Advanced Developmental

Psychology

PSY 620PJanuary 29, 2015

Discussion Leader Assignments

January 29th – Design, Measurement, & Analysis (ppt6)

Fraley, R. C., Roisman, G. I., & Haltigan, J. D. (2013). The legacy of early experiences in development: Formalizing alternative models of how early experiences are carried forward over time. Dev Psychol, 49(1), 109-126. Sunni1

Adolph, K. E., S. R. Robinson, et al. (2008). "What is the shape of developmental change?" Psychological Review 115(3): 527-543. Mike1

Brody, G. H., Chen, Y-F., Murry, V. M., Ge, X., Simons, R. L., Gibbons, F. X., Gerrard, M., & Cutrona, C. E. (2006). Perceived discrimination and the adjustment of African American youths: A five-year longitudinal analysis with contextual moderation effects. Child Development, 77, 1170-1189. BreAnne1

Oller DK, Niyogi P, Gray S, Richards JA, Gilkerson J, Xu D, Yapanel U, Warren SF: Automated vocal analysis of naturalistic recordings from children with autism, language delay, and typical development. Proceedings of the National Academy of Sciences 2010, 107:13354-13359. Carolyn1

Discussion Leader Assignments

February 5th – The biological basis of behavior and development   Champagne, F. A., & Mashoodh

, R. (2009). Genes in Context Gene–Environment Interplay and the Origins of Individual Differences in Behavior. Current Directions in Psychological Science, 18(3), 127-131. Cf. Szyf, M. and J. Bick (2012). "DNA Methylation: A Mechanism for Embedding Early Life Experiences in the Genome." Child Development. Ruth1

  Burgaleta, M., Johnson, W., Waber, D. P., Colom, R., & Karama, S. (2014).

Cognitive ability changes and dynamics of cortical thickness development in healthy children and adolescents. Neuroimage, 84(0), 810-819. doi: http://dx.doi.org/10.1016/j.neuroimage.2013.09.038 Ashley1

  Uddin, L. Q., Supekar, K., & Menon, V. (2013). Reconceptualizing

functional brain connectivity in autism from a developmental perspective. Frontiers in Human Neuroscience, 7. doi: 10.3389/fnhum.2013.00458 Emily1

  Chen, E., Cohen, S., & Miller, G. E. (2010). How low socioeconomic status affects 2-year hor

monal trajectories in children. Psychological Science, 21, 31-37. Kelly1

  Alternates: Lister, R., Mukamel, E. A., Nery, J. R., Urich, M., Puddifoot, C. A., Johnson, N. D., Lucero, J.,

Huang, Y., Dwork, A. J., Schultz, M. D., Yu, M., Tonti-Filippini, J., Heyn, H., Hu, S., Wu, J. C., Rao, A., Esteller, M., He, C., Haghighi, F. G., Sejnowski, T. J., Behrens, M. M., & Ecker, J. R. (2013). Global epigenomic reconfiguration during mammalian brain development. Science, 341(6146), 1237905. doi: 10.1126/science.1237905

 Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., Evans, A., Rapoport, J., & Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440, 676-679.

Discussion Leader Assignments

February 12th – Perceptual Development

Vogel, M., Monesson, A., & Scott, L. S. (2012). Building biases in infancy: The influence of race on face and voice emotion matching. Developmental Science, 15, 359-372.

  Maurer, D., & Werker, J. 

Perceptual narrowing during infancy: A comparison of language and faces. Developmental Psychobiology , 2014, 56 , 154-178.

  Papageorgiou

, K. A., Smith, T. J., Wu, R., Johnson, M. H., Kirkham, N. Z., & Ronald, A. (2014). Individual Differences in Infant Fixation Duration Relate to Attention and Behavioral Control in Childhood. Psychological Science. doi: 10.1177/0956797614531295

  Jones, W., & Klin, A. (2013). Attention to eyes is present but in decline in 2-6-month-o

ld infants later diagnosed with autism. Nature, 504(7480), 427-431. doi: 10.1038/nature12715

  Alternate: Maurer, D., Mondloch, C. J., & Lewis, T. L. (2007). Sleeper effects.

Developmental Science, 10, 40-47.

Design, Measurement & Analysisin Developmental Research

Measurement The shape of developmental change Modeling individual differences in

patterns of change Mediating and moderating variables

Modeling the impact of early experience

Natalie Time Lapse: Birth to 10 years old in 1 minute 25 sec.

http://www.youtube.com/watch?v=ZTjHLF3xKWo  Child growth face morph time-lapse (from birth to almost 4). 

Automated Vocal Analysis of Naturalistic Recordings from Children with Autism, Language Delay, and Typical DevelopmentOller et al (2010)

Rubenstein

Goal

The study of vocal development and its role in language has been labor intensive, requiring human transcribers and analysts code and take measurements from small recorded samples

Oller et al. (2010) illustrates a method to obtain measures of early speech development through automated analysis of massive quantities of day-long audio recordings collected naturalistically in children’s homes

Oller et al (2010) | Rubenstein

Naturalistic Recordings

Oller et al (2010) | Rubenstein

Automated Vocal Analysis1,486 all-day recordings from 232 children, with more than 3.1 million automatically identified child utterances

Identify speech-related child utterances (SCUs) and discard child cries and vegetative sounds

Divide SCUs into speech-related vocal islands (SVIs) high energy periods (salient syllables in SCUs) bounded by low-energy periods

SVIs were analyzed on 12 infrastructural acoustic features reflecting rhythmic/syllabic articulation and voice and known to play roles in speech development 4 Conceptual Groupings: RhSy, LtHp, BwLp, and duration Each SVI classified as present (+) or absent (-) for each feature

Oller et al (2010) | Rubenstein

Vocal parameters change with age for typical developing/language-delayed

Oller et al (2010) | Rubenstein

Vocal parameters did not change with age for children with autism

Blue—Typical Red—ASD

Oller et al (2010) | Rubenstein

Allowing for Identification

Oller et al (2010) | Rubenstein

Estimated posterior probability of “not typically developing” classification

Posterior probability threshold for “not typically developing” classification

Developmental Tracking and Group Differentiation

Primary factor: child’s control of infrastructural features of syllabification

Differentiated between children with and without a language disorder with higher accuracy than between the two language disorder groups (autism and language delay)

Oller et al (2010) | Rubenstein

Automated vs. Manual Analysis

Studying automated children’s vocalization is not just a method or technology. It’s a movement. – Oller

(Contemporary Pediatrics, 2014)

Strengths Weaknesses Solution?

What is the Shape of Developmental Change?Adolph et al, 2008

Developmental trajectories take many forms

Accurate depiction of trajectory depends on sampling rate of observations

“Microgenetic method” – small time intervals to observe developmental process

Hoffman

What is the Shape of Developmental Change?Adolph et al, 2008

Overly large sampling intervals can distort shape of change inaccurate picture of

developmental trajectory

How small is small enough? How large is too large?

Previously, measurement intervals chosen by: Intuition, convenience, and

traditionHoffman

What is the Shape of Developmental Change?Adolph et al, 2008

Call for a design based on a formal theoretical model about the shape of the underlying function Dictates number of data points and

optimal spacing BUT - hard to apply to psychology

We cannot mathematically calculate the shape of the function Causes a chicken or the egg

conundrumHoffman

Loss of sensitivity to detect trajectory

Hoffman

Study tracked motor development Systematically altered the time

between assessments to see how that impacted the trajectory

Sampling rate can misrepresent both form & age of development

Hoffman

Loss of sensitivity to detect trajectory

Hoffman

Results: Many kids are variable in achieving motor skills

Longer assessment periods mask that variability, even beyond 2-3 days

Questions: What is an appropriate amount of time between assessments?

When is a skill absolutely achieved?

Brody et al., 2006

Investigated Increases in discrimination related to increases in conduct

problems and depression Association attenuated by nurturant-involved parenting,

prosocial peer affiliation, and school efficacy Gender effects

Participants African American youth (from Georgia and Iowa) T1: Early childhood (ages 10-12) T2: 2 years later T3: 5 years later

Self-report, moderators assessed through self-report and parent-report

Structural Equation Modeling

Methods

Latent growth curve modeling uses data from 3 time points to assess change in constructs within individuals

Regression is fitted to three data points for each youth to see change in construct over time Intercept for each youth represents level of

construct at Time 1 Slope represents rate at which each

construct changes over time

Latent Growth Model

From Brody et al., 2006

Latent Growth Model

Findings

Increases in discrimination positively associated with development of conduct problems and depressive symptoms

High SES youth more likely to have increases in discrimination over time

Growth trajectories for discrimination and conduct problems stronger for boys than girls (no difference for depression)

Moderators

Used multigroup comparisons to test for moderation effects

Discrimination Conduct problems/Depression Weaker for youth with high

▪ Nurturant-involved parenting▪ School efficacy▪ Affiliation with prosocial peers

Brody et al., 2006

What does studying trajectories of change add?

Other potential moderators? Study limitations?

Developmental Psychology © 2012 American Psychological Association2013, Vol. 49, No. 1, 109 –126 0012-1649/12/$12.00 DOI: 10.1037/a0027852

The Legacy of Early Experiences in Development: Formalizing Alternative Models of How Early Experiences Are

Carried Forward Over Time

R. Chris Fraley, Glenn I. Roisman, and John D. HaltiganUniversity of Illinois at Urbana–Champaign

N. Sun-Suslow

Pre-paper discussion (T/F)

Experiences in the first few years of life are inconsequential or held in undue high regard.

N. Sun-Suslow

Example: maternal sensitivity on social competence

Age 1 Age 7

N. Sun-Suslow

Example: maternal sensitivity on social competence

Enduring

Diminishing

(McCartney & Rosenthal, 2000)

Age 1 Age 7

N. Sun-Suslow

The problem

Traditional, two-wave, longitudinal studies cannot discern pattern of association over time.

Stabilizes? If so, approaches zero or non-zero

value?

N. Sun-Suslow

Revisionist: Kagan’s (1980) tape recorder metaphor concerning fate and early experiences.

Alternative Models

Enduring: Early experiences play a unique and enduring role in development. (Sroufe, Egeland, & Dreutzer, 1990)

vs

N. Sun-Suslow

AssumptionsEarly

outcomes

Stability

External Factors

Enduring effects model

N. Sun-Suslow

Revisionist or Enduring?

N. Sun-Suslow

METHODS: Early Sensitivity onSocial Competence and Academic Skills National Institute of Child Health and

Human Development (NICHD) Study of Early Child and Youth Development (SECCYD).

Looked at maternal sensitivity in the first 3 years of life and its associations with:

Social competence (through age 15).▪ Parent and teacher report

Academic skills (through age 15).▪ Teacher report and WJ

N. Sun-Suslow

Maternal sensitivity + covariates have enduring effects on outcome

+ stability of maternal sensitivity

N. Sun-Suslow

Second-order stability paths. Early processes may be carried forward.

N. Sun-Suslow

Examined possibility that enduring association between early experiences and later social and academic functioning is due to…

Confounding influence of factors associated with sensitivity and child outcomes (e.g. maternal education)

Stability of caregiving environments over time

METHODS: Early Sensitivity onSocial Competence and Academic Skills

N. Sun-Suslow

Results

There may be enduring effects of early caregiving experiences in both social competence and academic skills.

Difficult to reconcile on basis of revisionist perspective on development.

N. Sun-Suslow

Post-paper Quiz

1. A two-wave, longitudinal studies can adequately discern pattern of association over time.

N. Sun-Suslow