Embodied Cognition

2025-10-31

Rick Gilmore

Department of Psychology

Prelude

Figure 1

Today’s topics

• Embodied cognition
  • General Readings: Pfeifer & Bongard (2006), Forward, Chapter 1, and Chapter 12; Smith, Jayaraman, Clerkin, & Yu (2018)

Today’s topics

• Student Presentation I: Does development gate input to prevent a “blooming, buzzing confusion?” (Presenter Luke Debec: Discussant: Alyssa Swift)
  • Read: Kretch, Franchak, & Adolph (2014); Franchak, Kretch, & Adolph (2018)
• Student Presentation J: How can AI & robotics inform developmental science? (Presenter: Pratt Srinivasan; Discussant: Hannah Huang)
  • Read: Ossmy et al. (2018); Ossmy et al. (2024)

Embodied Cognition

Development is a personal journey. The personal vantage point of the learner determines the data for learning for that individual.

– Smith et al. (2018)

Smith et al. (2018) Figure 1.

Egocentric vision

Smith et al. (2018) Figure 1.

“Natural” statistics

De Cesarei, Loftus, Mastria, & Codispoti (2017) Figure 1.

Illustrations

• Sample head camera video: https://databrary.org/volume/99/folder/9797
• Link to Jayaraman, Smith, & Gilmore Databrary volume: https://databrary.org/volume/81

Optic flow

– Dan (2015)

Types of optic flow

Gilmore, Raudies, & Jayaraman (2015)

Gilmore et al. (2015)

Gilmore et al. (2015)

Effects of locomotor posture

Kretch et al. (2014)

Gilmore et al. (2015)

Gilmore et al. (2015)

Embodied cognition

By embodiment, we mean that intelligence always requires a body.

– Pfeifer & Bongard (2006)

Challenges of embodied cognition

• Cognition and computation
• Degrees of freedom problem
• Not all problems seem embodied

What is cognition without a body?

• Computation
• But what is computation?
• Rule-based symbol manipulation
• But what is rule-based symbol manipulation?

Turing machines

Alan Turing

"Alan Turing" & the original (2001)

https://web.mit.edu/manoli/turing/www/turing.html

• Readable/writeable memory
• Rules for transitions between states

Universal Turing Machines (UTM)

• Can compute any computable sequence
• Modern digital computers are UTMs
• Everything’s a string of binary digits (bits): 00101100
  • Data
  • Memory addresses
  • Instructions (add, subtract, store, recall, etc.)

Universal Turing Machines

• Representation: Map from thing to binary string
• High-level computer languages
  • Map human-readable codes to machine-readable binary strings
• Human-readable computer languages \(\rightarrow\) machine code via intepreters or compilers (Resume, 2022)

Examples

```{r}
#| code-fold: false
a <- 4    # Assign the value 4 the name 'a'
b <- 2    # Assign the value 2 the name 'b'
c <- a^b  # Calculate a to the b power; assign the result to the name 'c'  
c         # Show the value of 'c'
```

[1] 16

• Not just numbers

some_letters <- c('A', 'b', 'c', 'D', 'e')
some_letters %in% LETTERS # LETTERS is a variable in base R

[1]  TRUE FALSE FALSE  TRUE FALSE

Symbols and operations

• {a, b, c}: ‘allowable’ names for things in R
• <-: a short symbol for the ‘assign()’ function

assign('a', 4) == (a <- 4) # `==` is a symbol that tests for equality

[1] TRUE

• ^: ‘exponential’ operator/symbol

From symbol manipulation to semantic networks

Source: https://www.researchgate.net/figure/Example-of-a-semantic-network-describing-animals-Source-own-work-A-new-vision-of-the_fig23_283328263

• See also Wikipedia contributors (2025c)

To cognitive architectures

• e.g., ACT-R (Wikipedia contributors, 2025b), see also Newell (1990)
• Declarative memory (“Gilmore wears glasses”)
• Procedural memory (productions or rules to carry out actions)

Wikipedia contributors (2025b)

Aren’t we forgetting a body?

giphy.com

flowchart LR
  A@{shape: rect, label: "person"}
  B@{shape: rounded, label: "computer" }
  A[person] -->|Types into| B
  B -->|Prints text| A

flowchart LR
  A@{shape: rect, label: "person"}
  B@{shape: rounded, label: "computer" }
  C@{shape: rect, label: "developer"}
  A[person] -->|Types into| B
  B -->|Prints text| A
  C -->|Programs| B

Degrees of freedom problem

• Nikolai Bernstein

Wikipedia contributors (2025d)

Arimoto (2005)

DFs to control human body

• ~650 skeletal muscles
• Millions? of motor neurons involved in control of somatic nervous system

Tangen & Crompton (2007)

Physical constraints can reduce DFs

• Braitenberg (2021) vehicles
• Simple “anatomy”, complex behavior

Wikipedia contributors (2025a)

Braitenberg vehicle simulator

http://www.harmendeweerd.nl/braitenberg-vehicles/

Benefits of embodied cognition

• Body as enabler
• Makes (some/many) tasks easier
• Improves perception
• Makes rapid/efficient movement possible
• Synthetic methodology: Understanding by building

– Pfeifer & Bongard (2006)

Cognition is embodied…

• Barsalou (1999)
• also embedded, enactive, and extended (4E) (Newen, De Bruin, & Gallagher, 2018)

From the 4E perspective, cognition, affect, and behavior emerge from the body being embedded in environments that extend cognition, as agents enact situated action reflecting their current cognitive and affective states.

– Barsalou (2020)

Challenges to embodied cognition

• Origins of abstract concepts?
  • e.g., mathematics, logic, philosophy
• Procedural vs. declarative memory
  • Differently impaired in medial temporal lobe-associated amnesia (Milner, 1970)
• Perceiving what vs. where (Mishkin, Ungerleider, & Macko, 1983) or how (Goodale, Milner, Jakobson, & Carey, 1991; Haan & Cowey, 2011)

Challenges to embodied cognition

• Micro-theories or just-so stories
• Vague terminology
• Body as constituent not cause

Wrap-up

Main points

• Individual-specific environments vary, between children & across time
• Advantages to measuring them
• “Development is a personal journey,” but what are regularities?
• Intelligence (so far) involves a body
• Does embodiment close an interactive/predictive loop?

Next time…

• Sociocultural development
  • Read: Siegler & Alibali (2021) Chapter 4; Tomasello (2016)

Resources

About

This talk was produced using Quarto, using the RStudio Integrated Development Environment (IDE), version 2025.5.1.513.

The source files are in R and R Markdown, then rendered to HTML using the revealJS framework. The HTML slides are hosted in a GitHub repo and served by GitHub pages: https://psu-psychology.github.io/psy-548-fall/

References

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Arimoto, S. (2005). A challenge to Bernstein’s degrees-of-freedom problem in both cases of human and robotic multi-joint movements. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, E88-A, 2484–2495. https://doi.org/10.1093/ietfec/e88-a.10.2484

Barsalou, L. W. (1999). Perceptions of perceptual symbols. The Behavioral and Brain Sciences, 22, 637–660. https://doi.org/10.1017/s0140525x99532147

Barsalou, L. W. (2020). Challenges and opportunities for grounding cognition. Journal of Cognition, 3, 31. https://doi.org/10.5334/joc.116

Braitenberg, V. (2021). Vehicles: Experiments in synthetic psychology. The MIT Press, Massachusetts Institute of Technology. Retrieved from https://mitpress.mit.edu/9780262521123/vehicles/

Dan. (2015). Toddler GoPro hide and seek. YouTube. Retrieved from https://www.youtube.com/watch?v=o27tIdYggY0&t=25s

De Cesarei, A., Loftus, G. R., Mastria, S., & Codispoti, M. (2017). Understanding natural scenes: Contributions of image statistics. Neuroscience and Biobehavioral Reviews, 74, 44–57. https://doi.org/10.1016/j.neubiorev.2017.01.012

Franchak, J. M., Kretch, K. S., & Adolph, K. E. (2018). See and be seen: Infant-caregiver social looking during locomotor free play. Developmental Science, 21, e12626. https://doi.org/10.1111/desc.12626

Gilmore, R. O., Raudies, F., & Jayaraman, S. (2015). What accounts for developmental shifts in optic flow sensitivity? In 2015 joint IEEE international conference on development and learning and epigenetic robotics (ICDL-EpiRob) (pp. 19–25). Providence, RI, USA: IEEE. https://doi.org/10.1109/devlrn.2015.7345450

Goodale, M. A., Milner, A. D., Jakobson, L. S., & Carey, D. P. (1991). A neurological dissociation between perceiving objects and grasping them. Nature, 349, 154–156. https://doi.org/10.1038/349154a0

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Milner, B. (1970). Memory and the medial temporal regions of the brain. In K. Pribram (Ed.), Biology of memory (pp. 29–50). Elsevier. https://doi.org/10.1016/b978-0-12-564350-4.50011-7

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Ossmy, O., Han, D., MacAlpine, P., Hoch, J., Stone, P., & Adolph, K. E. (2024). Walking and falling: Using robot simulations to model the role of errors in infant walking. Developmental Science, 27, e13449. https://doi.org/10.1111/desc.13449

Ossmy, O., Hoch, J. E., MacAlpine, P., Hasan, S., Stone, P., & Adolph, K. E. (2018). Variety wins: Soccer-playing robots and infant walking. Frontiers in Neurorobotics, 12, 19. https://doi.org/10.3389/fnbot.2018.00019

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