The bright future of quantitative developmental science

Rick O. Gilmore

2018-02-14 09:22:05



  • A temperature-check about open science
  • Is the ‘crisis’ in behavioral science also sinful?
  • Behavioral science is “big data” science
  • Quantitative developmental science has a bright future
  • Let’s not waste a “good” crisis

A temperature check about reproducibility and open science

Developmental science could be more open & transparent

Agree | Disagree

Developmental science should be more open and transparent

Agree | Disagree

Data from developmental research should be more widely and readily available

Agree | Disagree

Methods and materials used in developmental research should be more widely and readily available

Agree | Disagree

I have used data shared by others

Agree | Disagree

If data from publication X or project Y were more widely and readily available, I would use it

Agree | Disagree

Unless there are privacy or contractual limitations, data files described in published papers should be readily available in forms reusable by others

Agree | Disagree

I use video or audio recordings in my teaching

Agree | Disagree

I use video or audio recordings in my current research

Agree | Disagree

I can imagine using video or audio recordings in my research

Agree | Disagree

I use video or audio recordings to document my research procedures

Agree | Disagree

I could envision using video or audio recordings to document my research procedures

Agree | Disagree

It’s hard to find and access data that I might want to repurpose

Agree | Disagree

Once found and accessed, there can be a huge cost in “harmonizing” data from different sources

Agree | Disagree

I employ reproducible practices and tools (e.g., SPSS or SAS syntax, R code, Jupyter notebooks) in my research workflows

Agree | Disagree

Is there a reproducibility crisis?

  • Yes, a significant crisis
  • Yes, a slight crisis
  • No crisis
  • Don’t know

What does “reproducibility” mean?

Methods reproducibility

  • Enough details about materials & methods recorded (& reported)
  • Same results with same materials & methods

(Goodman et al., 2016)

Results reproducibility

  • Same results from independent study

(Goodman et al., 2016)

Inferential reproducibility

  • Same inferences from one or more studies or reanalyses

(Goodman et al., 2016)

Reproducibility crisis

  • Not just behavioral sciences
  • “Hard” sciences, too
  • Data collection to statistical analysis to reporting to publishing

Is the crisis in behavioral science also sinful?

The sin of unreliability

Studies are underpowered

“Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature.”

(Szucs & Ioannides, 2017)

The sin of hoarding…

…grants, …students, …data

The sin of corruptibility…

The sin of bias…

Bem, D.J. (2011). Experimental evidence for anomalous retroactive influences on cognition and affect. Journal of Personality and Social Psychology, 100(3), 407-425.

“This article reports 9 experiments, involving more than 1,000 participants, that test for retroactive influence by”time-reversing" well-established psychological effects so that the individual’s responses are obtained before the putatively causal stimulus events occur."

“We argue that in order to convince a skeptical audience of a controversial claim, one needs to conduct strictly confirmatory studies and analyze the results with statistical tests that are conservative rather than liberal. We conclude that Bem’s p values do not indicate evidence in favor of precognition; instead, they indicate that experimental psychologists need to change the way they conduct their experiments and analyze their data.”

(Wagenmakers et al., 2011)

The sin of hurrying…

(Nuijten et al., 2015)

The sin of narrowmindedness…

“…psychologists tend to treat other peoples’ theories like toothbrushes; no self-respecting individual wants to use anyone else’s.”

Walter Mischel, 2009

The sin of pragmatism…

“Reviewers and editors want novel, interesting results. Why would I waste my time doing careful direct replications?”

Any number of researchers I’ve talked with

In our defense…

Behavior multidimensional

(Adolph et al., 2016)

Embedded in networks

Humans are diverse

But much (lab-based) data collected are from Western, Educated Industrialized, Rich, Democratic (WEIRD) populations Henrich et al., 2010

Data about humans are sensitive, hard(er) to share

  • Protect participant’s identities
  • Protect from harm/embarrassment
  • Anonymize (effective?) or get permission

Psychology is harder than physics

Beyond sin

  • No physics envy, but we can learn from physics
  • Openness and transparency are means, not the ends
  • How do we accelerate, broaden, and deepen discovery?
  • Fixing the past vs. building the future

Behavioral science is “big data” science

“Mind-reading” in fMRI

A personal example

  • How does vision develop?
  • Experience
    • Input +
    • Visually-guided action
    • Physical (eye/brain/body) development

Measure (in the lab)

  • Behavioral sensitivity
  • Brain responses
  • At different ages

(Gilmore, 2014)

Children’s behavior

Adults’ behavior

Children’s brain responses

Adults’ brain responses

But, what’s the input? The real input?

(Gilmore et al., 2015)

(Gilmore et al., 2015)

Frame-by-frame video analysis

(Jayaraman et al., 2015)



  • Infant (passengers) experience faster visual speeds than mother
  • Controlling for speed of locomotion, environment
  • Motion “priors” for infants ≠ mothers

Are “fast” flow speeds common?

(Jayaraman et al., 2015)

Country Females Males Age (wks) Coded video Hrs
India 17 13 3-63 3.1 (0.5-6.0)
U.S. 15 19 4-62 4.6 (0.2-7.6)

(Gilmore et al, 2015)

Motion speeds - 6 weeks

U.S. | India

(Gilmore et al, 2015)

Motion speeds – 34 weeks

U.S. | India

(Gilmore et al, 2015)

Motion speeds – 58 weeks

U.S. | India

(Gilmore et al, 2015)

Linear > radial patterns

Simulating developmental change

\(\begin{pmatrix}\dot{x} \\ \dot{y}\end{pmatrix}=\frac{1}{z} \begin{pmatrix}-f & 0 & x\\ 0 & -f & y \end{pmatrix} \begin{pmatrix}{v_x{}}\\ {v_y{}} \\{v_z{}}\end{pmatrix}+ \frac{1}{f} \begin{pmatrix} xy & -(f^2+x^2) & fy\\ f^2+y^2 & -xy & -fy \end{pmatrix} \begin{pmatrix} \omega_{x}\\ \omega_{y}\\ \omega_{z} \end{pmatrix}\)

Geometry of environment/observer: \((x, y, z)\)
Translational speed: \((v_x, v_y, v_z)\)
Rotational speed: \((\omega_{x}, \omega_{y}, \omega{z})\)
Retinal flow: \((\dot{x}, \dot{y})\)

Parameters For Simulation

Parameter Crawling Infant Walking Infant
Eye height 0.30 m 0.60 m
Locomotor speed 0.33 m/s 0.61 m/s
Head tilt 20 deg 9 deg
Geometric Feature Distance
Side wall +/- 2 m
Side wall height 2.5 m
Distance of ground plane 32 m
Field of view width 60 deg
Field of view height 45 deg

Simulating Flow Fields

Simulated Flow Speeds (m/s)

Type of Locomotion Ground Plane Room Side Wall Two Walls
Crawling 14.41 14.42 14.43 14.62
Walking 9.38 8.56 7.39 9.18

Lessons learned

  • Data sharing is AWESOME
  • Bigger, densier, richer data sets are AWESOME
  • Simulations are AWESOME
  • I want to do more AWESOME science than I can do on my own

Quantitative developmental science has a bright future

Essentials for computationally intensive behavioral research

  • Computational resources
  • Technical expertise

Reproducible workflows

Kitzes, J., Turek, D., & Deniz, F. (Eds.). (2018). The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences. Oakland, CA: University of California Press. E-book.

Findable, Accessible, Interoperable, and Reusable (FAIR) data and materials

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. Retrieved from

Funded NSF (2012-16), NICHD (2013-18), SRCD (2014-16), & Sloan Fdn (2017-18)

Opened spring 2014

Approaching 1,000 researchers (690+ PIs + 300+ affiliates), 380+ institutions

540+ data/stimulus sets (~20% shared), 13,900+ hours

Free, open-source, multi-platform video/audio coding tool

Windows OS fix nearly complete

Updates for transcription

How Databrary differs

  • Open sharing among authorized researchers, not public
  • Share with community of researchers, not study-by-study
  • Share identifiable data with permission via consistent access levels

How Databrary differs

  • Store, search across, filter among participant & session characteristics
  • Active (during study) curation reduces post hoc burden
  • Gilmore, R. O., Kennedy, J. L., & Adolph, K. E. (2018). Practical Solutions for Sharing Data and Materials From Psychological Research. Advances in Methods and Practices in Psychological Science,

Play & Learning Across a Year (PLAY) Project

Play is the central context and activity of early development

What do parents and infants actually do when they play?

Adolph, K., Tamis-LeMonda, C. & Gilmore, R.O. (2016). PLAY Project: Webinar discussions on protocol and coding. Databrary. Retrieved January 24, 2018 from

Adolph, K., Tamis-LeMonda, C. & Gilmore, R.O. (2016). PLAY Project: Materials. Databrary. Retrieved January 24, 2018 from

  • \(n=900\) infant/mother dyads; 300 @ 12-, 18-, 24-months
  • 30 dyads from 30 sites across the US
  • 1 hr natural activity
    • 3 min solitary toy play
    • 2 min dyadic toy play
    • video tour of home

Demographics + parent-report questionnaires about health, family, temperament, vocabulary

Ambient sound levels

Census block group geocoding

Video as data AND documentation

What questions would you ask about these sorts of data?

How could the data be made maximimally (re)useful?

On the horizon..

  • Scientific process management (LabNanny), project sync, dataset cloning
  • Bringing machine learning, computer vision to behavioral scientists

Let’s not waste a “good” crisis

Collect & share video as data and documentation

Increase sample sizes

Or, “Building a CERN for Psychological Science”

Standardize metadata

  • participants (age, gender, race/ethnicity, …)
  • settings (times, dates, places)
  • measures & tasks

Improve statistical practices

  • Automated checking of paper statistics (in American Psychological Association formats) via Statcheck
  • Redefine “statistical significance” as \(p<.005\)? (Benjamin et al., 2017)
  • Or move away from NHST toward more robust and cumulative practices (Bayesian, CI/effect-size-driven)

Store data, materials, code in repositories

  • Data libraries
  • Funder, journal mandates for sharing increasing
  • But no long-term, stable, funding sources for curation, archiving, sharing
  • ArXiv model
    • Institutional (Cornell) support
    • Subscription

Build platforms for discovery

  • Data + analysis
  • e.g., PSU’s Biostars

Data from diverse domains

Web-based data visualization, analysis

Search, filtering by personal characteristics

Curate data & materials as they are generated

Consistent, clear sharing permissions structure

And/or let individuals own/profit from their own data

Boker, S. M., Brick, T. R., Pritikin, J. N., Wang, Y., von Oertzen, T., Brown, D., Lach, J., et al. (2015). Maintained Individual Data Distributed Likelihood Estimation (MIDDLE). Multivariate behavioral research, 50(6), 706–720. Taylor & Francis. Retrieved from

Compete over findings, not participants…


Example Multi-measure Indiv link/search Visualize Self-curate Permissions
Databrary tabular
Human Proj ? ?
Neurosynth fMRI BOLD group data public NA
OpenNeuro ? public
Open Humans ? ?
OSF public
WordBank M-CDI group metadata ? public

Keep in touch


This talk was produced on 2018-02-14 in RStudio using R Markdown and the reveal.JS framework. The code and materials used to generate the slides may be found at Information about the R Session that produced the code is as follows:

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