Cog Sci 1- Midterm 1

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What is cognitive science

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the interdisciplinary studyof mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience,
linguistics, and anthropology.

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Cog Sci History

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Ancient Greek Philosophers- study of the mind- metaphors, tabula rasa, knowledge comes from innately known concepts
Descartes- Dualism- mind and body separate.
1860's Wundt- laboratory methods for studying mental operations systematically.
Behaviorism (Pavlov) through 1950s.
1956- Miller short term memorory limits and chunking.
AI- McCarthy, Minsky, Newell, Simon
Chomsky- mental grammars.
1980s connectionist theories.

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Rationalism

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Knowledge only gained by thinking and reasoning. Held by Plato, Leibniz, Descartes,

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Empiricism

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Knowledge in terms of rules. Aristotle, Locke, Hume,

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Kant

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Attempted to combine Empiricism and Rationalism by arguing that human knowledge depends on both sense experience adn the innate capacities of the mind.

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mental representation

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a structure or process in the mind that stands for something. Knowledge in the mind consists of mental representations. Non-mental representations are like words on a page.

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mental procedures

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processes that happen on mental representations. multiplication for example.

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Wilhelm Wundt

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laboratory methods for studying mental operations.

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behaviorism

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denies existence of the mind- Pavlov, Watson, Skinner- belief that psychology should be restricted to the study of relationship between observable stimuli and observable responses. Talk of consciousness and mental representations was banished.

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George Miller

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Cog Sci founder- 1956- human capacity for short-term memory is limited to 7 items, more can be kept by breaking into chunks.

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Herbert Simon

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Cog Sci founder- proofs in formal logic, power of rules in accounting for aspects of human intelligence.

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Noam Chomsky

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Cog Sci founder- rejected behaviorist assumptions about language as a learned habit and proposed instead peoples ability to understand language in terms of mental grammars consisting of rules.

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methods

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1. Experimentation with human participants, computational modeling,

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Central hypothesis of Cog Sci

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Thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures.

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CRUM

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Computational-Representatioal Understanding of the Mind

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CRUM analogy

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Computer program: data structures + algorithms= running programs; Mind: mental representations+computational procedures = thinking.

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Wilhelm Wundt

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1870's laboratory methods for studying mental operations systematically- from philosophy to psychology.

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intersection vs. union

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disciplines working together to validate results, but not merging into one.

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3 circles of cog sci

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–Brain-the understanding of neurobiologicalprocesses and phenomena. –Behavior-the experimental methods and findings from the study of psychology, language, and the socioculturalenvironment –Computation-the powers and limits of various representations, coupled with studies of computational mechanisms

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mind

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refers to the collective aspects of intellect and consciousness which are manifest in some combination of thought, perception, emotion, will and imagination. –My definition: Ethereal entity that accounts for all phenomena that we can not empirically explain (yet!).

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brain

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control center of the central nervous system –Extremely complex structure, with more than ~100 billion neurons each of which is connected to ~10,000 others.

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computation

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refers to information processing –from simple calculations to human thinking

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Universal Turing machine:

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symbol-manipulating device capable of simulating the logic of any computer

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representation

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about how people store and process information- knowledge representation

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CRUM shortfallings, criticisms

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the mind not well explained by representations and computations.

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Computational Model

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Interprets structures and processes by analogy with computer programs

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What is Cog Sci?

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the interdisciplinary studyof mind andintelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology.

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syllogism

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Aristotle logic- two premises & a conclusion. All students are overworked, Mary is a student; therefore mary is overworked. Can analyze form rather than content.

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deductive inference

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conclusion follows necessarily from premise- if premise is true, conclusion is true. Syllogism is deductive.

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inductive inference

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reasoning that introduces uncertainty. For example, premise of syllogism is taken from a sampling rather than whole population (all the students I know are overworked- does not conclude that Mary is overworked.)

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Gottlob Frege

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1879- began modern logic (post aristotle) with much more general logic than aristotles

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p ➔ q

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Prepositional logic- if p, then q. If paula is in the library, then quincy is in the library.

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p v q

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Prepositional logic-p or q.

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(p v q) ➔ ~d

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Prepositional logic-If paula or quincy are in the library, then debra is not.

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S(p)

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Predicate calculus- Paula is a student ( item(s) in parenthesis is subset or member of capitol letter)

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(for-all x) (student (x) ➔ overworked (x))

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Predicate calculus- all students are overworked. (for any x, if x is a student, then x is overworked.

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(for-all x) (for-all y)[(student (x) & course (y) & take (x,y)) ➔ get-credit for (x,y)]

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Predicate calculus- students who take courses get credit for them. (For any x and y, if x is a student, y is a course and x takes y, x will get credit for y.

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First order logic (FOL)

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aka Predicate calculus: system of deduction extending propositional logic by the ability to express relations between individuals (e.g. people, numbers, and "things" more generally).

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modus ponens

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p ➔ q; p, therefore, q.

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modus tollens

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p ➔ q; not q, therefore not p

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deduction

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knowing all, and given a member

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induction

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knowing some and inducing based on experience.

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Criteria for Evaluating Approaches
to Mental Representations

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slide

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Why do people make the inferences that they do?

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People have mental representations similar to sentences in predicate logic. People have deductive and inductive procedures that operate on those sentences.The deductive and inductive procedures, applied to the sentences, produce inferences.

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logic

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Formal science that investigates the structure of statements and arguments through inference

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inference

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Process of deriving a conclusion based on existing knowledge

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formal logic

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is the study of inference with purely formal content, where that content is made explicit.

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problems with predicate logic

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–Hard to deal with time(“now”, ”later”...) –Metapropositions(“asks”involves and asker and the proposition that is asked)

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streghts and faults of Propositional & Predicate logic

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are good at true/false statements. –Can’t handle uncertainty • Use probability theory instead • e.g. P(j)= 0.9, high probability of Jose being tired

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rules of inference

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modus ponens, modus tollens

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Affirming the antecedent

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if p, then q; p therefore q

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denying the consequent

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if p, then q; not q therefore not p.

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affirming the consequent

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INVALID Reference: if p, then q: q therefore p

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denying the anticedent

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INVALID Reference: if p, then q; not p therefore not q

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Logic Theorists

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Newell, Simon, Starts with a series of logic propositions from which a theorem is to be proven• Analogy with a one person board game –Propositions are the starting positions –Rules of inference are the permitted moves –Proving a theorem = asking whether a given state of the board is reachable by a sequence of legal moves from one of the starting positions Addressed the combinatorial explosionproblem, and the need to use heuristics

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heuristics

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rules of thumb that contribute to satisfactory solutions without considering all possibilities. May help in processing, but doesn't guarantee a solution

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General problem solver

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(Newell and Simon, 1972)
• Universal problem solver machine
– i.e. any formalized symbolic problem could be solved, in principle, by GPS
– e.g. chess playing, theorems proof, geometric problems...• User defines objects and operations that can be done on the
objects• GPS generates heuristicsin order to solve problems
• It then creates subgoalsto get closer and closer to the goal• Problem: Can not solve any real-world problems (i.e. non-
symbolic)• Eventually evolved into SOAR

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None

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SOAR

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SOAR
(Newell, 1990, 1993)
• SOAR’smain element–the idea of a problem space: all cognitive acts are some
form of search task• Memory is unitary and procedural
–no distinction between procedural (skills) and declarative memory(facts and experiences)
• Chunking: primary mechanism for learning
–represents the conversion of problem-solving acts into long-term memory
• e.g. IF you want to get from campus to home, THEN drive

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None

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rules

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natural way of describing human knowledge-
–Innate
• biological circuitry, e.g. vision–Learned by inductive generalization
• formed from examples
• formed by chunking(SOAR) or composition(ACT)–Learned by specialization
• specific for a given situation–Learned by abduction
• rules run backward to provide explanation –Learned by their performance
• Incremental learning through associated usefulness value

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None