Connectionism and models of
memory and amnesia
Jaap Murre
University of Amsterdam
jaap@murre.com
http://www.neuromod.org/c...
The French neurologist Ribot
discovered more than 100 years
ago that in retrograde amnesia
one tends to loose recent
memor...
Overview
• Catastrophic interference and hypertransfer
• Brief review of neuroanatomy
• Outline of the TraceLink model
• S...
Catastrophic interference
• Learning new patterns in backpropation will
overwrite all existing patterns
• Rehearsal is nec...
Osgood surface (1949)
• Paired-associates in lists A and B will
interfere strongly if the stimuli are similar
but the resp...
Learned responses
Stimuli Target responses (after three learning
trials)
Phase 1: Learning list A
rist munk twup
gork gomp...
Problems with sequential
learning in backpropagation
• Reason 1: Strongly overlapping hidden-
layer representations
• Reme...
Problems with sequential
learning in backpropagation
• Reason 2: Satisfying only immediate
learning constraints
• Remedy 2...
Final remarks on sequential
learning
• Two-layer ‘backpropagation’ networks do
show plausible forgetting
• Other learning ...
Models of amnesia and memory
in the brain
• TraceLink
• Point-process model
• Chain-development model
Neuroanatomy of amnesia
• Hippocampus
• Adjacent areas such as entorhinal cortex
and parahippocampal cortex
• Basal forebr...
The position of the hippocampus
in the brain
Hippocampal connections
Hippocampus
Entorhinal cortex
7a
36 TF TH 46
7b
3aP-IP-BV1M 3b
Visual
areas
Somato-
sensory
and motor
areas
To and from se...
Trace-Link model: structure
System 1: Trace system
• Function: Substrate for bulk storage of
memories, ‘association machine’
• Corresponds roughly to ...
System 2: Link system
• Function: Initial ‘scaffold’ for episodes
• Corresponds roughly to hippocampus and
certain tempora...
System 3: Modulatory system
• Function: Control of plasticity
• Involves at least parts of the hippocampus,
amygdala, forn...
Stages in episodic learning
Dreaming and consolidation of
memory
• Theory by Francis Crick and Graeme
Mitchison (1983)
• Main problem: Overloading of ...
Dreaming and memory
consolidation
• When should this reverse learning take
place?
• During REM sleep
– Normal input is dea...
Consolidation may also
strengthen memory
• This may occur during deep sleep (as
opposed to REM sleep)
• Both hypothetical ...
Recent data by Matt Wilson and
Bruce McNaughton (1994)
• 120 neurons in rat hippocampus
• PRE: Slow-wave sleep before bein...
Wilson en McNaughton Data
• PRE: Slow-wave sleep before being in the experimental environment
(cage)
• RUN: During experim...
Some important characteristics of
amnesia
• Anterograde amnesia (AA)
– Implicit memory preserved
• Retrograde amnesia (RA)...
x
retrograde
amnesia
anterograde
amnesia
lesion presentpast
0
20
40
60
80
100
Amnesie patient
Normal forgetting
An example of retrograde
amnesia patient data
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
75-'8465-'7455-'6445-'5435-'44
Controls (n...
Retrograde amnesia
• Primary cause: loss of links
• Ribot gradients
• Shrinkage
Anterograde amnesia
• Primary cause: loss of modulatory system
• Secondary cause: loss of links
• Preserved implicit
memory
Connectionist implementation
of the TraceLink model
With Martijn Meeter from the
University of Amsterdam
Some details of the model
• 42 link nodes, 200 trace nodes
• for each pattern
– 7 nodes are active in the link system
– 10...
How the simulations work:
One simulated ‘day’
• A new pattern is activated
• The pattern is learned
• Because of low learn...
(Patient data)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
75-'84 65-'74 55-'64 45-'54 35-'44
Controls (n=16)
Korsakoff's (n=6)
Alzh...
A simulation with TraceLink
R2
= 0.932
R2
= 0.922
0
0.25
0.5
0.75
1
0 5 10 15
Control
Lesion
Frequency of consolidation of
patterns over time
0
0.5
1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Strongly and weakly encoded
patterns
• Mixture of weak, middle and strong
patterns
• Strong patterns had a higher learning...
0
0.5
1
0 5 10 15
No variance
strong variance
0
0.5
1
0 5 10 15
No variance
Strong variance
0
0.5
1
0 5 10 15
Weak pattern...
Other simulations
• Focal retrograde amnesia
• Levels of processing
• Transient Global Amnesia (TGA)
• Semantic dementia
•...
The Memory Chain Model: a
very abstract neural network
With Antonio Chessa from the
University of Amsterdam
Abstracting TraceLink (level 1)
• Model formulated within the mathematical
framework of point processes
• Generalizes Trac...
Learning and forgetting as a
stochastic process: 1-store example
• A recall cue (e.g., a face) may access
different aspect...
Neural
network
interpretation
Jo Brand
Single-store point process
• The expected number of points in the cue
area after learning is called µ
• This µ is directly...
Some aspects of the point process
model
• Model of simultaneous learning and
forgetting
• Clear relationship between signa...
Forgetting curve
( ) 1
at
e
p t e µ −
−
= −
If we need to find at least one point we obtain
the following curve (one-store...
Probe-digit experiment (Waugh & Norman 1965)
R
2
= 0.7295
0
20
40
60
80
100
0 2 4 6 8 10 12
Time (s)
Retention(%)Example: ...
Multi-store generalization
• Information about the current event passes
through many neural ‘stores’
• The retina, for exa...
General principles of the PPM
multi-store model
• A small part of the information is passed to
the next store before it de...
Two-store model
• While neural store 1 is decaying (with rate
a1) it induces new points (representations) in
store 2
• Ind...
Example of two neural stores
• Store 1: firing neural groups
• Store 2: synaptic connections between the
neural groups
• O...
Decomposition of intensity µ(t)
into encoding, storage, and
retrieval
( )
( ) 1 t
p t e µ−
= −
{ { {1
storage retrievalenc...
The contributions of S individual
neural stores can simply be added
{ }12... 1 1 1 2
1 2
( ) ( ) ( ) ( ) ... ( )
( ) ( ) ....
Two-store model retention
function: r12(t)= r1(t)+ r2(t)
( )1 21 2
2 1 2
2 1
( ) ( ) a t a t
r t r t e e
a a
µ µ
µ − −
= =...
The retention function for the
third-store of a three-store model
3 31 2
3 1 3
1 2 3
2 1 3 1 3 2
( ) ( )
a t a ta t a t
r ...
Recall probability p(t) as a function
of different learning times l
ν is the learning rate
l is the learning time
r(t) is ...
Saturation assumption
1
max
1
max
Simple learning was:
With an upper ceiling on
intensity we obtain:
( )
1
l
r
r t
l
r
µ ν...
Hellyer (1962). Recall as a function
of 1, 2, 4 and 8 presentations
0
0.2
0.4
0.6
0.8
1
0 10 20 30
Time (s)
Recallprobabil...
Amnesia: animal data
Retrograde amnesia
Cho & Kesner (1996). (mice)
R2
=0.96
b.
0
0.25
0.5
0.75
1
0 10 20 30 40 50
Time (days)
Recallprobability
Summary of animal data
a.
0
0.25
0.5
0.75
1
0 20 40 60
Time (days)
Recallprobability
b.
0
0.25
0.5
0.75
1
0 10 20 30 40 50...
Frankland et al. (2001) study
• α-CaMKB-dependent plasticity (in
neocortex) switched off in knock-out mice
• No LTP measur...
Forgetting after 3 shocks, using
three parameters
Freezing after 3 shocks
0
0.25
0.5
0.75
1
0 10 20 30 40 50
Retention del...
Using the same three parameters and
a massed-learning correction.
Freezing after 8 shocks
0
0.25
0.5
0.75
1
0 5 10 15
Rete...
Controls receive 1 shock, experimental
animals 3 shocks (no new free parameters).
Freezing after weak learning
0
0.25
0.5
...
Repeated learning for experimental
animals (no new free parameters)
Freezing after repeated learning
0
0.25
0.5
0.75
0 1 2...
Summary of ‘cortical amnesia’.
Using only 4 parameters for all
curves (R2
= 0.976).Freezing after 3 shocks
0
0.25
0.5
0.75...
Application to advertising data
• Advertisements as learning trials
• Zielske (1958): recall of printed
advertisements
• S...
Zielske 1958
• Printed advertisements were mailed
repeatedly to randomly selected house
wives
• After a week they were cal...
Zielske Data: Performance of weekly
advertising (R2
=87%, 1-store model)
0
20
40
60
80
100
0 10 20 30 40 50 60
Week
Recall...
Zielske Data: Performance of 4-weekly
advertising (R2
is 85%, 1-store model)
0
20
40
60
80
100
0 10 20 30 40 50 60
Week
Re...
SPOT data
• Research carried out by SPOT
• 43 brands were followed for half a year
• 50 phone calls per week per brand
• G...
IA1 IA3 IB10 IIA6 IIA7 IIA8
IB2 IB3 IB4 IIB2 IIC1 IIC2
IB5 IB8 IB9 IIC5 IIC6 IIC7
IC1 IC2 IC4 IID1 IID2 IID3
ID1 ID2 ID3 I...
Memory (impact) as a function of
advertising
0
40
80
120
160
200
1 6 11 16 21
0
10
20
30
40
Weeks
GRPs
Memory (impact)
GRP...
Sometimes, advertising has no
effect…
0
40
80
120
160
200
1 6 11 16 21
0
10
20
30
40
So, how is advertising forgotten?
0
40
80
120
160
200
1 6 11 16 21
0
10
20
30
40
SPOT brand with 1-store model
0
20
40
60
80
100
1 6 11 16 21 26
Week
GRPs
0
5
10
15
20
25
30
Impact
Week
GRP
Impact data
M...
High-learning brand, medium forgetting
Budget = 900 GRPs
Average impact = 26
0
20
40
60
80
100
120
140
1 5 9 13 17 21 25
W...
Optimized schedule for the same brand:
same GRPs but 7% impact improvement
Budget = 900 GRPs
Average impact = 28
0
20
40
6...
Conclusion
• Advertisements are learning trials
• Their learning and forgetting can be
described by our model
• If a good ...
Concluding remarks
• Given that the brain is exceedingly
complex, we need models at various levels
of abstraction to aid o...
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Sensory Humunculus

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  • Sensory Humunculus

    1. 1. Connectionism and models of memory and amnesia Jaap Murre University of Amsterdam jaap@murre.com http://www.neuromod.org/courses/kernthema2004
    2. 2. The French neurologist Ribot discovered more than 100 years ago that in retrograde amnesia one tends to loose recent memories Memory loss gradients in RA are called Ribot gradients
    3. 3. Overview • Catastrophic interference and hypertransfer • Brief review of neuroanatomy • Outline of the TraceLink model • Some simulation results of neural network model, focussing on retrograde amnesia • Recent work: – Mathematical point-process model – Detailed, more biological, neural network model • Concluding remarks
    4. 4. Catastrophic interference • Learning new patterns in backpropation will overwrite all existing patterns • Rehearsal is necessary • McCloskey and Cohen (1989), Ratcliff (1990) • This is not psychologically plausible
    5. 5. Osgood surface (1949) • Paired-associates in lists A and B will interfere strongly if the stimuli are similar but the responses vary • If stimuli are different, little interference (i.e., forgetting) occurs • Backpropagation also shows odd behavior if stimuli vary but responses are similar in lists A and B (hypertransfer)
    6. 6. Learned responses Stimuli Target responses (after three learning trials) Phase 1: Learning list A rist munk twup gork gomp toup wemp twub twup Phase 2: Learning interfering list B (after five learning trials) yupe munk muup maws gomp twup drin twub twub Phase 3: Retesting on list A rist munk goub gork gomp tomp Hypertransfer
    7. 7. Problems with sequential learning in backpropagation • Reason 1: Strongly overlapping hidden- layer representations • Remedy 1: reduce the hidden-layer representations – French, Murre (semi-distributed representations)
    8. 8. Problems with sequential learning in backpropagation • Reason 2: Satisfying only immediate learning constraints • Remedy 2: Rehearse some old patterns, when learning new ones – Murre (1992): random rehearsal – McClelland, McNaughton and O’Reilly (1995): interleaved learning
    9. 9. Final remarks on sequential learning • Two-layer ‘backpropagation’ networks do show plausible forgetting • Other learning networks do not exhibit catastrophic interference: ART, CALM, Kohonen Maps, etc. • It is not a necessary condition of learning neural networks; it mainly affects backpropagation • The brain does not do backpropagation and therefore does not suffer from this problem
    10. 10. Models of amnesia and memory in the brain • TraceLink • Point-process model • Chain-development model
    11. 11. Neuroanatomy of amnesia • Hippocampus • Adjacent areas such as entorhinal cortex and parahippocampal cortex • Basal forebrain nuclei • Diencephalon
    12. 12. The position of the hippocampus in the brain
    13. 13. Hippocampal connections
    14. 14. Hippocampus Entorhinal cortex 7a 36 TF TH 46 7b 3aP-IP-BV1M 3b Visual areas Somato- sensory and motor areas To and from sensory organs, via subcortical pathways Hippocampus Entorhinal cortex Unimodal and polymodal association areas (frontal, temporal, and parietal lobes) Parahippocampal cortex Perirhinal cortex (b)(a) Hippocampus has an excellent overview of the entire cortex
    15. 15. Trace-Link model: structure
    16. 16. System 1: Trace system • Function: Substrate for bulk storage of memories, ‘association machine’ • Corresponds roughly to neocortex
    17. 17. System 2: Link system • Function: Initial ‘scaffold’ for episodes • Corresponds roughly to hippocampus and certain temporal and perhaps frontal areas
    18. 18. System 3: Modulatory system • Function: Control of plasticity • Involves at least parts of the hippocampus, amygdala, fornix, and certain nuclei in the basal forebrain and in the brain stem
    19. 19. Stages in episodic learning
    20. 20. Dreaming and consolidation of memory • Theory by Francis Crick and Graeme Mitchison (1983) • Main problem: Overloading of memory • Solution: Reverse learning leads to removal of ‘obsessions’ “We dream in order to forget”
    21. 21. Dreaming and memory consolidation • When should this reverse learning take place? • During REM sleep – Normal input is deactivated – Semi-random activations from the brain stem – REM sleep may have lively hallucinations
    22. 22. Consolidation may also strengthen memory • This may occur during deep sleep (as opposed to REM sleep) • Both hypothetical processes may work together to achieve an increase in the definition of representations in the cortex
    23. 23. Recent data by Matt Wilson and Bruce McNaughton (1994) • 120 neurons in rat hippocampus • PRE: Slow-wave sleep before being in the experimental environment (cage) • RUN: During experimental environment • POST: Slow-wave sleep after having been in the experimental environment
    24. 24. Wilson en McNaughton Data • PRE: Slow-wave sleep before being in the experimental environment (cage) • RUN: During experimental environment • POST: Slow-wave sleep after having been in the experimental environment
    25. 25. Some important characteristics of amnesia • Anterograde amnesia (AA) – Implicit memory preserved • Retrograde amnesia (RA) – Ribot gradients • Pattern of correlations between AA and RA – No perfect correlation between AA and RA
    26. 26. x retrograde amnesia anterograde amnesia lesion presentpast 0 20 40 60 80 100 Amnesie patient Normal forgetting
    27. 27. An example of retrograde amnesia patient data 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 75-'8465-'7455-'6445-'5435-'44 Controls (n=16) Korsakoff's (n=6) Alzheimer's (n=8) Kopelman (1989) News events test
    28. 28. Retrograde amnesia • Primary cause: loss of links • Ribot gradients • Shrinkage
    29. 29. Anterograde amnesia • Primary cause: loss of modulatory system • Secondary cause: loss of links • Preserved implicit memory
    30. 30. Connectionist implementation of the TraceLink model With Martijn Meeter from the University of Amsterdam
    31. 31. Some details of the model • 42 link nodes, 200 trace nodes • for each pattern – 7 nodes are active in the link system – 10 nodes in the trace system • Trace system has lower learning rate that the link system
    32. 32. How the simulations work: One simulated ‘day’ • A new pattern is activated • The pattern is learned • Because of low learning rate, the pattern is not well encoded at first in the trace system • A period of ‘simulated dreaming’ follows – Nodes are activated randomly by the model – This random activity causes recall of a pattern – A recalled pattern is than learned extra
    33. 33. (Patient data) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 75-'84 65-'74 55-'64 45-'54 35-'44 Controls (n=16) Korsakoff's (n=6) Alzheimer's (n=8) Kopelman (1989) News events test
    34. 34. A simulation with TraceLink R2 = 0.932 R2 = 0.922 0 0.25 0.5 0.75 1 0 5 10 15 Control Lesion
    35. 35. Frequency of consolidation of patterns over time 0 0.5 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
    36. 36. Strongly and weakly encoded patterns • Mixture of weak, middle and strong patterns • Strong patterns had a higher learning parameter (cf. longer learning time)
    37. 37. 0 0.5 1 0 5 10 15 No variance strong variance 0 0.5 1 0 5 10 15 No variance Strong variance 0 0.5 1 0 5 10 15 Weak patterns Middle patterns Strong patterns 0 0.5 1 0 5 10 15 Weak patterns Middle patterns Strong patterns
    38. 38. Other simulations • Focal retrograde amnesia • Levels of processing • Transient Global Amnesia (TGA) • Semantic dementia • Implicit memory • More subtle lesions (e.g., only within-link connections, cf. CA1 lesions)
    39. 39. The Memory Chain Model: a very abstract neural network With Antonio Chessa from the University of Amsterdam
    40. 40. Abstracting TraceLink (level 1) • Model formulated within the mathematical framework of point processes • Generalizes TraceLink’s two-store approach to multiple neural ‘stores’ – trace system – link system – working memory, short-term memory, etc. • A store corresponds to a neural process or structure
    41. 41. Learning and forgetting as a stochastic process: 1-store example • A recall cue (e.g., a face) may access different aspects of a stored memory • If a point is found in the neural cue area, the correct response (e.g., the name) can be given LearningForgettingSuccessful Recall Unsuccessful Recall
    42. 42. Neural network interpretation Jo Brand
    43. 43. Single-store point process • The expected number of points in the cue area after learning is called µ • This µ is directly increased by learning and also by more effective cueing • At each time step, points die • The probability of survival of a point is denoted by a Link system Retrieval µ a Survival probability
    44. 44. Some aspects of the point process model • Model of simultaneous learning and forgetting • Clear relationship between signal detection theory (d'), recall (p), savings (Ebbinghaus’ Q), and Crovitz-type distribution functions • Multi-trial learning and multi-trial savings • Currently applied to over 250 experiments in learning and forgetting, since 1885
    45. 45. Forgetting curve ( ) 1 at e p t e µ − − = − If we need to find at least one point we obtain the following curve (one-store case): We predict a flex point when the initial recall is at least 63.01)0( 1 ≈−= − ep µ is the intensity of the process (expected number of points) and a is the decay parameter
    46. 46. Probe-digit experiment (Waugh & Norman 1965) R 2 = 0.7295 0 20 40 60 80 100 0 2 4 6 8 10 12 Time (s) Retention(%)Example: Single-store model fitted to short-term forgetting data R2 = 0,985
    47. 47. Multi-store generalization • Information about the current event passes through many neural ‘stores’ • The retina, for example, holds a lot of information very briefly • The cerebral cortex holds very little information (of the current event) for a very long time
    48. 48. General principles of the PPM multi-store model • A small part of the information is passed to the next store before it decays completely • Subsequent stores hold information for longer time periods: slower decay rates in ‘higher’ stores
    49. 49. Two-store model • While neural store 1 is decaying (with rate a1) it induces new points (representations) in store 2 • Induction rate is linear with the intensity in store 1 and has induction rate µ2 • The points in store immediately start to decay as well (at a lower rate a2)
    50. 50. Example of two neural stores • Store 1: firing neural groups • Store 2: synaptic connections between the neural groups • Other interpretation are possible as well, e.g.: – Store 1: hippocampus – Store 2: cerebral cortex Skip
    51. 51. Decomposition of intensity µ(t) into encoding, storage, and retrieval ( ) ( ) 1 t p t e µ− = − { { {1 storage retrievalencoding ( ) ( )t r t qµ µ= %
    52. 52. The contributions of S individual neural stores can simply be added { }12... 1 1 1 2 1 2 ( ) ( ) ( ) ( ) ... ( ) ( ) ( ) ... ( ) S S S r t r t r t r t r t r t r t r t µ µ= = + + + = + + + % % % %
    53. 53. Two-store model retention function: r12(t)= r1(t)+ r2(t) ( )1 21 2 2 1 2 2 1 ( ) ( ) a t a t r t r t e e a a µ µ µ − − = = − − % 1 1 1 1 1( ) ( ) a t r t r t eµ µ − = =% 12 ( ) ( ) 1 r t p t e− = −
    54. 54. The retention function for the third-store of a three-store model 3 31 2 3 1 3 1 2 3 2 1 3 1 3 2 ( ) ( ) a t a ta t a t r t r t e e e e a a a a a a µ µ µ µ − −− − = =  − − = −  − − −  %
    55. 55. Recall probability p(t) as a function of different learning times l ν is the learning rate l is the learning time r(t) is the decline function t time since learning ( ) ( ) 1 lr t p t e ν− = − %
    56. 56. Saturation assumption 1 max 1 max Simple learning was: With an upper ceiling on intensity we obtain: ( ) 1 l r r t l r µ ν µ ν =   = −   
    57. 57. Hellyer (1962). Recall as a function of 1, 2, 4 and 8 presentations 0 0.2 0.4 0.6 0.8 1 0 10 20 30 Time (s) Recallprobability Two-store model with saturation. Parameters are µ1= 7.4, a1= 0.53, µ2= 0.26, a2= 0.31, rmax= 85; R2 =.986 Skip
    58. 58. Amnesia: animal data Retrograde amnesia
    59. 59. Cho & Kesner (1996). (mice) R2 =0.96 b. 0 0.25 0.5 0.75 1 0 10 20 30 40 50 Time (days) Recallprobability
    60. 60. Summary of animal data a. 0 0.25 0.5 0.75 1 0 20 40 60 Time (days) Recallprobability b. 0 0.25 0.5 0.75 1 0 10 20 30 40 50 Time (days) Recallprobability c. 0 0.25 0.5 0.75 1 0 20 40 60 Time (days) Recallprobability d. 0 0.25 0.5 0.75 1 0 20 40 60 Time (days) Recallprobability f. 0 0.25 0.5 0.75 1 0 50 100 150 Time (days) Recallprobability e. 0 0.25 0.5 0.75 1 0 5 10 Time (days) Recallprobability d. 0 0.25 0.5 0.75 1 0 20 40 60 Time (days) Recallprobability
    61. 61. Frankland et al. (2001) study • α-CaMKB-dependent plasticity (in neocortex) switched off in knock-out mice • No LTP measurable in neocortex but LTP in hippocampus was largely normal • Forgetting curves with different levels of initial learning were measured • A learning curve was measured • Assumption: use r1[2](t) for knock-out mice
    62. 62. Forgetting after 3 shocks, using three parameters Freezing after 3 shocks 0 0.25 0.5 0.75 1 0 10 20 30 40 50 Retention delay (days) Freezing(fraction)
    63. 63. Using the same three parameters and a massed-learning correction. Freezing after 8 shocks 0 0.25 0.5 0.75 1 0 5 10 15 Retention delay (days) Freezing(fraction)
    64. 64. Controls receive 1 shock, experimental animals 3 shocks (no new free parameters). Freezing after weak learning 0 0.25 0.5 0.75 1 0 5 10 15 Retention delay (days) Freezing(fraction)
    65. 65. Repeated learning for experimental animals (no new free parameters) Freezing after repeated learning 0 0.25 0.5 0.75 0 1 2 3 Training day Freezing(fraction)
    66. 66. Summary of ‘cortical amnesia’. Using only 4 parameters for all curves (R2 = 0.976).Freezing after 3 shocks 0 0.25 0.5 0.75 1 0 10 20 30 40 50 Retention delay (days) Freezing(fraction) Freezing after 8 shocks 0 0.25 0.5 0.75 1 0 5 10 15 Retention delay (days) Freezing(fraction) Freezing after weak learning 0 0.25 0.5 0.75 1 0 5 10 15 Retention delay (days) Freezing(fraction) Freezing after repeated learning 0 0.25 0.5 0.75 0 1 2 3 Training day Freezing(fraction) (a) (b) (c) (d)
    67. 67. Application to advertising data • Advertisements as learning trials • Zielske (1958): recall of printed advertisements • SPOT study: TV commercials of over 40 brands
    68. 68. Zielske 1958 • Printed advertisements were mailed repeatedly to randomly selected house wives • After a week they were called • The memory for the advertisements was checked • Mailings were carried out weekly (massed) or every four weeks (distributed)
    69. 69. Zielske Data: Performance of weekly advertising (R2 =87%, 1-store model) 0 20 40 60 80 100 0 10 20 30 40 50 60 Week Recallpercentage Zielske data 1-Store Model
    70. 70. Zielske Data: Performance of 4-weekly advertising (R2 is 85%, 1-store model) 0 20 40 60 80 100 0 10 20 30 40 50 60 Week Recallpercentage Zielske data 1-Store Model
    71. 71. SPOT data • Research carried out by SPOT • 43 brands were followed for half a year • 50 phone calls per week per brand • Gross Rating Points (GRPs) were matched • A GRP is a measure for how many people will probably view the commercial
    72. 72. IA1 IA3 IB10 IIA6 IIA7 IIA8 IB2 IB3 IB4 IIB2 IIC1 IIC2 IB5 IB8 IB9 IIC5 IIC6 IIC7 IC1 IC2 IC4 IID1 IID2 IID3 ID1 ID2 ID3 IID5 IID6 IID7 IB6 IIA2 IB7 IID8 IIE2 IIE5 IIA3 IIA4 IIA5 IIF2 IIF3 IIF5 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16 21 0 20 40 60 80 0 40 80 120 160 200 1 6 11 16
    73. 73. Memory (impact) as a function of advertising 0 40 80 120 160 200 1 6 11 16 21 0 10 20 30 40 Weeks GRPs Memory (impact) GRPs Memory
    74. 74. Sometimes, advertising has no effect… 0 40 80 120 160 200 1 6 11 16 21 0 10 20 30 40
    75. 75. So, how is advertising forgotten? 0 40 80 120 160 200 1 6 11 16 21 0 10 20 30 40
    76. 76. SPOT brand with 1-store model 0 20 40 60 80 100 1 6 11 16 21 26 Week GRPs 0 5 10 15 20 25 30 Impact Week GRP Impact data Model m0 0.072514 m 0.000604 a1 0.843543 Base rate Learning Forgetting
    77. 77. High-learning brand, medium forgetting Budget = 900 GRPs Average impact = 26 0 20 40 60 80 100 120 140 1 5 9 13 17 21 25 Week GRPs 0 10 20 30 40 50 Impact Week GRP Impact Model fit
    78. 78. Optimized schedule for the same brand: same GRPs but 7% impact improvement Budget = 900 GRPs Average impact = 28 0 20 40 60 80 100 120 140 1 5 9 13 17 21 25 Week GRPs 0 10 20 30 40 50 Impact Week GRP Predicted
    79. 79. Conclusion • Advertisements are learning trials • Their learning and forgetting can be described by our model • If a good fit can be achieved, more optimal advertising schedules can be derived
    80. 80. Concluding remarks • Given that the brain is exceedingly complex, we need models at various levels of abstraction to aid our understanding • This is especially true when trying to unravel the link between the brain and human behavior, which is extremely complex itself • Hence, models are of particular use in the new, interdisciplinary field of cognitive neuroscience

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