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Data from: Social and emotional contexts predict the development of gaze following in early infancy

Citation

Astor, Kim (2020), Data from: Social and emotional contexts predict the development of gaze following in early infancy, Dryad, Dataset, https://doi.org/10.5061/dryad.v41ns1rs5

Abstract

The development of gaze following begins in early infancy and its developmental foundation has been under heavy debate. Using a longitudinal design (N = 118), we demonstrate that attachment quality predicts individual differences in the onset of gaze following, at 6 months of age, and that maternal postpartum depression predicts later gaze following, at 10 months. In addition, we report longitudinal stability in gaze following from 6 to 10 months. A full path model (using attachment, maternal depression, and gaze following at 6) accounted for 21 % of variance in gaze following at 10 months. These results suggest an experience-dependent development of gaze following, driven by the infant’s own motivation to interact and engage with others (the social-first perspective).

Methods

Gaze data was exported from Tobii Studio and processed in MATLAB version R2017b (9.3.0. 713579) using TimeStudio (version 3.18). See the time series analyses file BASIC_GF.study and visit timestudioproject.com for user instructions. 

Maternal postpartum depression was assessed using the Edinburgh Postnatal Depression Scale (EPDS) at 6 weeks, 6 months, and 12 months after child delivery.

The SSP was used to assess infant-mother attachment quality when the child was 12 months old. Two certified coders rated attachment according to the ABCD classification, providing a B (secure) vs. ACD (non-secure) score. For more information about the social context measures, including data reduction, see the Supplementary.

Usage Notes

This is the R code used for the path analysis:

library(lavaan)

library(influence.SEM)

 

GF_data <- read.csv2('BASIC_GF.csv')

 

Model <- '

GF_10 ~ GF_6 + Dep12mpp + Dep6mpp + Dep6vpp + BvsACD

GF_6 ~ Dep6mpp + Dep6vpp  + BvsACD

Dep12mpp ~ Dep6mpp + BvsACD

Dep6mpp ~ Dep6vpp + BvsACD

Dep6vpp ~ BvsACD

'

gCD.mod<- genCookDist(Model,data=GF_data,std.lv=TRUE)

plot(gCD.mod,pch=19,xlab="observations",ylab="Cook distance")

GF_data.gCDExc <- GF_data[-which(gCD.mod>1),]

Model.fit <- sem(Model, data = GF_data.gCDExc, estimator = 'ML', missing='fiml', std.ov = TRUE)

summary(Model.fit, fit.measures = TRUE,standardize = TRUE, rsquare = TRUE)