI-Generated	Human Forgery	0.00	-0.03	[-0.04, -0.01]	-4.06	< .001
AI-Generated	Human Original	0.28	-0.06	[-0.07, -0.05]	-9.27	< .001
Human Forgery	Human Original	0.28	-0.03	[-0.05, -0.02]	-5.42	< .001
AI-Generated	Human Forgery	0.28	-0.02	[-0.04, -0.01]	-3.89	< .001
AI-Generated	Human Original	0.50	-0.06	[-0.07, -0.04]	-8.22	< .001
Human Forgery	Human Original	0.50	-0.03	[-0.05, -0.02]	-5.18	< .001
AI-Generated	Human Forgery	0.50	-0.02	[-0.03, -0.01]	-3.10	0.002
AI-Generated	Human Original	1.00	-0.03	[-0.07, 0.00]	-1.74	0.083
Human Forgery	Human Original	1.00	-0.02	[-0.06, 0.01]	-1.25	0.213
AI-Generated	Human Forgery	1.00	-0.01	[-0.05, 0.03]	-0.44	0.657

rez_beauty$t_selfrelevance2

Beauty
Self-Relevance (Marginal Slopes)
Condition	SelfRelevance	Slope	95% CI	t(10548)	p
Human Original	0.00	0.32	[ 0.26, 0.39]	9.58	< .001
Human Original	0.25	0.26	[ 0.23, 0.29]	16.37	< .001
Human Original	0.50	0.20	[ 0.18, 0.23]	14.07	< .001
Human Original	0.75	0.14	[ 0.08, 0.21]	4.57	< .001
Human Original	1.00	0.08	[-0.02, 0.18]	1.65	0.098
Human Forgery	0.00	0.29	[ 0.22, 0.35]	8.52	< .001
Human Forgery	0.25	0.25	[ 0.21, 0.28]	15.52	< .001
Human Forgery	0.50	0.21	[ 0.17, 0.24]	13.31	< .001
Human Forgery	0.75	0.16	[ 0.10, 0.23]	4.98	< .001
Human Forgery	1.00	0.12	[ 0.02, 0.23]	2.34	0.020
AI-Generated	0.00	0.29	[ 0.23, 0.36]	8.70	< .001
AI-Generated	0.25	0.26	[ 0.23, 0.29]	16.16	< .001
AI-Generated	0.50	0.22	[ 0.19, 0.25]	15.00	< .001
AI-Generated	0.75	0.19	[ 0.13, 0.25]	5.90	< .001
AI-Generated	1.00	0.15	[ 0.05, 0.26]	2.99	0.003

Beauty 2

rez_beauty$p_beauty

rez_beauty$t_beauty

Beauty
Predicting Follow-up Beauty (Contrasts)
Level1	Level2	Beauty	Difference	95% CI	t(10548)	p
Human Forgery	Human Original	0.00	0.01	[-0.02, 0.04]	0.61	0.545
AI-Generated	Human Original	0.00	0.02	[-0.01, 0.05]	1.29	0.196
AI-Generated	Human Forgery	0.00	0.01	[-0.02, 0.04]	0.70	0.484
Human Forgery	Human Original	0.25	0.02	[ 0.00, 0.03]	2.61	0.009
AI-Generated	Human Original	0.25	0.02	[ 0.01, 0.03]	2.81	0.005
AI-Generated	Human Forgery	0.25	0.00	[-0.01, 0.01]	0.17	0.864
Human Forgery	Human Original	0.50	0.01	[ 0.00, 0.03]	2.73	0.006
AI-Generated	Human Original	0.50	0.01	[ 0.00, 0.02]	2.56	0.011
AI-Generated	Human Forgery	0.50	0.00	[-0.01, 0.01]	-0.18	0.854
Human Forgery	Human Original	0.75	0.00	[-0.01, 0.02]	0.75	0.455
AI-Generated	Human Original	0.75	0.01	[ 0.00, 0.02]	1.25	0.211
AI-Generated	Human Forgery	0.75	0.00	[-0.01, 0.02]	0.51	0.613
Human Forgery	Human Original	1.00	-0.01	[-0.05, 0.02]	-0.78	0.437
AI-Generated	Human Original	1.00	0.00	[-0.04, 0.04]	0.03	0.975
AI-Generated	Human Forgery	1.00	0.01	[-0.03, 0.05]	0.72	0.473

rez_beauty$t_beauty2

Beauty
Predicting Follow-up Beauty (Slopes)
Condition	Beauty	Slope	95% CI	t(10548)	p
Human Original	0.00	0.39	[0.30, 0.48]	8.45	< .001
Human Original	0.25	0.44	[0.39, 0.49]	17.36	< .001
Human Original	0.50	0.48	[0.46, 0.51]	35.07	< .001
Human Original	0.75	0.53	[0.47, 0.58]	18.56	< .001
Human Original	1.00	0.57	[0.48, 0.67]	11.49	< .001
Human Forgery	0.00	0.44	[0.35, 0.53]	9.39	< .001
Human Forgery	0.25	0.45	[0.40, 0.50]	18.57	< .001
Human Forgery	0.50	0.46	[0.43, 0.49]	30.92	< .001
Human Forgery	0.75	0.47	[0.41, 0.54]	14.23	< .001
Human Forgery	1.00	0.48	[0.37, 0.59]	8.52	< .001
AI-Generated	0.00	0.39	[0.30, 0.48]	8.41	< .001
AI-Generated	0.25	0.43	[0.38, 0.47]	18.19	< .001
AI-Generated	0.50	0.47	[0.44, 0.50]	30.08	< .001
AI-Generated	0.75	0.50	[0.43, 0.57]	14.37	< .001
AI-Generated	1.00	0.54	[0.42, 0.66]	9.17	< .001

Valence

rez_valence <- make_analysis(dftask, outcome="Valence")
rez_valence$p_desc

Condition

rez_valence$p_condition

rez_valence$t_condition

Valence
Marginal Contrasts
Level1	Level2	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	-0.06	[-0.07, -0.05]	-16.16	< .001
Human Forgery	Human Original	-0.03	[-0.04, -0.03]	-8.60	< .001
AI-Generated	Human Forgery	-0.03	[-0.04, -0.02]	-7.56	< .001

Style

rez_valence$p_style

rez_valence$t_style

Valence
Marginal Contrasts by Style
Level1	Level2	Style	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	Abstract and Avant-garde	-0.05	[-0.07, -0.04]	-6.90	< .001
Human Forgery	Human Original	Abstract and Avant-garde	-0.03	[-0.04, -0.01]	-3.50	< .001
AI-Generated	Human Forgery	Abstract and Avant-garde	-0.03	[-0.04, -0.01]	-3.43	< .001
AI-Generated	Human Original	Classical	-0.07	[-0.08, -0.05]	-8.92	< .001
AI-Generated	Human Forgery	Classical	-0.03	[-0.05, -0.02]	-4.48	< .001
Human Forgery	Human Original	Classical	-0.03	[-0.05, -0.02]	-4.43	< .001
AI-Generated	Human Original	Impressionist and Expressionist	-0.07	[-0.08, -0.05]	-8.99	< .001
Human Forgery	Human Original	Impressionist and Expressionist	-0.04	[-0.05, -0.02]	-4.77	< .001
AI-Generated	Human Forgery	Impressionist and Expressionist	-0.03	[-0.05, -0.02]	-4.20	< .001
AI-Generated	Human Original	Romantic and Realism	-0.06	[-0.07, -0.04]	-7.51	< .001
Human Forgery	Human Original	Romantic and Realism	-0.03	[-0.05, -0.02]	-4.50	< .001
AI-Generated	Human Forgery	Romantic and Realism	-0.02	[-0.04, -0.01]	-3.02	0.003

Emotion

rez_valence$p_emotion

rez_valence$t_emotion

Valence
Marginal Contrasts by Emotion
Level1	Level2	Emotion	Difference	95% CI	t(14625)	p
AI-Generated	Human Original	Negative - High intensity	-0.05	[-0.06, -0.03]	-6.19	< .001
AI-Generated	Human Forgery	Negative - High intensity	-0.04	[-0.05, -0.02]	-4.87	< .001
Human Forgery	Human Original	Negative - High intensity	-0.01	[-0.03, 0.00]	-1.33	0.182
AI-Generated	Human Original	Negative - Low intensity	-0.07	[-0.08, -0.05]	-8.67	< .001
Human Forgery	Human Original	Negative - Low intensity	-0.04	[-0.05, -0.02]	-5.00	< .001
AI-Generated	Human Forgery	Negative - Low intensity	-0.03	[-0.04, -0.01]	-3.58	< .001
AI-Generated	Human Original	Positive - High intensity	-0.08	[-0.09, -0.06]	-10.14	< .001
Human Forgery	Human Original	Positive - High intensity	-0.05	[-0.06, -0.03]	-6.09	< .001
AI-Generated	Human Forgery	Positive - High intensity	-0.03	[-0.05, -0.02]	-4.11	< .001
AI-Generated	Human Original	Positive - Low intensity	-0.06	[-0.07, -0.04]	-7.13	< .001
Human Forgery	Human Original	Positive - Low intensity	-0.04	[-0.05, -0.02]	-4.68	< .001
AI-Generated	Human Forgery	Positive - Low intensity	-0.02	[-0.03, 0.00]	-2.46	0.014

Complexity and Familiarity

rez_valence$p_complexity

Manipulation Belief

rez_valence$p_trust

rez_valence$t_trust

Valence
Manipulation Distrust
Level1	Level2	ManipulationDistrust	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	-5	-0.07	[-0.09, -0.04]	-5.62	< .001
Human Forgery	Human Original	-5	-0.04	[-0.06, -0.02]	-3.35	< .001
AI-Generated	Human Forgery	-5	-0.03	[-0.05, 0.00]	-2.28	0.023
AI-Generated	Human Original	0	-0.06	[-0.07, -0.05]	-16.06	< .001
Human Forgery	Human Original	0	-0.03	[-0.04, -0.03]	-8.61	< .001
AI-Generated	Human Forgery	0	-0.03	[-0.04, -0.02]	-7.46	< .001
AI-Generated	Human Original	5	-0.06	[-0.08, -0.03]	-5.05	< .001
AI-Generated	Human Forgery	5	-0.03	[-0.05, -0.01]	-2.72	0.007
Human Forgery	Human Original	5	-0.03	[-0.05, 0.00]	-2.34	0.020

Self-Relevance

rez_valence$p_selfrelevance

rez_valence$t_selfrelevance

Valence
Self-Relevance (Marginal Contrasts)
Level1	Level2	SelfRelevance	Difference	95% CI	t(10548)	p
AI-Generated	Human Original	0.00	-0.04	[-0.06, -0.03]	-6.43	< .001
AI-Generated	Human Forgery	0.00	-0.02	[-0.04, -0.01]	-3.43	< .001
Human Forgery	Human Original	0.00	-0.02	[-0.03, -0.01]	-2.99	0.003
AI-Generated	Human Original	0.28	-0.06	[-0.08, -0.05]	-9.46	< .001
Human Forgery	Human Original	0.28	-0.04	[-0.05, -0.03]	-6.17	< .001
AI-Generated	Human Forgery	0.28	-0.02	[-0.03, -0.01]	-3.33	< .001
AI-Generated	Human Original	0.50	-0.07	[-0.08, -0.05]	-9.45	< .001
Human Forgery	Human Original	0.50	-0.04	[-0.06, -0.03]	-6.21	< .001
AI-Generated	Human Forgery	0.50	-0.02	[-0.04, -0.01]	-3.31	< .001
AI-Generated	Human Original	1.00	-0.05	[-0.09, -0.01]	-2.63	0.009
AI-Generated	Human Forgery	1.00	-0.04	[-0.08, 0.00]	-1.78	0.074
Human Forgery	Human Original	1.00	-0.02	[-0.06, 0.03]	-0.76	0.448

rez_valence$t_selfrelevance2

Valence
Self-Relevance (Marginal Slopes)
Condition	SelfRelevance	Slope	95% CI	t(10548)	p
Human Original	0.00	0.34	[ 0.27, 0.41]	9.42	< .001
Human Original	0.25	0.27	[ 0.23, 0.30]	15.52	< .001
Human Original	0.50	0.19	[ 0.16, 0.22]	12.52	< .001
Human Original	0.75	0.12	[ 0.05, 0.19]	3.58	< .001
Human Original	1.00	0.05	[-0.06, 0.15]	0.87	0.383
Human Forgery	0.00	0.24	[ 0.17, 0.31]	6.65	< .001
Human Forgery	0.25	0.22	[ 0.19, 0.25]	12.92	< .001
Human Forgery	0.50	0.20	[ 0.17, 0.23]	12.01	< .001
Human Forgery	0.75	0.18	[ 0.11, 0.25]	5.03	< .001
Human Forgery	1.00	0.16	[ 0.05, 0.27]	2.77	0.006
AI-Generated	0.00	0.25	[ 0.18, 0.32]	7.09	< .001
AI-Generated	0.25	0.22	[ 0.18, 0.25]	12.88	< .001
AI-Generated	0.50	0.18	[ 0.15, 0.21]	11.56	< .001
AI-Generated	0.75	0.15	[ 0.08, 0.22]	4.36	< .001
AI-Generated	1.00	0.11	[ 0.01, 0.22]	2.07	0.038

Beauty 2

rez_valence$p_beauty

rez_valence$t_beauty

Valence
Predicting Follow-up Beauty (Contrasts)
Level1	Level2	Valence	Difference	95% CI	t(10548)	p
Human Forgery	Human Original	0.00	0.01	[-0.01, 0.04]	1.04	0.298
AI-Generated	Human Original	0.00	0.03	[ 0.00, 0.05]	1.94	0.052
AI-Generated	Human Forgery	0.00	0.01	[-0.01, 0.04]	0.90	0.366
Human Forgery	Human Original	0.25	0.01	[ 0.00, 0.03]	2.42	0.016
AI-Generated	Human Original	0.25	0.02	[ 0.01, 0.03]	2.82	0.005
AI-Generated	Human Forgery	0.25	0.00	[-0.01, 0.01]	0.38	0.701
Human Forgery	Human Original	0.50	0.01	[ 0.00, 0.02]	1.90	0.058
AI-Generated	Human Original	0.50	0.01	[ 0.00, 0.02]	1.37	0.170
AI-Generated	Human Forgery	0.50	0.00	[-0.01, 0.01]	-0.53	0.598
Human Forgery	Human Original	0.75	0.00	[-0.01, 0.01]	0.23	0.816
AI-Generated	Human Original	0.75	0.00	[-0.02, 0.01]	-0.32	0.751
AI-Generated	Human Forgery	0.75	0.00	[-0.02, 0.01]	-0.51	0.611
Human Forgery	Human Original	1.00	-0.01	[-0.05, 0.02]	-0.73	0.463
AI-Generated	Human Original	1.00	-0.01	[-0.05, 0.02]	-0.68	0.495
AI-Generated	Human Forgery	1.00	0.00	[-0.04, 0.04]	0.01	0.989

rez_valence$t_beauty2

Valence
Predicting Follow-up Beauty (Slopes)
Condition	Valence	Slope	95% CI	t(10548)	p
Human Original	0.00	0.35	[0.26, 0.44]	7.90	< .001
Human Original	0.25	0.37	[0.32, 0.42]	14.96	< .001
Human Original	0.50	0.38	[0.36, 0.41]	28.54	< .001
Human Original	0.75	0.40	[0.35, 0.45]	14.84	< .001
Human Original	1.00	0.41	[0.32, 0.51]	8.81	< .001
Human Forgery	0.00	0.36	[0.28, 0.45]	8.11	< .001
Human Forgery	0.25	0.36	[0.31, 0.41]	15.12	< .001
Human Forgery	0.50	0.36	[0.33, 0.38]	24.53	< .001
Human Forgery	0.75	0.35	[0.29, 0.41]	11.52	< .001
Human Forgery	1.00	0.35	[0.25, 0.45]	6.66	< .001
AI-Generated	0.00	0.32	[0.23, 0.40]	7.13	< .001
AI-Generated	0.25	0.33	[0.29, 0.37]	14.46	< .001
AI-Generated	0.50	0.34	[0.32, 0.37]	23.26	< .001
AI-Generated	0.75	0.36	[0.29, 0.42]	10.91	< .001
AI-Generated	1.00	0.37	[0.26, 0.48]	6.64	< .001

Meaning

rez_meaning <- make_analysis(dftask, outcome="Meaning")
rez_meaning$p_desc

Condition

rez_meaning$p_condition

rez_meaning$t_condition

Meaning
Marginal Contrasts
Level1	Level2	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	-0.10	[-0.11, -0.09]	-23.28	< .001
AI-Generated	Human Forgery	-0.06	[-0.07, -0.05]	-14.02	< .001
Human Forgery	Human Original	-0.04	[-0.05, -0.03]	-9.26	< .001

Style

rez_meaning$p_style

rez_meaning$t_style

Meaning
Marginal Contrasts by Style
Level1	Level2	Style	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	Abstract and Avant-garde	-0.08	[-0.10, -0.06]	-9.20	< .001
AI-Generated	Human Forgery	Abstract and Avant-garde	-0.04	[-0.06, -0.03]	-4.91	< .001
Human Forgery	Human Original	Abstract and Avant-garde	-0.04	[-0.05, -0.02]	-4.33	< .001
AI-Generated	Human Original	Classical	-0.11	[-0.13, -0.09]	-12.84	< .001
AI-Generated	Human Forgery	Classical	-0.07	[-0.08, -0.05]	-7.53	< .001
Human Forgery	Human Original	Classical	-0.05	[-0.06, -0.03]	-5.29	< .001
AI-Generated	Human Original	Impressionist and Expressionist	-0.11	[-0.12, -0.09]	-12.19	< .001
AI-Generated	Human Forgery	Impressionist and Expressionist	-0.06	[-0.07, -0.04]	-6.44	< .001
Human Forgery	Human Original	Impressionist and Expressionist	-0.05	[-0.07, -0.03]	-5.72	< .001
AI-Generated	Human Original	Romantic and Realism	-0.11	[-0.12, -0.09]	-12.34	< .001
AI-Generated	Human Forgery	Romantic and Realism	-0.08	[-0.10, -0.06]	-9.17	< .001
Human Forgery	Human Original	Romantic and Realism	-0.03	[-0.04, -0.01]	-3.18	0.001

Emotion

rez_meaning$p_emotion

rez_meaning$t_emotion

Meaning
Marginal Contrasts by Emotion
Level1	Level2	Emotion	Difference	95% CI	t(14625)	p
AI-Generated	Human Original	Negative - High intensity	-0.13	[-0.14, -0.11]	-14.27	< .001
AI-Generated	Human Forgery	Negative - High intensity	-0.08	[-0.10, -0.06]	-9.09	< .001
Human Forgery	Human Original	Negative - High intensity	-0.05	[-0.06, -0.03]	-5.21	< .001
AI-Generated	Human Original	Negative - Low intensity	-0.08	[-0.10, -0.07]	-9.68	< .001
AI-Generated	Human Forgery	Negative - Low intensity	-0.05	[-0.07, -0.04]	-6.11	< .001
Human Forgery	Human Original	Negative - Low intensity	-0.03	[-0.05, -0.01]	-3.48	< .001
AI-Generated	Human Original	Positive - High intensity	-0.11	[-0.12, -0.09]	-11.99	< .001
AI-Generated	Human Forgery	Positive - High intensity	-0.06	[-0.07, -0.04]	-6.38	< .001
Human Forgery	Human Original	Positive - High intensity	-0.05	[-0.07, -0.03]	-5.65	< .001
AI-Generated	Human Original	Positive - Low intensity	-0.09	[-0.11, -0.07]	-10.22	< .001
AI-Generated	Human Forgery	Positive - Low intensity	-0.05	[-0.07, -0.04]	-6.27	< .001
Human Forgery	Human Original	Positive - Low intensity	-0.04	[-0.05, -0.02]	-3.99	< .001

Complexity and Familiarity

rez_meaning$p_complexity

Manipulation Belief

rez_meaning$p_trust

rez_meaning$t_trust

Meaning
Manipulation Distrust
Level1	Level2	ManipulationDistrust	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	-5	-0.11	[-0.14, -0.08]	-8.06	< .001
Human Forgery	Human Original	-5	-0.06	[-0.09, -0.04]	-4.50	< .001
AI-Generated	Human Forgery	-5	-0.05	[-0.08, -0.02]	-3.58	< .001
AI-Generated	Human Original	0	-0.10	[-0.11, -0.09]	-23.13	< .001
AI-Generated	Human Forgery	0	-0.06	[-0.07, -0.05]	-13.73	< .001
Human Forgery	Human Original	0	-0.04	[-0.05, -0.03]	-9.41	< .001
AI-Generated	Human Original	5	-0.09	[-0.12, -0.07]	-7.30	< .001
AI-Generated	Human Forgery	5	-0.07	[-0.10, -0.05]	-5.68	< .001
Human Forgery	Human Original	5	-0.02	[-0.05, 0.00]	-1.63	0.103

Self-Relevance

rez_meaning$p_selfrelevance

rez_meaning$t_selfrelevance

Meaning
Self-Relevance (Marginal Contrasts)
Level1	Level2	SelfRelevance	Difference	95% CI	t(10548)	p
AI-Generated	Human Original	0.00	-0.09	[-0.10, -0.07]	-10.98	< .001
AI-Generated	Human Forgery	0.00	-0.05	[-0.06, -0.03]	-6.24	< .001
Human Forgery	Human Original	0.00	-0.04	[-0.05, -0.02]	-4.72	< .001
AI-Generated	Human Original	0.28	-0.10	[-0.11, -0.08]	-12.72	< .001
AI-Generated	Human Forgery	0.28	-0.05	[-0.07, -0.04]	-7.12	< .001
Human Forgery	Human Original	0.28	-0.04	[-0.06, -0.03]	-5.66	< .001
AI-Generated	Human Original	0.50	-0.09	[-0.11, -0.08]	-11.51	< .001
AI-Generated	Human Forgery	0.50	-0.05	[-0.07, -0.03]	-6.15	< .001
Human Forgery	Human Original	0.50	-0.04	[-0.06, -0.03]	-5.45	< .001
AI-Generated	Human Original	1.00	-0.06	[-0.11, -0.02]	-2.77	0.006
Human Forgery	Human Original	1.00	-0.04	[-0.09, 0.00]	-1.83	0.067
AI-Generated	Human Forgery	1.00	-0.02	[-0.07, 0.03]	-0.87	0.386

rez_meaning$t_selfrelevance2

Meaning
Self-Relevance (Marginal Slopes)
Condition	SelfRelevance	Slope	95% CI	t(10548)	p
Human Original	0.00	0.20	[ 0.12, 0.29]	4.99	< .001
Human Original	0.25	0.18	[ 0.14, 0.21]	8.93	< .001
Human Original	0.50	0.15	[ 0.11, 0.18]	8.25	< .001
Human Original	0.75	0.12	[ 0.04, 0.19]	3.02	0.003
Human Original	1.00	0.09	[-0.04, 0.21]	1.38	0.168
Human Forgery	0.00	0.18	[ 0.10, 0.26]	4.45	< .001
Human Forgery	0.25	0.16	[ 0.12, 0.20]	8.28	< .001
Human Forgery	0.50	0.14	[ 0.10, 0.18]	7.40	< .001
Human Forgery	0.75	0.12	[ 0.04, 0.20]	2.90	0.004
Human Forgery	1.00	0.09	[-0.03, 0.22]	1.47	0.141
AI-Generated	0.00	0.15	[ 0.07, 0.23]	3.67	< .001
AI-Generated	0.25	0.16	[ 0.12, 0.20]	8.15	< .001
AI-Generated	0.50	0.17	[ 0.13, 0.20]	9.20	< .001
AI-Generated	0.75	0.17	[ 0.10, 0.25]	4.50	< .001
AI-Generated	1.00	0.18	[ 0.06, 0.31]	2.92	0.003

Beauty 2

rez_meaning$p_beauty

rez_meaning$t_beauty

Meaning
Predicting Follow-up Beauty (Contrasts)
Level1	Level2	Meaning	Difference	95% CI	t(10548)	p
Human Forgery	Human Original	0.00	0.01	[-0.01, 0.03]	0.82	0.411
AI-Generated	Human Original	0.00	0.01	[-0.01, 0.02]	0.63	0.529
AI-Generated	Human Forgery	0.00	0.00	[-0.02, 0.02]	-0.24	0.809
Human Forgery	Human Original	0.25	0.01	[ 0.00, 0.02]	1.42	0.156
AI-Generated	Human Original	0.25	0.01	[ 0.00, 0.02]	1.57	0.117
AI-Generated	Human Forgery	0.25	0.00	[-0.01, 0.01]	0.16	0.872
Human Forgery	Human Original	0.50	0.00	[-0.01, 0.02]	0.71	0.475
AI-Generated	Human Original	0.50	0.01	[-0.01, 0.02]	0.93	0.351
AI-Generated	Human Forgery	0.50	0.00	[-0.01, 0.01]	0.23	0.815
Human Forgery	Human Original	0.75	0.00	[-0.02, 0.01]	-0.51	0.608
AI-Generated	Human Original	0.75	0.00	[-0.02, 0.01]	-0.56	0.577
AI-Generated	Human Forgery	0.75	0.00	[-0.02, 0.01]	-0.07	0.942
Human Forgery	Human Original	1.00	-0.02	[-0.05, 0.02]	-0.92	0.357
AI-Generated	Human Original	1.00	-0.02	[-0.06, 0.01]	-1.15	0.249
AI-Generated	Human Forgery	1.00	-0.01	[-0.04, 0.03]	-0.27	0.785

rez_meaning$t_beauty2

Meaning
Predicting Follow-up Beauty (Slopes)
Condition	Meaning	Slope	95% CI	t(10548)	p
Human Original	0.00	0.28	[ 0.20, 0.35]	7.34	< .001
Human Original	0.25	0.23	[ 0.19, 0.27]	11.49	< .001
Human Original	0.50	0.18	[ 0.15, 0.21]	13.87	< .001
Human Original	0.75	0.13	[ 0.08, 0.18]	4.79	< .001
Human Original	1.00	0.08	[-0.01, 0.17]	1.77	0.076
Human Forgery	0.00	0.29	[ 0.21, 0.36]	7.69	< .001
Human Forgery	0.25	0.22	[ 0.18, 0.26]	11.65	< .001
Human Forgery	0.50	0.16	[ 0.13, 0.18]	11.07	< .001
Human Forgery	0.75	0.09	[ 0.03, 0.15]	3.02	0.003
Human Forgery	1.00	0.03	[-0.07, 0.12]	0.51	0.608
AI-Generated	0.00	0.31	[ 0.23, 0.38]	8.25	< .001
AI-Generated	0.25	0.23	[ 0.19, 0.27]	12.57	< .001
AI-Generated	0.50	0.15	[ 0.12, 0.18]	10.12	< .001
AI-Generated	0.75	0.08	[ 0.01, 0.14]	2.39	0.017
AI-Generated	1.00	0.00	[-0.10, 0.11]	0.04	0.971

Worth

rez_worth <- make_analysis(dftask, outcome="Worth")
rez_worth$p_desc

Condition

rez_worth$p_condition

rez_worth$t_condition

Worth
Marginal Contrasts
Level1	Level2	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	-0.12	[-0.12, -0.11]	-33.56	< .001
Human Forgery	Human Original	-0.06	[-0.07, -0.06]	-18.68	< .001
AI-Generated	Human Forgery	-0.05	[-0.06, -0.04]	-14.89	< .001

Style

rez_worth$p_style

rez_worth$t_style

Worth
Marginal Contrasts by Style
Level1	Level2	Style	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	Abstract and Avant-garde	-0.09	[-0.11, -0.08]	-13.36	< .001
Human Forgery	Human Original	Abstract and Avant-garde	-0.05	[-0.06, -0.03]	-6.79	< .001
AI-Generated	Human Forgery	Abstract and Avant-garde	-0.05	[-0.06, -0.03]	-6.63	< .001
AI-Generated	Human Original	Classical	-0.13	[-0.14, -0.12]	-18.99	< .001
Human Forgery	Human Original	Classical	-0.08	[-0.09, -0.06]	-10.92	< .001
AI-Generated	Human Forgery	Classical	-0.06	[-0.07, -0.04]	-8.04	< .001
AI-Generated	Human Original	Impressionist and Expressionist	-0.12	[-0.14, -0.11]	-17.78	< .001
Human Forgery	Human Original	Impressionist and Expressionist	-0.07	[-0.09, -0.06]	-10.53	< .001
AI-Generated	Human Forgery	Impressionist and Expressionist	-0.05	[-0.06, -0.04]	-7.22	< .001
AI-Generated	Human Original	Romantic and Realism	-0.12	[-0.13, -0.10]	-16.99	< .001
Human Forgery	Human Original	Romantic and Realism	-0.06	[-0.08, -0.05]	-9.12	< .001
AI-Generated	Human Forgery	Romantic and Realism	-0.05	[-0.07, -0.04]	-7.89	< .001

Emotion

rez_worth$p_emotion

Ignoring unknown labels:
• alpha : "Emotion"

rez_worth$t_emotion

Worth
Marginal Contrasts by Emotion
Level1	Level2	Emotion	Difference	95% CI	t(14625)	p
AI-Generated	Human Original	Negative - High intensity	-0.11	[-0.12, -0.09]	-15.39	< .001
AI-Generated	Human Forgery	Negative - High intensity	-0.06	[-0.07, -0.04]	-7.94	< .001
Human Forgery	Human Original	Negative - High intensity	-0.05	[-0.07, -0.04]	-7.50	< .001
AI-Generated	Human Original	Negative - Low intensity	-0.12	[-0.13, -0.11]	-17.37	< .001
Human Forgery	Human Original	Negative - Low intensity	-0.06	[-0.08, -0.05]	-9.11	< .001
AI-Generated	Human Forgery	Negative - Low intensity	-0.06	[-0.07, -0.04]	-8.07	< .001
AI-Generated	Human Original	Positive - High intensity	-0.12	[-0.13, -0.10]	-16.60	< .001
Human Forgery	Human Original	Positive - High intensity	-0.07	[-0.09, -0.06]	-10.63	< .001
AI-Generated	Human Forgery	Positive - High intensity	-0.04	[-0.06, -0.03]	-6.09	< .001
AI-Generated	Human Original	Positive - Low intensity	-0.12	[-0.13, -0.11]	-17.13	< .001
Human Forgery	Human Original	Positive - Low intensity	-0.07	[-0.08, -0.05]	-9.79	< .001
AI-Generated	Human Forgery	Positive - Low intensity	-0.05	[-0.06, -0.04]	-7.39	< .001

Complexity and Familiarity

rez_worth$p_complexity

Manipulation Belief

rez_worth$p_trust

rez_worth$t_trust

Worth
Manipulation Distrust
Level1	Level2	ManipulationDistrust	Difference	95% CI	t(14613)	p
AI-Generated	Human Original	-5	-0.13	[-0.15, -0.11]	-11.78	< .001
Human Forgery	Human Original	-5	-0.07	[-0.09, -0.05]	-6.46	< .001
AI-Generated	Human Forgery	-5	-0.06	[-0.08, -0.04]	-5.35	< .001
AI-Generated	Human Original	0	-0.12	[-0.12, -0.11]	-33.37	< .001
Human Forgery	Human Original	0	-0.06	[-0.07, -0.06]	-18.56	< .001
AI-Generated	Human Forgery	0	-0.05	[-0.06, -0.04]	-14.82	< .001
AI-Generated	Human Original	5	-0.10	[-0.12, -0.08]	-10.37	< .001
Human Forgery	Human Original	5	-0.06	[-0.08, -0.04]	-5.89	< .001
AI-Generated	Human Forgery	5	-0.04	[-0.06, -0.03]	-4.50	< .001

Self-Relevance

rez_worth$p_selfrelevance

rez_worth$t_selfrelevance

Worth
Self-Relevance (Marginal Contrasts)
Level1	Level2	SelfRelevance	Difference	95% CI	t(10548)	p
AI-Generated	Human Original	0.00	-0.09	[-0.10, -0.07]	-14.27	< .001
Human Forgery	Human Original	0.00	-0.05	[-0.06, -0.04]	-8.24	< .001
AI-Generated	Human Forgery	0.00	-0.04	[-0.05, -0.02]	-5.98	< .001
AI-Generated	Human Original	0.28	-0.10	[-0.11, -0.09]	-17.70	< .001
Human Forgery	Human Original	0.28	-0.07	[-0.08, -0.05]	-11.43	< .001
AI-Generated	Human Forgery	0.28	-0.04	[-0.05, -0.03]	-6.34	< .001
AI-Generated	Human Original	0.50	-0.11	[-0.13, -0.10]	-18.03	< .001
Human Forgery	Human Original	0.50	-0.07	[-0.08, -0.06]	-11.48	< .001
AI-Generated	Human Forgery	0.50	-0.04	[-0.05, -0.03]	-6.68	< .001
AI-Generated	Human Original	1.00	-0.14	[-0.17, -0.10]	-7.64	< .001
AI-Generated	Human Forgery	1.00	-0.07	[-0.11, -0.03]	-3.75	< .001
Human Forgery	Human Original	1.00	-0.07	[-0.10, -0.03]	-3.66	< .001

rez_worth$t_selfrelevance2

Worth
Self-Relevance (Marginal Slopes)
Condition	SelfRelevance	Slope	95% CI	t(10548)	p
Human Original	0.00	0.23	[0.16, 0.29]	7.08	< .001
Human Original	0.25	0.21	[0.18, 0.24]	13.71	< .001
Human Original	0.50	0.19	[0.16, 0.22]	14.02	< .001
Human Original	0.75	0.17	[0.12, 0.23]	5.87	< .001
Human Original	1.00	0.16	[0.06, 0.25]	3.27	0.001
Human Forgery	0.00	0.15	[0.09, 0.22]	4.84	< .001
Human Forgery	0.25	0.16	[0.13, 0.19]	10.93	< .001
Human Forgery	0.50	0.17	[0.15, 0.20]	11.96	< .001
Human Forgery	0.75	0.18	[0.12, 0.24]	5.91	< .001
Human Forgery	1.00	0.19	[0.10, 0.29]	3.88	< .001
AI-Generated	0.00	0.16	[0.10, 0.23]	5.22	< .001
AI-Generated	0.25	0.15	[0.12, 0.18]	10.12	< .001
AI-Generated	0.50	0.14	[0.11, 0.17]	9.97	< .001
AI-Generated	0.75	0.13	[0.07, 0.19]	4.21	< .001
AI-Generated	1.00	0.11	[0.02, 0.21]	2.35	0.019

Beauty 2

rez_worth$p_beauty

rez_worth$t_beauty

Worth
Predicting Follow-up Beauty (Contrasts)
Level1	Level2	Worth	Difference	95% CI	t(10548)	p
Human Forgery	Human Original	0.00	0.02	[ 0.00, 0.03]	2.29	0.022
AI-Generated	Human Original	0.00	0.01	[ 0.00, 0.03]	2.20	0.028
AI-Generated	Human Forgery	0.00	0.00	[-0.01, 0.01]	-0.15	0.880
Human Forgery	Human Original	0.25	0.02	[ 0.00, 0.03]	2.78	0.006
AI-Generated	Human Original	0.25	0.03	[ 0.02, 0.04]	5.08	< .001
AI-Generated	Human Forgery	0.25	0.01	[ 0.00, 0.03]	2.32	0.020
Human Forgery	Human Original	0.50	0.01	[ 0.00, 0.03]	2.18	0.029
AI-Generated	Human Original	0.50	0.02	[ 0.01, 0.04]	2.83	0.005
AI-Generated	Human Forgery	0.50	0.01	[-0.01, 0.02]	0.82	0.412
Human Forgery	Human Original	0.75	0.01	[-0.02, 0.04]	0.69	0.493
AI-Generated	Human Original	0.75	-0.02	[-0.05, 0.02]	-0.97	0.331
AI-Generated	Human Forgery	0.75	-0.03	[-0.06, 0.01]	-1.37	0.170
Human Forgery	Human Original	1.00	0.00	[-0.07, 0.07]	0.05	0.961
AI-Generated	Human Original	1.00	-0.08	[-0.16, -0.01]	-2.09	0.036
AI-Generated	Human Forgery	1.00	-0.08	[-0.17, 0.00]	-1.88	0.060

rez_worth$t_beauty2

Worth
Predicting Follow-up Beauty (Slopes)
Condition	Worth	Slope	95% CI	t(10548)	p
Human Original	0.00	0.47	[ 0.40, 0.54]	13.25	< .001
Human Original	0.25	0.37	[ 0.34, 0.40]	22.48	< .001
Human Original	0.50	0.27	[ 0.23, 0.30]	13.53	< .001
Human Original	0.75	0.16	[ 0.08, 0.24]	4.03	< .001
Human Original	1.00	0.06	[-0.06, 0.18]	0.94	0.349
Human Forgery	0.00	0.48	[ 0.41, 0.56]	12.23	< .001
Human Forgery	0.25	0.37	[ 0.33, 0.40]	22.06	< .001
Human Forgery	0.50	0.25	[ 0.19, 0.31]	8.43	< .001
Human Forgery	0.75	0.14	[ 0.02, 0.25]	2.33	0.020
Human Forgery	1.00	0.02	[-0.15, 0.19]	0.22	0.823
AI-Generated	0.00	0.60	[ 0.51, 0.68]	14.48	< .001
AI-Generated	0.25	0.38	[ 0.35, 0.42]	21.78	< .001
AI-Generated	0.50	0.17	[ 0.10, 0.24]	4.84	< .001
AI-Generated	0.75	-0.04	[-0.18, 0.09]	-0.67	0.505
AI-Generated	1.00	-0.26	[-0.45, -0.06]	-2.58	0.010

Syntheticness

rez_reality <- make_analysis(filter(dftask, !is.na(Reality)), outcome="Reality")
rez_reality$p_desc

Condition

rez_reality$p_condition

rez_reality$t_condition

Reality
Marginal Contrasts
Level1	Level2	Difference	95% CI	t(14325)	p
AI-Generated	Human Original	-0.05	[-0.06, -0.04]	-8.40	< .001
AI-Generated	Human Forgery	-0.04	[-0.05, -0.03]	-6.14	< .001
Human Forgery	Human Original	-0.01	[-0.03, 0.00]	-2.26	0.024

Style

rez_reality$p_style

rez_reality$t_style

Reality
Marginal Contrasts by Style
Level1	Level2	Style	Difference	95% CI	t(14325)	p
AI-Generated	Human Original	Abstract and Avant-garde	-0.05	[-0.08, -0.03]	-4.27	< .001
AI-Generated	Human Forgery	Abstract and Avant-garde	-0.03	[-0.05, 0.00]	-2.34	0.019
Human Forgery	Human Original	Abstract and Avant-garde	-0.02	[-0.05, 0.00]	-1.94	0.052
AI-Generated	Human Original	Classical	-0.06	[-0.08, -0.03]	-4.73	< .001
AI-Generated	Human Forgery	Classical	-0.04	[-0.06, -0.01]	-3.00	0.003
Human Forgery	Human Original	Classical	-0.02	[-0.04, 0.00]	-1.73	0.084
AI-Generated	Human Original	Impressionist and Expressionist	-0.05	[-0.07, -0.03]	-4.06	< .001
AI-Generated	Human Forgery	Impressionist and Expressionist	-0.04	[-0.06, -0.01]	-3.01	0.003
Human Forgery	Human Original	Impressionist and Expressionist	-0.01	[-0.04, 0.01]	-1.04	0.301
AI-Generated	Human Forgery	Romantic and Realism	-0.05	[-0.07, -0.02]	-3.93	< .001
AI-Generated	Human Original	Romantic and Realism	-0.05	[-0.07, -0.02]	-3.74	< .001
Human Forgery	Human Original	Romantic and Realism	0.00	[-0.02, 0.03]	0.19	0.847

Emotion

rez_reality$p_emotion

rez_reality$t_emotion

Reality
Marginal Contrasts by Emotion
Level1	Level2	Emotion	Difference	95% CI	t(14337)	p
AI-Generated	Human Original	Negative - High intensity	-0.06	[-0.08, -0.03]	-4.74	< .001
AI-Generated	Human Forgery	Negative - High intensity	-0.04	[-0.06, -0.02]	-3.19	0.001
Human Forgery	Human Original	Negative - High intensity	-0.02	[-0.04, 0.00]	-1.56	0.118
AI-Generated	Human Forgery	Negative - Low intensity	-0.04	[-0.07, -0.02]	-3.65	< .001
AI-Generated	Human Original	Negative - Low intensity	-0.04	[-0.07, -0.02]	-3.63	< .001
Human Forgery	Human Original	Negative - Low intensity	0.00	[-0.02, 0.02]	0.04	0.970
AI-Generated	Human Original	Positive - High intensity	-0.06	[-0.09, -0.04]	-5.15	< .001
AI-Generated	Human Forgery	Positive - High intensity	-0.03	[-0.06, -0.01]	-2.61	0.009
Human Forgery	Human Original	Positive - High intensity	-0.03	[-0.05, -0.01]	-2.57	0.010
AI-Generated	Human Original	Positive - Low intensity	-0.04	[-0.06, -0.01]	-3.12	0.002
AI-Generated	Human Forgery	Positive - Low intensity	-0.03	[-0.06, -0.01]	-2.79	0.005
Human Forgery	Human Original	Positive - Low intensity	0.00	[-0.03, 0.02]	-0.36	0.722

Complexity and Familiarity

rez_reality$p_complexity

Manipulation Belief

rez_reality$p_trust

rez_reality$t_trust

Reality
Manipulation Distrust
Level1	Level2	ManipulationDistrust	Difference	95% CI	t(14325)	p
AI-Generated	Human Original	-5	-0.07	[-0.10, -0.03]	-3.34	< .001
AI-Generated	Human Forgery	-5	-0.05	[-0.09, -0.01]	-2.70	0.007
Human Forgery	Human Original	-5	-0.01	[-0.05, 0.03]	-0.65	0.517
AI-Generated	Human Original	0	-0.05	[-0.06, -0.04]	-8.42	< .001
AI-Generated	Human Forgery	0	-0.04	[-0.05, -0.03]	-6.20	< .001
Human Forgery	Human Original	0	-0.01	[-0.03, 0.00]	-2.22	0.026
AI-Generated	Human Original	5	-0.04	[-0.07, 0.00]	-2.14	0.033
AI-Generated	Human Forgery	5	-0.02	[-0.06, 0.01]	-1.32	0.188
Human Forgery	Human Original	5	-0.01	[-0.05, 0.02]	-0.82	0.410

Self-Relevance

rez_reality$p_selfrelevance

rez_reality$t_selfrelevance

Reality
Self-Relevance (Marginal Contrasts)
Level1	Level2	SelfRelevance	Difference	95% CI	t(10404)	p
AI-Generated	Human Original	0.00	-0.06	[-0.08, -0.03]	-5.01	< .001
AI-Generated	Human Forgery	0.00	-0.05	[-0.07, -0.03]	-4.54	< .001
Human Forgery	Human Original	0.00	-0.01	[-0.03, 0.02]	-0.48	0.632
AI-Generated	Human Original	0.28	-0.04	[-0.06, -0.02]	-3.56	< .001
AI-Generated	Human Forgery	0.28	-0.03	[-0.05, -0.01]	-2.60	0.009
Human Forgery	Human Original	0.28	-0.01	[-0.03, 0.01]	-0.97	0.331
AI-Generated	Human Original	0.50	-0.03	[-0.06, -0.01]	-3.00	0.003
AI-Generated	Human Forgery	0.50	-0.02	[-0.04, 0.00]	-1.70	0.089
Human Forgery	Human Original	0.50	-0.02	[-0.04, 0.01]	-1.32	0.186
AI-Generated	Human Original	1.00	-0.07	[-0.13, 0.00]	-1.97	0.049
AI-Generated	Human Forgery	1.00	-0.03	[-0.10, 0.03]	-1.01	0.315
Human Forgery	Human Original	1.00	-0.03	[-0.10, 0.04]	-0.90	0.367

rez_reality$t_selfrelevance2

Reality
Self-Relevance (Marginal Slopes)
Condition	SelfRelevance	Slope	95% CI	t(10404)	p
Human Original	0.00	0.15	[ 0.03, 0.26]	2.55	0.011
Human Original	0.25	0.14	[ 0.09, 0.19]	5.11	< .001
Human Original	0.50	0.13	[ 0.08, 0.18]	5.36	< .001
Human Original	0.75	0.13	[ 0.02, 0.23]	2.36	0.018
Human Original	1.00	0.12	[-0.05, 0.29]	1.36	0.173
Human Forgery	0.00	0.13	[ 0.02, 0.24]	2.31	0.021
Human Forgery	0.25	0.12	[ 0.07, 0.17]	4.49	< .001
Human Forgery	0.50	0.11	[ 0.06, 0.16]	4.11	< .001
Human Forgery	0.75	0.09	[-0.02, 0.21]	1.67	0.094
Human Forgery	1.00	0.08	[-0.10, 0.26]	0.91	0.362
AI-Generated	0.00	0.24	[ 0.13, 0.35]	4.21	< .001
AI-Generated	0.25	0.18	[ 0.13, 0.23]	6.75	< .001
AI-Generated	0.50	0.12	[ 0.07, 0.17]	4.88	< .001
AI-Generated	0.75	0.06	[-0.04, 0.17]	1.19	0.235
AI-Generated	1.00	0.01	[-0.17, 0.18]	0.08	0.939

Beauty 2

rez_reality$p_beauty

rez_reality$t_beauty

Reality
Predicting Follow-up Beauty (Contrasts)
Level1	Level2	Reality	Difference	95% CI	t(10404)	p
Human Forgery	Human Original	0.00	0.00	[-0.02, 0.03]	0.29	0.770
AI-Generated	Human Original	0.00	0.00	[-0.03, 0.02]	-0.33	0.742
AI-Generated	Human Forgery	0.00	-0.01	[-0.03, 0.02]	-0.64	0.525
Human Forgery	Human Original	0.25	0.01	[ 0.00, 0.02]	1.57	0.116
AI-Generated	Human Original	0.25	0.00	[-0.01, 0.01]	0.35	0.723
AI-Generated	Human Forgery	0.25	-0.01	[-0.02, 0.00]	-1.25	0.212
Human Forgery	Human Original	0.50	0.01	[-0.01, 0.02]	0.94	0.349
AI-Generated	Human Original	0.50	0.00	[-0.02, 0.01]	-0.07	0.947
AI-Generated	Human Forgery	0.50	-0.01	[-0.02, 0.01]	-1.01	0.311
Human Forgery	Human Original	0.75	-0.01	[-0.02, 0.01]	-1.06	0.287
AI-Generated	Human Original	0.75	-0.01	[-0.02, 0.00]	-2.03	0.042
AI-Generated	Human Forgery	0.75	-0.01	[-0.02, 0.01]	-0.97	0.332
Human Forgery	Human Original	1.00	-0.03	[-0.05, -0.01]	-2.72	0.007
AI-Generated	Human Original	1.00	-0.03	[-0.05, -0.01]	-2.92	0.003
AI-Generated	Human Forgery	1.00	0.00	[-0.03, 0.02]	-0.25	0.799

rez_reality$t_beauty2

Reality
Predicting Follow-up Beauty (Slopes)
Condition	Reality	Slope	95% CI	t(10404)	p
Human Original	0.00	-0.08	[-0.17, 0.02]	-1.63	0.104
Human Original	0.25	0.02	[-0.03, 0.07]	0.70	0.482
Human Original	0.50	0.11	[ 0.09, 0.13]	10.80	< .001
Human Original	0.75	0.20	[ 0.16, 0.25]	8.31	< .001
Human Original	1.00	0.30	[ 0.21, 0.39]	6.46	< .001
Human Forgery	0.00	-0.03	[-0.12, 0.06]	-0.60	0.551
Human Forgery	0.25	0.02	[-0.02, 0.07]	0.98	0.325
Human Forgery	0.50	0.08	[ 0.06, 0.10]	7.53	< .001
Human Forgery	0.75	0.13	[ 0.08, 0.18]	5.20	< .001
Human Forgery	1.00	0.18	[ 0.09, 0.27]	3.90	< .001
AI-Generated	0.00	-0.03	[-0.12, 0.05]	-0.75	0.452
AI-Generated	0.25	0.02	[-0.02, 0.07]	1.03	0.303
AI-Generated	0.50	0.08	[ 0.06, 0.10]	8.06	< .001
AI-Generated	0.75	0.14	[ 0.09, 0.19]	5.60	< .001
AI-Generated	1.00	0.20	[ 0.11, 0.29]	4.30	< .001

Authenticity

rez_authenticity <- make_analysis(filter(dftask, !is.na(Authenticity)), outcome="Authenticity")
rez_authenticity$p_desc

Condition

rez_authenticity$p_condition

rez_authenticity$t_condition

Authenticity
Marginal Contrasts
Level1	Level2	Difference	95% CI	t(14325)	p
Human Forgery	Human Original	-0.02	[-0.03, -0.01]	-3.41	< .001
AI-Generated	Human Original	-0.02	[-0.03, -0.01]	-3.25	0.001
AI-Generated	Human Forgery	0.00	[-0.01, 0.01]	0.16	0.874

Style

rez_authenticity$p_style

rez_authenticity$t_style

Authenticity
Marginal Contrasts by Style
Level1	Level2	Style	Difference	95% CI	t(14325)	p
Human Forgery	Human Original	Abstract and Avant-garde	-0.01	[-0.03, 0.01]	-0.89	0.372
AI-Generated	Human Original	Abstract and Avant-garde	0.00	[-0.02, 0.03]	0.30	0.766
AI-Generated	Human Forgery	Abstract and Avant-garde	0.01	[-0.01, 0.04]	1.19	0.234
AI-Generated	Human Original	Classical	-0.04	[-0.06, -0.01]	-3.00	0.003
Human Forgery	Human Original	Classical	-0.02	[-0.04, 0.01]	-1.52	0.128
AI-Generated	Human Forgery	Classical	-0.02	[-0.04, 0.01]	-1.48	0.140
Human Forgery	Human Original	Impressionist and Expressionist	-0.03	[-0.05, -0.01]	-2.46	0.014
AI-Generated	Human Original	Impressionist and Expressionist	-0.01	[-0.03, 0.02]	-0.65	0.516
AI-Generated	Human Forgery	Impressionist and Expressionist	0.02	[ 0.00, 0.04]	1.81	0.071
AI-Generated	Human Original	Romantic and Realism	-0.04	[-0.06, -0.01]	-3.15	0.002
Human Forgery	Human Original	Romantic and Realism	-0.02	[-0.05, 0.00]	-1.95	0.052
AI-Generated	Human Forgery	Romantic and Realism	-0.01	[-0.04, 0.01]	-1.21	0.228

Emotion

rez_authenticity$p_emotion

rez_authenticity$t_emotion

Authenticity
Marginal Contrasts by Emotion
Level1	Level2	Emotion	Difference	95% CI	t(14337)	p
Human Forgery	Human Original	Negative - High intensity	-0.02	[-0.05, 0.00]	-1.94	0.052
AI-Generated	Human Original	Negative - High intensity	-0.01	[-0.03, 0.02]	-0.49	0.623
AI-Generated	Human Forgery	Negative - High intensity	0.02	[-0.01, 0.04]	1.44	0.150
AI-Generated	Human Original	Negative - Low intensity	-0.02	[-0.04, 0.00]	-1.66	0.097
AI-Generated	Human Forgery	Negative - Low intensity	-0.02	[-0.04, 0.01]	-1.31	0.190
Human Forgery	Human Original	Negative - Low intensity	0.00	[-0.03, 0.02]	-0.33	0.739
Human Forgery	Human Original	Positive - High intensity	-0.04	[-0.06, -0.01]	-3.15	0.002
AI-Generated	Human Original	Positive - High intensity	-0.03	[-0.05, 0.00]	-2.17	0.030
AI-Generated	Human Forgery	Positive - High intensity	0.01	[-0.01, 0.03]	0.92	0.356
AI-Generated	Human Original	Positive - Low intensity	-0.02	[-0.05, 0.00]	-2.07	0.038
Human Forgery	Human Original	Positive - Low intensity	-0.02	[-0.04, 0.01]	-1.34	0.179
AI-Generated	Human Forgery	Positive - Low intensity	-0.01	[-0.03, 0.01]	-0.73	0.464

Complexity and Familiarity

rez_authenticity$p_complexity

Manipulation Belief

rez_authenticity$p_trust

rez_authenticity$t_trust

Authenticity
Manipulation Distrust
Level1	Level2	ManipulationDistrust	Difference	95% CI	t(14325)	p
AI-Generated	Human Original	-5	-0.05	[-0.08, -0.01]	-2.53	0.012
Human Forgery	Human Original	-5	-0.03	[-0.07, 0.01]	-1.55	0.120
AI-Generated	Human Forgery	-5	-0.02	[-0.06, 0.02]	-0.98	0.328
AI-Generated	Human Original	0	-0.02	[-0.03, -0.01]	-3.45	< .001
Human Forgery	Human Original	0	-0.02	[-0.03, -0.01]	-3.45	< .001
AI-Generated	Human Forgery	0	0.00	[-0.01, 0.01]	0.00	0.996
Human Forgery	Human Original	5	-0.01	[-0.05, 0.02]	-0.68	0.499
AI-Generated	Human Original	5	0.01	[-0.03, 0.04]	0.39	0.694
AI-Generated	Human Forgery	5	0.02	[-0.02, 0.05]	1.07	0.284

Self-Relevance

rez_authenticity$p_selfrelevance

rez_authenticity$t_selfrelevance

Authenticity
Self-Relevance (Marginal Contrasts)
Level1	Level2	SelfRelevance	Difference	95% CI	t(10404)	p
AI-Generated	Human Original	0.00	-0.03	[-0.06, -0.01]	-3.19	0.001
Human Forgery	Human Original	0.00	-0.03	[-0.05, -0.01]	-2.69	0.007
AI-Generated	Human Forgery	0.00	-0.01	[-0.03, 0.02]	-0.48	0.630
Human Forgery	Human Original	0.28	-0.02	[-0.04, 0.00]	-1.64	0.102
AI-Generated	Human Original	0.28	0.00	[-0.02, 0.02]	0.21	0.834
AI-Generated	Human Forgery	0.28	0.02	[ 0.00, 0.04]	1.84	0.065
Human Forgery	Human Original	0.50	-0.01	[-0.04, 0.01]	-1.28	0.200
AI-Generated	Human Original	0.50	0.00	[-0.02, 0.03]	0.38	0.702
AI-Generated	Human Forgery	0.50	0.02	[ 0.00, 0.04]	1.67	0.095
AI-Generated	Human Original	1.00	-0.08	[-0.14, -0.01]	-2.42	0.016
AI-Generated	Human Forgery	1.00	-0.04	[-0.11, 0.02]	-1.34	0.180
Human Forgery	Human Original	1.00	-0.03	[-0.10, 0.03]	-1.00	0.317

rez_authenticity$t_selfrelevance2

Authenticity
Self-Relevance (Marginal Slopes)
Condition	SelfRelevance	Slope	95% CI	t(10404)	p
Human Original	0.00	-0.06	[-0.17, 0.05]	-1.11	0.267
Human Original	0.25	0.03	[-0.02, 0.08]	1.06	0.290
Human Original	0.50	0.12	[ 0.07, 0.16]	4.97	< .001
Human Original	0.75	0.21	[ 0.10, 0.31]	3.98	< .001
Human Original	1.00	0.30	[ 0.13, 0.46]	3.52	< .001
Human Forgery	0.00	0.00	[-0.11, 0.11]	0.03	0.980
Human Forgery	0.25	0.06	[ 0.01, 0.11]	2.23	0.026
Human Forgery	0.50	0.11	[ 0.06, 0.16]	4.48	< .001
Human Forgery	0.75	0.17	[ 0.06, 0.28]	3.10	0.002
Human Forgery	1.00	0.23	[ 0.05, 0.40]	2.57	0.010
AI-Generated	0.00	0.14	[ 0.03, 0.24]	2.48	0.013
AI-Generated	0.25	0.10	[ 0.05, 0.16]	4.08	< .001
AI-Generated	0.50	0.07	[ 0.03, 0.12]	3.04	0.002
AI-Generated	0.75	0.04	[-0.06, 0.15]	0.81	0.420
AI-Generated	1.00	0.01	[-0.16, 0.18]	0.13	0.894

Beauty 2

rez_authenticity$p_beauty

rez_authenticity$t_beauty

Authenticity
Predicting Follow-up Beauty (Contrasts)
Level1	Level2	Authenticity	Difference	95% CI	t(10404)	p
Human Forgery	Human Original	0.00	0.00	[-0.03, 0.03]	0.17	0.865
AI-Generated	Human Original	0.00	0.01	[-0.02, 0.04]	0.62	0.538
AI-Generated	Human Forgery	0.00	0.01	[-0.02, 0.03]	0.46	0.646
Human Forgery	Human Original	0.25	0.01	[-0.01, 0.02]	0.95	0.341
AI-Generated	Human Original	0.25	0.00	[-0.01, 0.02]	0.25	0.805
AI-Generated	Human Forgery	0.25	0.00	[-0.02, 0.01]	-0.71	0.475
Human Forgery	Human Original	0.50	0.00	[-0.01, 0.02]	0.61	0.542
AI-Generated	Human Original	0.50	-0.01	[-0.02, 0.01]	-1.03	0.303
AI-Generated	Human Forgery	0.50	-0.01	[-0.03, 0.00]	-1.65	0.100
Human Forgery	Human Original	0.75	0.00	[-0.02, 0.01]	-0.78	0.435
AI-Generated	Human Original	0.75	-0.02	[-0.03, -0.01]	-3.15	0.002
AI-Generated	Human Forgery	0.75	-0.01	[-0.02, 0.00]	-2.36	0.018
Human Forgery	Human Original	1.00	-0.02	[-0.04, 0.00]	-1.87	0.062
AI-Generated	Human Original	1.00	-0.03	[-0.05, -0.01]	-2.89	0.004
AI-Generated	Human Forgery	1.00	-0.01	[-0.03, 0.01]	-0.99	0.321

rez_authenticity$t_beauty2

Authenticity
Predicting Follow-up Beauty (Slopes)
Condition	Authenticity	Slope	95% CI	t(10404)	p
Human Original	0.00	-0.15	[-0.25, -0.05]	-3.07	0.002
Human Original	0.25	-0.05	[-0.10, 0.01]	-1.66	0.096
Human Original	0.50	0.06	[ 0.04, 0.08]	5.28	< .001
Human Original	0.75	0.17	[ 0.12, 0.21]	7.17	< .001
Human Original	1.00	0.27	[ 0.18, 0.36]	6.10	< .001
Human Forgery	0.00	-0.12	[-0.22, -0.03]	-2.50	0.012
Human Forgery	0.25	-0.04	[-0.09, 0.01]	-1.57	0.117
Human Forgery	0.50	0.04	[ 0.02, 0.06]	3.42	< .001
Human Forgery	0.75	0.12	[ 0.07, 0.16]	4.92	< .001
Human Forgery	1.00	0.20	[ 0.11, 0.29]	4.33	< .001
AI-Generated	0.00	-0.18	[-0.27, -0.08]	-3.67	< .001
AI-Generated	0.25	-0.08	[-0.13, -0.03]	-2.92	0.003
AI-Generated	0.50	0.02	[ 0.00, 0.04]	1.84	0.066
AI-Generated	0.75	0.12	[ 0.07, 0.16]	5.05	< .001
AI-Generated	1.00	0.22	[ 0.13, 0.30]	4.86	< .001

Self-Relevance

rez_selfrelevance <- make_analysis(filter(dftask, !is.na(SelfRelevance)), outcome="SelfRelevance")
rez_selfrelevance$p_desc

Condition

rez_selfrelevance$p_condition

rez_selfrelevance$t_condition

SelfRelevance
Marginal Contrasts
Level1	Level2	Difference	95% CI	t(10533)	p
AI-Generated	Human Original	0.00	[-0.01, 0.00]	-0.96	0.339
Human Forgery	Human Original	0.00	[-0.01, 0.01]	-0.66	0.509
AI-Generated	Human Forgery	0.00	[-0.01, 0.01]	-0.30	0.768

Style

rez_selfrelevance$p_style

rez_selfrelevance$t_style

SelfRelevance
Marginal Contrasts by Style
Level1	Level2	Style	Difference	95% CI	t(10533)	p
Human Forgery	Human Original	Abstract and Avant-garde	-0.01	[-0.03, 0.01]	-0.55	0.581
AI-Generated	Human Original	Abstract and Avant-garde	0.00	[-0.02, 0.02]	0.11	0.914
AI-Generated	Human Forgery	Abstract and Avant-garde	0.01	[-0.01, 0.03]	0.66	0.508
Human Forgery	Human Original	Classical	-0.02	[-0.04, 0.00]	-1.71	0.088
AI-Generated	Human Original	Classical	-0.01	[-0.03, 0.01]	-1.40	0.160
AI-Generated	Human Forgery	Classical	0.00	[-0.02, 0.02]	0.30	0.761
AI-Generated	Human Forgery	Impressionist and Expressionist	-0.02	[-0.04, 0.00]	-1.61	0.107
AI-Generated	Human Original	Impressionist and Expressionist	-0.01	[-0.03, 0.01]	-0.70	0.486
Human Forgery	Human Original	Impressionist and Expressionist	0.01	[-0.01, 0.03]	0.91	0.362
Human Forgery	Human Original	Romantic and Realism	0.00	[-0.02, 0.02]	0.02	0.982
AI-Generated	Human Forgery	Romantic and Realism	0.00	[-0.02, 0.02]	0.06	0.955
AI-Generated	Human Original	Romantic and Realism	0.00	[-0.02, 0.02]	0.08	0.937

Emotion

rez_selfrelevance$p_emotion

Ignoring unknown labels:
• alpha : "Emotion"

rez_selfrelevance$t_emotion

SelfRelevance
Marginal Contrasts by Emotion
Level1	Level2	Emotion	Difference	95% CI	t(10545)	p
AI-Generated	Human Forgery	Negative - High intensity	0.00	[-0.02, 0.02]	-0.04	0.965
AI-Generated	Human Original	Negative - High intensity	0.01	[-0.01, 0.03]	0.59	0.558
Human Forgery	Human Original	Negative - High intensity	0.01	[-0.01, 0.03]	0.64	0.523
AI-Generated	Human Original	Negative - Low intensity	-0.01	[-0.03, 0.01]	-1.40	0.161
AI-Generated	Human Forgery	Negative - Low intensity	-0.01	[-0.03, 0.01]	-1.34	0.181
Human Forgery	Human Original	Negative - Low intensity	0.00	[-0.02, 0.02]	-0.07	0.947
Human Forgery	Human Original	Positive - High intensity	-0.02	[-0.04, 0.00]	-1.90	0.058
AI-Generated	Human Original	Positive - High intensity	-0.02	[-0.04, 0.00]	-1.83	0.068
AI-Generated	Human Forgery	Positive - High intensity	0.00	[-0.02, 0.02]	0.06	0.948
Human Forgery	Human Original	Positive - Low intensity	0.00	[-0.02, 0.02]	-0.01	0.995
AI-Generated	Human Original	Positive - Low intensity	0.01	[-0.01, 0.03]	0.78	0.437
AI-Generated	Human Forgery	Positive - Low intensity	0.01	[-0.01, 0.03]	0.79	0.430

Complexity and Familiarity

rez_selfrelevance$p_complexity

Manipulation Belief

rez_selfrelevance$p_trust

rez_selfrelevance$t_trust

SelfRelevance
Manipulation Distrust
Level1	Level2	ManipulationDistrust	Difference	95% CI	t(10533)	p
Human Forgery	Human Original	-5	-0.04	[-0.07, -0.01]	-2.52	0.012
AI-Generated	Human Original	-5	-0.01	[-0.04, 0.02]	-0.38	0.701
AI-Generated	Human Forgery	-5	0.03	[ 0.00, 0.06]	2.13	0.033
AI-Generated	Human Original	0	0.00	[-0.01, 0.00]	-0.96	0.337
Human Forgery	Human Original	0	0.00	[-0.01, 0.01]	-0.89	0.375
AI-Generated	Human Forgery	0	0.00	[-0.01, 0.01]	-0.07	0.943
AI-Generated	Human Forgery	5	-0.03	[-0.06, -0.01]	-2.31	0.021
AI-Generated	Human Original	5	0.00	[-0.03, 0.03]	-0.24	0.810
Human Forgery	Human Original	5	0.03	[ 0.00, 0.06]	2.07	0.038

Beauty 2

rez_selfrelevance$p_beauty

rez_selfrelevance$t_beauty

SelfRelevance
Predicting Follow-up Beauty (Contrasts)
Level1	Level2	SelfRelevance	Difference	95% CI	t(10548)	p
Human Forgery	Human Original	0.00	-0.01	[-0.02, 0.01]	-0.98	0.328
AI-Generated	Human Original	0.00	-0.01	[-0.02, 0.00]	-1.86	0.062
AI-Generated	Human Forgery	0.00	-0.01	[-0.02, 0.01]	-0.88	0.378
Human Forgery	Human Original	0.25	0.00	[-0.01, 0.01]	-0.01	0.989
AI-Generated	Human Original	0.25	-0.01	[-0.02, 0.00]	-1.84	0.065
AI-Generated	Human Forgery	0.25	-0.01	[-0.02, 0.00]	-1.83	0.067
Human Forgery	Human Original	0.50	0.00	[-0.01, 0.01]	0.16	0.874
AI-Generated	Human Original	0.50	-0.01	[-0.02, 0.00]	-1.57	0.117
AI-Generated	Human Forgery	0.50	-0.01	[-0.02, 0.00]	-1.74	0.081
Human Forgery	Human Original	0.75	0.00	[-0.02, 0.01]	-0.35	0.727
AI-Generated	Human Original	0.75	-0.01	[-0.03, 0.00]	-1.37	0.171
AI-Generated	Human Forgery	0.75	-0.01	[-0.02, 0.01]	-0.98	0.326
Human Forgery	Human Original	1.00	-0.01	[-0.05, 0.03]	-0.61	0.544
AI-Generated	Human Original	1.00	-0.01	[-0.05, 0.02]	-0.64	0.519
AI-Generated	Human Forgery	1.00	0.00	[-0.04, 0.04]	-0.02	0.981

rez_selfrelevance$t_beauty2

SelfRelevance
Predicting Follow-up Beauty (Slopes)
Condition	SelfRelevance	Slope	95% CI	t(10548)	p
Human Original	0.00	0.55	[0.48, 0.61]	16.86	< .001
Human Original	0.25	0.48	[0.45, 0.51]	30.86	< .001
Human Original	0.50	0.41	[0.38, 0.43]	29.35	< .001
Human Original	0.75	0.34	[0.28, 0.40]	11.23	< .001
Human Original	1.00	0.27	[0.17, 0.37]	5.51	< .001
Human Forgery	0.00	0.58	[0.52, 0.64]	17.95	< .001
Human Forgery	0.25	0.49	[0.46, 0.52]	32.26	< .001
Human Forgery	0.50	0.40	[0.37, 0.43]	27.29	< .001
Human Forgery	0.75	0.31	[0.25, 0.38]	9.89	< .001
Human Forgery	1.00	0.22	[0.13, 0.32]	4.42	< .001
AI-Generated	0.00	0.55	[0.49, 0.61]	17.10	< .001
AI-Generated	0.25	0.48	[0.45, 0.51]	31.14	< .001
AI-Generated	0.50	0.41	[0.38, 0.43]	29.35	< .001
AI-Generated	0.75	0.34	[0.28, 0.39]	11.09	< .001
AI-Generated	1.00	0.26	[0.17, 0.36]	5.31	< .001

Beauty 2

rez_beauty2 <- make_analysis(filter(dftask, !is.na(Beauty2)), outcome="Beauty2")
rez_beauty2$p_desc

Condition

rez_beauty2$p_condition

rez_beauty2$t_condition

Beauty2
Marginal Contrasts
Level1	Level2	Difference	95% CI	t(10533)	p
AI-Generated	Human Original	-0.01	[-0.02, 0.00]	-2.86	0.004
AI-Generated	Human Forgery	-0.01	[-0.02, 0.00]	-2.03	0.042
Human Forgery	Human Original	0.00	[-0.01, 0.01]	-0.83	0.409

Style

rez_beauty2$p_style

rez_beauty2$t_style

Beauty2
Marginal Contrasts by Style
Level1	Level2	Style	Difference	95% CI	t(10533)	p
AI-Generated	Human Original	Abstract and Avant-garde	-0.02	[-0.03, 0.00]	-1.68	0.093
Human Forgery	Human Original	Abstract and Avant-garde	-0.01	[-0.03, 0.01]	-1.26	0.208
AI-Generated	Human Forgery	Abstract and Avant-garde	0.00	[-0.02, 0.01]	-0.43	0.670
Human Forgery	Human Original	Classical	-0.02	[-0.04, 0.00]	-2.35	0.019
AI-Generated	Human Original	Classical	-0.01	[-0.03, 0.00]	-1.51	0.131
AI-Generated	Human Forgery	Classical	0.01	[-0.01, 0.03]	0.84	0.399
AI-Generated	Human Forgery	Impressionist and Expressionist	-0.02	[-0.04, -0.01]	-2.58	0.010
AI-Generated	Human Original	Impressionist and Expressionist	-0.02	[-0.04, 0.00]	-2.19	0.028
Human Forgery	Human Original	Impressionist and Expressionist	0.00	[-0.01, 0.02]	0.39	0.694
AI-Generated	Human Forgery	Romantic and Realism	-0.02	[-0.03, 0.00]	-1.90	0.058
AI-Generated	Human Original	Romantic and Realism	0.00	[-0.02, 0.01]	-0.33	0.741
Human Forgery	Human Original	Romantic and Realism	0.01	[ 0.00, 0.03]	1.57	0.117

Emotion

rez_beauty2$p_emotion

rez_beauty2$t_emotion

Beauty2
Marginal Contrasts by Emotion
Level1	Level2	Emotion	Difference	95% CI	t(10545)	p
AI-Generated	Human Forgery	Negative - High intensity	-0.01	[-0.03, 0.01]	-1.33	0.184
AI-Generated	Human Original	Negative - High intensity	-0.01	[-0.02, 0.01]	-0.68	0.495
Human Forgery	Human Original	Negative - High intensity	0.01	[-0.01, 0.02]	0.65	0.517
AI-Generated	Human Original	Negative - Low intensity	-0.01	[-0.03, 0.00]	-1.45	0.146
AI-Generated	Human Forgery	Negative - Low intensity	-0.01	[-0.03, 0.00]	-1.45	0.148
Human Forgery	Human Original	Negative - Low intensity	0.00	[-0.02, 0.02]	-0.01	0.994
AI-Generated	Human Original	Positive - High intensity	-0.03	[-0.05, -0.01]	-3.26	0.001
Human Forgery	Human Original	Positive - High intensity	-0.03	[-0.04, -0.01]	-2.86	0.004
AI-Generated	Human Forgery	Positive - High intensity	0.00	[-0.02, 0.01]	-0.40	0.689
AI-Generated	Human Forgery	Positive - Low intensity	-0.01	[-0.03, 0.01]	-0.82	0.415
AI-Generated	Human Original	Positive - Low intensity	0.00	[-0.02, 0.02]	-0.24	0.807
Human Forgery	Human Original	Positive - Low intensity	0.01	[-0.01, 0.02]	0.57	0.569

Complexity and Familiarity

rez_beauty2$p_complexity

Manipulation Belief

rez_beauty2$p_trust

rez_beauty2$t_trust

Beauty2
Manipulation Distrust
Level1	Level2	ManipulationDistrust	Difference	95% CI	t(10533)	p
AI-Generated	Human Original	-5	-0.02	[-0.05, 0.01]	-1.48	0.139
Human Forgery	Human Original	-5	-0.01	[-0.04, 0.02]	-0.83	0.405
AI-Generated	Human Forgery	-5	-0.01	[-0.04, 0.02]	-0.65	0.519
AI-Generated	Human Original	0	-0.01	[-0.02, 0.00]	-2.90	0.004
AI-Generated	Human Forgery	0	-0.01	[-0.02, 0.00]	-2.02	0.043
Human Forgery	Human Original	0	0.00	[-0.01, 0.00]	-0.88	0.379
AI-Generated	Human Forgery	5	-0.01	[-0.04, 0.02]	-0.68	0.497
AI-Generated	Human Original	5	-0.01	[-0.03, 0.02]	-0.39	0.696
Human Forgery	Human Original	5	0.00	[-0.02, 0.03]	0.29	0.772

Figures

fig_stimselection