This document outlines research on modeling socio-cognitive learning processes in collaborative environments. It discusses: - The interplay between primary memory (attention control) and secondary memory (long-term memory) during reflection on resources. - Three studies examining how semantic stabilization evolves through this interplay and affects individual learning. Stabilization was found to support learning by priming memory searches. - A computational model called CMR that formalizes these memory dynamics and allows modeling how people's reflections lead to intersubjective understandings and semantic stabilization in collaborative tagging systems.

1. The role of primary and secondary memory
in organism-environment dynamics
...
computa(onal modeling of accruing data in collabora(ve
learning scenarios
paul seitlinger
27. 05. 2016, tallinn
1

2. Outline of my research
• Knowledge building in open/self-directed learning seDngs
• Challenges from a psychological perspec(ve
– Organism-Environment dynamics
• Interplay of primary memory (scope and control of aIen(on) and
secondary (long-term) memory during reflec(on
– Secondary memory (SM): Integra(ng episodic memory (evolving in collabora(ve learning
seDng) into seman(c (pre-exis(ng) memory [1]
– Primary memory (PM): controlled use of contextual cues (environmental, internal) to
search secondary memory (interpreta(on, reflec(on) [1]
– Socio-cogni(ve processes/co-crea(on of environments
• How stabiliza(on (paIerned prac(ces, grounding) evolves and affects PM-
SM interplay
[1] Usworth, N. & Engle, R. (2007). The nature of individual differences in working memory capacity: Ac(ve maintenance in
primary memory and controlled search of from secondary memory. Psychological Review, 114, 104-132.
2

3. Outline of my research
• Observing socio-cogni(ve learning in Web environments
– Collabora(ve learning so`ware (school, university)
– Social informa(on systems (bookmarking, tagging, informa(on search)
• Making use of accruing datasets to validate models of socio-cogni(ve learning
• 3 studies by the example of social tagging
– Study 1: Field study (university course) about effects of seman(c
stabiliza(on on individual learning [2]
– Studies 2 and 3: Measurement and computa(onal modeling to shed light
on variables giving rise to stabiliza(on [3,4]
[2] Ley, T. & Seitlinger, P. (2015). Dynamics of human categoriza(on in a collabora(ve tagging system:
how social processes of seman(c stabiliza(on shape individual ssensemaking. Computers in
Human Behavior, 51, 140-151.
[3] Seitlinger, P., Ley, T. & Albert, D. (2015). Verba(m and seman(c imita(on inindexing resources on the
Web: a fuzzy-trace account of social tagging. Applied Cogni?ve Psychology, 29, 32-48.
[4] Seitlinger, P. & Ley, T. (2016). Reconceptualizing imita(on in social tagging: a reflec(ve search model
of human web interac(on. In P. Parigi & S. Staab (Eds.), Proceedings of the 8th Interna?onal ACM
conference on Web Science Conference (in press). New York: ACM press. 3

4. • From a post-phenomenological perspec(ve (e.g., [5]): Tags as hermeneu(c means of
collabora(ve learning
• Hypothe(cal stabiliza(on cycle
i) Provide contextual cues that trigger search of secondary memory (seman(c priming; e.g.,
[6])
ii) Subsequent resource reflec(ons lead to similar interpreta(ons/conceptualiza(ons
(intersubjec(vity)
iii) Similar tag choices
Study 1: Effects of seman(c stabiliza(on on individual learning
à Mutual reinforcement in choosing similar
words for similar conceptualiza(ons
à Small and consistent (= stable) tag
vocabulary and elaborated knowledge
about underlying concepts
[5] Verbeek, P. (2005). What things do: philosophical reflec?ons on technology, agency, and design. University Park,
Pennsylvania: The Pennsylvania State University Press.
[6] Fu, W.-T., Kannampallil, T., Kang, R. & He, J. (2010). Seman(c imita(on in social tagging. ACM Transac(ons on
Computer-Human Interac(ons, 17, 12:1-12:37.
Tag
(contextual cue)
Search of memory
(PM-SM interplay)
Semantic priming
Reflection
(PM-SM interplay)
Intersubjectivity
Imitation
Semantic
stabilization
4

5. Study 1: Effects of seman(c stabiliza(on on individual learning
• N=24 students of a university course on cogni(ve models in TEL
– Social bookmarking system (SOBOLEO) to collect and tag Web resources
– Training phase to become familiar with purpose of tagging
• Manipula(ng stabiliza(on (λ) of tag vocabulary (high vs. low stabiliza(on)
– Low λ (n=12): ‘Old’ and interfering tags of training phase remain in the system
– High λ (n=12): Environmental switch
• Elici(ng individual learning: Performing an aIribute lis(ng task
– Lis(ng aIributes to general, medium, and specific tags (level of specificity)
• Basic-level shiN (e.g. [7])
• Hypothesis: Individuals of the high λ group gain more knowledge about medium
and specific tags than individuals of the low λ group.
[7] Close, J. & Pothos, E. (2012). “Object categoriza(on: Reversals and explana(ons of the basic-level advantage” (Rogers
& PaIerson, 2007): A simplicity account . Quarterly Journal of Experimental Psychology, 65, 1615-1632.
5

6. Study 1: Effects of seman(c stabiliza(on on individual learning
0 50 100 150
010305070
Consecutive tag assignments
Numberuniquetags
λ high
λ low
N = H * (1 – e-λt)
λ high = .009
λ low = .006
Stabiliza(on on group level: Higher
stabiliza(on in high than in low λ group
12345
Specificity
Numberlistedattributes
General Medium Specific
λ high
λ low
Individual learning: More knowledge about
medium and specific tags (basic-level shi`) in
high than low λ group
[2] Ley, T. & Seitlinger, P. (2015). Dynamics of human categoriza(on in a collabora(ve tagging system: how social
processes of seman(c stabiliza(on shape individual ssensemaking. Computers in Human Behavior, 51, 140-151.
F2,21 = 5.06, p < .05
6

7. Study 1: Effects of seman(c stabiliza(on on individual learning
• Stabiliza(on during collabora(on supports learning
– Tag-based (seman(c) priming inves(gated
by [6]
– Goals of studies 2 and 3: Revealing
interplay of remaining variables
• Study 2: Measuring i) contribu(ons of
PM-SM interplay and ii) impact of
intersubjec(vity on imita(on
• Study 3: Computa(onal model of
mechanisms underlying these variables
[6] Fu, W.-T., Kannampallil, T., Kang, R. & He, J. (2010). Seman(c imita(on in social tagging. ACM Transac?ons on
Computer-Human Interac?ons, 17, 12:1-12:37.
Tag
(contextual cue)
Search of memory
(PM-SM interplay)
Semantic priming
Reflection
(PM-SM interplay)
Intersubjectivity
Imitation
Semantic
stabilization
7

8. Study 2: Measuring the impact of intersubjec(vity on imita(on
• Web-based experiment
• 48 students conduc(ng an
informa(on search
• Incidental learning: Browsing
pictures (taken by famous
photographers; e.g., Henri Car(er-
Bresson) interpreted and
annotated by tag clouds
• Tagging phase: Re-exposed to
pictures to reflect on it and derive
own interpreta(ons and tag
assignments
• Frequency distribu(ons for the act of imita(ng (I) vs. not imita(ng (N) previously seen tags
• Analysis in terms of theore(cal constructs (e.g., PM, SM, intersubjec(vity) through
Mul(nomial Processing Tree (MPT) derived from Fuzzy-Trace Theory (e.g., [8])
[8] Brainerd, C. & Reyna, V. (2010). Recollec(ve and non-recollec(ve recall. Journal of Memory and Language, 63, 425-445. 8

9. Study 2: Measuring the impact of intersubjec(vity on imita(on
Automatic unloading
from PM
Reflective search
of memory
PM-SM interplay
Similar reflection
(Intersubjectivity)
1-S
Same tag choice
1-C
1
1-C
2
Imitation, VI
Web
resource
No Imitation, N
Imitation, I
D
1-D S
C
1
C
2 Imitation, I
No Imitation, N
Same tag choice
Different
reflection
[3] Seitlinger, P., Ley, T. & Albert, D. (2015). Verba(m and seman(c imita(on inindexing resources on the Web: a fuzzy-
trace account of social tagging. Applied Cogni?ve Psychology, 29, 32-48.
• Performing maximum likelihood es(ma(on to test model fit and quan(fy contribu(on of
cogni(ve processes
9

10. Automatic unloading
from PM
Reflective Search
of memory
PM-SM interplay
Similar reflection
(Intersubjectivity)
1-S=0.81
Same tag choice
1-C
1
=0.37
1-C
2
=0.97
Imitation, I
Web
resource
No Imitation, N
Imitation, ID=0.15
1-D=0.85 S=0.19
C
1
=0.63
Imitation, I
No Imitation, N
Same tag choice
C
2
=0.03Different
reflection
Model fit, G2(4)=0.78, n.s.
P(I)=0.10
P(I)=0.02
Probability
P(I)=0.15
Study 2: Measuring the impact of intersubjec(vity on imita(on
• PM-SM interplay crucial to model students’ interpreta(ons and annota(ons
• Intersubjec(vity (state of reflec(ve agreement) as a driving force behind imita(on
and thus, stabiliza(on
[3] Seitlinger, P., Ley, T. & Albert, D. (2015). Verba(m and seman(c imita(on inindexing resources on the Web: a fuzzy-
trace account of social tagging. Applied Cogni?ve Psychology, 29, 32-48.
10

11. Study 3: Applying CMR to model students’ reflec(ons on Web
resources as a PM-SM interplay
• Mechanisms behind PM-SM interplay to model reflec(ons and states of reflec(ve
agreement (intersubjec(vity)
– Drawing on contemporary theory of human memory
– Context Maintenance and Retrieval (CMR) model [9]
• SM: Integra(on of episodic and seman(c knowledge
• PM: Controlling internal context state (aIen(on/’spotlight’) to search SM
[9] Polyn, S., Norman, K. & Kahana, M. (2009). A context maintenance and retrieval model of organiza(onal processes in
free recall. Psychological Review, 116, 129-156.
11

12. Context Maintenance and Retrieval Model (CMR [9])
PM-SM interplay when reflec(ng on environmental objects
Article about
learning and
memory
“Brain” “Synapse”
“Kandel”
Item layer F
Context layer C
Context evolution (internal spotlight)
Episodic learning (integration of item-context associations into MFC
and MCF
MFC
MFC MFC
MCF
MCF MCF
Stream of thoughts triggered by environmental item
* PM: Turning environmental cues into context
** Using context to search SM
[9] Polyn, S., Norman, K. & Kahana, M. (2009). A context maintenance and retrieval model of organiza(onal processes in
free recall. Psychological Review, 116, 129-156.
*
**
12

13. Study 3: Applying CMR to model students’ reflec(ons on Web
resources as a PM-SM interplay
• CMR: A valid model of PM-SM dynamics
– Tested by a series of laboratory experiments on episodic learning (e.g., [9,10])
• RQs: Does PM-SM dynamics formalized by CMR allow for modeling
– peoples’ reflec(ons on Web resources?
– the effect of intersubjec(vity on seman(c stabiliza(on?
• RQs inves(gated in a large-scale social tagging system (Delicious)
– Dataset [11]: 1,685 tags for 49,691 Bookmarks of 2,003 Wikipedia ar(cles
from 1,968Users
• Tes(ng a CMR-specific hypothesis about stabiliza(on (consensual tag use)
• Simula(ng empirical paIerns by means of a CMR-based mul(-agent
simula(on (MAS)
[9] Polyn, S., Norman, K. & Kahana, M. (2009). A context maintenance and retrieval model of organiza(onal processes in free
recall. Psychological Review, 116, 129-156.
[10] Healey, M. & Kahana, M. (2016). A four component model of age-related memory change. Psychological Review, 123, 23-69.
[11] Zubiaga, A. (2009). Enhancing naviga(on on wikipedia with social tags. In Wikimania 2009. Wikimedia Founda(on, 2009.
13

14. Hypothesis: Decreasing intersubjec(vity during reflec(ons
F
C
F
C
F
C
F
C
Evolving spotlight
tag1
tag2
tag3
tag4
Tag assignment TAS
• TAS as a manifesta(on of
resource reflec(on (Study 2)
• Each search itera(on yields a
single tag (posi(on t) within
TAS
• Dri`ing spotlight hypothesis
• The longer we reflect, the more individualis(c the spotlight (internal context
state) should be
• Intersubjec(vity should decrease along consecu(ve search itera(ons t
(TAS posi(ons)
à Less imita(on and thus, seman(c stabiliza(on (consensual tag use)
at later TAS posi(ons
14

15. 0.40.50.60.70.80.91.0
Probabilitynewtag
Consecutive TAS
1 2 3 4 5 6 7 8 9 10
With each new TAS, the probability of a new
tag declines ~ Stabiliza(on
Web
resource
TAS
1
= {Kandel, brain, synapse, learning}
TAS
2
TAS
3
TAS
10
…
Micro dynamics
Macro
dynamics
TAS…Tag assignment
Indica(on of intersubjec(vity, implicit agreement on conceptualizing an object (e.g.,[12] )
Criterion: Seman(c stabiliza(on in a social tagging system
[12] S. Sen, S., Lam, S., Rashid, A., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F. & Riedl, J. (2006). Tagging, communi(es,
vocabulary, evolu(on. In Proc. 20th anniversary conference on Computer Supported Coopera(ve Work (pp. 181- 190). ACM press. 15

16. Study 3: Applying CMR to model students’ reflec(ons on Web
resources as a PM-SM interplay
• DriNing spotlight hypothesis
– Stabiliza?on more strongly pronounced at early than later TAS posi?ons t
0.40.60.81.0
Probabilitynewtagpnew(r,t)
Consecutive TAS r
1 2 3 4 5 6 7 8 9 10
1st position
2nd position
3rd position
4th position
Expected paIern of stabiliza(on
Predic(on: Stabiliza(on (slope)
decreases as TAS posi(on increases
[4] Seitlinger, P. & Ley, T. (2016). Reconceptualizing imita(on in social tagging: a reflec(ve search model of human web interac(on.
In P. Parigi & S. Staab (Eds.), Proceedings of the 8th Interna?onal ACM conference on Web Science Conference (in press). New York:
ACM press. 16

17. …
…
MFC
MCF
1) Category combination of present Wikipedia article fi
3) Context evolution
ci
= ci-1
+ β*cIN
2) Context retrieval
cIN
= MFC
fi
4) Activation of
item layer
fIN
= MCF
ci
Semantic
Pre-exist.
Episodic
Evolving
(1 − !)!!"#
!"
+ !!!"#
!!
Study 3: Applying CMR to model students’ reflec(ons on Web
resources as a PM-SM interplay
• MAS
– Each agent behaves according to CMR model
1) Training phase based on a real user history (sequence of bookmarked ar(cles)
Developing individual stream of consciousness (episodic learning and spotlight evolu(on)
2) Tagging phase: All agents assign 4 tags to each of 10 further ar(cles
Semantic utility
based on reflection
u(w) = p(w|fIN
)
u’(w) = u(w)[1+s(w)]Φ
O E
Environmental salience
based on previous TAS
s(w) = p(w|fi
)
5)
• Gene(c algorithm
exploring parameter space
• 500 simula(on runs with
best-fiDng parameter set
[4] Seitlinger, P. & Ley, T. (2016). Reconceptualizing imita(on in social tagging: a reflec(ve search model of human web interac(on.
In P. Parigi & S. Staab (Eds.), Proceedings of the 8th Interna?onal ACM conference on Web Science Conference (in press). New York:
ACM press. 17

18. Study 3: Applying CMR to model students’ reflec(ons on Web
resources as a PM-SM interplay
0.40.60.81.0
Probabilitynewtagpnew(r,t)
Consecutive TAS r
Data
CMR
1 2 3 4 5 6 7 8 9 10
t = 1
0.40.60.81.0
Probabilitynewtagpnew(r,t)
Consecutive TAS r
Data
CMR
1 2 3 4 5 6 7 8 9 10
t = 3
0.40.60.81.0
Probabilitynewtagpnew(r,t)
Consecutive TAS r
Data
CMR
1 2 3 4 5 6 7 8 9 10
t = 4
• Model fit: χ2(29) = 13.74, χ2
crit=42.56
– CMR-based modeling of reflec(ng on resources
explains paIerns qualita(vely and quan(ta(vely
• Dri`ing spotlight hypothesis HDS
– Slope λ of pnew(r,t) along consecu(ve r decreases
with increasing t
Data CMR
pnew λ pnew λ
t = 1 .580 .093 .584 .089
t = 2 .639 .077 .633 .078
t = 3 .669 .069 .665 .069
t = 4 .708 .060 .700 .064
0.40.60.81.0
Probabilitynewtagpnew(r,t)
Consecutive TAS r
Data
CMR
1 2 3 4 5 6 7 8 9 10
t = 2
[4] Seitlinger, P. & Ley, T. (2016). Reconceptualizing imita(on in social tagging: a reflec(ve search model of human web interac(on.
In P. Parigi & S. Staab (Eds.), Proceedings of the 8th Interna?onal ACM conference on Web Science Conference (in press). New York:
ACM press. 18

19. Tag
(contextual cue)
Search of memory
(PM-SM interplay)
Semantic priming
Reflection
(PM-SM interplay)
Intersubjectivity
Imitation
Semantic
stabilization
Conclusion
• A valid model of peoples’ reflec(ons on Web resources
– PM-SM interplay (spotlight-driven search of memory)
• Precise predic(ons and modeling of stabiliza(on
– By implemen(ng result of study 2: Imita(on as an epiphenomenon of
intersubjec(vity (state of reflec(ve agreement)
• Studies 1-3 as a triangula(on of
• Field experiment: Iden(fying mutual
influences between observable variables on
group and individual
• Mul(nomial modeling of Web-based
experiments: Quan(fying contribu(ons of
latent variables to observable behavior
• Mul(-Agent Simula(on: Tes(ng assump(ons
on dynamics between mul(ple latent and
observable variables
19

20. Methodological implica(ons
• Collabora(ve learning scenario well captured by nonlinear organism-
environment dynamics
– No simple cause-effect rela(onships [13]
– Non-linear processes and mutual influences between variables
• Methodological approach
– Going beyond correla(onal analysis
– Computa(onal modeling
• Model-based simula(ons/predic(ons of system development
• Model-based representa(on and computa(on/analysis of
contextual informa(on about a student (temporal, seman(c,
social)
[13] Larsen-Freeman, D. Cameron, L. (2008). Research methodology on language development from a complex systems perspec(ve.
The Modern Language Journal, 08, 200-213.
20

21. Going beyond correla(onal analysis
• Advantage of computa(onal modeling
– Close to phenomena to be observed
• Distribu(on of informa(on through non-linear and itera(ve processes
– Intertwining theory and sta(s(cs
• Parameters directly represen(ng theore(cal constructs
– Independence of domain and data
• Fundamental mechanisms of
– learning (Hebbian learning of seman(c and episodic associa(ons)
– execu(ve func(ons (scope and control of aIen(on ~ Spotlight and spotlight-
driven search)
• should account for different behavioral data
– Self-directed naviga(on (forma(on of informa(on goals, meta-cogni(ve
processes/control)
» spotlight -> informa(on goal
» PM-SM interplay to account for meta-cogni(ve processes
– Crea(ve group cogni(on (trade-off between stabiliza(on and divergent
thinking)
» Interplay of aIen(on control and scope of aIen(on when re-combining
pre-exis(ng associa(ons
21

22. Contribu(ons to research infrastructure
• Summerschool on theory-driven analyses of human-web interac(ons
– Prof. Wai-Tat Fu (Partner in two currently running FWF projects)
• Department of Computer Science, University of Illinois at Urbana-Champaign
– Topic1: Computa(onal modeling of user behavior in crea(ve and self-directed learning
environments
– Topic 2: Design of crea(vely s(mula(ng recommenda(on mechanisms: „Escaping the
echo chamber“
• Summerschool on web-based experiments on „access to knowledge“
– Prof. Harry Bahrick (Partner in a current EU project proposal)
• Department of Psychology, Ohio Wesleyan University
– Topic: Applying MPTs to analyze learning in Web-based experiments
• Availability vs. Accessibility of knowledge
22

23. Contribu(ons to research infrastructure
• Courses on R: A flexible environment for learning analy(cs
– to perform conven(onal inferen(al sta(s(cs (e.g., ANOVA, SME, factor
analysis, etc.)
– to perform theory-driven analyses of log files
– implemen(ng and running simula(ons
• Courses on JAVA Script for psychologists and educa(onal scien(sts
– making use of exis(ng libraries to turn research designs into Web-based
experiments
23