1. History and Philosophy (3 and 4): A Brief History of Cognitive Science
CLPS0020: Introduction to Cognitive Science
Professor Dave Sobel
Fall 2016

2. Review from last time We want to solve the problem of other minds
How do we know others have consciousness?
We can’t observe their subjective phenomenology.
What we can observe is behavior and physiology
We talked about physiology last time.
To contextualize how to understand the problem of other minds from others’ behavior, we need to know a little about the history of cognitive science

3. Observing Behavior Recall “Functionalism”
It was a solution to the Mind-Body Problem
There is only physical stuff. Mental states are defined by physical stuff, by other mental states, and by behaviors
William James (1890) suggested that we can understand the mind by solving “The Problem of Knowledge”
The basis of modern psychology
What’s the problem of knowledge:
How do we go from Sensation/Perception to Thinking?

4. An Answer from the early 20th Century: Behaviorism
Behaviorism: Watson (1929)
Introspection is unreliable
Behavior is a function of conditioning
Extreme version of this view: Skinner (1950):
No cognition, only propensities to behave
Example: Language learning

5. Advantages Objective! Explains effects in “high level” cognition
Memory (e.g., Skinner, Deese)
Categorization (e.g., Hull)
Problem Solving (e.g., Luchins)

6. Disadvantages Do you believe you have no cognition?
Cognition as a “Black Box”
Chomsky’s (1960) review of Skinner:
Poverty of the Stimulus. The input one receives is insufficient to generate all possible outputs.
“Colorless green ideas sleep furiously”

7. The Cognitive Revolution
Poverty of the stimulus suggests that you need a generative system to understand cognition
Various different systems began to be proposed
Language: Chomsky
Spatial Cognition: Tolman
Categorization: Bruner
Memory and Problem Solving: Miller
General system was proposed by Alan Turing
Turing Machines (and subsequent Turing test)

8. What is a Turing Machine?
Imagine a box with a very long tape, divided into squares.
Tape
Reader
Box
On each square is a symbol (or not).
The box can read the symbols and write new ones to the tape

9. More about Turing Machines
The box is “programmed” with a set of states and rules
The box reads the symbol on the square
Looks at its current state
The rules tell it to write a new symbol or move the reader left or right on the tape (or end)
What’s this for?
Turing built real-world versions of these machines to break German codes during WWII
Input was coded message. Output was decoded message
You probably saw “The Imitation Game” (“Enigma” is better)

10. Why should we care?
A Turing Machine is a symbol processor – given input, it generates an output
Turing proposed more complex machines, which laid the groundwork for the modern day computer
Imagine a Turing machine that took the specs of other Turing machines and their inputs as input, and output what that Turing machine would then output. (This is a computer)
This is nice, but what does this have to do with Cognitive Science?

11. Intelligent Behavior
Behaviorism was concerned with analyzing input and output
Turing Machines are also input-output devises
But they are not black boxes!
They have sets of rules that we can analyze

12. Computational Model of the Mind
If we build a Turing Machine to do something intelligent, it might tell us how to model intelligent behavior
Mind is to Brain as Software is to Hardware
If we understand the rules, then we’ll understand the mind
But, how do we know that something is intelligent?

13. Turing Test
Human beings are intelligent. Can we build a Turing Machine that acts like a human being.
Imagine you are in a room with two teletypes.
On the other end of one is a human, on the other end of the other is a Turing Machine
You engage in a conversation with each teletype. You type things in and each replies.
If after a certain amount of time, you can’t tell which is which, the TM passes the Turing Test, and is said to be “Intelligent”
Turing called this “The Imitation Game” – hence the title of the movie.

14. Objections and Extensions
Can a Turing Machine have phenomenology or consciousness?
Chinese Room Argument (Searle, 1983)
Imagine you don’t know Chinese.
You are in a room with an input slot that presents you with a Chinese symbol.
You have a big rulebook. You look up this particular symbol and it tells you to write a different symbol
You write it, and send it out the output slot.
From outside the room, it looks like you are speaking Chinese. But do you, inside the room, understand Chinese?
No! You are just manipulating symbols.

15. Modern Functionalism
Cognitive Science is not just about manipulating symbols
But much can be learned from understanding how the symbols are manipulated
There must be an interaction between symbol manipulation and other processes
In Functionalism, mental states can be understood by:
1) Physical states of the brain
2) Interactions with other mental states
3) Behaviors
Dirty Secret: Functionalism predated Behaviorism (James was a functionalist).

16. An analogy Animals have digestive organs
Stomachs, livers, etc.
Basic way we understand these organs in terms of their function, not what they reduce to (cells)
Stomachs digest food
Liver produces bile
Etc.
Think of mental states in the same way:
What does attention do?
How does attention and perception interact?

17. Minds and Machines We can also talk about machines this way
Artificial hearts, which decompose into parts.
If we understand the parts, we’ll understand the function
What about the brain?
If we understand the parts of the brain, we will understand their function, and hence cognition.
Sounds the same as analyzing the rules of a Turing Machine

18. Levels of analysis (Marr)
What does it mean to “understand the parts”?
Marr (1982) suggested three levels of understanding (analysis):
Computational
Algorithmic
Implementation

19. Computational Level Defines the function of a system
What are the inputs and outputs?
What is the system supposed to do?
Behaviorism was a computational-level approach
Marr was interested in vision
Visual system takes a 2D retinal image and translates it to a 3D visual representation of space and objects

20. Algorithmic Level
Algorithm: Step by step process that describes some computation
How one goes from the input to the output?
But, there are potentially many algorithmic levels
How do you brush your teeth?

21. Recursive Decomposition
How do you analyze an algorithm?
Functional Analysis: Recursive Decomposition
Each step of an algorithm is decomposed into its component algorithms
This is done until we find the primitive processes: Lowest level of algorithm
What are the primitive in Cognition?
I’ll answer this in a few slides

22. An Example
Describing our understanding of human action (Wellman, 1990)

23. Example (Continued)

24. Strong vs. Weak Functionalism
Strong Functionalism (James) is that algorithmic level is sufficient to explain cognition.
Primitive level is what the brain can do
If we do enough recursive decomposition, we get to the brain
Weak Functionalism (a.k.a. Information Processing – Marr and others)
Primitive level are representations and processes described by brain functions
We need a deeper level to describe how we instantiate those representations and processes

25. A note on the nature of algorithms
All the algorithms we’ve talked about have been linear:
First do this
Then do this
Then do this…
Not the only type of algorithm:
E.g., Distributed Algorithms (Neural Networks)
No representations or processes – just nodes interacting

26. Implementation Level
For Weak Functionalism: How does the physical system actually instantiate these algorithms?
a.k.a. Cognitive Neuroscience
What is the nature of the physical system that runs these algorithms?
a.k.a. Systems neuroscience
Warning: Brain-o-scopes

27. General Conclusions
Marr’s level of analysis still hold as a benchmark for thinking about problems in Cognition
What is the system doing? What input does it take, what output does it provide?
How does the system move from input to output? What does that tell us?
How does the physical system (e.g., brain or computer) instantiate that algorithm?

28. Some questions to think about
What can computational models tell us about cognition?
How well does contemporary cognitive neuroscience (e.g., fMRI) describe the implementational level?
What would building Mr. Data (from Star Trek) or any robot that passed the Turing Test tell us about human cognition?