Exam 2: Physical symbol system hypothesis

2/11/2026

one way to view information processing perspective:

  • useful for understanding both artificial and biological minds

  • help ask questions

  • remain agnostic to whether bio minds are strictly information processing systems

stronger interpretation:

  • minds aren’t as studied from info processing angle

  • both bio and artificial are info processing systems

  • info processing is all you need to explain things like intelligence

60s/70s: Newell and Simon with first overarching theory for cog sci

  • follows info processing ideas, but uses them to articulate hypothesis about bio and artificial minds (Physical Symbol System Hypothesis)

Physical Symbol System Hypothesis (PSSH):

  • forms basis of almost all AI (good old-fashioned AI - GOFAI) until late 20th century

  • since 90s a diff form of AI has become more prominent (learn after exam 2)

  • meant for this to apply to biological intelligence

  • claim: being a physical symbol system is necessary and sufficient for intelligent action

    • if it’s not a PSS, it can’t act intelligently - if it is a PSS, it acts intelligently

    • implication: if a human acts intelligently, it must be a PSS, and if something is a PSS, we don’t need anything else to explain its intelligent behavior

  • PSS 4-point definition:

    • a PSS has symbols (elements that stand for something); these are physical patterns (these physically exist), ex. words on a page, bits in computer

    • these symbols can be combined to form symbol structures (more complex expressions that jointly stand for something), ex. sentences made of words, group of 0s and 1s that represent something

    • PSS has processes/rules for manipulating its symbols (creating them, changing them, combining them), these processes have to be purely mechanical: no intuition or insight allowed

    • these processes or rules of point #3 can themselves be represented as symbols within the system, ex. operations programmed into calculator

  • Newell and Simon were inspired by Turing’s work; Turing machines are textbook example of Physical Symbol System

Questions:

Q1 (point 1 and 2). Which parts of a Turing machine would correspond to the symbols and symbol structures of points 1 and 2?

  • symbols: actual symbols printed on the tape,

  • symbol structures: whole line of tape after computation, groups of symbols that jointly mean something

Q2 (point 3 and 4). Which part of a Turing machine would correspond to the stored rules of points 3 and 4?

  • the Turing machines table to follow the program through it’s process, for point 4, the Turing machines table uses the programming of the 0s and 1s that the machine is reading from the input, the system knows what to do based on whether is it a 0 or 1

  • rules contained in machine table, machine has programming that responds to those rules

  • machine head allows machine to see what is on the tape

Q3 (point 3 and 4). We have said that “representations” and “algorithms” play a central role in cognitive science. Which of Newell and Simon’s concepts corresponds closely to representations, and which one to algorithms?

  • representations: first 2 points, all the numbers are representations (can stand for other numbers, pixels, etc)

  • algorithms: point 4, purely mechanical, no insight, rules are concrete

the PSSH consists of 4-points definition of a physical symbol system, but the understanding that being a PSS is necessary and sufficient for intelligent action = the claim is that the human mind is a PSS

Newell and Simon summarized view:

  • a physical symbol system is a machine that produces through time an evolving collection of symbol structures

  • thinking is simply transforming symbol structures from one into the other according to rules

Newell and Simon 2 key components to intelligence:

  1. essence of intelligence is the ability to solve problems (ability to work out, when confronted with a range of options, which option best matches certain constraints)

all AI machines they built are meant to solve problems

  1. intelligence requires generality: an ability to respond effectively to a wide range of conceivable situations, not just being effective in some restricted domain (others have also thought generality is key component of intelligence)

put together: intelligent system has a general ability to solve many different problems it may be confronted with

Newell and Simon’s General Problem Solver:

  • example of good old-fashioned AI

  • purpose: solving problems

  • fairly abstract description of what a problem is, so using their approach many different problems can be phrased in abstract terms that the GPS can deal with

  • terms in which problems need to be phrased

    • states:

      • initial state: ex. mouse wants to get to cheese, but there is a maze separating them

      • solution state: ex. mouse gets the cheese

      • all other states in between: ex. the mouse in the maze

    • permissible transformations: an action that connects one state to another different state, that changes the situation from one state to a different state

      • ex. mouse moving up in the maze, moving right, etc.

  • in the language the GPS speaks. a problem is a list of states, one of them initial, one of them solution, and a list of permissible transformations

  • in the language of GPS, a solution is a sequence of permissible transformations that transforms initial state into solution state

Point of PSS according to N and S:

  • take the symbol structure that represents input

  • use the system’s processes/rules for manipulating the symbols in that symbol structure, until you have the symbol structure that represents the output

GPS does this by:

  • takes symbol structure that represents problem: set of states and permissible transformations

  • uses some processes/rules for manipulating the symbols in that symbol structure, until it has the symbol structure that represents the solution: a series of permissible transformation that link from initial state to solution state