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Evidence of Top-Down Sensory Prediction in Neonates Within 2 Days of Birth.

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The authors' code

R · 154 lines · 7 KB · no license

  1. library(dplyr)
  2. library(reshape)
  3. library(ggplot2)
  4. library(gridExtra)
  5. library(grid)
  6. library(RColorBrewer)
  7. library(ggpattern)
  8. library(lsr)
  10. ## Experiment 1 data
  11. Main <- read.csv("Expt1_anonymized.csv", stringsAsFactors = F)
  13. Main <- filter(Main, HRF != "HbT")
  15. Main$Condition <- sub("SinglePresent", "sA+V+: short Audio-visual block", Main$Condition)
  16. Main$Condition <- sub("SingleOmitted", "A+V-: Visual absent block", Main$Condition)
  17. Main$Condition <- sub("TestBlock", "A+V+: Audio-visual block", Main$Condition)
  19. Main$Condition <- factor(Main$Condition,
  20. levels = c("A+V+: Audio-visual block",
  21. "A+V-: Visual absent block",
  22. "sA+V+: short Audio-visual block"))
  24. ## analysis
  25. ## t-tests
  26. Agg <- cast(Main, anonymizedName + Condition + HRF ~., mean, value = "Signal", subset = Time > 0 & Time <= 40)
  27. names(Agg)[4] <- "Signal"
  29. by(Agg, list(Agg$Condition, Agg$HRF), function(x){t.test(x$Signal, mu = 0)})
  31. # Cohen's d
  32. by(Agg, list(Agg$Condition, Agg$HRF), function(x){cohensD(x$Signal, mu = 0)})
  34. by(Agg, list(Agg$HRF), function(x){t.test(x[x$Condition == "A+V-: Visual absent block",]$Signal,
  35. x[x$Condition == "sA+V+: short Audio-visual block",]$Signal, paired = T)})
  36. # Cohen's d
  37. by(Agg, list(Agg$HRF), function(x){cohensD(x[x$Condition == "A+V-: Visual absent block",]$Signal,
  38. x[x$Condition == "sA+V+: short Audio-visual block",]$Signal, method = "paired")})
  40. by(Agg, list(Agg$HRF), function(x){t.test(x[x$Condition == "A+V+: Audio-visual block",]$Signal,
  41. x[x$Condition == "sA+V+: short Audio-visual block",]$Signal, paired = T)})
  42. # Cohen's d
  43. by(Agg, list(Agg$HRF), function(x){cohensD(x[x$Condition == "A+V+: Audio-visual block",]$Signal,
  44. x[x$Condition == "sA+V+: short Audio-visual block",]$Signal, method = "paired")})
  46. by(Agg, list(Agg$HRF), function(x){t.test(x[x$Condition == "A+V+: Audio-visual block",]$Signal,
  47. x[x$Condition == "A+V-: Visual absent block",]$Signal, paired = T)})
  48. # Cohen's d
  49. by(Agg, list(Agg$HRF), function(x){cohensD(x[x$Condition == "A+V+: Audio-visual block",]$Signal,
  50. x[x$Condition == "A+V-: Visual absent block",]$Signal, method = "paired")})
  51. ## figure
  52. myColors <- brewer.pal(6, "Set1")[c(1, 2, 4)]
  54. Agg <- cast(Main, Condition + Time + HRF ~., mean_se, value = "Signal")
  56. theme_set(theme_bw() + theme(axis.title = element_text(family = "Times"),
  57. axis.text = element_text(family = "Times"),
  58. strip.text = element_text(family = "Times"),
  59. legend.text = element_text(family = "Times")))
  61. # time series chart
  62. TimeSeries <- ggplot(Agg[Agg$Time <= 40, ], aes(Time, y, color = Condition, linetype = HRF)) +
  63. geom_abline(slope = 0, intercept = 0, linetype = 1, linewidth = .5) +
  64. geom_abline(slope = 90, intercept = 0, linetype = 1, linewidth = .5) + geom_line() +
  65. scale_x_continuous("Time (s)", expand = c(0, 0)) +
  66. scale_y_continuous("Changes of Hemoglobin Concentration (Mm mm)\nin Occipital Channels",
  67. limits = c(-.0015, .0018), expand = c(0, 0),
  68. labels = function(x) format(x, scientific = TRUE)) +
  69. scale_colour_manual(values = myColors) +
  70. scale_linetype_discrete() +
  71. guides(col="none") +
  72. facet_wrap("Condition", nrow = 3, strip.position = "top") +
  73. theme(axis.text.x = element_text(color = "black", size = 6),
  74. axis.text.y = element_text(color = "black", size = 6),
  75. axis.title = element_text(color = "black", size = 8, face = "bold")) +
  76. theme(panel.grid.minor = element_blank(),
  77. panel.grid.major.x = element_blank()) +
  78. theme(
  79. legend.position = c(.85, .78),
  80. legend.title = element_blank(),
  81. legend.box.just = "left",
  82. legend.direction="vertical",
  83. legend.text = element_text(color = "black", size = 6, face = "bold"),
  84. legend.key.width= unit(1, 'cm'),
  85. legend.key.height= unit(0.2, 'cm'),
  86. legend.key = element_rect(fill = "transparent", colour = "transparent"),
  87. legend.background = element_rect(fill = "transparent", colour = "transparent")) +
  88. theme(strip.text = element_text(color = "white",
  89. face = "bold", size = 6, hjust = 0, margin = margin(.03, 0, .03, 0.2, "cm")),
  90. strip.background = element_rect(color = "black", fill = "black"))
  93. # bar chart
  94. Agg <- cast(Main, anonymizedName + Condition + HRF ~., mean, value = "Signal", subset = Time > 0 & Time <= 40)
  96. Agg <- cast(Agg, Condition + HRF ~., mean_se, value = "(all)")
  98. bracket <- cast(Agg, HRF + Condition ~., max, value = "ymax")
  100. bracket <- bracket[c(2), ]
  102. bracket <- rbind(bracket, bracket, bracket, bracket)
  104. bracket$X <- c(2, 2, 3, 3)
  106. bracket$Y <- rep(c(.00008, .00008 + .00005, .00008 + .00005, .00008), 1)
  107. bracket$Y <- bracket$`(all)` + bracket$Y
  109. bracket1 <- bracket[1:4,]
  110. bracket1$HRF <- "HbO"
  112. Sig <- cast(Agg, Condition + HRF ~., max, value = "ymax")
  114. Sig$`(all)` <- Sig$`(all)` + .000165
  115. Sig$asterisk <- rep(c("*", ""), 3)
  116. Sig$x <- rep(1:3, each = 2)
  118. Sig[5,]$x <- 2.5
  119. Sig[5,]$`(all)` <- max(Sig$`(all)`) + .00003
  122. BarChart <- ggplot(Agg, aes(x = Condition, y = y, fill = Condition)) +
  123. geom_bar(stat = "identity", width = .7, position='dodge') +
  124. geom_errorbar(aes(ymin = ymin, ymax = ymax), width = .3, size = 0.4) +
  125. geom_text(aes(label = asterisk, x = x, y = `(all)`), size = 6, data = Sig) +
  126. geom_path(data = bracket1, aes(x = X, y = Y), size = .4) +
  127. scale_x_discrete("", labels = c("A+V+", "A+V-", "sA+V+")) +
  128. scale_y_continuous("Changes of Hemoglobin Concentration (Mm mm)\n in Occipital Channels", limits = c(-.0006, .0013),
  129. labels = function(x) format(x, scientific = TRUE)) +
  130. scale_fill_manual(values= myColors) +
  131. scale_linetype() +
  132. geom_abline(slope = 0, intercept = 0, linetype = 1, size = .5) +
  133. facet_grid(.~HRF) +
  134. theme(axis.text.y = element_text(color = "black", size = 6),
  135. axis.title = element_text(color = "black", size = 8, face = "bold"),
  136. axis.text.x = element_text(color = "black", face = "bold", size = 6, angle = 30)) +
  137. theme(panel.grid.minor = element_line(color = "white"),
  138. panel.grid.major = element_line(color = "white")) +
  139. theme(legend.position = "none",
  140. legend.title = element_blank(),
  141. legend.justification = c("left", "top"),
  142. legend.box.just = "left",
  143. legend.direction="vertical",
  144. legend.text = element_text(color = "black", size = 6, face = "bold"),
  145. legend.key.size = unit(2, "mm"),
  146. legend.background = element_rect(fill = "transparent", colour = "transparent"))+
  147. theme(strip.text = element_text(color = "white",
  148. face = "bold", size = 6),
  149. strip.background = element_rect(color = "black", fill = "black"))
  151. quartz("", 6.4, 3)
  152. Ml <- grid.arrange(TimeSeries, BarChart, widths = c(4, 2.4), ncol = 2)
  154. ggsave("Results_Expt1_hbobhr.pdf", Ml, dpi = 300)

Expt1_analysis.R, no license · at the source

Overview

Authors: Naiqi G Xiao1,2, Claire E Robertson2,3, Lauren L Emberson2,4
ORCID iDs: Naiqi G Xiao
  1. Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada
  2. Department of Psychology, Princeton University, Princeton, New Jersey, USA
  3. Department of Psychology, Colby College, Waterville, Maine, USA
  4. Department of Psychology, the University of British Columbia, Vancouver, British Columbia, Canada
Institutions: Princeton University (United States); McMaster University (Canada); Colby College (United States); University of British Columbia (Canada)
Journal: Developmental science, volume 29, issue 2, article e70114
Dates: received 13 December 2024; accepted 5 December 2025; published online 30 December 2025; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/desc.70114 · PMID 41467615 · PMCID PMC12750968 · OpenAlex W7117529890
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fNIRS (modality), human (organism), developmental (subfield)
MeSH: Auditory Perception*, Occipital Lobe*, Visual Perception*, Acoustic Stimulation, Female, Humans, Infant, Newborn, Learning, Male, Photic Stimulation, Spectroscopy, Near-Infrared (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH HHS (R00 4R00HD076166-02); Foundation for the National Institutes of Health; James S. McDonnell Foundation (220020505); National Institutes of Health (R00 4R00HD076166‐02); NICHD NIH HHS (R00 HD076166); Natural Sciences and Engineering Research Council of Canada (RGPIN-2020-07129, RGPIN‐2020‐07129); McDonnell Foundation (220020505)
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

Recent studies have demonstrated top‐down modulation in perceptual cortices in infants as young as 6 months. However, it is unclear when and how this ability emerges given conflicting evidence available. This study investigates top‐down perceptual modulation by focusing on a neural signature referred to as top‐down sensory prediction, where the prediction of upcoming sensory information is exhibited in the modulation of activity in perceptual cortices. We extended a paradigm previously used to identify top‐down sensory prediction in 6‐month‐old infants to neonates. Using functional near‐infrared spectroscopy (fNIRS), we monitored occipital lobe activity in sleeping neonates held by their caregivers. The study consisted of a Learning session, where neonates were exposed to a novel auditory‐visual stimulus combination (A+V+), followed by sessions presenting occasional visual stimulus omissions (A+V−). Results showed that fNIRS channels over the occipital lobe, which were active during the Learning session, also responded to the unexpected visual omissions, indicating neonatal brains’ capability for top‐down sensory prediction. Experiment 2 confirmed that this response depended on learning the audiovisual association, ruling out non‐specific mechanisms such as heightened arousal or an increase in the visual response when a non‐specific auditory stimulus is presented. These findings offer the first evidence of top‐down modulation of visual responses in neonates, suggesting this capacity exists at birth, significantly earlier than previously thought. This study suggests that top‐down predictive processing is crucial for early perceptual and cognitive development.

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

Repository

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OSF zvgue

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (3)
Size: 5 files, 3 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: reshape2 (3 files), tidyverse (3 files), ggplot2 (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/zvgue/

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;
  • 3 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 Availability Statement

Anonymized fNIRS data and corresponding analysis scripts can be found on the OSF website (https://osf.io/zvgue/). These data and scripts will be accessible for anyone.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 11 MeSH terms, 7 funders, 64 references.

Cite

This paper

Xiao, N. G., Robertson, C. E., & Emberson, L. L. (2026). Evidence of Top-Down Sensory Prediction in Neonates Within 2 Days of Birth. Developmental science, 29(2), e70114. https://doi.org/10.1111/desc.70114

BibTeX

@article{xiao2026evidence,
author = {Xiao, Naiqi G and Robertson, Claire E and Emberson, Lauren L},
title = {{Evidence of Top-Down Sensory Prediction in Neonates Within 2 Days of Birth}},
journal = {Developmental science},
year = {2026},
month = mar,
volume = {29},
number = {2},
pages = {e70114},
publisher = {Wiley},
issn = {1363-755X},
doi = {10.1111/desc.70114},
url = {https://doi.org/10.1111/desc.70114},
pmid = {41467615},
pmcid = {PMC12750968}
}

RIS

TY - JOUR
AU - Xiao, Naiqi G
AU - Robertson, Claire E
AU - Emberson, Lauren L
TI - Evidence of Top-Down Sensory Prediction in Neonates Within 2 Days of Birth
T2 - Developmental science
J2 - Dev Sci
PY - 2026
DA - 2026/03/01
VL - 29
IS - 2
SP - e70114
SN - 1363-755X
PB - Wiley
DO - 10.1111/desc.70114
UR - https://doi.org/10.1111/desc.70114
LA - en
ER -

CSL-JSON

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"container-title": "Developmental science",
"author": [
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"family": "Xiao",
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"given": "Claire E"
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{
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"given": "Lauren L"
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],
"container-title-short": "Dev Sci",
"volume": "29",
"issue": "2",
"page": "e70114",
"DOI": "10.1111/desc.70114",
"PMID": "41467615",
"PMCID": "PMC12750968",
"ISSN": "1363-755X",
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