Course Introduction

2025-08-29

Rick Gilmore

Department of Psychology

Prelude

Today’s topics

  • Course introduction
  • A cognitive sensibility
  • On cognitive development
  • Your turn

Course introduction

Introductions all around

PSY 548

  • Structure
    • Group discussion (Gilmore leads)
    • Student presentation and student-led discussion
  • Material
    • Theoretical & empirical
    • Classic & modern
  • Schedule
  • Evaluation

A cognitive sensibility

  • On cognition
  • On computation
  • On representation
  • On modeling
  • On explicating behavior

On cognition

Bayne et al. (2019)

Common themes

  • Processes, concepts, representations (Bayne)
  • Perception vs. cognition (Brainerd)
  • “acquisition, storage, retrieval and processing of information” (Byrne)
  • cognition vs. learning theory (Byrne)
  • representations and how coded in the brain (Byrne)

Common themes

  • mental processes; biological problem-solving; knowing about knowing; more complex than associative learning (Chittka)
  • thinking, knowing, understanding; non-human animal cognition (Clayton)
  • conservative, e.g., related to human thought vs. liberal, adaptive information handling and can be modelled as computation; attention; categorization (Heyes)

Common themes

  • how sensory input “is transformed, reduced, elaborated, stored, recovered and used”; animal umwelt (Mather)
  • less vs. more introspection/deliberation; ““requires learning”; “isn’t a reflex”; “depends on internally generated brain dynamics”; “needs access to stored models and relationships”; “relies on spatial maps” (Ölveczky)
  • “flexibility, contingency and freedom from immediacy” (Shadlen)

Common themes

  • not uniquely human; intentional vs. unintentional, conscious vs. unconscious, effortful vs. automatic, slow vs. fast processes (Suddendorf)
  • “I know that I am cognizing, but I can only surmise that cognition has occurred in other animals by observing their actions.”; “behaviours in which an animal performs an action directed towards a goal it cannot currently perceive” (Webb)

Student insights

On computation

Is the brain a digital computer?

  • From neuronal action potentials to Boolean logic

McCulloch & Pitts (1943)

How do digital computers compute?

  • Strings of binary digits (bits) represent information
    • 0001: the number one; 1111: the number 15 (in base 2)
    • 00110001: the character symbol ‘1’ in ASCII (Wikipedia contributors, 2025f)
    • (some other binary ‘word’): symbol meaning ‘add’, or ‘store’, etc.

How do digital computers compute?

  • Sequences of binary commands can be data or a program
  • Computer has Central Processing Unit (CPU), addressable memory (addresses are also binary words), interfaces (move info in and out), etc.

High-level computer languages

x <- 3
y <- 4
h <- sqrt(x^2 + y^2)
h
  • Represent
    • data as variables, e.g. h, x, and y
    • functions or operators as character or strings, e.g., <-, sqrt(), ^
    • Convert to binary, then back again to give human-readable output

High-level computer languages

Code 
x <- 3
y <- 4
h <- sqrt(x^2 + y^2)
h
[1] 5

“What might cognition be, If not computation?”

Analog

Algorithmic

Van Gelder (1995)

On representation

Surface forms

Deep forms

  • Map (relate) multiple surface forms to
    • A single deep form \(\rightarrow\) the letters “A” and “G”
  • “Re-presentation”
  • Why? Some forms more easily “computable”, more efficiently stored, less “lossy”

https://twiistedmedia.com/bitmap-vs-vector-printing

A cognitive sensibility

  • Asks what info is available
    • In the world
    • In the mind
  • In what form(s)
  • Representations all the way down1

https://commons.wikimedia.org/w/index.php?curid=2747463

On modeling

All models are wrong…

“…but some are useful.”

Wikipedia contributors (2025d)

George Box (Wikipedia)

Simmering, Triesch, Deák, & Spencer (2010)

  • Mathematical vs. verbal models
  • Are verbal models poorly specified?
  • Do mathematical models “just move the ambiguity to the next level”?

Simmering et al. (2010)

Phil: That’s an interesting point—in a way, all researchers use mathematical models when they apply statistical methods.

Mira: Hm, I suppose that’s true. I wouldn’t normally include statistics in my definition of computational models. I’m thinking about models that emphasize the processes underlying behavior and development.

What do statistics model?

  • What does a correlation (r) mean?
Code 
x <- rnorm(n = 500, mean = 0, sd = 1)
y <- x + rnorm(n = 500, mean = 0, sd = 1)
cor.test(x, y)

    Pearson's product-moment correlation

data:  x and y
t = 24.056, df = 498, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.6897779 0.7712334
sample estimates:
     cor 
0.733124

What do statistics model?

Figure 1

Plot your data!

“The Datasaurus dozen - same stats, different graphs | Autodesk Research” (n.d.); Matejka & Fitzmaurice (2017)

Aside…

  • Simulating your data can help reveal hidden assumptions about what your statistical model actually means, what kind of plots will best show predicted effects, etc.
  • Plotting your data can reveal patterns that the statistics alone obscure.
  • So, do statistical models inform us about processes?

Simmering et al. (2010)

Phil: …Lewis Thomas once wrote an essay listing the seven wonders of the modern world—it was a challenge put to him in the form of a dinner invitation which he, interestingly, declined. Do you know what his Seventh Wonder of the modern world was?

Phil: Thomas’s Seventh Wonder was the development of a human child. How, my brilliant companions, are you going to explain that?

Cognitive models

  • Wikipedia contributors (2025b)
  • Many types

Box and Arrow

flowchart LR
  A[Stimulus] --> B[Sensation]
  B --> C[Perception]
  C --> D[Cognition]
  D --> E[Action]

flowchart LR
  A[Stimulus] --> B[Sensation]
  B --> C[Perception]
  C --> D[Cognition]
  D --> E[Action]
  B --> F[Memory]
  F --> C
  D --> G[Attention]
  G --> C
  G --> F
  • See also Figures 3.1 and 3.4 in Siegler & Alibali (2021)

A computer program…

  • That simulates some complex behavior
  • Is one type of model
  • Production systems (pp. 59-64 in Siegler & Alibali (2021))

Unified Theories of Cognition (Wikipedia contributors, 2025a)

Newell (1990)

Why a unified theory?

Fig 1-6 from Newell (1990)

Constraints on minds

  1. Behave flexibly as a function of the environment
  2. Exhibit adaptive (rational, goal-oriented) behavior
  3. Operate in real time
  4. Operate in a rich, complex, detailed environment
  1. Use symbols and abstractions
  2. Use language, both natural and artificial
  3. Learn from the environment and from experience
  4. Acquire capabilities through development

(Newell, 1990)

Constraints on minds

  1. Operate autonomously
    but within a social community
  2. Be self-aware and have a sense of self
  3. Be realizable as a neural system
  1. Be constructable by an embryological growth process
  2. Arise through evolution

(Newell, 1990)

On explicating behavior

The cognitive revolution 1

B.F. Skinner
  • Can’t (rigorously) look inside the mind.

Noam Chomsky
  • Must (rigorously) look inside the mind.

Ritter, Baxter, & Churchill (2014)

  • Task Analysis (TA) as a tool for designing “user-centered systems”
  • What do (people, e.g. children) have to do?
  • Often the basis of creating a computational cognitive model

Ritter et al. (2014)

Figure 11.1 from Ritter et al. (2014)

On cognitive development

Big questions

  • Are some capabilities innate?
  • Does children’s thinking progress through qualitatively different stages?
  • How do changes in children’s thinking occur?
  • Why do individual children differ so much from each other in their thinking?

Chapter 1 in Siegler & Alibali (2021)

Big questions

  • How does development of the brain contribute to cognitive development?
  • How does the social world contribute to cognitive development?

Chapter 1 in Siegler & Alibali (2021)

Big questions (condensed)

  • Starting (or current at age X) state
  • Patterns of change
  • Causes of change

Your turn

Let’s do a hierarchical task analysis!

  • Pick a task you know well, use in your research
  • What is required to perform it?
  • (Later) what cognitive components are associated with the task?

Is the analysis useful?

Take homes

  • A “cognitive” sensibility is about…
  • Information, representations, operations/processes
  • Sequences, hierarchical structures
  • Solving real-world problems
  • Inspired by computational systems

Next time…

  • Theoretical foundations: Piaget
    • General Readings: Siegler & Alibali (2021) Chapter 2; Piaget (1953); Newcombe (2013)

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

Bayne, T., Brainard, D., Byrne, R. W., Chittka, L., Clayton, N., Heyes, C., … Webb, B. (2019). What is cognition? Current Biology: CB, 29, R608–R615. https://doi.org/10.1016/j.cub.2019.05.044
Matejka, J., & Fitzmaurice, G. (2017). Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing. In Proceedings of the 2017 CHI conference on human factors in computing systems. New York, NY, USA: ACM. https://doi.org/10.1145/3025453.3025912
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5, 115–133. https://doi.org/10.1007/BF02478259
Newcombe, N. S. (2013). Cognitive development: Changing views of cognitive change. Wiley Interdisciplinary Reviews. Cognitive Science, 4, 479–491. https://doi.org/10.1002/wcs.1245
Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.
Piaget, J. (1953). The Origins of Intelligence in Children. New York: Routledge. https://doi.org/10.1037/11494-000
Ritter, F. E., Baxter, G. D., & Churchill, E. F. (2014). Foundations for designing user-centered systems: What system designers need to know about people (2014th ed.). London, England: Springer. https://doi.org/10.1007/978-1-4471-5134-0
Siegler, R., & Alibali, M. (2021). Children’s Thinking (5th ed.). Pearson.
Simmering, V. R., Triesch, J., Deák, G. O., & Spencer, J. P. (2010). To model or not to model? A dialogue on the role of computational modeling in developmental science: To model or not to model? Child Development Perspectives, 4, 152–158. https://doi.org/10.1111/j.1750-8606.2010.00134.x
The Datasaurus dozen - same stats, different graphs | Autodesk Research. (n.d.). Retrieved June 2, 2019, from https://www.autodeskresearch.com/publications/samestats
Van Gelder, T. (1995). What might cognition be, if not computation? The Journal of Philosophy, 92, 345–381. https://doi.org/10.2307/2941061
Wikipedia contributors. (2025a, February 24). Unified theories of cognition. Retrieved from https://en.wikipedia.org/wiki/Unified_Theories_of_Cognition
Wikipedia contributors. (2025b, May 24). Cognitive model. Retrieved from https://en.wikipedia.org/wiki/Cognitive_model
Wikipedia contributors. (2025c, June 28). Turtles all the way down. Retrieved from https://en.wikipedia.org/wiki/Turtles_all_the_way_down
Wikipedia contributors. (2025d, July 23). All models are wrong. Retrieved from https://en.wikipedia.org/wiki/All_models_are_wrong
Wikipedia contributors. (2025e, August 13). Cognitive revolution. Retrieved from https://en.wikipedia.org/wiki/Cognitive_revolution
Wikipedia contributors. (2025f, August 25). ASCII. Retrieved from https://en.wikipedia.org/wiki/ASCII