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Psyc 317: Cognitive Psychology. Lecture 8: Knowledge. Outline. Approaches to Categorization – Definitions – Prototypes – Exemplars • Is there a special level of category? • Semantic Networks • Connectionism • Categories in the brain. Categorization is hierarchical.

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Lecture 8 knowledge l.jpg

Psyc 317: Cognitive Psychology

Lecture 8: Knowledge


Outline l.jpg
Outline

  • Approaches to Categorization

    – Definitions

    – Prototypes

    – Exemplars

    • Is there a special level of category?

    • Semantic Networks

    • Connectionism

    • Categories in the brain


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Categorization is hierarchical

• So we have levels of categories

• How can all of this be represented in the mind?

• Semantic network approach


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Collins & Quillian’s Model

  • Nodes are bits of information

    • Links connect them together

Semantic network template

Simple semantic network


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Get more complicated!

  • Add properties to nodes:


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How does the network work?

  • Example: Retrieve properties of canary


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Why not store it all at the node?

  • To get “can fly” and “has feathers,” you must travel up to bird

    • Why not put it all at canary?

    • Cognitive economy: Putting common properties at each node is too inefficient

    • More efficient to put “cannot fly” at exception nodes


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How do we know this works?Collins & Quillian (1969)

  • Ask participants about canaryproperties that require more traversal

vs.


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Link Traversal Demo

Yes or no:

• A German Shepherd is a type of dog.

• A German Shepherd is a mammal.

• A German Shepherd barks.

• A German Shepherd has skin.



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Spreading activation:Priming the Network

  • An activated node spreads its activation to connected links


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Spreading Activation WorksMeyer & Schvaneveldt (1971)

  • Lexical decision task: Are the two letter strings both words?

Associated


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Meyer & Schvaneveldt Results

* Associated words prime each other


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Collin & Quillian Criticisms

  • Typicality effect is not explained - ostrich and canary are one link away from bird

    • Incongruent results (Rips et al., 1972):

    – A pig is a mammal 1476 ms

    – A pig is an animal 1268 ms


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Collins & Loftus’ Model

  • No more hierarchy

    • Shorter links between more connected concepts


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(Dis)advantages of the model

“A fairly complicated theory with enough generality to apply to results from many different experimental paradigms.”

• This is bad. Why?


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The model is unfalsifiable

  • The theory explains everything – How long should links be between nodes?

Result B says nodes look like this

Result A says nodes look like this


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Everything is arbitrary

  • Cannot disprove theory: what does link length mean for the brain?

    • You can make connections as long as you want/need to explain your results


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Outline

  • Approaches to Categorization

    – Definitions

    – Prototypes

    – Exemplars

    • Is there a special level of category?

    • Semantic Networks

    • Connectionism

    • Categories in the brain


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Connectionism is a new version of semantic network theories

  • McClelland & Rummelhart (1986)

    • Concepts are represented in networks with nodes and links

    – But they function a lot differently than in semantic networks

    • Theory is biologically based

    • A quick review of neurons…


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Physiological Basis of Connectionism

  • Neural circuits: Processing happens between many neurons connected by synapses

    • Excitatory and inhibitory connections:


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Physiological Basis of Connectionism

  • Strength of firing: Number of inputs onto a neuron (+ and -) determines rate of firing

1.5

0.2

Fires at 1.6

-0.75



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Basics of Connectionism

  • Instead of nodes, you have units

    – Units are “neuronlike processing units”

    • Units are connected together

    • Parallel Distributed Processing (PDP)

    – Activation occurs in parallel

    – Processing occurs in many units


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Basic PDP network

Mental representation

Processing

Weights

5.6

From the environment


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How a PDP network works

  • Give the network stimuli via the input units

  • Information is passed through the network by hidden units

    – Weights affect activation of nodes

  • Eventually, the stimulus is represented as a pattern via the output units


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Example output

  • The brain represents things from the environment differently


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PDP Learning: Stage 1

  • Give it input, get output


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Learning: Error signals

• The output pattern is not the correct pattern

• Figure out what the difference is

– That difference is the error signal

• Use the error signal to fine-tune weights

• Error signal is sent back using back propagation


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Learning : Stage 2

  • Back propagate error signal through network, adjust weights

5.7

5.2


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Learning: Stage 3, 4, 5… 1024

  • Now that weights are adjusted, give network same input

  • Lather, rinse, repeat until error signal is 0


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So this is learning?

• Repeated input and back propagation changes weights between units

• When error signal = 0, the network has learned the correct weights for that stimulus

– The network has been trained


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So where is the knowledge?

  • Semantic networks

    – One node has “canary” and is connected to “can fly” and “yellow”

    • PDP networks

    – A bunch of nodes together represent “canary” and another bunch represent “yellow”

    – Distributed knowledge in neural circuits


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PDP: The GoodNetworks based on neurons

  • All nodes can do is fire (they are dumb)

    • Knowledge is distributed amongst many nodes

    • Sounds a lot like neurons and the brain!

    • Emergence: Lots of little dumb things form one big smart thing


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PDP: The GoodNetworks are damage-resistant

  • “Lesion” the network by taking out nodes

    • This damage does not totally take out the system

    – Graceful degradation

    • These networks can adapt to damage


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PDP: The GoodLearning can be generalized

  • Related concepts should activate many of the same nodes

    – Robin and sparrow should share a lot of the same representation

    • PDP networks can emulate this – similar inputs can operate with similar networks


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PDP: The GoodSuccessful computer models

  • Not just a theory, but can be programmed in a computer

    • Computational modeling of the mind

    – Object perception

    – Recognizing words


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PDP: The BadCannot explain everything

  • More complex tasks cannot be explained

    – Problem solving

    – Language processing

    • Limitation of computers?

    – We have trillions of neurons

    – PDP networks can’t support that many nodes (yet)


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PDP: The BadRetroactive interference

  • Learning something new interferes with something already learned

    Example: Train network on “collie”

    – Weights are perfectly adjusted for collie

    • Give network “terrier”

    – Network must change weights again for terrier

    • Weights must change to accommodate both dogs


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PDP: The BadCannot explain rapid learning

• It does not take thousands of trials to remember that you parked in Lot K

– How does rapid learning occur?

• Two separate systems?


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How the connectionists explain rapid learning

• Two separate systems

PDP in the Cortex:

Rapid learning in

the Hippocampus:


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Outline

  • Approaches to Categorization

    – Definitions

    – Prototypes

    – Exemplars

    • Is there a special level of category?

    • Semantic Networks

    • Connectionism

    • Categories in the brain


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Categories in the brain

  • Imaging studies have localized face and house areas

    – Still not very exciting (“light-up” studies)

    • Does this mean one brain area processes houses, another one for heads, and chairs, and technology, etc. etc.?


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Visual agnosia for categories

  • Damage to inferior temporal cortex causes inability to name certain objects

    – Visual agnosia

    • Double dissociation for living/nonliving things


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Double Dissociation

  • Double dissociation for living/nonliving things


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Living vs. Non-living?

  • fMRI studies have shown different brain areas for living and non-living things

    • There is a lot of overlap for the two areas, though

    • Damage for categories is not well understood


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Category-specific neurons

  • Some neurons only respond to certain categories

    • A “Bill Clinton” neuron? Probably not.

    • A “Bill Clinton” neural circuit? More likely.


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Not categories, but continuum

  • There are probably no distinct face, house, chair, etc. areas in the brain

    • But everything’s not all stored in the same place, either

    • A mix of overlapping areas and distributed processes

    – Living vs. non-living is a big distinction


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