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STM LTM

STM LTM. STM LTM. STM LTM. Retrieval. Storage; Consolidation. Case of H. M. Hippocampus. Not loss of LTM, Not loss of STM, but loss of consolidation. Tolman: Cognitive Maps. G. St. Olton: Radial Arm Maze. How?. Intramaze (e.g., smell food, scratch on floor)

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STM LTM

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  1. STM LTM STM LTM 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 G St

  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 > 1 cue Hippocampus and Configural learning: If Hippocampal damage, can do: discrimination, but not configual A—US B—US AB— A—US 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. 100 Percent Correct Trials 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 Difference = Surprise External Internal Vmax V Get Expect Sensory detection and activation Retrieval of a node

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