BioCog 7AB long-term memory II Flashcards

1
Q

recognition triggers…

A
  • …response selection
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2
Q

categorization

A
  • neccessary and sufficient properties

- can be blurry around the edges

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3
Q

prototype

A
  • most average or central object
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4
Q

graded representation

A

grade of similarity to the prototype

- tested on rating scale

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5
Q

sentece verification

A
  • to test representativeness of an object

- measured by reaction time

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6
Q

priming

A

(here)

  • participants have to decide whether two colours are the same
  • hear “green”
  • are quicker to decide for green circles
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7
Q

tendency towards prototype

A
  • Bartlett 1932
  • people see mask
  • have to draw it from memory at different timepoints
  • tend to draw a face rather than the mask over time
  • also explained by parellel ditributed porcessing
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8
Q

advantages prototype approach

A
  • efficient storage
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9
Q

disadvantage prototype approach

A
  • are prototypes stores seperatly?
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10
Q

exemplar approach

A
  • we remember every object we ever experienced

- prototypes are only genereted when asked to do so

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11
Q

advantages exemplar approach

A
  • no categorization problem

- everything is remembered

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12
Q

disadvantages exemplar approach

A
  • required huge storage capacity
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13
Q

evidence for structure of semantic memory

A
  • one recalled fact often triggers mulptiple others

- mistakes are often near-misses

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14
Q

hierarchical network

A
  • pyramide like structure
  • every concept has all properties of the concept above it and adds one extra thing
  • probably false, cannot deal with typicality problem
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15
Q

multiple trace model

A
  • a memory trace is created with each interaction
  • forgetting by traces fading
  • recognition by collective allocation of old traces
  • explains typicality effect by exposure
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16
Q

semantization of episodic memory

A
  • recall that you did something but not your actual experiences
  • too many memory traces firing in concert drowning the episodic memory
17
Q

automatization of memory

A
  • by obligatory retrieval of many traces
18
Q

parallel distributed coding

A
  • input units = sensory neurons
  • output units = motor neurons
  • connected by hidden units = interneurons
  • biologically inspired
  • weighted and adjustable connections
  • NO local representation enabling “graceful degradation”
  • explains generalization and why we learn so effectively