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Working Memory: The Feature Model. Presented by: Umer Fareed. Outline. Basic Concept of Working Memory (WM) Three Component Model of WM Working Memory Capacity The Feature Model Feature Model Operation Simulations using Feature Model Pros and Cons of Feature Model Conclusion.

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working memory the feature model

Working Memory:The Feature Model

Presented by: Umer Fareed

outline
Outline

Basic Concept of Working Memory (WM)

Three Component Model of WM

Working Memory Capacity

The Feature Model

Feature Model Operation

Simulations using Feature Model

Prosand Cons of Feature Model

Conclusion

basic concept of working memory
Basic Concept of Working Memory

A dedicated system that maintains and stores information in the short term

Supports human thought processes by providing an interface between perception, long-term memory and action

Information decays quickly unless actively rehearsed

Sometimes referred to as short term memory

slide4

Three Component Model of WM

Proposed by Baddeley and Hitch

Comprises a control (supervisory) system and two storage (slave) systems;

The Central Executive

The Visuospatial Sketchpad

The Phonological Loop

working memory capacity
Working Memory Capacity
  • Measures the extent to which a person can control and sustain attention in face of interference and distraction
  • Ability to activate items in memory and to ignore or disregard interfering items
  • Correlate highly with many cognitive tasks including reading comprehension, spelling, vocabulary learning, writing and reasoning
  • Not about storage and processing but is about retention over a period in which there is distraction from stored information
working memory capacity1
Working Memory Capacity
  • Measured by a variety of tasks, commonly used are Reading Span and Operation Span
  • Reading span includes reading of a number of sentences and trying to recall the last word of each sentence in their correct order
  • In operation span, subject performs mathematical calculations and word reading and then asked to recall the words in order
  • Larger WM capacity means better ignorance to irrelevant or distracting information
the feature model
The Feature Model
  • Proposed by Nairne in 1988
  • Computational model of Serial Recall guided by a set of “Primary Memory” (PM) cues of varying effectiveness in identifying the target item from a search set defined within “Secondary Memory” (SM)
  • Items are assumed to be represented as a set of features
  • A simplifying assumption is that only immediately adjacent items interfere
the feature model1
The Feature Model
  • Items in PM and SM comprise sets of internally-generated modality-independent (MI) and externally generated modality-dependent (MD) features organized as row vectors
  • MD features represent the conditions of presentation whereas MI features represent the nature of the item itself
  • For each partially degraded trace in PM, the subject tries to select an appropriate recall candidate by comparing degraded traces with intact traces in SM search set
slide9

The Feature Model

Serial Position Function: Effect of change in MD features

while keeping MI features constant

the feature model2
The Feature Model

Item Presented Primary Memory

  • [ +1 -1 +1 +1, +1 -1 +1 -1] [ +1 -1 +1 +1, +1 -1 +1 -1]

[ +1 0 0 +1, +1 0 +1 0]

  • [ -1 -1 +1 -1, -1 -1 -1 -1] [ -1 -1 +1 -1, -1 -1 -1 -1]

[ +1 0 0 +1, +1 0 +1 0]

[ -1 0 +1 0, -1 -1 0 -1]

  • [ +1 -1 -1 -1, +1 +1 -1 +1] [ +1 -1 +1 +1, +1 -1 +1 -1]

[+1 0 0 +1, +1 0 +1 0]

[ -1 0 +1 0, -1 -1 0 -1]

[+1 -1 0 0, +1 0 -1 0]

4 [ -1 +1 -1 -1, -1 +1 +1 +1] [ -1 +1 -1 -1, -1 +1 +1 +1]

Example : Four Items are presented

f eature model operation
Feature Model Operation
  • Each to-be-remembered item is made up of features
  • Assume there are 20 MI features and 20 MD features, each of which is randomly set to a value of 1 or -1
  • These 40 features represent the first item presented, second item presented also contains 40 features
  • Item2 features overwrite Item1 features due to retroactive interference
f eature model operation1
Feature Model Operation
  • If MI feature no 5 of item2 has the same value as MI feature no 5 of item1, then original value of item1’s feature 5 is overwritten with a value of 0
  • Then item3 and item4 are presented, the final item presented is not followed by any external information but by rehearsal
  • At end of list presentation, primary memory contains trace of each item presented but these traces are degraded due to certain features being overwritten
f eature model operation2
Feature Model Operation
  • Only the modality-independent features of final item are overwritten whereas its modality dependent features remain intact
  • The subject then tries to match each primary item with an intact secondary memory trace
  • Beginning with first item, each PM item is compared with SM items in the comparison set, SM item with fewest mismatching features will be selected as a candidate for recall
slide14

Simulations using Feature Model

  • The Serial Position Function
  • Recall generally declines over serial positions due to output interference
  • If feature x of item n+1 is identical to feature x of item n, then the value representing feature x of item n is lost and cannot be used as a recall cue
  • The recency effect seen in serial recall of auditory items arises because the MD features of the last list item are not overwritten whereas recency effect is not seen in visual presentation
slide15

Simulations using Feature Model

  • The Suffix Effect
  • Occurs whenever modality-dependent features of the final item are overwritten
  • Speech suffix significantly reduces serial recall of auditory presented lists overwriting modality-dependent features of list of speech items
  • Visual suffixes have little or no effect on auditory list items
simulations using feature model
Simulations using Feature Model
  • Effects of Irrelevant Speech and Articulatory Suppression
  • Immediate serial recall of verbal material is reduced significantly if participants are exposed to irrelevant speech while studying the list items
  • If the target item is presented auditorily then irrelevant speech reduce serial recall performance under articulatory suppression
  • Feature adoption occurs when some of the MI features in PM are replace by features of word that is articulated
simulations using feature model1
Simulations using Feature Model
  • Effects of Irrelevant Speech, Phonological Similarity and Word Length
  • With visual presentation of list items, the feature model predicts that there will be no effect of phonological similarity under conditions of irrelevant speech
  • Recall of visually presented material depends heavily on MI features, therefore, phonological similarity will produce no further damage to them
  • World length effect is eliminated by articulating suppression
simulations using feature model2
Simulations using Feature Model

Suffix Effect and Articulatory Suppression Effect

pros cons of feature model
Pros & Cons of Feature Model
  • Can address effects at different serial positions, including modality and suffix effects observable with auditory stimuli
  • Readily explains the absence of time-based word-length effects whereas working memory (Baddeley) has to predict that they will occur
  • Provides precise and unambiguous predictions as numbers used are easy to compare
pros cons of feature model1
Pros & Cons of Feature Model
  • No role of time in the Feature Model
  • Only addresses serial recall not free recall or recognition
  • Recall of single list implemented in the model whereas most experiments ask subjects to recall multiple lists
conclusion
Conclusion
  • Differs from other working memory models as it does not use the concept of decay, rather interference degrades memory performance
  • Accounts for suffix effect remains even when subjects engage in articulatory suppression
  • Directly addresses modality effects
questions
Questions
  • How has working memory capacity been found to affect dichotic listening?
  • Identify two shortcomings of Nairne’s feature model?
  • Identify two advantages that Nairne’s feature model enjoys over Baddeley’s working memory model?