Components of Working Memory Updating

1
Components of Working Memory Updating

• Ullrich Ecker1, Stephan Lewandowsky1, Klaus
  Oberauer2, Abby Chee1
• 1 University of Western Australia, 2 University
  of Bristol (now University of Zurich)

2
Working memory updating (WMU)

• Working memory (WM) holds selected
  representations available for ongoing processing
• To maintain accurate representations of
  information that changes over time, WM content
  needs to be updated
• Example of WMU Keeping score in a tabletennis
  match
• Important for
• Mental arithmetic
• Language comprehension
• Navigating through traffic
• etc.

3
Aims of study (1)

• WMU not a unitary process
• WMU needs to allow for stability and flexibility
  at the same time (Kessler Meiran, 2008)
• If X is replaced with Y
• and then Z
• Unclear
• What are the component processes of WMU?

4
Aims of study (1)

• WMU not a unitary process
• WMU needs to allow for stability and flexibility
  at the same time (Kessler Meiran, 2008)
• If X is replaced with Y
• and then Z
• Unclear
• What are the component processes of WMU?

5
Aims of study (2)

• Individual differences research
• WMU specifically predicts higher cognitive
  abilities (e.g., fluid intelligence, Friedman et
  al., 2006)
• However, WMU often measured with tasks that
  conflate WMU WM capacity (WMC)
• Running memory task E G H R O F
• Unclear
• What is the relationship between WMU WMC?
• WMU WMC may rely on common WM abilities
  (Schmiedek et al., in press)
• WMU WMC may be distinct and dissociable (van
  Raalten et al., 2008 Radvansky Dijkstra, 2007)
  
• Predictive power of WMU over and above WMC?

6
Aims of study (2)

• Individual differences research
• WMU specifically predicts higher cognitive
  abilities (e.g., fluid intelligence, Friedman et
  al., 2006)
• However, WMU often measured with tasks that
  conflate WMU WM capacity (WMC)
• Running memory task E G H R O F T
• Unclear
• What is the relationship between WMU WMC?
• WMU WMC may rely on common WM abilities
  (Schmiedek et al., in press)
• WMU WMC may be distinct and dissociable (van
  Raalten et al., 2008 Radvansky Dijkstra, 2007)
  
• Predictive power of WMU over and above WMC?

7
The present study

• Decomposition of WMU into three distinct
  components
• Retrieval
• Transformation
• Substitution
• Implemented into a standard WMU paradigm
• Plus WMC battery
• 97 subjects

8
The present study

• Decomposition of WMU into three distinct
  components
• Retrieval
• Transformation
• Substitution
• Implemented into a standard WMU paradigm
• Plus WMC battery
• 97 subjects

actually, 5 more than the 20 initially expected
9
The present study

• Decomposition of WMU into three distinct
  components
• Retrieval
• Transformation
• Substitution
• Implemented into a standard WMU paradigm
• Plus WMC battery
• 97 subjects

actually, 5 more than initially expected
10
The present study

• Decomposition of WMU into three distinct
  components
• Retrieval
• Transformation
• Substitution
• Implemented into a standard WMU paradigm
• Plus WMC battery
• 97 subjects

actually, were expecting 25
11
Processes of WMU
12
The present study

• Decomposition of WMU into three distinct
  components
• Retrieval
• Transformation
• Substitution
• Implemented into a standard WMU paradigm (MU
  task, e.g., Oberauer, 2002)
• Plus WMC battery
• 97 subjects

13
Updating task Trial structure

• Encoding
• Remember 3 letters-in-frames
• Updating
• 6 steps
• Update individual frames (alphabet arithmetic)
• Remember and type result (? RT, accuracy)
• Finall recall (not analysed)
• To make sure subjects updated until the end,
  given predictable sequence
• All frames in random order

14
Design

• Updating
• 3 factors Retrieval, Transformation, Substitution
• fully crossed within-subjects

15
Updating conditions

• Pure Substitution
• type N, remember N F A

16
Updating conditions

• Pure Transformation
• type F, remember V F A

17
Updating conditions

• Pure Retrieval
• type A, remember V F A

18
Updating conditions

• All 3 combined
• type H, remember V H A

?2
19
Updating conditions

• Baseline condition
• type V, remember V F A

V
20
Updating conditions

• Retrieval plus Substitution
• type F, remember V F F

21
Updating conditions

• Transformation plus Retrieval
• type A, remember V F A

?0
22
Condition prompts at a glance
Assuming C is currently remembered
23
Sample trial

Im ready
24
Sample trial Initial encoding
B-Q-J
Encoding time 2 s
25
Sample trial Updating step 1
?1
C-Q-J
26
Sample trial Updating step 2
I2
C-Q-K
Note frame switch on every step
27
Sample trial Updating step 3
?
C-Q-K
Note frame switch on every step
28
Sample trial Updating step 4
S
S-Q-K
Note frame switch on every step
29
Sample trial Updating step 5
S-K-K
Note frame switch on every step
30
Sample trial Updating step 6
J1
S-K-K
Note frame switch on every step
31
Sample trial Final recall 1
?
K
Random order
32
Sample trial Final recall 2
?
S
Random order
33
Sample trial Final recall 3
?
K
Random order
34
Results
2 MODELS
35
Modelling

• Multilevel regression SEM
• Starting point
• Maximal parsimony Could there be independence?

36
Multilevel regression

• permits an aggregate analysis of data from all
  participants without confounding within- and
  between-subject variability

37
Multilevel regression (RT)

• Additional frame-switch in ? condition
• frame-switch cost (Garavan, 1998 483 ms) was
  subtracted from all ? RTs
• Transformations were represented by 3 dummy
  variables
• T1 1 (baseline)
• T2 2 (coded as RT increase relative to
  baseline T1)
• T0 0 (coded as RT decrease relative to
  baseline T1)
• UpdRT 1.30 .04R 1.20T1 .94T0 .58T2
  .30S
• Coefficient of discrimination r2(obs, fitted)
  across all 1197 data points .91
• Likelihood ratio tests Fit not improved by
  adding interactions ? Independence

38
Multilevel regression (Accuracy)

• Additional frame-switch in ? condition
• Additional frame-switch factor SW estimated
• Transformations were represented by 3 dummy
  variables
• T1 1 (baseline)
• T2 2 (coded as accuracy decrease relative to
  baseline T1)
• T0 0 (coded as accuracy increase relative to
  baseline T1)
• UpdAcc .99 .89R .91T1 1.07T0 .93T2
  .97S .97SW
• Coefficient of discrimination r2(obs, fitted)
  across all 1197 data points .76
• Likelihood ratio tests Fit not improved by
  adding interactions ? Independence

39
SEM

• SEM..
• is typically used to
• capture individual differences
• correlational dependencies between latent
  variables

40
SEM WMC measurement model
41
SEMRT model
Strong constraints that impose additive
structure All loadings fixed to 1 all manifest
intercepts and error means fixed to 0
RT.1
e1
S
RT.2
e2
RT.3
e3
T
e4
RT.4
RT.5
e5
R
RT.6
e6
e7
RT.7
GenRT
e8
RT.8
42
SEMRT model
Strong constraints that impose additive
structure All loadings fixed to 1 all manifest
intercepts and error means fixed to 0
RT.1
e1
S
RT.2
e2
RT.3
e3
T
e4
RT.4
RT.5
e5
R
RT.6
e6
e7
RT.7
GenRT
e8
RT.8
43
SEMRT model
Relaxed additivity constraints Fixed e3 to
frame-switch estimate (Garavan, 1998) freely
estimated TRT.5 weight allowed error covariation
RT.1
e1
S
RT.2
e2
e3
RT.3
T
.48
e4
RT.4
RT.5
e5
R
Condition 3 ? (2nd frame switch) Condition 5 ?0
RT.6
e6
e7
RT.7
GenRT
e8
RT.8
44
SEMRT model
?2(47) 87.23 CFI .94 RMSEA .094 SRMR
.084
RT.1
e1
S
.34
RT.2
e2
e3
RT.3
T
.48
.52
1.47
e4
RT.4
.20
RT.5
e5
R
.48
.03
RT.6
e6
.34
e7
RT.7
GenRT
-.21
1.28
e8
RT.8
45
SEMRT model
Methodological advancement Simultaneous
estimation of latent weights and means
RT.1
e1
S
.34
RT.2
e2
e3
RT.3
T
.48
.52
1.47
e4
RT.4
.20
RT.5
e5
R
.48
.03
RT.6
e6
.34
e7
RT.7
GenRT
-.21
1.28
e8
RT.8
46
Estimated ? observed means

• Estimated means can be used to accurately
  re-calculate observed experimental data
• For example, in case of T S
• 1.28 1.47 .34 3.08 3.01 s
• Median deviation 35 ms

47
SEMAcc model
?2(45) 68.01 CFI .95 RMSEA .073 SRMR
.048
Acc.1
S
.02
Acc.2
Acc.3
T
-.46
.46
.08
Acc.4
-.11
-.56
.84
Acc.5
R
.11
Acc.6
-.33
Acc.7
GenAcc
.25
.97
Acc.8
48
Summary

• Data suggest that R, T, and S
• make orthogonal and additive contributions to WMU
  task performance
• probably run serially R?T?S
• Transformation
• strong effect on both WMU accuracy and RT
• transformation accuracy covaries with WMC
• No actual retrieval process operating (no effect
  on RT)
• subjects likely keep all 3 letters in a region
  of direct access (Oberauer, 2002)
• Accuracy effect of retrieval
• direct access vs. integrity / accuracy of
  representation
• covaries with WMC
• Substitution
• relatively small but significant effect on both
  WMU accuracy and RT
• the only factor with own variance separate from
  WMC

49
Conclusion

• Only substitution is unique to WMU.
• Previous reports that WMU specifically predicts
  higher cognitive abilities (e.g., fluid
  intelligence, Friedman et al., 2006) are likely
  due to a conflation of WMU and WMC factors.
• Studies that reported dissociation of WMU and WMC
  (e.g., in schizophrenia, van Raalten et al.,
  2008) used WMU tasks that relied heavily on
  substitution.
• To-be-tested Are substitution skills in
  themselves useful in predicting higher cognitive
  abilities?

50
Thank you!
Steve Lewandowsky Steve Lewandowski
Klaus Oberauer Abby Chee
Toby Danny
51
References

• Chen, T., Li, D. (2007). The roles of working
  memory updating and processing speed in mediating
  age-related differences in fluid intelligence.
  Aging, Neuropsychology, and Cognition, 14,
  631-646.
• Friedman, N. P., Miyake, A., Corley, R. P.,
  Young, S. E., DeFries, J. C., Hewitt, J. K.
  (2006). Not all executive functions are related
  to intelligence. Psychological Science, 17,
  172-179.
• Galletly, C. A., MacFarlane, A. S., Clark, C.
  R. (2007). Impaired updating of working memory in
  schizophrenia. International Journal of
  Psychophysiology, 63, 265-274.
• Garavan, H. (1998). Serial attention within
  working memory. Memory Cognition, 26, 263-276.
• Kessler, Y., Meiran, N. (2008). Two dissociable
  updating processes in working memory. Journal of
  Experimental Psychology Learning, Memory, and
  Cognition, 34, 1339-1348.
• Kirchner, W. K. (1958). Age differences in short
  term retention of rapidly changing information.
  Journal of Experimental Psychology, 55, 352-358.
• McElree, B. (2001). Working memory and focal
  attention. Journal of Experimental Psychology
  Learning, Memory, and Cognition, 27, 817-835.
• Miyake, A., Friedman, N. P., Emerson, M. J.,
  Witzki, A. H., Howerter, A., Wager, T. D.
  (2000). The unity and diversity of executive
  functions and their contributions to complex
  frontal lobe tasks A latent variable analysis.
  Cognitive Psychology, 41, 49-100.
• Morris, N., Jones, D. M. (1990). Memory
  updating in working memory The role of the
  central executive. British Journal of Psychology,
  81, 111-121.
• Oberauer, K. (2002). Access to information in
  working memory Exploring the focus of attention.
  Journal of Experimental Psychology Learning,
  Memory, and Cognition, 28, 411-421.
• Oberauer, K., Süss, H.-M., Schulze, R., Wilhelm,
  O., Wittmann, W. W. (2000). Working memory
  capacityfacets of a cognitive ability construct.
  Personality and Individual Differences, 29,
  1017-1045
• Radvansky, G. A., Dijkstra, K. (2007). Aging
  and situation model processing. Psychonomic
  Bulletin Review, 14, 1027-1042.
• Salthouse, T. A., Babcock, R. L., Shaw, R. J.
  (1991). Effects of adult age on structural and
  operational capacities in working memory.
  Psychology and Aging, 6, 118-127.
• Schmiedek, F., Hildebrandt, A., Lovden, M.,
  Wilhelm, O., Lindenberger, U. (in press).
  Complex span versus updating tasks of working
  memory The gap is not that deep. Journal of
  Experimental Psychology Learning, Memory, and
  Cognition.
• Van Raalten, T. R., Ramsey, N. F., Jansma, J. M.,
  Jager, G., Kahn, R. S. (2008). Automatization
  and working memory capacity in schizophrenia.
  Schizophrenia Research, 100, 161-171.
• Yntema, D. B. (1963). Keeping track of several
  things at once. Human Factors, 5, 7-17.

52
Oberauers WM model (2002)