Subjects on Objects in Contexts: Using GICA method to quantify epistemological subjectivity Timo Honkela 1 , Juha Raitio 1 , Krista Lagus 1 , Ilari T. Nieminen 1 , Nina Honkela 2 , Mika Pantzar 3 1 Aalto University (former Helsinki University of Technology) Department of Information and Computer Science (former Neural Networks Research Center, Adaptive Informatics Research Center) 2 University of Helsinki 3 National Consumer Research Center Finland
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Timo Honkela: Subjects on objects in contexts: Using GICA method to quantify epistemological subjectivity
We have developed a novel method, Grounded Intersubjective Concept Analysis (GICA), for the analysis and visualization of individual differences in language use and conceptualization. The GICA method first employs a conceptual survey or a text mining step to elicit to elicit from varied groups of individuals the particular ways in which terms and associated concepts are used among the individuals. The subsequent analysis and visualization reveals potential underlying groupings of subjects, objects and contexts.
In order to demonstrate the use of the GICA method, we present the results of two case studies. In the first case, a GICA analysis of health-related concepts is conducted. In the second one, the State of the Union addresses by US presidents are analyzed.
The GICA method can be used, for instance, to support education of heterogeneous audiences, public planning processes and participatory design, conflict resolution, environmental problem solving, interprofessional and interdisciplinary communication, product development processes, mergers of organizations, and building enhanced knowledge representations in semantic web.
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Subjects on Objects in Contexts:
Using GICA method to quantify epistemological subjectivity
Timo Honkela1, Juha Raitio1, Krista Lagus1,Ilari T. Nieminen1, Nina Honkela2, Mika Pantzar3
1Aalto University(former Helsinki University of Technology)
Department of Information and Computer Science(former Neural Networks Research Center, Adaptive Informatics Research Center)
2University of Helsinki
3National Consumer Research Center
Finland
Subjects on Objects in Contexts:
Using GICA method to quantify epistemological subjectivity
Timo Honkela Juha Raitio Krista Lagus
Ilari T. Nieminen Nina Honkela Mika Pantzar
Traditional representation of meaning:Generalized (non-contextual, non-subjective)
λ : Ci × Cj → R, i ≠ jA distance between two points in the concept spaces of different agents
S: symbol space,The vocabulary of anagent that consists of discrete symbols
: sξ i S∈ i → CAn individual mapping function from symbols to concepts
φi: Si D→An individual mapping from agent i's vocabulary to the signal space D andan inverse mappingφ1
i from the signal space to the symbol space
Ci: Ndimensional metric concept space
Observing f1 and after symbol selection process, agent 1 communicates a symbol s*to agent 2 as signal d. When agent 2 observes d, it maps it to some s2 S∈ 2 by using the function φ 1
1. Then it maps the symbol to some point in its concept space by using ξ2. If this point is close to its observation f2 in the sense of λ, the communication process has succeeded.
Subjectifying: adding subjective views into object-context matrices
Outcome: Subject-Object-Context (SOC) Tensors
More on subjectification
● A central question in GICA is how to obtain the data on subjectivity for expanding an object-context matrix into the tensor that accounts additionally for subjectivity.
● The basic idea is that for each element in the object-context matrix one needs several subjective evaluations.
● Specifically, the GICA data collection measures for each subject s
i the relevance x
ijk of
an object oj in a context c
k
Potential sources for subjectification
● Conceptual surveys: ● individual assessment of contextual
appropriateness
● Text mining:● statistics of word/phrase-context patterns
● Empirical psychology:● reaction times, etc.
● Brain research
Flattening: unfolding 3-way tensorfor traditional 2-way analysis
GICA:Grounded
Intersubjective Concept Analysis
Examples of use
Case 1: Wellbeing concepts
● A conceptual survey was conducted among the participants of the EIT ICT Labs activity “Wellbeing Innovation Camp” that took place between 26th and 29th of October 2010 in Vierumäki, Finland.
● The participants were asked to fill in a data matrix that consisted of the objects as rows and the contexts as columns.
● Each individual’s task was to determine how strongly an object is associated with a context, using Likert scale from 1 to 5
OBJECTS:
Relaxation
Happiness
Fitness
Wellbeing
CONTEXTS:
SUBJECTS: Event participants
Data collection
MDS: Objects x Subjects
Fitness
NeRV: Objects x Subjects
Fitness
J. Venna, J. Peltonen, K. Nybo, H. Aidos, and S. Kaski. Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization. Journal of Machine Learning Research, 11:451-490, 2010.
NeRV:
SOM: Objects x Subjects
SOM: Distribution of contexts
SOM: Contexts
Case 2: State of the Union Addresses
● In this case, text mining is used for populating the Subject-Object-Context tensor
● This took place by calculating the frequencies on how often a subject uses an object word in the context of a context word● Context window of 30 words
SOM: Subjects (presidents)
SOM: Objects x Subjects
Analysis of the word 'health'
Related research andfuture plans
Our related research on subjectivity:User-specific difficulty assessment