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115 Cards in this Set

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What is cognitive science
the interdisciplinary studyof mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience,
linguistics, and anthropology.
Cog Sci History
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.
Knowledge only gained by thinking and reasoning. Held by Plato, Leibniz, Descartes,
Knowledge in terms of rules. Aristotle, Locke, Hume,
Attempted to combine Empiricism and Rationalism by arguing that human knowledge depends on both sense experience adn the innate capacities of the mind.
mental representation
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.
mental procedures
processes that happen on mental representations. multiplication for example.
Wilhelm Wundt
laboratory methods for studying mental operations.
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.
George Miller
Cog Sci founder- 1956- human capacity for short-term memory is limited to 7 items, more can be kept by breaking into chunks.
John McCarthy
Cog Sci founder- leader of AI in formal logic
Marvin Minsky
Cog Sci founder- concept like frames are central form of knowledge representations.
Allen Newell
Cog Sci founder- proofs in formal logic, power of rules in accounting for aspects of human intelligence.
Herbert Simon
Cog Sci founder- proofs in formal logic, power of rules in accounting for aspects of human intelligence.
Noam Chomsky
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.
Case-based reasoning
analogical thinking
1980s developments
connectionist theories of mental representation and processing based loosely on neural networks.
1. Experimentation with human participants, computational modeling,
Central hypothesis of Cog Sci
Thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures.
Computational-Representatioal Understanding of the Mind
CRUM analogy
Computer program: data structures + algorithms= running programs; Mind: mental representations+computational procedures = thinking.
4 elements in cog sci models
theory (postulate), model (design test), program (perform test), platform
criterion for evaluating theories of mental representation.
1 representational power 2 computational power, (problem solving; i planning, ii decision making, iii explanation 3 psychological plausibility, 4 neurological plausibility, 5 practical applicability
Wilhelm Wundt
1870's laboratory methods for studying mental operations systematically- from philosophy to psychology.
Christopher Longuet-Higgins
coined term Cognitive Science in 1973- Edinburgh- Dept of Machine intelligence perception.
Lighthill report
AI winter- scalability problem- stopped funding for AI work in UK for years.
intersection vs. union
disciplines working together to validate results, but not merging into one.
3 circles of cog sci
–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
interdisciplinary problems
breadth vs. depth, importance in having a strong foundation in a core discipline.
CS requires hypothesis
• suggested explanation of a phenomenon • reasoned proposal suggesting a possible correlation between multiple phenomena
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!).
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.
refers to information processing –from simple calculations to human thinking
Universal Turing machine:
symbol-manipulating device capable of simulating the logic of any computer
Von-Neumann architecture
implementation of Turning machine. Input, cpu (instruction adddress register, instruction unit, arithmatic unit, accumulator) memory, output.
about how people store and process information- knowledge representation
internal symbol in cog. Psychology
refers to external reality
80s revolution
Rodney Brooks proposed intelligence without representation.
CRUM hypothesis
thinking is performed by computationsoperating on representations
CRUM shortfallings, criticisms
the mind not well explained by representations and computations.
Cognitive theory
Set of representational structures and set of process that operate in these structures
Computational Model
Interprets structures and processes by analogy with computer programs
3 stages in process
discovery, modification, evaluation
Criteria- representational power
How much information is expressed by this representation?e.g. University guidelines on enrolment
Criteria- computational power
Problem solving: how do we accomplish our goals? * Planning how(find a successful sequence of actions) * Decision which(chose the best plan) * Explanation why(did it happened)?-Learning: generalization(through experience)
Criteria- psychological plausability
Since we aim to understand human cognition, any theory must address how people think (as opposed to machine learning). Use of quantitative measures in human subjects
What is Cog Sci?
the interdisciplinary studyof mind andintelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology.
Aristotle logic- two premises & a conclusion. All students are overworked, Mary is a student; therefore mary is overworked. Can analyze form rather than content.
deductive inference
conclusion follows necessarily from premise- if premise is true, conclusion is true. Syllogism is deductive.
inductive inference
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.)
Gottlob Frege
1879- began modern logic (post aristotle) with much more general logic than aristotles
p ➔ q
Prepositional logic- if p, then q. If paula is in the library, then quincy is in the library.
p v q
Prepositional logic-p or q.
(p v q) ➔ ~d
Prepositional logic-If paula or quincy are in the library, then debra is not.
Predicate calculus- Paula is a student ( item(s) in parenthesis is subset or member of capitol letter)
(for-all x) (student (x) ➔ overworked (x))
Predicate calculus- all students are overworked. (for any x, if x is a student, then x is overworked.
(for-all x) (for-all y)[(student (x) & course (y) & take (x,y)) ➔ get-credit for (x,y)]
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.
First order logic (FOL)
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).
modus ponens
p ➔ q; p, therefore, q.
modus tollens
p ➔ q; not q, therefore not p
monotonic deductive planning
problem with deductive planning: can only infer new deductions, cannot learn from and reject previous ones. Doesn't allow you to discard previous beliefs found to be wrong. When programmed to make nonmonotonic, it becomes processor expensive. Also, cannot learn from experience- it goes through the same process over and over again, when presented with the same problem.
knowing all, and given a member
knowing some and inducing based on experience.
inference where you form a hypothesis in order to generate an explanation.
Criteria for Evaluating Approaches
to Mental Representations
2 senses. 1. Any inference that introduces uncertainty. 2. Only inductive generalization which general conclusions are reached from particular examples.
pragmatic reasoning schemas
how people use logic to solve problems- instead of if p, then q- they translated to real life situations.
Why do people make the inferences that they do?
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.
thoughts/feelings arise ONLY from the brain
Astonishing hypothesis: You–your joys and sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells and their associated molecules.F. Crick
Formal science that investigates the structure of statements and arguments through inference
Process of deriving a conclusion based on existing knowledge
formal logic
is the study of inference with purely formal content, where that content is made explicit.
modal logic
allows manipulation of necessity and possibility • “It is possible that Jose is tired today”
epistimic logic
knowledge and belief • e.g. operator “Kp”: it is known that p
deontic logic
morality • e.g. pis permissible or forbidden
problems with predicate logic
–Hard to deal with time(“now”, ”later”...) –Metapropositions(“asks”involves and asker and the proposition that is asked)
streghts and faults of Propositional & Predicate logic
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
rules of inference
modus ponens, modus tollens
Deductive reasoning not carried out by formal logic rules nor schemas, but by mental models • mental representations that correspond in structureto the situations they represent
categorical syllogisms
–Universal / affirmative • AllX are Y –Particular / affirmative • SomeX are Y–Universal / negative • NoX are Y–Particular / negative • SomeX are notY
Affirming the antecedent
if p, then q; p therefore q
denying the consequent
if p, then q; not q therefore not p.
affirming the consequent
INVALID Reference: if p, then q: q therefore p
denying the anticedent
INVALID Reference: if p, then q; not p therefore not q
Logic Theorists
Newell, Simon, Shaw 1Starts 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
rules of thumb that contribute to satisfactory solutions without considering all possibilities. May help in processing, but doesn't guarantee a solution
General problem solver
(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
(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
“Cognitive skills are realized by production rules”
• These rules operate on problems
–e.g. addition• Production rules organized around a set of goals
• One goal always active at any given time
• Working memory: knowledge that the system is currently attending to
natural way of describing human knowledge-
• 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
rule based systems
–Had psychological aims from the beginning
• as opposed to logic-based models–Abandon FOL‘sexpressiveness for simple if-thenrules
–Computational advantages
• concise and independent representations–Psychological plausibility
• strongest among all CRUM based approaches
FOL vs. rules
–It is apparent that logic is not the way to go in cognitive science because of the lack of core irepresentation and computation
Mental representations of stereotypical situations or entities (objects) in a given category. Can be used as a single entity.
concept acquisition
old school- innately (plato, chomsky)- contemporary- through experience and from other concepts.
when did concepts come into Cog Sci
Minsky(1975): “Thinking should be understood as frame application rather than logical deduction”
concepts comprised of;
schemas (Rumelhardt), frames (Minsky), Scripts (Schank and Abeson)
Data-structure for representing a stereotyped situation- have slots
frame slots contain
–Frame identification
–Relation with other frames
• e.g. hotel phone phone communication device–Requirements for frame match
• e.g. object specifications –Default information
• e.g. chairs have 4 legs, hotel beds made by the staff –New instance (unspecified)
• e.g. color of bedspread
multiple frame issues
Ambiguous multiple inheritance (which frame to use when multiple apply)- new class is created.
Frame-like structures representing a stereotyped sequence of eventsin a particular context
script components
"–Entry conditions
"–Entry conditions
–Scenes "
script problems
"1. Impossibility to know ahead of time what occurrences will break a script
–e.g. “John was eating dinner at his favorite restaurant when a large piece of plaster fell from the ceiling and
landed on his date...”–Q’s: Was John eating a date salad?
What did John do next?Was John’s date plastered?
–Structured representations can be inflexible! ...
2Script match
–e.g. “John visited his favorite restaurant on the way to the concert. He was pleased by the bill because he liked
–“Bill”can refer to dinner check orconcert playbill
–Which script should be called?"
concepts, like rules- can be
"• Innate: basic concepts + mechanisms to form new ones
–“hardwired”to recognize faces, properties of physical objects...• Formed from examples
–Sample sizeneeded for generalization varies–Plasticity (tuning) from further examples, i.e. online adaptation
• Formed from other concepts"
Analogyis a correspondence or partial similarity between two situations (analogs) – It usually uses “like”or “as”. John is like a pig
is an indirect comparison between two (or more) seemingly unrelated subjects – It usually uses “is a” john is a pig
analogy in AI
case-based reasoning (AI)
how to distinguish superficial similarities from important ones in analogies
Find the causal relations that give the right and relevant (to the goal) outcome • i.e. analogy representation requires causality
Framework for computational models of analogical reasoning
retrieva(remembering), comparison, adaptation
3 kinds of learningin analogical reasoning
storage, adaptation, generalization
Literal and metaphorical processing interact with each other (Keysar, 1990) – i.e. metaphorical interpretation happens simultaneouslywith literal processing, not after
Glucksberg& Keysar
subjects asked to signal when sentence is literally false
zapping left prefrontal cortex (PFC) speeds up analogical reasoning (!)
Glasgow & Papadias
Knowledge representation scheme that brings to the foreground the most important visual and spatial properties of an image
Computational Imagery
Glasgow & Papadia’sscheme on computational imagery: – 3 representations for 3 kinds of processing Deep, spacial and shape