• What is optic flow?
• Why is optic flow important?
• How does optic flow sensitivity develop?
• How do brain systems for processing optic flow develop?
• What shapes these patterns of development?

• EEG measures of brain responses to optic flow
• Psychophysical measures of optic flow perception
• Computational simulations of optic flow experiences across developmental milestones
• Empirical measures of experienced optic flow across development from head-mounted video cameras

• Brain and behavioral responses to optic flow develop throughout childhood
  • Still immature in 5-8 year-olds
• Changes in the statistics of experienced optic flow shape development in infancy, and likely beyond

• Open, transparent, and reproducible research practices – including open data sharing – have changed my work
• Within 10 years (maybe 5) it will be impossible to get funded or published if you have not adopted them
• It will be good for us and for science

• Structured pattern of visual motion generated by observer movement

• Geometry of environment
  • Surface layout, orientation
  • Object motion
• Direction, speed of self-motion
  • Rotation, translation
  • Visual proprioception (eye vs. head vs. body)

• Sensitivity at birth, (Jouen et al. 2000)
• Infants
  • Brain responses stronger to fast, translational flow, (Hou et al. 2009; Rick O. Gilmore et al. 2007)
  • Behavioral responses stronger to fast translational flow, (Kiorpes and Movshon 2004)
  • Primate universal pattern? Not adult-like until late adolescence?

• Adults
  • Brain responses stronger to radial flow, (Rick O. Gilmore et al. 2007; Fesi, Thomas, and Gilmore 2014).

• Brain and behavioral responses in childhood
• Linking brain and behavioral responses
• What influences developmental shifts?
  • Why fast speeds and linear flows?

• If infant-like: stronger responses to fast, linear flows
• If adult-like: stronger responses to slow, radial flows
• If in-between:
  • fast + radial OR
  • slow + linear

• Time-varying optic flow patterns
• Steady-state visual evoked potentials (SSVEPs)
  • Event-related EEG technique
  • Focus on phase-locked, low-order harmonics
• n=29 4-8 year-olds

• Modulate coherence/signal-to-noise ratio (SNR), 100%/0%
• Modulation frequency 1.2 Hz (1F1), dot update rate 24 Hz (1F2)
• Cross pattern and speed

• Fourier analysis (frequency domain)
  • generates complex domain (real, imaginary) components
  • time-varying signals have amplitude, phase
• Codepen Demo

• Mixed effects MANOVA to capture phase, amplitude
  • Pattern (radial, rotation, linear) and Speed (2, 4, 8 deg/s) as fixed effects
  • Individual means as random factors
• Analyze channels independently with conservative \(\alpha\) (.0005)

• Highly responsive channels over right lateral cortex
• Radial & rotation >> translation
• Amplitude and phase differences

• Highly responsive channels over medial cortex
• Speed, but not pattern tuned, 2 < 4 = 8 deg/s
• Amplitude and phase differences

• Anatomical separation of responses
  • speed (medial)
  • vs. pattern (lateral)
• Radial & rotation != translation, phase & amplitude
• Speed tuning (local and global)

• Children adult-like in many respects
  • Lateral "pattern" responses @ 1F1
  • Medial "speed" responses @ 3F1 and 1F2
• Activate smaller # of channels, more focal

• Time-varying optic flow
  • Radial, linear
  • 2, 8 deg/s
  • 5, 10, 15, 20% coherence (adults)
  • 15, 30, 45, 60% and 20, 40, 60, 80% (children)

• Side by side displays
  • Signal/noise
  • Choose side with signal
  • 2AFC, 10 s response period

https://nyu.databrary.org/volume/218

• Generalized linear mixed effects modeling (lmer in R)
  • Random intercepts by participant
  • Coherence, Pattern, Speed as fixed effects
• Probit link function (2AFC)

• Children
• Adults

• Children faster and more accurate to detect fast, radial flow
• Adults faster, more accurate to detect radial flow, small advantage for slow speeds
• Children's evoked brain responses to fast, radial flow higher amplitude
• Adults' EEG responses to speed vary by scalp location, radial patterns higher

• Predictions
  • Fast, linear flows common in natural experiences of infants
  • Eye/head movements, head instability
  • Infants' motion "prior" is not slow (Florian Raudies and Gilmore 2014; F. Raudies et al. 2012)

• Jayaraman, Gilmore, & Raudies 2016
• (R. O. Gilmore, Raudies, and Jayaraman 2015)
• (F. Raudies et al. 2012)
• (Florian Raudies and Gilmore 2014)

• Infants commonly experience fast, laminar flows.
  • Head, eye movements >> forward-facing locomotion
• Transition from infancy to adulthood shaped by changing statistics of visual input
  • Body dimensions, posture, locomotor speed

• Brain and behavioral responses to optic flow develop throughout childhood
  • Still immature in 5-8 year-olds
• Changes in the statistics of experienced optic flow shape development in infancy, and likely beyond

• Open, transparent, and reproducible research practices – including open data sharing – have changed my work
  • Video data sharing
  • Computer vision analyses of video

• Within 10 years (maybe 5) it will be impossible to get funded or published if you have not adopted them
• It will be good for us and for science

"We have empirically assessed the distribution of published effect sizes and estimated power by extracting more than 100,000 statistical records from about 10,000 cognitive neuroscience and psychology papers published during the past 5 years…False report probability is likely to exceed 50% for the whole literature. In light of our findings the recently reported low replication success in psychology is realistic and worse performance may be expected for cognitive neuroscience."

• Reproducible methods, results, and inferences (Goodman, Fanelli, and Ioannidis 2016)
• Databrary.org: share video, procedures/tasks, data
  • Video the best way to capture, share procedures
• Open Science Framework (OSF): share non-identifiable materials, data

• RStudio or Jupyter for reproducible notebooks, workflows, analyses, & presentations
• GitHub: share flat-files & code, version control
• PSY 511 Spring 2016 course, https://github.com/psu-psychology/psy-5xx-reproducible-research

• This talk: https://github.com/gilmore-lab/psu-action-club-2016-10-28
• Lab site
• GitHub
• Databrary
• Email: rogilmore@psu.edu