Mindmap
Multi-Store Model

Mind map: Multi-Store Model
Inhalt der Mindmap
Multi-Store Model
Atkinson & Shiffrin
Coding in STM & LTM
- Baddeley
- 4 groups of ppts, each given a different word list
- Condition 1: acoustically similar list (cat, cab, can)
- Condition 2: acoustically dissimilar list (pit, few, cow)
- Condition 3: semantically similar list (great, large, big)
- Condition 4: semantically dissimilar list (good, huge, hot)
- STM
- Ppts shown original word list & asked to recall the list immediately
- LTM
- Ppts asked to recall original list after 20 mins
- Findings
- Tended to do worse on semantically similar list
Capacity in STM
- Jacobs
- Ppts given strings of unrelated digits that increase by one digit each time
- Digit span measured when ppts could no longer recall digits in the correct sequence
- Mean digit span was 9.3, mean span for letters was 7.4
- Capacity limited to 7 give or take 2 items
- Miller
- Digit span can be increased by 'chunking'
- Jacobs
Duration in STM
- Peterson & Peterson
- Each ppt took part in 8 trials
- Ppts shown a nonsense trigram (YCG) and then immediately after a 3 digit number
- Ppt told to count backwards from their 3 digit number until told to stop (prevented rehearsal)
- On each trial they were told to stop after a different amount of time (3 secs, 6, 9, 12 etc) - called the retention interval
- Found ppts were 90% correct over 3 seconds, but this diminished over time
- Only 2% ppts correctly recalled trigram after 18 secs
- Suggests STM has duration between approx. 18-30 secs
Duration in LTM
- Bahrick
- 392 American high school grads tested on memory of former classmates
- Condition 1: Matching names to photos
- Condition 2: Recalling names with no photo cue
- Found when tested after 15 years ppts were 90% accurate in naming classmates
- After 48 years recognition dropped to 70%
- Shows LTM can last a long time
Informationen
- Beschreibung
- A2 Psychology (P1 Memory) Mindmap am Multi-Store Model, erstellt von Abigail Taylor am 06/05/2018.
- Erstellt von
- Abigail Taylor
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