Replication of Study affective congruence & donation by Genevsky et al. (2025, Social Cognitive and Affective Neuroscience)

Introduction

I chose to replicate Experiment 1a from Genevsky et al. (2025) because it directly connects with my research interests in affective decision-making and prosocial behavior, particularly how emotional cues and affective congruence shape value-based choices. Genevsky and colleagues proposed that the congruence between emotional features in charitable appeals—such as facial expressions and message framing—can enhance donation behavior by eliciting positive aroused affect, which in turn engages reward-related neural systems like the nucleus accumbens (NAcc). This framework integrates affective science and decision neuroscience, offering a mechanistic account of how emotion and valuation processes jointly drive prosocial giving. Replicating Experiment 1a, the foundational behavioral study in their series, provides an opportunity to test the robustness and generalizability of the affective congruence effect across participant samples. It also offers a strong behavioral foundation for my broader research program, which examines how affective and neural signals forecast real-world decisions, such as consumer or charitable choices. By reproducing this experiment, I aim to evaluate whether affective congruence consistently amplifies giving through emotional engagement, and to better understand the affective mechanisms that support prosocial decision-making.

Methods

Power Analysis

Original effect size and power considerations were estimated based on the reported results from Experiment 1a. Because the original data are not publicly available, we used the reported paired mean difference (\(M_{congruent} - M_{incongruent} = 0.425\)) sample size (\(n=56\)), and t-values to estimate Cohen’s d, which was found to range approximately from 0.27 to 0.38. Using this estimated effect size, we conducted a power analysis to determine the required sample sizes to achieve \(80\%\), \(90\%\), and \(95\%\) power for detecting the effect. The resulting sample size requirements are illustrated in Figure 1. For conservative planning, at least 110 participants would be needed to achieve \(80\%\) power at the lower bound of the effect size (\(d = 0.27\)). The original experiment, with \(56\) participants, would only achieve \(80\%\) power if the effect size were at the upper bound (\(d > 0.37\)).

library(pwr)
library(tidyverse)

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✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

dz_values <- seq(0.27, 0.4, by = 0.01)

n_80 <- sapply(dz_values, function(d) ceiling(pwr.t.test(d=d, power=0.8, type="paired", alternative="two.sided")$n))
n_90 <- sapply(dz_values, function(d) ceiling(pwr.t.test(d=d, power=0.9, type="paired", alternative="two.sided")$n))
n_95 <- sapply(dz_values, function(d) ceiling(pwr.t.test(d=d, power=0.95, type="paired", alternative="two.sided")$n))

df <- data.frame(
  dz = dz_values,
  n_80 = n_80,
  n_90 = n_90,
  n_95 = n_95
)

df_long <- pivot_longer(df, cols = c(`n_80`, n_90, n_95),
                        names_to = "Power", values_to = "SampleSize")

p1 <- ggplot(df_long, aes(x=dz, y=SampleSize, color=Power, linetype=Power)) +
  geom_line(size=1.2) +
  geom_hline(yintercept=56, color="black", linetype="dashed", size=1) +
  annotate("text", x=0.3, y=57, label="orignal n=56", color="black", hjust=0, vjust = -1.2) +
  scale_color_manual(values=c("n_80"="#F6A6B2", 
                              "n_90"="#FFE066",  
                              "n_95"="#6CA0DC"), 
                     labels=c("80% power","90% power","95% power")) +
  scale_linetype_manual(values=c("solid","dashed","dotted"),
                        labels=c("80% power","90% power","95% power")) +
  labs(title="Required Sample Size vs Cohen's d",
       x="Cohen's d",
       y="Required Sample Size",
       color="Power Level",
       linetype="Power Level") +
  theme_minimal(base_size = 14)

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

ggsave(filename = "fig1.png", plot = p1, width = 9, height = 5)

Fig 1. Power analysis with estimated effect size given statistics from original study

Planned Sample

The planned sample size was determined based on a power analysis using the estimated effect size from Experiment 1a (\(d ≈ 0.27–0.38\)). To achieve \(80\%\) power at the lower bound of the effect size (\(d = 0.27\)), a conservative sample of at least 110 participants is required (can be more liberal with funding constraint). Participants will be recruited from Prolific and will be adults aged 18–60 years, fluent in English. No additional preselection criteria will be applied based on prior experience or other characteristics, and no demographic information will be collected or used for inclusion or exclusion.

Materials

The experiment will use the original image and message stimuli from Genevsky et al. (2025), which will be conducted through Pavlovia (https://run.pavlovia.org/cwu0701/genevsky2025_psych251). The image set consists of photographs depicting individuals in need who display either happy or sad facial expressions, representing the manipulation of image valence. Each image is paired with a brief aid message that is either positively or negatively framed, creating a 2 × 2 within-subjects factorial design that varies affective congruence between the two affective features. Stimuli will be presented on a computer screen using a program that allows participants to indicate their donation decision with a slider bar ranging from $0.00 to $10.00. Additional materials include task instructions, comprehension checks, demographic questions, and post-task self-reports of emotional responses.

Procedure

Participants will receive a $10 endowment at the beginning of the session and complete 48 trials organized in a counterbalanced block design. On each trial, participants will view one image–message pair and decide how much of their endowment to donate using the slider. Each decision will be recorded as the dollar amount donated, along with response time and stimulus identifiers. Trial order within blocks will be randomized to minimize order and carryover effects. At the end of the session, one trial will be randomly selected to determine the participant’s real payout: if a donation was made on that trial, the chosen amount will be deducted from their endowment and donated to the corresponding cause; otherwise, the participant will retain the full $10. Finally, participants will complete brief demographic and manipulation-check questionnaires assessing perceived emotional tone of the stimuli.

Analysis Plan

The analysis strategy closely follows the original study. Data cleaning, exclusion rules, and modeling choices are reported as faithfully as possible. Trials with missing responses will be removed according to the original criteria. The main analysis focuses on the interactive effect of affective congruence on donation amount, using hierarchical mixed-effects regression models that include random effects of subject-level intercepts. Fixed effects include image valence, message valence, and their interaction. While the primary statistical inference is conducted using hierarchical mixed-effects regression, group-level averages for congruent versus incongruent trials were visualized in the original study. For descriptive purposes, an independent-samples t-test will be conducted comparing congruent and incongruent affective trials.

Differences from Original Study

While the current study closely follows the original experiment, several differences are worth noting. First, the sample was recruited from Prolific, whereas the original study recruited university students in a laboratory setting. Second, the study was conducted online rather than in-person, which may influence participants’ engagement or environmental control. Third, the choice-making duration was increased from 1 second to 2 seconds to account for potentially lower attention and slower responses among online participants. These differences are not expected to substantially affect the primary effect of affective congruence on donation amount, as prior research indicates that the effect is robust across different adult populations and experimental settings. The increased response window is anticipated to facilitate task performance without altering the underlying effect.

Methods Addendum (Post Data Collection)

You can comment this section out prior to final report with data collection.

Actual Sample

Sample size, demographics, data exclusions based on rules spelled out in analysis plan

Differences from pre-data collection methods plan

Any differences from what was described as the original plan, or “none”.

Results

Data preparation

Data preparation following the analysis plan.

Confirmatory analysis

library(ggplot2)
library(ggsignif)
summary(lm(donation ~ img_aff * mes_aff, df_filtered))

Call:
lm(formula = donation ~ img_aff * mes_aff, data = df_filtered)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2000 -0.2000  0.0000  0.2667  4.3333 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        6.20000    0.51107  12.131 8.21e-15 ***
img_affp           1.60000    0.72277   2.214   0.0328 *  
mes_affp          -6.20000    0.70615  -8.780 8.91e-11 ***
img_affp:mes_affp  0.06667    0.98869   0.067   0.9466    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.616 on 39 degrees of freedom
  (5 observations deleted due to missingness)
Multiple R-squared:  0.8088,    Adjusted R-squared:  0.794 
F-statistic: 54.98 on 3 and 39 DF,  p-value: 4.498e-14

t_res <- t.test(df_filtered %>% filter(congruency == "Congruent") %>% select(donation),
                df_filtered %>% filter(congruency != "Congruent") %>% select(donation))

df_summary <- df_filtered %>%
  group_by(congruency) %>%
  summarise(
    mean_donation = mean(donation, na.rm = TRUE),
    se_donation = sd(donation, na.rm = TRUE)/sqrt(n())
  )

p2 <- ggplot(df_summary, aes(x=congruency, y=mean_donation, fill=congruency)) +
  geom_bar(stat="identity", width=0.6, color="black") +
  geom_errorbar(aes(ymin=mean_donation - se_donation,
                    ymax=mean_donation + se_donation), 
                width=0.2, size=0.8) +
  scale_fill_manual(values=c("Congruent"="#F6A6B2", "Incongruent"="gray80")) +
  geom_signif(
    comparisons = list(c("Congruent", "Incongruent")),
    annotations = ifelse(t_res$p.value < 0.001, "***",
                         ifelse(t_res$p.value < 0.01, "**",
                                ifelse(t_res$p.value < 0.05, "*", "ns"))),
    y_position = max(df_summary$mean_donation + df_summary$se_donation) + 0.5
  ) +
  labs(title="Average Donation by Affective Congruency",
       x="Congruency",
       y="Mean Donation") +
  theme_minimal(base_size = 14) +
  theme(legend.position = "none")

ggsave(filename = "fig2.png", plot = p2, width = 6, height = 5)

df_summary <- df_filtered %>%
  group_by(img_aff, mes_aff) %>%
  summarise(
    mean_donation = mean(donation, na.rm = TRUE),
    se_donation = sd(donation, na.rm = TRUE)/sqrt(n())
  ) %>%
  ungroup()

`summarise()` has grouped output by 'img_aff'. You can override using the
`.groups` argument.

p3 <- ggplot(df_summary, aes(x=mes_aff, y=mean_donation, fill=mes_aff)) +
  geom_bar(stat="identity", color="black", width=0.6) +
  geom_errorbar(aes(ymin=mean_donation - se_donation,
                    ymax=mean_donation + se_donation),
                width=0.2, size=0.8) +
  facet_wrap(~img_aff) +  # separate panels for image valence
  scale_fill_manual(values=c("p"="#F6A6B2", "n"="#6CA0DC")) +
  labs(title="Average Donation by Image and Message Valence",
       x="Message Valence",
       y="Mean Donation",
       fill="Message Valence") +
  theme_minimal(base_size = 14)

ggsave(filename = "fig3.png", plot = p3, width = 8, height = 5)

The analyses as specified in the analysis plan.
Side-by-side graph with original graph is ideal here

Exploratory analyses

Any follow-up analyses desired (not required).

Discussion

Summary of Replication Attempt

Open the discussion section with a paragraph summarizing the primary result from the confirmatory analysis and the assessment of whether it replicated, partially replicated, or failed to replicate the original result.

Commentary

Add open-ended commentary (if any) reflecting (a) insights from follow-up exploratory analysis, (b) assessment of the meaning of the replication (or not) - e.g., for a failure to replicate, are the differences between original and present study ones that definitely, plausibly, or are unlikely to have been moderators of the result, and (c) discussion of any objections or challenges raised by the current and original authors about the replication attempt. None of these need to be long.