Psychological Services
(Laboratory of Prof. Sulis,
Department of Psychiatry and Behavioural Neurosciences,
Department of Psychology, Neuroscience and Behaviour,
Process Algebra and Collective
Intelligence
Architecture
Modeling of diversity and evolutionary
phenomena: diagonal evolution, EVS-approach, etc.
Functional
ensemble of temperament (neurochemical model) and psychiatric taxonomies
Psychosemantic
study of connections between meaning attribution and age, gender and
temperament.
Educational
activity
Organisational Activity
Staff and contacts
Project name: Process Algebra and Collective
Intelligence Architecture
The purpose of the Collective Intelligence Laboratory is to study the dynamics, capabilities, limitations and applications of systems composed of embodied, strongly situated, weakly coupled agents interacting without hierarchical control. These topics are currently known as computational psychiatry and mathematical modeling in psychology. Systems studied in our Lab range from social organisms, through social insect colonies and on to societies and economic systems. Methodologies include computer simulations, especially cellular automata and graphical dynamical systems, formal theoretical modeling and analysis, and observation of natural systems. Applications to parallel processing algorithms , neural representation, functional differentiation in biological development and psychotherapeutics are also under investigation.
Directions of the project:
Development
of formal models of collective intelligence.
Study of transient induced global
response synchronization (TIGoRS) in complex systems.
Application of TIGoRS to the
design of parallel processing algorithms and neural representation.
Study of intrinsic linguistic
behavior in complex systems.
Study of collective
intelligence as a model for a dynamic unconscious.
Study of symbol sign
relationships and communication in biological systems
Books
Sulis, W. and Combs,
A. (eds.) (1996) Nonlinear Dynamics in Human Behavior.
Trofimova I.N.,
Budanov V.G. (eds.) (1997). Synergetics and Psychology. Texts. Volume 1.
Methodological questions.
Trofimova I.N. (ed.) (1999). Synergetics and
Psychology. Texts. Volume 2. Social Processes.
Sulis W.,
Shendyapin V.,
Sulis, W.
Archetypal Dynamics. Manuscript in progress.
Trofimova I.N. Diversity in Natural Systems.
Manuscript in progress.
Articles:
Sulis, W.(1992)
Tempered Neural Networks. Proceedings of the International Joint Conference on
Neural Networks 1992.
Sulis, W. (1993)
Emergent Computation in Tempered Neural Networks 1: Dynamical Automata.
Proceedings of the WCNN'93.
Sulis, W. (1993)
Emergent Computation in Tempered Neural Networks 2: Computation Theory.
Proceedings of the WCNN'93.
Sulis, W. (1993)
Naturally Occurring Computational Systems. World Futures 39(4) 225-241
Sulis, W. (1995)
Naturally Occurring Computational Systems. In Chaos Theory In Psychology and
the Life Sciences. R. Robertson, A. Combs (eds). 103-122.
Sulis, W. (1995)
Driven Cellular Automata. In 1993 Lectures on Complex Systems. Lecture Volume
VI in the Santa Fe Institute Studies in the Sciences of Complexity. 565-578.
Addison-Wesley
Sulis, W. (1995)
Causality in Naturally Occurring Computational Systems. World Futures 44 (2- 3)
129-148
Sulis, W. (1995)
Driven Cellular Automata, Adaptation, and the Binding Problem. In Advances in
Artificial Life, Lectures Notes in Artificial Intelligence 929. F. Moran, A.
Moreno, JJ Merelo, P Chacon (eds.). 824-840. Springer-Verlag.
Sulis, W. (1996) A
Formal Framework for the Study of Collective Intelligence. 5th Conference on
Artificial Life,
Sulis, W. (1996)
TIGoRS and Neural Codes. In Nonlinear Dynamics in Human Behavior, W. Sulis and
A. Combs (Eds.)
Sulis, W. (1997)
Fundamentals of Collective Intelligence. Nonlinear Dynamics, Psychology, and
Life Science, 1(1), 30-65.
Sulis, W. (1997)
TIGoRS and Neural Codes. In Nonlinear Dynamics in Human Behaviour, W. Sulis and
A. Combs (Eds.)
Sulis, W. (1997)
Collective Intelligence as a Model for the Unconscious. Psychological
Perspectives, 35, Spring, 64-93.
Sulis, W. (1997)
Collective Intelligence. In: Trofimova I.N., Budanov V.G. (eds.) (1997).
Synergetics and Psychology. Texts. Volume 1. Methodological questions.
Sulis W. (1997)
Tigors and Neural Codes. In: M.A.Basin, S.V. Charitonov (Eds.). Synergetics and
Psychology. Sanct-Petersburg: SPBUVK.
Sulis, W. (1998)
Dynamical Systems in Psychology: Linguistic Approaches. In The Complex Matters
of Mind and Brain, F.Orsucci (ed.), 33-58.
Sulis, W. (1998)
TIGoRS as an Associative Memory in Complex Systems. Complex Systems.
Proceedings of International Conference.
Sulis, W. (1998)
Dynamical systems in psychobiology. In Chaos, Fractals, Models, F.M. Guindani
and G. Salvadori (eds.).
Sulis W. (1999)
Archetypal Dynamics. Internet World Congress on Biomedical Sciences.
Sulis W. (1999) A
Formal Theory of Colletive Intelligence. In: W.Tschacher, J.P.Dauwalder (Eds.)
Dynamics, Synergetics, Autonomous Agents.
Sulis W. (1999)
Collective Intelligence. In: Trofimova I.N. (Ed.) (2000). Synergetics and
Psychology. Texts. Volume 2. Social Processes.
Sulis W.
Information representation in neural and complex systems. In : Sulis W.,
Sulis W., Gupta A.
Nonlinear dynamics in psychiatry. In: Sulis W.,
Sulis W. (2001) A
formal theory of collective intelligence. In: Szuba, T. Computational
Collective Intelligence. Wiley Book Series on Parallel and Distributed
Computing.
Sulis W. (2001)
Collective intelligence as a model for the unconscious. In: Szuba, T.
Computational Collective Intelligence. Wiley Book Series on Parallel and
Distributed Computing.
Sulis, W. (2002) Archetypal
Dynamics and Emergence. In Nation J.,
Sulis W. (2003) Effect TIGoRS
in stimuli recognition. In: Arshinov V., Shendyapin V.,
Sulis, W. (2004) Archetypal
Dynamical Systems and Semantic Frames in Vertical and Horizontal Emergence.
Emergence: Complexity and Organizations, 6(3).
Sulis, W. (2007) Archetypal
Dynamical Systems and Semantic Frames in Vertical and Horizontal Emergence in
Cilliers, P (Ed.) (2007) Thinking Complexity. Complexity and Philosophy Vol. 1,
ISCE Publishing.
Sulis, W. (2008) Stochastic
phase decoupling in dynamical networks. Nonlinear dynamics, psychology, and
life science. 12(4) 327-358
Sulis, W. (2009) Collective
intelligence: Observations and models. In
Sulis, W. (2010) Archetypal
dynamics, emergent situations and the reality game. Nonlinear dynamics,
psychology, and life sciences. 14(3) 209-238.
Sulis, W. (2012) Causal
Tapestries for Psychology and Physics. Nonlinear dynamics, psychology, and life
sciences, 16(2), 113-136.
Sulis, W. (2014) A Process
Model of Quantum Mechanics. Journal of Modern Physics. DOI:
10.4236/jmp.2014.516176
Sulis, W. (2016) A Process
Algebra Model of QED. Journal of Physics. 701 (1) 012032
Sulis, W. (2016)
Synchronization, TIGoRS, and Information Flow in Complex Systems: Disposiitonal
Cellular Automata. Nonlinear Dynamics in Psychology and Life Sciences 20(2)
293-317
Sulis, W. (2016) Transient
Induced Global Response Synchronization. International Journal of Design &
Nature and Environment, 11(4) 712-21
Sulis, W. (2017) Completing
Quantum Mechanics. In K. Sienicki, Quantum Mechanics Interpretations. Open
Academic Press 350-421
Sulis, W, (2017) A Process
Algebra Approach to Quantum Electrodynamics: Physics from the Top Up. In: R.
Martinez (ed.) Complex Systems: Theory and Applications. Nova Publishing
Sulis, W. (2017) A Process
Algebra Approach to Quantum Electrodynamics. International Journal of
Theoretical Physics. DOI 10.1007/s10773-017-3366-y
10.Sulis, W. (2017) Modeling
stochastic complexity in complex adaptive systems: Non-Kolmogorov probability
and the process algebra approach. Nonlinear Dynamics in Psychology and Life
Sciences, 21(4) 407-440
Sulis, W. (2019) Transients as
the basis for information flow in complex adaptive systems. Entropy, 21(1) 94
DOI: 10.3390/e21010094
Sulis, W. (2019) Fractals
transcendant: Bridging the transpersonal chasm. In A Fractal Epistemology for a
Scientific Psychology: Bridging the Personal with the Transpersonal. Friedman,
H., Wolfe, K. (Eds.(To appear).
Sulis, W. (2019) Lessons from
collective intelligence. In Chaos Theory in the Social Sciences, Elliot, E.,
Kiel, D. (Eds.) (To appear)
Project name: Modeling of Diversity and Evolutionary phenomena: diagonal
evolution, EVS-approach, etc.
Diagonal evolution: see:
Trofimova,
I. (2011) Are men evolutionarily wired to love the "Easy" buttons? Nature Neuroscience Preceding, 5562 http://precedings.nature.com/documents/5562/version/1
Trofimova, I. (2015)
Do psychological sex differences reflect evolutionary bi-sexual partitioning? American Journal of Psychology, 128(4) 485-514 DOI:10.5406/amerjpsyc.128.4.0485,
PMID:26721176 PDF
Trofimova, I. (2016) Phenomena of Functional
Differentiation (FD) and Fractal Functionality (FF). International Journal
of Design & Nature and Ecodynamics, 11, 4, 508-521. Wessex Institute of
Technology Press: UK. DOI: 10.2495/DNE-V11-N4-508-521
Trofimova I. (2017)
Functional constructivism: in search of formal descriptors. Nonlinear
Dynamics in Psychology, 21/4, 441-474. .PDF doi: 10.13140/RG.2.2.32098.43205
EVS modeling
Consideration of multi-element
systems, be it brain or body of a subject, groups of subject or organizations,
leads to a necessity of formal analysis of an interaction between the elements.
Random graph theory (Palmer, 1985), percolation models (Grimmett, 1989),
cellular automata (Burks, 1970), random boolean networks (the best review is
Arbib, 1995) self-organized criticality (Bak, Tang, Wiesenfeld, 1987), or the
Kauffman model (Kauffman, 1993) all constitute populations of interacting
agents. Most models however consider formal populations with identical elements
or possessing only a small diversity of types, strategies or rules. Also agents
of some of these models interacted only locally (cellular automata, networks),
or the connections, once established, are fixed, as are the vertices
(percolation model, random graph model) that simulates equilibrium conditions.
A set of models that we
developed in collaboration with Alexey Potapov and Nicolay Mitin from Keldysh
Institute of Applied Mathematics (
Briefly the main properties of EVS models
are:
o Similarity with cellular automata, as the characteristics of each element
are discrete numbers, and evolution occurs in discrete time;
o
Non-locality of
connections between agents.
o
Population has a
diversity of elements, defined via some parameters or vectors.
o
Agents randomly
check other agents in the matter of compatibility.
o
The number of
connections to be checked/established is limited by the parameter of
sociability.
o
The structure of
connections between elements is very dynamic and stochastic.
o
Mutual agreement
principle: connections between agents appear only when both agents `agree` to
establish it, and if one agent wants to terminate it, the connection breaks.
o Each agent receives and spends some resource at each time step, allowing the simulation of resource flow through the agent and through the system.
EVS modeling did show that…
n …one doesn’t
need to have stable components or even links between them in order to have a
visible and consistent structure. To show that in mid-1990s we proposed modeling with
Ensembles of Variable Structures (EVS), to observe the emergence of structures
even while the connections between elements were highly stochastic and
volatile. Refs:
Trofimova,
n … diversity of agents within populations and their interconnectivity ("sociability") - these two formal characteristics of populations compete and suppress each other, and this competing dynamics drives the evolution of distributions. Refs:
Trofimova I. & Mitin N. (2002) Self-organization and resource exchange in EVS modeling. Nonlinear Dynamics, Psychology, and Life Sciences, 6, 4, 351-562. See .PDF
n …it is
possible to have formal codes for "functions on the flow of resources"
within sets of diverse elements. Refs:
Relevant Publications of the lab on
this project:
Trofimova
I.N., Mitin N.A., Potapov A.B., Malinetzky G.G. (1997)Description of Ensembles
with Variable Structure. New Models of Mathematical Psychology. Preprint N 34
of KIAM RAS. (in Russian).
Trofimova
I.N. (Ed.) (2000). Synergetics and Psychology. Texts. Volume 2. Social
Processes.
Sulis
W.,
Shendyapin
V.,
Trofimova
I.N. Strategies of behavior as stable characteristics of individuality //
Nature of Psyche. Perm. 1994. (in Russian)
Trofimova
I.N. Evolutionary determination of individual differences // Individuality in
the modern world.
Trofimova
I.N. Universal principles of evolution as the foundation for analysis of human
nature // Philosophical researches, N 3 1995. P.5-23. (in Russian)
Trofimova
I.N. Parameters of behavioral strategies and style characteristics // Human
styles: structure and functions. Ed.A.V.Libin. Moscow. Smisl, 1996. (in
Russian)
Trofimova
I.N. Individual differences from the point of view of the evolutionary approach
// Questions of psychology, N 1 - 1996. (in Russian)
Malinetzky
G.G, Mitin N.A., Potapov A.B., Trofimova I.N. (1997). Individual differences:
in search of universal characteristics// Informatics in science and education.
Reports of the Conference.
Trofimova
I.N. Analysis of behavioral strategies using cellular automaton models.
Abstracts of International Psychological Congress.
Trofimova
I.N. (1997) Precursors of the Synergetic Approach in Psychology // Synergetics
and Psychology. Texts. Volume 1. Methodological questions.
Trofimova
I.N., Potapov A.B. (1998) The definition of parameters for measurement in
psychology. In: F.M. Guindani & G. Salvadori (Eds.) "Chaos, Models,
Fractals".
Trofimova
I. Potapov, A., Sulis, W. (1998) Collective Effects on Individual Behavior: In
search of Universality. International Journal of Chaos Theory and Applications.
V.3, N.1-2. Pp.53-63.
Trofimova
I. & Mitin N. (2002) Self-organization and resource exchange in EVS
modeling. Nonlinear Dynamics, Psychology, and Life Sciences, 6, 4, 351-562.
Project name: FET framework for temperament (click here for more details) and psychiatric taxonomies
The Functional Ensemble of
Temperament (FET) model suggests that:
§ ...the structure
of adult temperament (biologically based individual differences) cannot and
should not be presented as 3-5 independent dimensions. She offers the "Functional
Ensemble of Temperament" model consisting of
12 components, which reflect the regulation of
behaviour in four perspectives of functional tasks: dynamical aspects of tasks (energetic,
integration- and orientation-related), probabilistic (known task vs.
uncertain), activity-specific (physical vs. verbal-social vs. mental) and
emotional amplifier of dynamical aspects. Her model is a derivative of Rusalov's STQ
model.
References:
Rusalov, V.M. & Trofimova, I.N. (2007) The Structure
of temperament and its measurement. PSP: Psychological
Services Press,
Trofimova,
Trofimova,
Trofimova, I. (2010). An
investigation into differences between the structure of temperament and the
structure of personality. American
Journal of Psychology. 123, 4, 467-480. DOI:
10.5406/amerjpsyc.123.4.0467 PDF
Trofimova, I. &
Sulis W. (2010). The lability
of behavior as a marker of comorbid depression and anxiety. Advances in Bioscience and Biotechnology, 1,
3, 190-199.
DOI: 10.4236/abb.2010.13027. See PDF
Trofimova, I. & Sulis W. (2011). Is temperament
activity-specific? Validation of the Structure of
Temperament Questionnaire - Compact (STQ-77). International
Journal of Psychology and Psychological Therapy, 11(3), 389-400. PDF
Rusalov,
V.M., Trofimova, I.N. (2011). Representation of psychological types
in various models of temperament (О представленности типов психической деятельности в различныхмоделях темперамента). Psychological
Journal, 32, 3, 74-84. PDF
Trofimova,
Trofimova, I. & Christiansen, J. (2016) Coupling of
temperament traits with mental illness in four age groups. Psychological
Reports, 118, 2. DOI 10.1177/0033294116639430. PDF
Trofimova,
I. & Robbins, T.W. (2016) Temperament
and arousal systems: a new synthesis of differential psychology and functional
neurochemistry. Neuroscience and Biobehavioral Reviews, 64, 382–402. DOI:
10.1016/j.neubiorev.2016.03.008. PDF
Trofimova,
I. & Sulis W. (2016a). Benefits of distinguishing between physical and
social-verbal aspects of behaviour: an example of generalized anxiety. Frontiers
in Psychology, 7:338. doi: 10.3389/fpsyg.2016.00338 PDF
Trofimova,
I. & Sulis, W. (2016b). A study of the coupling of FET temperament traits
with Major Depression. Frontiers in Psychology, 7: 1848. doi: 10.3389/fpsyg.2016.01848.
PDF
Trofimova,
I. & Sulis W. (2018). There is more to mental
illness than just negative affect: comprehensive temperament profiles in
depression and anxiety. BMC
Psychiatry. 18: 125. Doi: 10.1186/s12888-018-1695-x
Trofimova,
I. (2018). Functionality
vs dimensionality in psychological taxonomies, and a puzzle of emotional
valence. Philosophical Transactions
of the Royal Society, Biology, 373 (1744), doi: 10.1098/rstb.2017.0167.
Sulis,
W. (2018) Assessing
the continuum between temperament and affective illness: psychiatric and
mathematical perspective. Philosophical Transactions of the Royal Society,
Biology, 373, 1744, doi:10.1098/rstb.2017.0168
Uher,
J., Trofimova, I., Sulis, W., Netter, P., Pessoa, L., Posner, M., Rothbart, M.,
Rusalov, V., Petersen, I., Schmidt, L. Diversity in
action: Exchange of perspectives and reflections on taxonomies of individual
differences. Philosophical
Transactions of the Royal Society, Biology. 373 (1744) doi:
10.1098/rstb.2017.0172
Trofimova, I. (2019). An overlap between mental abilities and temperament traits. In: McFarland, D. (Ed.) General and specific mental abilities. Cambridge, UK: Cambridge Scholars Publishing. Pp. 176-225. ISBN-13: 978-1-5275-3310-3.
Trofimova, I. (2019). An overlap between mental abilities and temperament
traits. In: McFarland, D. (Ed.)
General and
specific mental abilities. Cambridge, UK: Cambridge Scholars
Publishing.
A neurochemistry-based model of
the structure of temperament FET ("Functional Ensemble of
Temperament") that was implemented in
the Compact Structure
of Temperament Questionnaire (see forms testing here):
|
Situation of: emergency complexity |
|
Dynamical aspects of an action with a function to... to orient/select to initiate/mobilize to
sustain an action |
|||
|
|
Sensitivity to reinforces: |
Endurance |
|||
|
low -► |
higher--► |
Probabilistic (Mental) tuning |
Sensitivity
to Probabilities |
Plasticity |
Intellectual
Endurance |
|
|
lower --► |
Social-verbal |
Empathy sensitivity
to others |
Social
Tempo |
Social Endurance |
|
|
Physical-motor Deterministic |
Sensitivity
to (seek for) sensations |
Physical
Tempo |
Physical
Endurance |
|
|
high -► |
high --► |
Emotional Amplifier |
Neuroticism sensitivity
to novelty |
Impulsivity |
(dispositional) Satisfaction |
...
The Big
Five model of personality reflects the nature of the lexical material that it
is based upon. Language possesses a strong sociability bias, reflective of its
role in socialization. Language thus has more personal descriptors related to
affiliation and sociability than other descriptors of individual differences.
Moreover, there is a strong negativity bias of emotionality and as a
consequence there are more words related to negative than to positive emotions.
These two biases affect the content of lexical descriptors of individual
differences. As the result, the largest factors in lexical approach models
(Extraversion and Neuroticism) as artefacts of the sociability bias of language
and the negativity bias of emotionality, and do not reflect the structure of consistent
individual differences. Moreover, many personality and temperament models miss
important distinctions between traits related to social, mental and physical
activities.
References:
Trofimova,
Trofimova,
Trofimova,
Trofimova,
I., Robbins, T., Sulis, W., Uher, J. (2018) Taxonomies
of psychological individual differences: biological perspectives on
millennia-long challenges. Philosophical Transactions of the Royal Society,
Biology. doi: 10.1098/rstb.2017.0152
|
Functional aspects: |
Behavior.
orientation to..
(NE+...) |
Integration aspects (DA+...) |
Energetic aspects
(5-HT, ACh +...) |
|
Probabilistic/ >
analytic
aspects |
to learning probabilities, PRO NE+ACh+Glu, DA |
Plasticity vs. rigidity,
PL
DA+ACh,
5-HT,
GABA |
Intellectual Endurance, ERI
ACh+NE |
|
Social-verbal |
to others:
Empathy,
EMP
NE+OXY, VSP |
Social Tempo,
TMS
DA+ Estr, OXY |
Social Endurance,
ERS
5-HT+Estr, H, OXY |
|
Physical-motor
(>deterministic) |
to sensations,
SS
NE+αAR+
NP/SubP |
Motor Tempo,
TMM
DA+GABA, A, NP |
Motor Endurance
ERM,
5-HT+ORE, H, NP |
|
Emotional amplifier |
Neuroticism, NEU
KOR→NE-HPA KOR > MOR |
Impulsivity,
IMP
DOR(DA, MOR)>KOR |
Satisfaction, SLF
MOR→(5-HT, DA) MOR > KOR |
References:
Trofimova,
I.
(2016)
The interlocking between functional aspects of activities and a neurochemical
model of adult temperament. In: Arnold, M. C (Ed.)
Temperaments: Individual Differences, Social
and Environmental Influences and Impact on Quality of Life. Nova
Science Publishers, Inc., NY pp. 77-147
Trofimova, I. & Robbins, T.W. (2016)
Temperament and arousal systems: a new synthesis of differential psychology and
functional neurochemistry. Neuroscience and Biobehavioral Reviews,
64, 382-402. DOI: 10.1016/j.neubiorev.2016.03.008.
PDF
Trofimova, I. (2018) Functionality vs dimensionality in psychological taxonomies, and a puzzle of emotional valence. Philosophical Transactions of the Royal Society, Biology, 373, 1744, doi: 10.1098/rstb.2017.0167.
Trofimova, I. (2019). An overlap between mental abilities and temperament
traits. In: McFarland, D. (Ed.)
General and
specific mental abilities. Cambridge, UK: Cambridge Scholars
Publishing.
You-tube videos on the FET model:
Presentation of the FET model at the Karolinska Institute, Sweden (in English)
Presentation of the FET model at the World Congress of Mental Health (in Russian)
Neuroscience of Polymathy: Interview with Irina Trofimova at the Annual Creativity Conference 2019
·
...depression and
anxiety have a significant impact on dynamical (speed of integration of an
action) aspects of activity (tempo, plasticity and impulse control),
compromising the responsiveness of behavior through
an increase in rigidity and impulsivity. Depression and anxiety appear to be
associated mostly with aspects of physical activity and less with intellectual
or verbal activity: