The naive brain detects face-to-face biological motion.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches

[1] § STAR★Methods › Quantification and statistical analysis ↔ biomotion_cFos_analysis.Rmd, lines 280–356 · score 0.70 · partial eta squared, permutation ANOVA, models, aovperm, permuco, Hemisphere
[2] § STAR★Methods › Quantification and statistical analysis ↔ biomotion_cFos_analysis.Rmd, lines 280–356 · score 0.64 · post hoc, rank biserial, pairwise, raw, MD, correlation
[3] § STAR★Methods › Quantification and statistical analysis ↔ biomotion_cFos_analysis.Rmd, lines 358–449 · score 0.63 · FOV lateralization, Fos activity, bootstrapping, Pearson, Wilcoxon, rank
[4] § Results › Stronger right NCL activation is associated with greater left visual field use ↔ biomotion_cFos_analysis.Rmd, lines 358–449 · score 0.54 · FOV lateralization, fos activity, bootstrapped, Pearson, eye, correlated

Paper

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The authors' code

R Markdown · 452 lines · 15 KB · CC-BY-4.0 · 4 matches

---
title: "biomotion"
author: "Mirko Zanon"
date: '2024-11-05'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r cars}
# ==== LOAD PACKAGES ====
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(emmeans)
library(ez)
library(afex)
library(ggpubr)
library(ggeffects)
library(car)
library(rstatix)
library(lmPerm)
library(perm)
library(effectsize)
library(boot)
library(permuco)
# ==== LOAD AND CLEAN DATA ====
data <- read_excel("biomotion.xlsx") %>%
filter(preferred_looking_side != "x") %>% # remove invalid trials
filter(Group %in% c("face2face", "back2back")) # keep only selected groups
# ==== Define which transformation to use ====
cfos_transform <- "raw" # ← # Options for activity transformation: "raw", "sqrt", "log"
# ==== COMPUTE MEAN PER HEMISPHERE ====
data_avg <- data %>%
group_by(Brain, Group, Area, Hemi, preferred_looking_side, lateralization_eye_DLC) %>%
summarise(
avg_cfos = mean(cfos, na.rm = TRUE),
avg_cfos_sqrt = mean(sqrt(cfos), na.rm = TRUE),
avg_cfos_log = mean(log1p(cfos), na.rm = TRUE), # log1p per gestire eventuali zeri
.groups = "drop"
) %>%
mutate(
activity = case_when(
cfos_transform == "raw" ~ avg_cfos,
cfos_transform == "sqrt" ~ avg_cfos_sqrt,
cfos_transform == "log" ~ avg_cfos_log,
TRUE ~ NA_real_
)
)
# ==== COUNT UNIQUE SUBJECTS PER GROUP ====
subject_counts <- data %>%
group_by(Group) %>%
summarise(num_subjects = n_distinct(Brain), .groups = "drop")
print(subject_counts)
```
# CHECK WITHOUT OUTLIERS
```{r pressure, echo=FALSE}
# ==== PARAMETERS ====
outlier_sigma <- 2
# ==== 1. Compute mean and SE for each Group × Area × Hemi ====
summary_df <- data_avg %>%
group_by(Group, Area, Hemi) %>%
summarise(
mean_cfos = mean(activity, na.rm = TRUE),
se_cfos = sd(activity, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
# ==== 2. Count subjects per Group ====
subject_counts <- data_avg %>%
group_by(Group) %>%
summarise(num_subjects = n_distinct(Brain), .groups = "drop")
# ==== 3. Merge counts and compute SD ====
summary_df2 <- summary_df %>%
left_join(subject_counts, by = "Group") %>%
mutate(cfos_sd = se_cfos * sqrt(num_subjects))
# ==== 4. Flag outliers (by Group × Area × Hemi) ====
data_flagged <- data_avg %>%
left_join(
summary_df2 %>% select(Group, Area, Hemi, mean_cfos, se_cfos, cfos_sd),
by = c("Group", "Area", "Hemi")
) %>%
rename(cfos_avg = mean_cfos, cfos_se = se_cfos) %>%
mutate(outlier = abs(activity - cfos_avg) > (outlier_sigma * cfos_sd))
# ==== 5. Identify outlier subjects ====
outlier_subjects <- data_flagged %>%
filter(outlier) %>%
pull(Brain) %>%
unique()
# ==== 6. Remove all rows for outlier Brains ====
data_no_outliers <- data_avg %>%
filter(!Brain %in% outlier_subjects)
# Keep only Area "NCL" and "NRL"
data_no_outliers_filtered <- data_no_outliers %>%
filter(Area %in% c("NCL", "NRL"))
# ==== Count subjects per Group after exclusion ====
subject_counts <- data_no_outliers %>%
group_by(Group) %>%
summarise(num_subjects = n_distinct(Brain), .groups = "drop")
print(subject_counts)
# ==== 7. Boxplot ====
p0 <- ggplot(data_no_outliers, aes(x = Area, y = activity, fill = Group)) +
geom_boxplot(na.rm = TRUE, position = position_dodge(width = 0.9)) +
facet_wrap(~ Hemi) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
title = "Distribution of cfos by Area and Group",
x = "Area", y = "avg_cfos"
)
print(p0)
# ==== 8. Summary for bar plot ====
summary_df_no_outliers <- data_no_outliers %>%
group_by(Group, Area, Hemi) %>%
summarise(
mean_cfos = mean(activity, na.rm = TRUE),
se_cfos = sd(activity, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
# ==== 9. Bar and violin plot ====
p1 <- ggplot(summary_df_no_outliers, aes(x = Area, y = mean_cfos, fill = Group)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
geom_errorbar(
aes(ymin = mean_cfos - se_cfos, ymax = mean_cfos + se_cfos),
width = 0.2, position = position_dodge(width = 0.9)
) +
facet_wrap(~ Hemi) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(
title = "Mean cfos by Area and Group",
x = "Area", y = "Mean cfos"
)
print(p1)
# Create a numeric x-position with custom spacing
data_no_outliers$Area_numeric <- as.numeric(factor(data_no_outliers$Area)) * 2 # Multiply by 1.5 for more space
summary_df_no_outliers$Area_numeric <- as.numeric(factor(summary_df_no_outliers$Area)) * 2
# Get area labels for the axis
data_no_outliers$Group <- factor(data_no_outliers$Group, levels = c("face2face", "back2back"))
summary_df_no_outliers$Group <- factor(summary_df_no_outliers$Group, levels = c("face2face", "back2back"))
area_levels <- levels(factor(data_no_outliers$Area))
area_positions <- seq(1, by = 1.5, length.out = length(area_levels))
# Increase spacing significantly
dodge_width <- 1.2
violin_width <- 1.0
# Violin plot with colorblind-friendly colors that print well in grayscale
p2 <- ggplot(data_no_outliers, aes(x = Area_numeric, y = avg_cfos, fill = Group)) +
geom_violin(aes(group = interaction(Area, Group)),
color = NA, alpha = 0.7, trim = FALSE,
width = violin_width,
position = position_dodge(width = dodge_width),
scale = "width") +
geom_pointrange(
data = summary_df_no_outliers,
aes(x = Area_numeric, y = mean_cfos, ymin = mean_cfos - se_cfos,
ymax = mean_cfos + se_cfos, group = Group),
color = "black", size = 0.1, fatten = 1,
position = position_dodge(width = dodge_width)
) +
facet_wrap(~ Hemi) +
scale_x_continuous(
breaks = area_positions,
labels = area_levels,
expand = expansion(mult = 0.05)
) +
scale_fill_manual(values = c("face2face" = "#D55E00", "back2back" = "#0072B2")) + # Blue and orange
theme_minimal(base_size = 12) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 11),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
panel.spacing = unit(2, "lines"),
plot.margin = margin(15, 25, 15, 15),
strip.text = element_text(face = "bold", size = 13)
) +
labs(
title = "Mean cfos by Area and Group",
x = "Area",
y = "Mean cfos"
)
ggsave("cfos_violinplot.svg", p2, width = 10, height = 6)
print(p2)
```
# CHECK NORMALITY
```{r pressure, echo=FALSE}
df <- data_no_outliers %>%
mutate(Group = as.factor(Group),
Area = as.factor(Area),
Hemi = as.factor(Hemi))
# ==== 1. Test per normalità e omoschedasticità ====
results <- data.frame()
areas <- unique(df$Area)
for (a in areas) {
for (h in unique(df$Hemi)) {
# subset per area/emisfero
sub_df <- df %>% filter(Area == a, Hemi == h)
# test di Levene per omoschedasticità (tra gruppi)
if (length(unique(sub_df$Group)) > 1) {
lev <- car::leveneTest(avg_cfos ~ Group, data = sub_df)
p_levene <- lev[1, "Pr(>F)"]
} else {
p_levene <- NA
}
# loop su ogni gruppo per test di normalità
for (g in unique(sub_df$Group)) {
dat <- sub_df %>% filter(Group == g) %>% pull(avg_cfos)
# Shapiro test
p_shapiro <- if (length(dat) >= 3 && length(dat) <= 5000) {
tryCatch(shapiro.test(dat)$p.value, error = function(e) NA)
} else {
NA
}
# suggerimento: parametric se entrambi i test > 0.05
suggestion <- if (!is.na(p_shapiro) && p_shapiro > 0.05 &&
(is.na(p_levene) || p_levene > 0.05)) {
"-"
} else {
"*"
}
results <- rbind(results, data.frame(
Area = a,
Hemi = h,
Group = g,
n = length(dat),
p_shapiro = round(p_shapiro, 4),
p_levene = round(p_levene, 4),
normality_violation = suggestion
))
}
}
}
# ==== 2. Ordina e stampa tabella finale ====
results <- results %>%
arrange(Area, Hemi, Group)
print(results)
```
# PERMUCO 10000
```{r pressure, echo=FALSE}
# =====================================================================
# c-Fos Analysis & Behavioral Correlations
# =====================================================================
# ==== 1. PERMUTATION ANOVAS & POST-HOCS PER AREA ====
areas <- unique(data_no_outliers$Area)
for (a in areas) {
cat("\n============================\n")
cat("Area:", a, " - permuco::aovperm: Group × Hemi\n")
cat("============================\n")
df_sub <- subset(data_no_outliers, Area == a)
# The Modern Permutation ANOVA (Guarantees exactly 10,000 iterations)
set.seed(123)
perm_model <- aovperm(activity ~ Group * Hemi + Error(Brain/Hemi),
data = df_sub,
np = 10000) # 'np' stands for number of permutations
print(perm_model)
# Post-hoc pairwise comparisons (This part stays exactly the same)
df_sub <- df_sub %>% mutate(GroupHemi = paste(Group, Hemi, sep = "_"))
combo_groups <- combn(unique(df_sub$GroupHemi), 2, simplify = FALSE)
posthoc_area <- data.frame()
for (pair in combo_groups) {
dat1 <- df_sub %>% filter(GroupHemi == pair[1]) %>% pull(activity)
dat2 <- df_sub %>% filter(GroupHemi == pair[2]) %>% pull(activity)
if (length(dat1) > 1 && length(dat2) > 1) {
wt <- wilcox.test(dat1, dat2, exact = FALSE)
posthoc_area <- rbind(posthoc_area, data.frame(
Area = a,
Comparison = paste(pair, collapse = "_vs_"),
W = wt$statistic,
p_raw = wt$p.value
))
}
}
if (nrow(posthoc_area) > 0) {
posthoc_area <- posthoc_area %>%
mutate(p_fdr_BH = p.adjust(p_raw, method = "BH"), p_holm = p.adjust(p_raw, method = "holm"))
}
print(posthoc_area)
}
# ==== 2. EFFECT SIZES EXTRACTION (NCL FOCUS) ====
df_ncl <- data_no_outliers %>% filter(Area == "NCL")
cat("\n=========================================\n")
cat("NCL EFFECT SIZES (ANOVA Partial Eta Squared)\n")
cat("=========================================\n")
aov_ncl <- aov(activity ~ Group * Hemi + Error(Brain/Hemi), data = df_ncl)
print(eta_squared(aov_ncl, partial = TRUE))
cat("\n=========================================\n")
cat("NCL POST-HOC EFFECT SIZES (MD and r_rb)\n")
cat("=========================================\n")
# Right NCL (Face-to-Face vs Back-to-Back)
dat_f2f_R <- df_ncl %>% filter(Group == "face2face", Hemi == "R") %>% pull(activity)
dat_b2b_R <- df_ncl %>% filter(Group == "back2back", Hemi == "R") %>% pull(activity)
cat("\n--- Right Hemisphere: Face-to-Face vs Back-to-Back ---\n")
cat("Mean Difference:", round(mean(dat_f2f_R) - mean(dat_b2b_R), 3), "\n")
print(rank_biserial(dat_f2f_R, dat_b2b_R))
# Face-to-Face Group (Right vs Left Hemisphere)
dat_f2f_L <- df_ncl %>% filter(Group == "face2face", Hemi == "L") %>% pull(activity)
cat("\n--- Face-to-Face Group: Right vs Left Hemisphere ---\n")
cat("Mean Difference:", round(mean(dat_f2f_R) - mean(dat_f2f_L), 3), "\n")
print(rank_biserial(dat_f2f_R, dat_f2f_L))
```
# LINKING BEHAVIOUR
```{r pressure, echo=FALSE}
# ==== 1. FOV LATERALIZATION DISTRIBUTION ====
df_behav <- data_no_outliers %>%
filter(Group %in% c("face2face", "back2back")) %>%
distinct(Brain, Group, lateralization_eye_DLC)
cat("\n======================================================\n")
cat("LATERALIZATION INDEX DISTRIBUTION: face2face vs back2back\n")
cat("======================================================\n")
wt_fov <- wilcox.test(lateralization_eye_DLC ~ Group, data = df_behav, exact = FALSE)
cat("Wilcoxon p-value:", round(wt_fov$p.value, 4), "\n")
print(rank_biserial(lateralization_eye_DLC ~ Group, data = df_behav))
# ==== 2. BRAIN-BEHAVIOR CORRELATIONS (PEARSON + CIs) ====
# Changed to Pearson
pearson_func <- function(data, indices) {
d <- data[indices, ]
if(sd(d$activity) == 0 || sd(d$lateralization_eye_DLC) == 0) return(NA)
cor(d$lateralization_eye_DLC, d$activity, method = "pearson", use = "complete.obs")
}
master_boot_table <- data.frame()
data_corr <- data_no_outliers %>% filter(Group %in% c("face2face", "back2back"))
areas <- unique(data_corr$Area)
for(a in areas) {
df_area <- data_corr %>% filter(Area == a)
for(h in unique(df_area$Hemi)) {
for(g in unique(df_area$Group)) {
df_sub <- df_area %>% filter(Hemi == h, Group == g)
if(nrow(df_sub) > 4) {
# Standard Pearson
cor_test <- cor.test(df_sub$lateralization_eye_DLC, df_sub$activity, method = "pearson")
# Bootstrapped CI (10,000 iterations)
set.seed(123)
boot_res <- boot(data = df_sub, statistic = pearson_func, R = 10000)
boot_ci <- boot.ci(boot_res, type = "perc")
# Add to master table (changed rho to r)
master_boot_table <- rbind(master_boot_table, data.frame(
Area = a,
Hemi = h,
Group = g,
r = round(cor_test$estimate, 3),
CI_low = round(boot_ci$percent[4], 3),
CI_high = round(boot_ci$percent[5], 3),
p_value = round(cor_test$p.value, 4),
n = nrow(df_sub)
))
}
}
}
}
# ==== 3. PRINT FINAL SUMMARY TABLE ====
cat("\n======================================================\n")
cat("FINAL MASTER BOOTSTRAPPED CORRELATION TABLE (PEARSON)\n")
cat("======================================================\n")
print(master_boot_table, row.names = FALSE)
# ==== 4. GLOBAL CORRELATION PLOT ====
p_global <- ggplot(data_corr, aes(x = lateralization_eye_DLC, y = activity, color = Group, fill = Group)) +
geom_point(alpha = 0.7, size = 2) +
geom_smooth(method = "lm", se = TRUE, alpha = 0.2) +
facet_grid(Hemi ~ Area) +
scale_color_manual(values = c("face2face" = "#D55E00", "back2back" = "#0072B2")) +
scale_fill_manual(values = c("face2face" = "#D55E00", "back2back" = "#0072B2")) +
theme_minimal(base_size = 11) +
theme(strip.text = element_text(face = "bold"),
panel.spacing = unit(1, "lines"),
legend.position = "bottom") +
labs(title = "c-Fos vs Eye Lateralization (Pearson Linear Trends)",
x = "Lateralization (Eye Index)", y = "c-Fos Activity")
print(p_global)
ggsave("correlation_plot_pearson.svg", p_global, width = 18, height = 7)
# ==== 5. PERMUTATION ANCOVA ====
# This tests the linear interaction across all areas
cat("\n======================================================\n")
cat("GLOBAL LINEAR INTERACTION (PERMUTATION ANCOVA)\n")
cat("======================================================\n")
global_perm_ancova <- aovperm(activity ~ Group * Hemi * lateralization_eye_DLC,
data = data_corr,
np = 10000)
summary(global_perm_ancova)
```

biomotion_cFos_analysis.Rmd, under CC-BY-4.0 · at the source

Overview

Authors: Anastasia Morandi-Raikova¹, Mirko Zanon¹, Giorgio Vallortigara¹

Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy

Institutions: University of Trento (Italy)

Journal: iScience, volume 29, issue 8, article 116695

Dates: received 9 December 2025; accepted 19 June 2026; published online 10 July 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.isci.2026.116695 · PMID 42472101 · PMCID PMC13380427 · OpenAlex W7167942959

Open access: gold, a free copy (OpenAlex)

Status: code verified

Methods: Connectivity, Statistics

Keywords: Social predisposition, animacy detection, agency perception, pallial processing, evolution of social cognition

Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex

Funding: Ministero dell'Istruzione dell'Universita e della Ricerca

Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

Detecting agents and their interactions is a cornerstone of social cognition. Vertebrates are known to possess innate sensitivity to animacy cues, such as self-propelled or biological motion, supported by subpallial circuits within the social behavior network (SBN). However, the neural mechanisms distinguishing the detection of animate motion from the recognition of socially meaningful relations remain unclear. Using visually naive female domestic chicks, we investigated the brain regions activated when observing point-light displays of hens moving face-to-face or back-to-back, a behavioral paradigm previously shown to reveal spontaneous sensitivity to social interaction. We found selective immediate early-gene expression in the nidopallium caudolaterale, homologous to the mammalian prefrontal cortex, but not in the nucleus taeniae of the amygdala and septum, subpallial areas in the SBN. This pattern suggests that while basic sensitivity to animate motion may rely on evolutionarily ancient subpallial circuits, the detection of socially relevant relations engages higher order pallial processing.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

figshare 30490487

License: CC-BY-4.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Languages: R (1)

Size: 4 files, 1 script

Software Heritage: not checked

Found in: “Data and code availability”

Holds: 1 notebook

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: afex (1 file), car (1 file), easystats (1 file), emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), permuco (1 file), rstatix (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

1 file

biomotion_cFos_analysis.Rmd, R, 452 lines, 4 matches

At the source: doi.org/10.6084/m9.figshare.30490487

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
1 script, each with its path and the digest of its content;
4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data and code availability

Data are available in Figshare: https://doi.org/10.6084/m9.figshare.30490487.

Code used for the analysis of this work is available in Figshare: https://doi.org/10.6084/m9.figshare.30490487.

Any additional information required to reanalyze the data reported in this paper is available from the corresponding contact upon request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 1 funder, 59 references.

Cite

This paper

Morandi-Raikova, A., Zanon, M., & Vallortigara, G. (2026). The naive brain detects face-to-face biological motion. iScience, 29(8), 116695. https://doi.org/10.1016/j.isci.2026.116695

BibTeX

@article{morandiraikova2026naive, author = {Morandi-Raikova, Anastasia and Zanon, Mirko and Vallortigara, Giorgio}, title = {{The naive brain detects face-to-face biological motion}}, journal = {iScience}, year = {2026}, month = jul, volume = {29}, number = {8}, pages = {116695}, publisher = {Elsevier}, issn = {2589-0042}, doi = {10.1016/j.isci.2026.116695}, url = {https://doi.org/10.1016/j.isci.2026.116695}, pmid = {42472101}, pmcid = {PMC13380427} }

RIS

TY - JOUR AU - Morandi-Raikova, Anastasia AU - Zanon, Mirko AU - Vallortigara, Giorgio TI - The naive brain detects face-to-face biological motion T2 - iScience J2 - iScience PY - 2026 DA - 2026/07/10 VL - 29 IS - 8 SP - 116695 SN - 2589-0042 PB - Elsevier DO - 10.1016/j.isci.2026.116695 UR - https://doi.org/10.1016/j.isci.2026.116695 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.isci.2026.116695", "type": "article-journal", "title": "The naive brain detects face-to-face biological motion", "container-title": "iScience", "author": [ { "family": "Morandi-Raikova", "given": "Anastasia" }, { "family": "Zanon", "given": "Mirko" }, { "family": "Vallortigara", "given": "Giorgio" } ], "container-title-short": "iScience", "volume": "29", "issue": "8", "page": "116695", "DOI": "10.1016/j.isci.2026.116695", "PMID": "42472101", "PMCID": "PMC13380427", "ISSN": "2589-0042", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.isci.2026.116695", "language": "en", "issued": { "date-parts": [ [ 2026, 7, 10 ] ] } }

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[10] doi:10.1126/sciadv.aeb8106 [code]: A thyroid hormone-mediated opsin switch initiates metamorphosis in a proto-vertebrate.
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