2015-02-25 research seminal, paul seitlinger

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Marks of Stabilizing Patterns on the Web Blessing or Curse for Individual Thought… Austrian Science Fund omefispo P 27709-G22 Overcoming Mental Fixation by switching the internal Spotlight Memory retrieval in tagging: A Socio-Cognitive Model Paul Seitlinger 25 February 2015

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Marks of Stabilizing Patterns on the Web Blessing or Curse for Individual Thought…

Austrian Science Fund

omefispo

P 27709-G22

Overcoming Mental Fixation by switching the internal Spotlight

Memory retrieval in tagging: A Socio-Cognitive Model

Paul Seitlinger 25 February 2015

Understanding Behavior of Collectives

Individuals and their intentions

Non-human actants (e.g., artifacts) introduced to manifest intentions

How the web of relations between human and non-human actants evolves in time (Law, 2009)

Organism environment coupling (Järvilehto, 1998)

/162

Organism Environment Coupling

2 empirical examples and simulation-based analyses

Stabilization in interpreting and tagging Web resources

Evolution of information needs in searching for Web resources

/163

Example of stabilization

Quick non-linear stabilization in tagging objects in Delicious

Difference between every two points in time of the tag distribution for a given resource

Kullback-Leibler Divergence KLD

KLD reaches a value of 0 after only a few time points

Result triggered several studies to develop models of semantic stabilization

Halpin et al. (2007; similar results found by Golder & Huberman, 2007; Wagner et al., 2014)

/164

Models of stabilization

Artifact-mediated feedback cycles

/165

Resource Semantic field

Tag choice

Tag recommendations (tx, ty, tz)

y

z

x1

2

Future interpretations

Semantic imitation: Tags of previous users shape the interpretation of the resource (Tag-based priming; Fu, 2008; Fu & Dong, 2012)

1. Tag-based priming 2. Implicit learning

2

Implicit learning: Long-term effects of other users’ tags (Seitlinger & Ley, 2011; Seitlinger, Ley & Albert, 2015)

romanticthreatening

Simulating non-linear stabilization by implementing implicit reinforcement learning in connectionist networks

(Ley, Seitlinger & Pata, in press)

Is the mechanism of implicitly learning associations between perceived tags and semantic fields/clusters sufficient to explain stabilization?

Multi-Agent simulation

Each agent is a connectionist network (Hutchins & Hazlehurst, 1995; Overwalle & Heylighen 2006)

Learns to categorize and tag objects

Objects are taken from a large-scale data set of Delicious bookmarks of Wikipedia articles (Arkaitz et al., 2013)

Articles described by Wikipedia categories

/166

Simulating non-linear stabilization by implementing implicit reinforcement learning in connectionist networks

(Ley, Seitlinger & Pata, in press)

• SUSTAIN-Network (Love et al., 2004) spreads activation across 3 layers to choose a set of tags TAS probabilistically (numbers 1-3)

• Network can “see” MPT = the 7 most popular tags at a given point in time (number 4)

• Output-Encoding (Rizzuto & Kahana, 2002): Associations wjk for tags included in TAS and MPT are strengthened = Artifact-mediated output encoding (number 5)

/167

Simulating non-linear stabilization by implementing implicit reinforcement learning in connectionist networks

(Ley, Seitlinger & Pata, in press)

• M=50 agents, N=300 unique objects, 100 simulation runs

• Training phase

• 50 users randomly sampled from a large-scale data set from Delicious (Arkaitz et al., 2013)

• Each user’s history to train each of the M agents -> Non-random behavior at the beginning

• Two measures of stabilization during “communication” phase

• 1) Similarity of TAS and MPT: Jaccard Index J

• 2) Kullback-Leibler Divergence KLD

/168

Simulating non-linear stabilization by implementing implicit reinforcement learning in connectionist networks

(Ley, Seitlinger & Pata, in press)

• Results

0.00

0.05

0.10

0.15

Consecutive agent tagging events ATE

Sim

ilarit

y be

twee

n TA

S an

d M

PT

6 65 124 182 241 300

J(TAS,MPT)

0.0

0.2

0.4

0.6

0.8

1.0

Consecutive object tagging events OTE

Kullb

ack−

Leib

ler D

iverg

ence

3 12 22 31 41 50

KLD

• A high degree of randomness changes quickly into a stable state

• Artifact-mediated output encoding as an organism-environment coupling causing stabilization

/169

Semantic Stabilization – A Conclusion

Social cues influence interpretations and descriptions

Disambiguation (Tag-based priming)

Implicit reinforcement learning

Automatic processes result in homogeneous categorization patterns

Do the influenced interpretations in turn affect our information needs and hence, search patterns?

A further stabilizing mechanism?

/1610

DNMs (e.g., Polyn et al., 2009) to account for a non-linear evolution of information needs

Cues of a Web resource, such as tags, activate a pattern fi on a feature layer F

fi illuminates a pattern of activation tIN on a latent topic layer T via MFT (neural network)

tIN is integrated into already illuminated pattern ti-1 (previous information need)

ti then actualizes the feature pattern on F through

fIN = ti MTF

fIN might be applied to form a new search cue and trigger the retrieval of a new Web resource.

In each cycle of this environment organism coupling, MFT and MTF are adjusted through Hebbian learning of new associations between fi and tIN.

T

F

“Neural” network representation of tag topic associations/1611

Proximal cuesResource features F

Latent topicsInformation need T

MFTMTF Searching the Web

fi

tINti

fIN

ti = �ti-1 + �tIN

DNMs to account for non-linear organism-environment coupling

Asymmetric search patterns in Delicious

A tag of a current bookmark fi is more likely to occur in a subsequent fi+1 than in a preceding bookmark fi-1

Interpretation of current bookmark tIN on T might influence fIN which drives search for new bookmark fi+1

DNM-based search simulation also produces an asymmetric similarity curve

Might capture cue-dependent memory processes involved in searching the Web

/1612

−20 −10 0 10 20

0.00

0.10

0.20

0.30

Distance from last bookmark collected

Sim

ilarit

y of

tag

assi

gnm

ents

−20 −10 0 10 20

0.00

0.10

0.20

0.30

Distance from last bookmark collected

Sim

ilarit

y of

tag

assi

gnm

ents

Empirical

Simulation

f1 f2 fi-1 fi fi+1 fi+2 f20

……..tx, ty tx, tztu, tv

Summary

Examples of organism environment coupling

Environmental cues influence mental states

they make people learn similar interpretations

The influenced states determine future behavior

the influenced interpretations cause a drift in information needs

What might be a consequence for collaboration within a social information system?

Homogeneous interpretation of different objects

Homogeneous search patterns

Consensual state of minds

/1613

Virtual Echo Rooms

Current semantic technologies create “virtual echo rooms”

Complicate the emergence and retrieval of novel ideas

Make us circulate around ourselves by searching for familiar things

Slavoj Zizek

• Concerns are justified particularly in social media applications for political deliberation (e.g., Stieglitz & Xuan, 2012)

• Echo rooms counteract a multi-discoursive ordering (Law, 2009)

• Homogeneity accelerates polarization (Huckfeldt et al., 2005)

/1614

Questions we have to consider in the context of Collective Awareness Platforms for Sustainability

CAPS

How can both collaboration and deliberation within a social information system take place?

What are alternatives to current aggregating technologies based on frequency, recency and similarity?

/1615

Thank you for your attention!