STMLTM - PowerPoint PPT Presentation

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STMLTM

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3. Spread of activation 'Neural networks' and prototype ... 4 legs. 3. Spread of activation. tail. fur. ears. 4 legs. Learning rule: Delta Rule. External input ... – PowerPoint PPT presentation

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Title: STMLTM


1
STM LTM
Retrieval
Storage Consolidation
Case of H. M.
Hippocampus
Not loss of LTM, Not loss of STM, but loss of
consolidation
2
Tolman Cognitive Maps
3
Olton Radial Arm Maze
How?
Intramaze (e.g., smell food, scratch on
floor) Extramaze (stimuli around the maze)
4
Choose 4
Then rotate maze




5
Which will they visit?
Go to space, not arm
Use extramaze cues




6
Morris Water Maze
Hence Spatial, and not any single cue.
7
Morris Water Maze
Further tests Probe trial
8
Role of Hippocampus in Spatial Learning
Hippocampal Damage Still get a little faster at
finding platform
Probe Trial Shows no spatial strategy
9
Role of Hippocampus in Spatial Learning
If visible platform, do as well as normal
Suggests role of hippocampus specifically in the
spatial aspects of the water maze task
10
Configural Learning Hypothesis
Spatial Learning is type of configural
learning response is based on gt 1 cue
Hippocampus and Configural learning
If Hippocampal damage, can do
but not configual
discrimination,
AUS BUS AB
AUS B
11
Integrating across experiences
Rule learning (learning set) Strategies
Win-stay
Win-shift
Strategy is then applied to new situations More
rapid learning of new (same strategy) task.
12
Hypothesis Testing
New task Try different hypotheses
(strategies) If wrong, throw it out If correct,
stick with it
Leads to step function learning curves looks
like insight
13
Concept Formation
Concept Category- a set of objects/events
having common features/relationships
Abstraction How a concept is formed.
Abstract (summarize) common features.
Form prototype (composite average) based on
experiences with specific exemplars (examples)
14
Neural networks and prototype formation
Common assumptions 1. Universal connectivity
(note preparedness perhaps not universal)
2. Contiguity
3. Spread of activation
15
Neural networks and prototype formation
Common assumptions 1. Universal connectivity
2. Contiguity
3. Spread of activation
4 legs
ears
tail
fur
16
Learning rule Delta Rule
4 legs
ears
tail
External input
fur
Internal input
17
Learning rule Delta Rule
Sensory neuron
Sensory neuron
External input
T
E
Internal input
? synaptic efficacy
c (Ext - Int)
? Int
REM ?V c (Vmax - V)
18
Similar Ideas
External Internal
Vmax V Get Expect
Sensory detection and activation
Retrieval of a node
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