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62 Cards in this Set
- Front
- Back
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paradigmatic approaches |
symbolic (rule based) sub-symbolic (connectionist) |
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what is ACT-R |
a production-system cognitive architecture designed to redict human behavior by processng info (cognition, visual attention, movement etc. ) and generating behavior models many cognitive phenomenon |
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types of knowledge |
declarative - conscious - facts procedural - unconscious |
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chunks of declarative knowledge (2) |
type - category (bird) slots - attributes (color, size) |
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basic program structure for a production |
if - goal then - subcoal |
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productions in general comprised of (2) |
conditions actions |
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conditions |
- dp on declarative knowledge (chunks) - &/or sensory input - specify the goal, and number of chunks - often tests contents of buffers |
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actions |
- can alter declarative knowlege - initiate actions - produce changes in buffers |
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Architecture of ACT-R modules --> buffers what are these relationships (4) |
intentional (n/k) - goal buffer DLPFC declarative (temp/hippo) - retrival VLPFC visual (occ) - visual Par manual (motor/cerebellum) - manual motor |
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Architecture of ACT-R productions |
matching (striatum) selection (pallidum) execution (thalamus
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modules devoted to
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idetifying objects controlling hands retrieving declr info keeping track of goals
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role of central production system |
respods to info which is deposited as chunks in buffers then fed forward for central processing |
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visual buffers (2) |
dorsal 'where' path - object locations ventral 'what' path - object identities |
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info processed in parallel or serial? |
mixed |
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parallel processing: |
visual system - whole viusal feild declarative system - retreival of mems |
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what are the 2 serial bottle necks? |
content buffer limited to 1 declarative chunk so: - one mem retr. at a time/ - one object encoded from visual feild
one production is selected to fire ea. cycle |
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hybrid cognitive archetecture consists of (2) |
symbolic production sub-symbolic production |
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sub-symb. parallel processign implemented by _____ controls many of the _____ processes |
equations symbolic |
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productions and chunks have ______ parameters which reflect _____ |
subsymbolic past content |
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Activatsion of delclarative memorie to what degree does ACT-R makes chunks active? |
to degree tht they will be useful/are relevent in particular moment |
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conflict resolution for when multiple productions may match (but only 1 may fire) dp on the _________ utility f(x) |
subsymbolic utility fx
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sub-symbolic utility function estimates (3) to select proudction with highest ___ |
for a given production fx estimates: - probability that current goal will be acheived if fires - relative cost (time to acheive goal) - benefit (value of goal) highest utility |
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learning in ACT-R - symbolic (2) - sub-symbolic |
symbolic - declarative : new chunks - procedural : production compilations sub-s - rational analysis (optimized enviro stats, bayes rule) |
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in the stick-building problem, what are the strategies |
- undershoot - overshoot - hill-climbing |
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production compilation |
combining 2 existing rules if A --> B, if B --> C compiles if A --> C |
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result of production compilation is this is refered to as the _______ of practice |
more efficience, speed power law of practice |
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compilation with past-tense model predicted: |
faster prfrm on irregulars (regular = defalt) - contradictory to neural network findings |
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what does W represent |
the attentional weighting of elements that are part of current goal |
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why vary W? |
to represent individual differences |
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hybrid cognitive architecture chuncks and productions are _____ controling _____ |
symbolic components overall info flow |
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hybrid cognitive architecture chuncks have sub-s __________ while productions have sub-s __________ |
activations utilities |
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learning can involve
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- acquiring new hchuncks/ prodct. - find -tuning sub-s parameters |
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limitations |
- learing rule and parameters is difficult - cant learn from scratch - extensive engineering rq - autoomus dvlp seems out - many wrong predictions (past-tense latencies) |
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connectionism |
network of units and weights - unit computs weighted sum of inputs |
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modify weight to ++____________ |
reduce error |
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why must brain be a parallel processor? |
rapid computation performed by sluggish units |
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computational properties of brain |
robust flexible approximate parallel compact and efficient |
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tranlate into neural net: neuron activity synapse synaptic reception threshold |
unit activation weight sum of products (activation * weight) S-shaped function |
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translate into psych equivalents - pattern of activation across net - connection weights - adjustment of weights |
= active/working memory = LT memory = learning |
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back-propogation network structure |
output units ^ hidden units ^ input units |
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activation function is ________--- |
sigmoid 1/(1+e^-x) |
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3 Boolean problems |
AND - both true - linearly seperable OR - 1/2 true - lineraly seperable XOR- only 1 true - lineraly non-seperable |
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how are inputs calculated? |
unit type (0/1) * weight) |
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prospect theory differs from EV theory |
ps replaed with subjective decision weights |
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gains vs losses in prospect theory |
gains - concave - risk averse losses - convex - risk seeking |
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reference point |
customary wealth, at origin x = 0 |
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loss aversion |
steeper for gains than losses |
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problems with prospect theory |
- predicts preferences that are never observed - limited to binary - poor predictions when more possible outcomes |
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desicion making biases |
allais stochastic dominance preference reversals similarity attraction compromise |
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allais paradox ex: |
:: a) 100% win of 1m b) 89% win of 1m and 10% win of $5m |
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stochastic domminance |
:: a) 85% $98m, 5% 90m, 10% 12m b) 90% 98m, 5% 14m, 5% 12m preference for A>B hw, remove items with same value and you see preference reversal |
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preference reversals: choice and price |
- would choose lower risk gamble option but, - would assign higher price to higher risk gamble option |
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preference reversals: min selling price vs max buying price |
would choose lower risk gamble but would assign higher price to higher risk gamble framing effects |
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violatiosn of independence from irrelevance |
- similarity - attraction - compromise |
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similarity |
A: IQ = 60 motivation = 90 B: IQ = 78, motivatio = 24 C: IQ = 75, motivation = 29
B when [A.B] A when [A.B.C]
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attraction effect |
3rd item that is similar to another item but slightly inferior will ^^ attractivness of that item |
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compromise effect |
3rd item is an intermediate, will compromise and pick that item |
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computer models capturing paradoxes abound, currently the most effective one is ______ |
decision field theory shows preference emergence, as a function of time |
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self feedback in DFT |
explains primacy/recency |
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lateral inhibition in DFT |
explains context effects |
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weigts |
depend on perceived prob that prospect I delivers outcome j |
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limitations to DFT |
hand designed and highly engineered no learning no autonomous neowrk crct arbitrary specifications lots of randomness but still the champ! |
