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113 Cards in this Set
- Front
- Back
Decision problems
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At their simplest, we can consider the job of a computer to be:
given a particular input, produce a specific output for example, given 2*3, produce 6 Let’s consider a simple version of this: f(N) = {1, 0} for any given input N (which is a natural number), this function outputs either a one or zero odd(12) = 0 prime(13) = 1 This is known as a “decision problem” --Function that can spit out a 1/0 based on a decision |
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The Turing machine
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Turing proposed a simple theoretical computer with 4 components
Machine table rules of the form “if state == y and input == j, do z” this is the “program” of the computer Machine state A number that represents the states y Tape Of infinite length, divided into discrete squares On each square, the machine can write a 1 or 0 Represents the inputs, outputs, and parts of program Read/write head for reading/writing 1/0 to the tape |
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Turing machine: example
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A Turing machine that adds 1 to a natural number
Represent numbers as a sequence of 1’s 1 -> 1 ; 5 -> 11111 Internal State=0 -Read=0; new internal state=0; write=0; Action=R -Read=1; new internal state=1; write=1; Action=R Internal State=1 -Read=0; new internal state=0; write=1; Action=STOP -Read=1; new internal state=1; write=1; Action=R So 1111 would become 11111 |
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The Universal Turing Machine
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Imagine a Turing machine whose input tape specifies the machine table of another Turing machine
This Turing machine would be programmable It could imitate any other Turing machine It could compute any computable function Most of us have a UTM on our desk (and perhaps even in our pocket or on our wrist) |
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The Turing-Church hypothesis
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If a particular decision problem can be computed by a Turing Machine, then it can be computed by any reasonable computer
Conversely, if it cannot be computed by at Turing Machine, then it cannot be computed by any reasonable computer (no stopping point) All known models of computing are equivalent! What does it mean for a decision problem to be computable? |
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Computability
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A problem is computable if:
The algorithm contains a finite number of steps The algorithm makes use of a finite set of basic operations The algorithm comes to a halt for any valid input data Problems for which no algorithm exist are called “undecideable” or “uncomputable” example: what is the value of Pi? Is the following sentence true or false: “This sentence is false” Just because it is solvable doesn’t mean that it’s solvable in human time This is the domain of computational complexity theory -Computational steps |
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Is the human mind a Turing machine?
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There are many problems that are computable but that the human mind can’t solve
e.g., problem-space search in chess We don’t have unlimited memory like a Turing Machine We prefer to use heuristics rather than algorithmic computations |
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Humans and uncomputable functions
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There are many functions that humans perform that are in theory uncomputable
inverse optics estimating transcendental numbers (e.g., Pi) induction We solve these unsolvable problems by using heuristics |
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What is intelligence (average "human" level of thinking from a computer)?
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How do we decide if a machine is intelligent?
The Turing Test (1950) Put a judge in one room connected by teletype to a computer in second room and a human in a third room If the judge can’t tell which one is the computer and which is the human, then the computer is intelligent --Rarefied form of interaction |
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The Turing Test
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The Turing Test provides a principled definition of intelligence that only refers to behavior
It is a behaviorist approach to intelligence It doesn’t say anything about what is going on in the person or computer’s mind |
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An early example of AI: ELIZA
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A simulation of a psychiatrist written by Weizenbaum in the 1970’s
Uses a set of very simple strategies Eliza worked by simple parsing and substitution of key words into canned phrases. Depending upon the initial entries by the user the illusion of a human writer could be instantly dispelled, or could continue through several interchanges. It was sometimes so convincing that there are many anecdotes about people becoming very emotionally caught up in dealing with ELIZA for several minutes until the machine's true lack of understanding became apparent. All this was due to people's tendency to attach to words meanings which the computer never put there. |
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Problems with the Turing Test
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Who gets to be the judge?
A naïve judge might think that anything was intelligent In a public test, 5/10 people thought that a version of ELIZA was intelligent A system with canned answers to every possible question could pass the test without any intelligence (reasoning, awareness, etc.) at all |
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Strong vs. Weak AI
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Different AI researchers have different goals (Searle, 1980)
Weak AI The idea that computers can be useful to simulate mental processes Strong AI The idea that an appropriately programmed computer really is a mind, in the sense that it understands the world The mind is just another kind of program |
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Intentionality
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Computers are “syntactic engines”
automatic formal systems They manipulate symbols according to a specific set of rules These rules apply on the basis of form, not meaning e.g., “Boston” versus Boston -Experienced, exists in the world and has memories/knowledge attached to it Human minds are “semantic engines” Our thoughts have meaning, in that they refer to things in the outside world (e.g., Boston the city) The symbols in the human mind have intentionality (about-ness) |
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The language of thought
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Fodor (1975) proposed that the symbols in the human mind are in a “language of thought”
It is this language on which the computations of the mind are performed This language is like a natural language but not necessarily the same The LOT hypothesis explains the productivity of thought Just as the syntax of language explains its productivity |
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The Chinese Room
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Searle (1980) argued that a computer program can’t understand anything (not learned but stored)
It doesn’t have intentionality He did this using a thought experiment fashioned after the Turing Test |
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Locked in the Chinese Room
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Jack (an English speaker who knows nothing about Chinese writing) is locked in a room and is given a batch of Chinese writing
He is then given a second batch of Chinese writing along with a a set of rules (in English) for correlating the first batch with the second batch and producing an appropriate Chinese character |
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The Chinese Room problem
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Unbeknownst to Jack, he is part of a Chinese Turing Test
The first batch of Chinese writing was his “knowledge” The second batch was “questions” His response was “answers to the questions” To someone observing from outside the Chinese room, it appears that the person in the room “understands” Chinese But: Does anyone really believe that Jack understands Chinese? -Doesn't know content of symbols -Doesn't know relationship between symbols and outside world |
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Can computers understand?
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If we take the program to be analogous to the rules provided to Jack, and Jack as the computer, the Chinese Room suggests that computers cannot understand
They simply manipulate symbols on the basis of formal rules (syntax) These symbols do not relate to anything in the world (they don’t have intentionality) |
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New approaches to AI
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The GOFAI (“good old-fashioned artificial intelligence”) approach has yielded great success in many domains, but has failed to approach the flexibility of human intelligence
Recent work in AI has tried to build intelligence from the ground up rather than from the top down |
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The evolution of intelligence
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Timeline of biological evolution
Single-cell organisms 3,500mya Photosynthetic plants 2,500 Fish and vertebrates 550 Insects 450 Reptile 370 Dinosaurs 330 Mammals 250 Primates 120 Humans 2.5 |
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The hard part of intelligence
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The things that we commonly think of as “intelligent” are very new
Other things are much harder Mobility in a dynamic environment Sensing surroundings well enough to maintain life and reproduce Workers in AI have begun to focus on making machines that exhibit these kinds of simple intelligence |
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How smart are insects?
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Beetle Brain
just ganglions rather than brain, but a dung beetle can execute its egg-laying technique and solve simple problems caused by obstacles |
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The lowly roach
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Escape skills of the roach (Clark, 1997)
It senses the wind disturbance caused by the motion of an attacking predator It distinguishes winds caused by predators from normal winds and air currents It does not avoid contact with other roaches When it does initiate an escape motion, it does not run at random. Instead, it takes into account its own initial orientation, the presence of obstacles (such as walls), the degree of illumination, and the direction of the wind |
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The roach-brained car
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Imagine there was a car with a computer as intelligent as a roach
It would: sense approaching vehicles, but only those moving in abnormal ways in the case of an impending collision, initiate a turn taking into account its own current state (speed and position), road conditions, and the presence of other dangers Doesn’t sound so bad, does it? |
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Brooks’ approach
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Rodney Brooks of MIT has argued against the classic AI approach
Intelligence can be produced without the explicit representations and reasoning processes of GOFAI Rather, intelligence is an emergent process of certain complex systems Intelligence grows out of basic processes Mobility in a changing environment Survival-related tasks (feeding, reproduction) Acute sensation of the environment Brooks and his colleagues have focused on building robots that exhibit these basic behaviors -only programmed to get around the world generally |
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Can intelligence really emerge?
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The example of cellular slime mold
As long as there is sufficient food locally, the mold remains in a vegetative state individual mold cells grow and divide like amoeba When food runs out, the cells cluster together into a tissue-like mass acts like a miniature slug is attracted to light, and follows temperature and humidity gradients Once new food is found, the cluster forms into a stalk-like creature and releases spores, which start a new |
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Physical symbol systems
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Newell (1980) argued that both brains and computers can be treated as “physical symbol systems”
These are necessary and sufficient for general intelligent action Any intelligent system will be a symbol system A symbol system has several properties: Memory - to hold information about the world Symbols - to represent the world or internal goals Operations - to manipulate symbols Two essential properties of symbols They can designate things in the world They can represent things or operations |
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Cognition as search
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In a symbolic system, the problem of cognition boils down to searching for the appropriate operator in any particular state
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Heuristic search
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Blindly searching the problem space will fail for complex problems
combinatorial explosion! Solution: use rules (assoc?>)to guide search |
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Newell & Simon’s GPS
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Problem solving has both task-dependent and task-independent aspects
Task-independent: “If you can’t solve all of the problem, try to solve part of it” Task-dependent: “If the Windows Registry gets corrupted, the machine may hang” The “General Problem Solver” was designed to be able to solve any problem Explicitly separates the task-independent and task-dependent aspects The user defined objects and operations that could be done on the objects and GPS generated heuristics by Means-ends analysis in order to solve problems. It focused on the available operations, finding what inputs were acceptable and what outputs were generated. It then created subgoals to get closer and closer to the goal. |
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Representation in GPS
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Problem solving in GPS (general problem solver) occurs by heuristic search through a state space
Each state is a snapshot of what the system knows at a particular point in time composed of one or more objects, which contain symbolic structures and/or operators (programs) |
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Operators in GPS
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Operators are like programs that take symbols as input and output
GPS applies operators to the current state in order to generate new states that are closer to the goal state Operators can do various things: compare symbol structures create new symbol structures read input and write output store symbol structures to memory |
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Means-ends analysis
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Problem: How to decide which operator to apply to any given state?
Option 1: Apply them all (exhaustive search) This is not possible for complex problems Option 2: Apply the best one How do we know which that is? Means-ends analysis says that one should apply the operator that eliminates the most differences between the current state and the goal state Each operator is classified in terms of the differences that it eliminates (the GPS uses his information to choose) |
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GPS: defining operators
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(defparameter *school-ops*
(list (make-op :action 'drive-son-to-school :preconds '(son-at-home car-works) :add-list '(son-at-school) :del-list '(son-at-home)) (make-op :action 'shop-installs-battery :preconds '(car-needs-battery shop-knows-problem shop-has-money) :add-list '(car-works)) (make-op :action 'tell-shop-problem :preconds '(in-communication-with-shop) :add-list '(shop-knows-problem)) (make-op :action 'telephone-shop :preconds '(know-phone-number) :add-list '(in-communication-with-shop)) (make-op :action 'look-up-number :preconds '(have-phone-book) :add-list '(know-phone-number)) (make-op :action 'give-shop-money :preconds '(have-money) |
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A GPS trace
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> (gps '(son-at-home car-works)
'(son-at-school) *school-ops*) Achieve SON-AT-SCHOOL ... Trying DRIVE-SON-TO-SCHOOL ... Achieve SON-AT-HOME ... Achieve CAR-WORKS ... Executing DRIVE-SON-TO-SCHOOL State = (SON-AT-SCHOOL CAR-WORKS) SOLVED |
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Testing GPS
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GPS was meant as a theory of human problem solving
It was tested by having humans solve the same problems while they thought out loud This is called verbal protocol These protocols were then analyzed to classify what kind of behavior the subject was exhibiting In many cases, humans behaved very similarly to GPS Newell & Simon (1972): 84% of utterances showed patterns that were also exhibited by GPS only 10% of utterances showed patterns that were not exhibited by GPS |
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Importance of GPS
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One of the first attempts to provide a general theory of problem solving behavior
“weak methods” AI: search heuristics that don’t rely upon knowledge about the specific domain Introduced means-ends analysis into cognitive science/AI |
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Production system models
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Production systems are currently the most common symbolic approach within cognitive science
A production system models cognition using condition-action sets known as productions If a certain condition is met, then perform a certain action IF person 1 is the father of person 2 and person 2 is the father of person 3 THEN person 1 is the grandfather of person 3 [Simulating understanding (of relationships)] |
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Unified theories of cognition
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Two well-known production-system frameworks aim to provide full accounts of cognition
They argue that all of cognition can be modeled using a single architecture ACT-R (John Anderson) More heavily tested against psychological data SOAR (Newell, Laird, & Rosenbloom) More comprehensive implementation |
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Expert Systems
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One of the most successful areas in AI has been the development of expert systems
Two components of an expert system: knowledge base derived from extensive interviews with human experts inference engine works with the knowledge base to reason about the domain |
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MYCIN
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MYCIN was an expert system developed to assist physicians in the treatment of certain bacterial infections
It asks specific questions about symptoms, test results, suspected organisms, etc It then provides recommendations for treatment Also provides an explanation for its reasoning, and provides a measure of uncertainty for its recommendations |
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MYCIN rules
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MYCIN uses production (if-then) rules, such as:
if the stain of the organism is gram-positive and the morphology of the organism is coccus and the conformation of the organism is clumps then (0.7) the identity of the organism is staphyloccus |
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Evaluating Expert Systems
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MYCIN was compared to the performance of actual physicians on its recommendation
Expert raters didn’t know that MYCIN was included in the comparison MYCIN outperformed all of the physicians including Medical School faculty! Particularly good at prescribing exact dosages and dealing with drug interactions, which human doctors aren’t so good at |
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Problems with expert systems
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Limited to verbalizable knowledge
If human experts rely upon intuition or other forms of implicit knowledge, then expert system will fail Expert systems don’t have common sense knowledge Learning Most expert systems do not learn from their experience e.g., in MYCIN, new diseases have to be coded in by hand |
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CYC: Formalizing Common Sense
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The CYC (“Encyclopedia”) project began in 1984
Goal: Build a “universal” expert system that can understand natural language and detect violations of common sense as well as humans can More than 3 million hand-coded rules Trees usually grow outside When people die, they stop buying things Glasses of liquid should be carried right-side up |
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The problem of context (CYC)
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The CYC project ran into huge problems because rules almost always depend upon their context
Vampires are not real, but in fictional settings they may be treated as real This has increased the size of the knowledge base by a factor of 10 The hype of CYC has not lived up to the reality > $50 million has been spent so far |
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Expertise and problem solving
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What makes a person an expert in a particular domain (e.g., chess)?
Experts are not just smarter You wouldn’t want to visit Einstein if you needed brain surgery They have extensive knowledge in a particular domain They know how to solve many problems in that domain They can diagnose novel problems |
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Chess expertise and memory
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Chess experts are better able to remember real chess positions
They are actually worse at remembering random positions They chunk the entire position as one item Experts see the world differently They process the world in terms of their knowledge Chase & Simon (1975) |
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Chase & Simon (1975)
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Chess positions to analyze expertise in chess
Beginner: worst at meaningful Class A: middle-ish Master: best at meaningful but worst at random chess positions |
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Experts categorize differently than novices do (Chi et al., 1983)
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Physics problem as example: block on incline
Novices categorize in terms of surface features of the problem e.g., "block on an inclined plane," "...coefficient of friction" Experts categorize in terms of underlying physical laws. e.g., "Conservation of energy," "work-energy theorem, these are straightforward problems," |
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Deep Blue
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Deep Blue is an expert system for chess
Beat Gary Kasparov in 1997 It contains extensive knowledge of the game from human chess experts It can search ~200 million positions/second Gary Kasparov can consciously evaluate ~ 3 positions/second |
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Problems with symbolic AI
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Brittleness
The systems fail completely whenever the problem extends beyond their built-in knowledge Example: The robot ant won’t be able to climb a 3-inch telephone book if its program only includes instructions for climbing a 2-inch book The frame problem |
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The frame problem in AI (Robot R1)
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Imagine a robot R1 (Dennett, 1984)
its only job is to fend for itself One day its designers arrange for it to learn that its precious spare battery is locked in a room with a time bomb set to go off soon. R1 located the room, found a key to open the door, saw that the battery was on the wagon, and executed the operator PULLOUT(wagon, room) Unfortunately the bomb was also on the wagon, and R1 didn’t realize that an implication of its action would be to move the bomb with the wagon -context |
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the robot-deducer R1D1
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The designers create a new robot that deduces not just the implication of its acts, but also of their side-effects
Put in the same situation, R1D1 also chose the PULLOUT(wagon,room) operator, and began to deduce its implications and side effects It had just finished deducing that pulling the wagon out of the room would not change the color of the walls, and was embarking on a proof that pulling the wagon from the room would cause its wheels to turn more revolutions than there are wheels on the wagon - when the bomb exploded! |
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the robot-relevant-deducer R2D1
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The designers set out to build a robot that knows the difference between relevant and irrelevant implications, and ignores irrelevant ones
When put to the same test, the robot simply sat outside the room “Do something!” the designers yelled “I am!” said the robot. “I’m busily ignoring the thousands of implications I’ve deemed irrelevant. Every time I discover a new irrelevant implication, I put it on the list to ignore”. Then the bomb exploded. |
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The frame problem
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The frame problem refers to the fact that humans seem to just know which facts are relevant in any particular situation
Computers, on the other hand, must be programmed to know which of the infinite number of possible implications of any action are relevant This has proven to be the most difficult problem for strong symbolic AI |
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Neural computation
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How does a computer compute?
Complex operations built-in Very fast and error-free Fixed architecture How does a neural network compute? Lots of simple computational units Each unit is slow and noisy Highly connected units Plastic connections |
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What is the simplest thing we could try to compute?
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Simple Boolean logic
A/B/AND/OR/XOR 0/0/0/0/0 0/1/0/1/1 1/0/0/1/1 1/1/1/1/0 |
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The perceptron
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A simple neural network that can compute some Boolean logic functions
Input units: A and B (with modifiers that can adjust strength of inputs separately) Output Unit: O Decision: 1/0 OR gate and AND (with 0.5xinput) gates work, but XOR causes problems |
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Linearly separable OPERATIONS
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AND and OR are linearly separable problems
AND A0B1 A1B1--N Y A0B0 A1B0--N N OR A0B1 A1B1--Y Y A0B0 A1B0--N Y |
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XOR gate
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XOR is not linearly
separable A0B1 A1B1--Y N A0B0 A1B0--N Y So, it can’t be solved by a simple perceptron |
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How can we solve more complex problems? (XOR and above)
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Minsky & Papert’s 1969 book “Perceptrons” highlighted the limitations of the perceptron
Had a chilling effect on neural network research until the 1980’s In 1986, a set of books was published (“the PDP books”) that brought this approach back to life Outlined an approach that solved many of the limitations of perceptrons |
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Components of a PDP model
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AKA neural net, connectionist model
Set of processing units, each having: a state of activation an output function Pattern of connectivity between units and rule for propagation of activity Activation rule for combining the inputs to a unit to produce a new activation level Learning rule for changing the patterns of connectivity based on experience Representation of the environment coding of features of the the environment |
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Processing units of a PDP model
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Each unit (Input, Hidden, and Output) has a level
of activation ai The output of the unit is determined by the output function: oi = f(ai) Usually identity function, threshold function, or stochastic function |
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Connectivity of a PDP model
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Changing the patterns of connectivity based on experience (and the activation function).
Modifying operations of input passed along connectivity lines |
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Activation function
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In PDP models...
Determines the level of activation in a unit based on the net input (which is determined by the input unit activity * weights) The type of problem that can be learned depends upon the activation function Needs to be nonlinear to learn interesting problems threshold sigmoid |
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Learning rules
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Rules for altering the connectivity of the network depending upon experience
Hebb rule increase connectivity when two units fire at the same time Delta rule Change weights in proportion to the amount of error between desired and actual output |
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Beyond Perceptrons (PDP benefits)
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The main aspect of PDP models that makes them more powerful than Perceptrons is that they have multiple layers
Allows solution of problems that are not linearly separable The main advance of Rumelhart, McClelland, & colleagues was to (re)discover a method to train a multi-layer network Known as “backpropagation of errors” |
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Solving XOR using a multi-layer network
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[Image of proper network]
Backpropagation can look at out put, see if it matches what was desired and then change weights accordingly |
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Forms of machine (statistical) learning
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There are several different ways in which a neural network can learn
Unsupervised learning Reinforcement learning Supervised learning The intersection of machine learning and computational neuroscience is one of the hottest areas in cognitive science right now |
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Unsupervised learning
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Learning about the environment without being told what is going on
E.G., An infant learning to see How can a system do this? By being sensitive to the statistics of the input, such as correlations between different features |
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Hebbian learning
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When an axon of cell A is near enough to excite cell B or repeatedly or consistently takes part in firing it, some growth or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased. Donald Hebb (1949)
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Hebbian learning
An example: Ocular dominance |
Cells in the visual cortex respond preferentially to one eye
This develops with experience Deprivation of input to one eye reduces its representation in the cortex Plasticity of ocular dominance: shows that deprivation can affect growth of brain regions typically dedicated to the deprived eye |
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A model of ocular dominance
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Miller et al (1989)
Showed that Hebbian learning can result in ocular dominance Relies on fact that nearby inputs from same eye are more correlated than inputs from different eyes -Cells sensitive to how correlated their firing is |
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A neural mechanism for Hebbian learning
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Long-term potentiation (LTP)
A persistent change in the firing of neurons due to previous activity potentiation = increase in firing strength The mechanisms by which LTP is induced and maintained are still a matter of controversy But the basics of the mechanism are fairly well understood |
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Parallels between LTP and memory
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LTP is prominent in hippocampus, which is important for memory
LTP develops rapidly (within 1 minute) LTP is long-lasting (up to several weeks) LTP is specific to active synapses LTP is associative detects simultaneous activity across neurons |
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The Morris water maze
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The rodent is placed in a tub of cloudy water
The tub has a small platform that the rodent can stand on Rodents don’t like being in the water! |
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LTP and the water maze
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Blocking LTP (by blocking NMDA receptors) does not impair the ability to swim or find the platform
However, it does impair the rodent’s long-term memory for the location of the platform |
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Reinforcement learning
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Learning how to act without being told what the right action is, but receiving reinforcement when the right action is taken
A problem: credit assignment How do you know which of the 20 things you did in the last minute are responsible for the reinforcement? Prediction of reward affects repetition of action -reward higher than predicted=more responses The basic paradigm of reinforcement learning is as follows: The learning agent observes an input state or input pattern, it produces an output signal (most commonly thought of as an "action" or "control signal"), and then it receives a scalar "reward" or "reinforcement" feedback signal from the environment indicating how good or bad its output was. The goal of learning is to generate the optimal actions leading to maximal reward. In many cases the reward is also delayed (i.e., is given at the end of a long sequence of inputs and outputs). In this case the learner has to solve what is known as the "temporal credit assignment" problem (i.e., it must figure out how to apportion credit and blame to each of the various inputs and outputs leading to the ultimate final reward signal). |
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The actor/critic model
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Two components
Actor Implements a policy for which actions should be chosen depending upon their potential value (‘advantage’) Critic Provides an error signal comparing the predicted outcome to the actual outcome and allowing the potential value of actions to be revised |
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Reinforcement diagram
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(I)Evaluate actions (the ACTOR)
-Assess reward, delay, risk --Striatum; Frontal and Parietal Cortex ---place expected value on available actions (II)Choose an Action -Biased toward richest options --Same areas as (I) or downstream (III)Learn from Experience -Compare predicted and actual reward --Dopaminergic error signal (the CRITIC--leads to plasticity) |
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Dopamine neurons signal
reward prediction error |
Schultz, 1988
[graphs that show effect in relation to prediction errors: no prediction and reward (Dopa at reward), prediction and reward (Dopa at CS), and prediction and no reward (DIP in Dopa at approximate time of reward)] |
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An example: TD-Gammon
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A neural network model that learned to play backgammon using a reinforcement learning algorithm (the temporal difference, or TD, algorithm)
Was able to learn to play world-class backgammon, and actually changed the way that top players played the game negative points/game smaller each revision while number of training games increased from 300,000 to 800,000 to 1,500,000 TD-Gammon is a neural network that trains itself to be an evaluation function for the game of backgammon by playing against itself and learning from the outcome. |
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Supervised learning
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Learning by being taught what to do
Requires a “teacher” |
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Supervised learning
An example: NetTalk |
Learned how to read
-backpropagation correction -told right answers TRAINING TEXT (from Carterette & Jones, first-grade conversation): You mean uh um like England or something. When we walk home from school I walk home with two friends and Because um one girl where every time she wants to runs she gets the And then she cant breathe very well and she gets sick. Thats why we cant run. I like to go to my grandmothers house. Well because she gives us candy. Well um we eat there sometimes. Sometimes we sleep over night there. Sometime when I go to go to my cousins I get to play soft ball or Thing I hate to play is doctor. Oh. I hate to play doctor or house or that. Dont like it or stuff. Weve been learning a lot of Spanish words. Our teacher speaks Spanish sometimes. So does my father. Well my father doesnt know very much Spanish but he doesnt know what In Spanish. |
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The “rules” controversy
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The classical view in cognitive science is that knowledge is stored in a explicit symbol system
Connectionist models do not explicitly store symbols, but instead store knowledge in connection weights |
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Radical connectionism
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The classical symbolic view of cognition is wrong
It can’t explain: Graceful degradation with damage Spontaneous generalization of knowledge Context-sensitivity of knowledge Others take a weaker view Neural nets may implement a symbolic system Final word The neural computing approach has been much more productive than the symbolic approach over the last 20 years There are many models that show how symbolic processing can arise from neural networks |
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Consciousness
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Consciousness is probably the most baffling problem in cognitive science
Consciousness can mean many things many of these meaning are things that we have already discussed conscious sensation attention (direction of) the ability to describe mental states using language The “hard problem” of consciousness relates to subjective experience How do things seem to you? -subjective experience This is also known as the problem of qualia -qualities of conscious perceptions |
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Scientific approaches to consciousness
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Scientific interest has mostly centered on asking what kinds of cognitive processes require conscious awareness
These studies avoid the hard problem Conscious awareness is always defined in terms of specific tests for awareness |
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The implicit/explicit distinction
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Most research regarding the role of consciousness has centered around the implicit/explicit distinction
Explicit phenomena are those that involve conscious awareness and memory of the past (declarative memory) Your perception of my voice Your memory for breakfast Implicit phenomena do not involve conscious awareness or memory Your memory for how to ride a bicycle |
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Implicit memory
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Think about your ability to drive a car
When you are driving, is it necessary to think back to all of the times when you were learning to drive? Implicit memory is the memory that underlies skills and other effects of experience that don’t require conscious memory for the past |
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Multiple memory systems
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Research with amnesic patients has shown that they can learn some kinds of information normally
Even though they can’t consciously remember the past! This has led to the suggestion that there are multiple memory systems in the brain Separate, independent structures that support separate kinds of memory Damaging one system does not necessarily impact the other system |
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Memory systems
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The most common distinction is between declarative and nondeclarative memory systems
Declarative memory: supports conscious memory for facts and events “knowing that” relies upon the hippocampus Nondeclarative memory supports effects of experience without conscious memory skill learning, repetition priming “knowing how” relies upon a number of brain structures |
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Skill learning
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Patients with amnesia can exhibit normal learning of many different types of skills
Even though they don’t remember having learned them! |
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Mirror-reading
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Read the following words aloud as quickly as possible from right to left:
ambitious bedraggle plaintiff The amnesics were able to learn the mirror-reading skill just as well as the normal controls However, when later tested on their memory for the words, the amnesics were much worse. |
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Repetition priming
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Experience with a stimulus leads to enhanced processing of that stimulus later
A commonly studied form of implicit memory |
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Priming in amnesia
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Amnesic patients show normal repetition priming effects
At the same time, they are impaired on tests of recognition memory |
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Graf et al. (1984)
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Amnesic patients and controls studied lists of words
MOTEL, WINDOW After each list, they were given word stems (MOT__, HOU__) and asked to do one of two tasks: Cued recall: Complete the stem with a word from the study list This is a “direct” test of memory Word stem completion: Complete the stem with the first word that comes to mind This is an “indirect” test of memory |
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Measuring priming (Graf et al. (1984))
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Repetition priming is measured by:
The increase in the likelihood of completing the word stem with a studied word, compared to the case when that word was not studied |
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Priming in amnesia (Graf et al. (1984))
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Amnesics were badly impaired on free recall and cued recall
They were actually somewhat better on stem completion |
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Double dissociation of memory systems
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Do recognition memory and repetition priming rely upon separate brain systems?
Gabrieli and colleagues demonstrated a double dissociation Patients with amnesia demonstrate impaired recognition memory but normal repetition priming Patients with lesions to the occipital lobe show impaired repetition priming but normal recognition memory |
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Subliminal perception?
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Implicit memory shows that people can be affected by stimuli that they no longer consciously remember
Can people be affected by stimuli that they aren’t even aware of when they occur? This is known as subliminal perception Source of great controversy over the last 30 years |
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Subliminal priming
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Semantic priming:
The subject performs a lexical decision (word/nonword) task On some trials, a related stimulus (“prime”) precedes the stimulus in the task Example: bread -> butter The subject is faster to say that butter is a word than if they had seen an unrelated word or a letter string “XXXXX” In the 1980’s, Tony Marcel claimed that semantic priming occurred even if the subject wasn’t aware of the prime prime is presented for a very short period (less than 50 milliseconds) with a masking stimulus following it |
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Does subliminal priming occur?
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There is substantial evidence that priming can occur even when subjects cannot discriminate the identity of the prime
The subject can still detect whether the prime was present There is little evidence for priming when the subject cannot detect whether the prime was present or not |
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Can subliminal ads sell products?
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James Vicary claimed in 1957 that the messages “Eat Popcorn” and “Drink Coca-Cola” were shown subliminally during the movie “Picnic”
He claimed that these messages led to a 57.7% increase in popcorn sales and an 18.1% increase in Coke sales Vicary later claimed that these results had been fabricated There is no empirical evidence that subliminal messages can influence behavior in this way |
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Subliminal messages in music
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In the 1980s and 1990s, there was great controversy over whether rock music contained backwards messages
Judas Priest was sued (unsuccessfully) over the suicide attempt by the parents of two teenagers in Reno who attempted suicide They claimed that backwards message in the music had driven them to it This debate confounded two issues: Are the messages put there intentionally? Do backwards messages have any effect? |
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Effects of backwards speech (Vokey & Read (1985))
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Vokey & Read (1985) examined whether people could perceive the contents of backwards speech
Subjects were able to determine: gender (98.9%) same versus different individuals (78.5%) language (English/French/German) (46.7% vs. 33% chance) |
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Vokey & Read (1985)
(What couldn't be done) |
Subjects were unable to accurately:
say how many words were were spoken whether it was a question or a statement whether it was a sensible sentence or not The backwards speech was equally ineffective at causing implicit memory effects |
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Unconscious perception
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The upshot from research on unconscious perception in normal subjects is:
Stimuli that are detectable but not discriminable can cause effects These effects are small and limited in time They are probably not sufficient to cause people to buy particular products or commit suicide Stimuli that are not detectable do not cause effects |
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Unconscious perception in prosopagnosia
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Prospopagnosia results in the inability to recognize faces
They cannot consciously distinguish between familiar and unfamiliar faces However, prosopagnosics show differential galvanic skin response (GSR) to familiar and unfamiliar faces Shows that their autonomic nervous system registers familiarity even when conscious awareness does not |
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Capgras syndrome
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Patients with Capgras syndrome think that significant others have been replaced by imposters, aliens, or robots
These patients do not show differential GSR between familiar and unfamiliar faces Flipside of prosopagnosia -Recognition but lacks "feeling" --the implicit recognition |
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Blindsight
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People with lesions to the visual cortex are unable to consciously see part of their visual field
Weiskrantz et al. (1974) showed that these people can exhibit some aspects of vision They can accurately point at the location of a flash of light Even though they claim not to have seen the light! Blindsight does not occur following earlier lesions in visual system (e.g., optic nerve, retina) Probably relies upon visual pathways other than the visual cortical pathway, such as the superior colliculus |