Lecture 2: Paradigms of Cognitive Science - David Vernon

An Introduction to 

Artificial Cognitive Systems 

David Vernon 

Institute for Cognitive Systems 

Technical University of Munich 

www.vernon.eu 

Copyright © 2012 David Vernon

Lecture 2 

Paradigms of Cognitive Science 


Cognitive Science 

Cybernetics 

1943-53 

Cognitivism 

1956- 

(AI) 

Emergent Systems 

1958- 

Network Dynamics 

Synergetics 

Dynamical Systems Theory 

Enactive Systems 

1970- 

[Varela 88] 


COGNITION 

Self-Organization 

Cognitivist 

Hybrid 

Emergent 

Physical Symbol Systems 

Newell’s Unified Theories 

of Cognition 

Computational paradigm 

Dynamical 

Systems 

Connectionist 

Systems 

Enactive 

Systems 


Some Background 

on 

Cybernetics 


Cybernetics 

• The word cybernetics has its roots in the Greek word κυβερνητης or 

kybernetes, meaning steersman 

• It was defined in Norbert Wiener’s book Cybernetics, first published 

in 1948, as “the science of control and communication” (this was the 

sub-title of the book) 

• W. Ross Ashby notes in his book An Introduction to Cybernetics, 

published in 1956 that cybernetics is essentially “the art of 

steersmanship” 

– co-ordination, regulation, and control. 

• The word governor derives from gubernator, the Latin version of 

κυβερνητης 


Cybernetics 

• The word cybernetics has its roots in the Greek word κυβερνητης or 

kybernetes, meaning steersman 

• It was defined in Norbert Wiener’s book Cybernetics, first published 

in 1948, as “the science of control and communication” (this was the 

sub-title of the book) 

• W. Ross Ashby notes in his book An Introduction to Cybernetics, 

published in 1956 that cybernetics is essentially “the art of 

steersmanship” 

– co-ordination, regulation, and control. 

• The word governor derives from gubernator, the Latin version of 

κυβερνητης 


Cybernetics 

• Closed-loop control 

• An action by the system causes some change in its environment 

and that change is fed to the system via feedback that enables the 

system to change its behaviour 

• This "circular causal" relationship is necessary and sufficient for a 

cybernetic perspective [Wikipedia] 


Cybernetics 

• “The essential goal of cybernetics is to understand and define the 

functions and processes of systems that have goals and that 

participate in circular, causal chains that move from action to 

sensing to comparison with desired goal, and again to action.” 

[Wikipedia] 


Cybernetics 

• Recent definition proposed by Louis Kauffman, President of the 

American Society for Cybernetics 

"Cybernetics is the study of systems and processes that interact with 

themselves and produce themselves from themselves." 


COGNITION 


Cognitivist 

Hybrid 

Emergent 





Dynamical 

Systems 

Connectionist 

Systems 

Enactive 

Systems 


Different Paradigms of 

Cognitive Systems 

The Cognitivist Approach 

Information Processing 


Representationist Approach 


‘The world we perceive is isomorphic with our perceptions of it 

as a geometric environment’ 

[Shepard & Hurwitz ‘84] 


‘Cognition is a type of computation’ 

‘People “instantiate” … representations physically as cognitive 

codes and that their behaviour is a causal consequence of 

operations carried out on these codes’ 

[Pylyshyn ‘84] 


From The Tree of Knowledge – The Biological Roots of Human Understanding 

H. R. Maturana and F. J. Varela, 1988 


Cognitivist Systems 

• Strong representations 

– Explicit & symbolic 

– Representations denote external objects 

– Isomorphic 

– Projection 

– Implies an absolute and accessible ontology 

– That is consistent with human expression … 




• Representations 

– Descriptive product of a human designer 

– Can be directly accessed & understood by humans 

– Human knowledge can be directly injected into an 

artificial cognitive system 



• But … 

Programmer-dependent representations bias the system 

‘blind’ the system (cf. Winograd & Flores) 

• … can you anticipate every eventuality in your design? 

• The semantic gap … and the Symbol Grounding problem 



• Plugging the gap by … 

– Machine learning 

– Probabilistic modelling 

– Better models 

– Better logics 

– Better reasoning 

– Better everything 

– … and still … 


Cognitive science 

is involved in an escalating retreat 

from the inner symbol: 

a kind of inner symbol flight 

A. Clark, Mindware 


Cognitivism and Artificial Intelligence 


Cognitivism & Artificial Intelligence 

• Physical symbol system approach to AI 

• Intelligence 

– The degree to which a system approximates a knowledge-level system 

[Unified Theories of Cognition, Newell 90] 

– A knowledge-level system: can bring all its knowledge to bear on every 

problem 

• Perfect knowledge -> complete use of knowledge 

• Humans aren’t there yet!! 

– Principle of maximum rationality [Newell 82] 

• ‘If an agent has knowledge that one of its actions will lead to one of its goals, 

then the agent will select that action’ 

– Rational analysis [Anderson 89] 

• ‘The cognitive system optimizes the adaptation of the behaviour of the 

organism’. 



• Physical Symbol Systems 

– Symbols are abstract entities that can be instantiated as tokens 

– A physical symbol system has [Newell 90]: 

• Memory (to contain the symbolic information) 

• Symbols (to provide a pattern to match or index other symbols) 

• Operations (to manipulate symbols) 

• Interpretations (to allow symbols to specify operations) 

• Capacities for 

– Composability 

– Interpretability 

– Sufficient memory 

– Symbol systems can be instantiated but … behaviour is 

independent of the particular form of the instantiation 



• Physical Symbol Systems [Newell and Simon 1975] 

– The Physical Symbol System Hypothesis 

• A physical symbol system has the necessary and sufficient means for 

general intelligent action 

• Any system that exhibits general intelligence is a physical symbol 

system 

• A physical symbol system is ‘a machine that produces through time an 

evolving collection of symbol structures’ 




Symbol Systems 

comprise 

comprise 

Symbol Structures / 

Expresssions / 

Patterns 

Produce, destroy, modify 

Processes 

designate 

Objects 

designate 

Processes 

Can affect objects 

Can be affected by objects 

Can be interpreted: carry out the designated process 




– The Heuristic Search Hypothesis 

• The solutions to problems are represented as symbol structures 

• A physical symbol system exercises its intelligence in problem-solving 

by search 

– Generating and progressively modifying symbol structures until ti produces 

a solution structure 

– Effective and efficient search 

• ‘The task of intelligence is to avert the ever-present threat of the 

exponential explosion of search’ 



• Physical Symbol Systems 

– The Physical Symbol System Hypothesis 

A physical symbol system has the necessary and sufficient means 

of general intelligence 

– What are the implications for humans??? 

– Natural and artifical intelligence is equivalent (why?) 



Newell’s four levels 

Social 

10 5 –10 7 s; behaviours 

Requires a symbolic level 

Rational 

10 2 –10 4 s; tasks requiring reasoning (find way home) 

Cognitive 

10 -1 –10 1 s; deliberate acts (reaching), 

composed operations (shifting gear) 

actions (steering a car into a gate) 

Biological 

10 -4 –10 -2 s; organelle, neuron, neural circuit 

All knowledge is here 



• (Cognitive) Architecture: defines the manner in which a cognitive agent 

manages the primitive resources at its disposal 

• Dictates representations and their deployment 

• Dictates properties of cognitive system 

– Organization & control strategies (coordination/cooperation; 

modular/hierarchical) 

– Memory, knowledge, representation (world models, delarative 

representations, procedural representations, associate memory, episodic 

knowledge, meta-knowledge, represenational structures) 

– Types of learning (deliberative vs reflexive; monotonic vs non-monotonic) 

– Types of planning 

– Behaviour (coherence, saliency, & adequacy: consistency, relevance, 

sufficiency) 



• Unified Theories of Cognition 

– Attempts to explain all the mechanisms of all problems in its 

domain 

– Now plausible (Newell) cf the Soar project 

– Applies to both natural and artificial cognition 



• Non-functional Attributes of Cognitive Architectures 

– Generality (breadth of tasks) 

– Versatility (ibid) 

– Rationality (cf. consistency and repeatability) 

– Scalability (cf. complexity) 

– Reactivity (cf. unpredictability) 

– Efficiency (cf. time and space constraints) 

– Extendability (cf. reconfigurability) 

– Taskability (cf. external direction) 

– Psychological validity (cf. human models) 





Emergent Approaches 


COGNITION 


Cognitivist 

Hybrid 

Emergent 





Dynamical 

Systems 

Connectionist 

Systems 

Enactive 

Systems 


Self-generated AI: 

AI by orchestrating the processes that generate it 

Luc Steels 



• Cognition is the process whereby an autonomous system 

becomes viable and effective in its environment 

• It does so through a process of self-organization 

– System is continually re-constituting itself 

– In real-time 

– To maintain it operational identity 

– Through moderation of mutual system-environment interaction and 

co-determination 

Maturana & Varela 87 


• Co-determination 


– Cognitive agent is specified by its environment 

– Cognitive process determines what is real or meaningful for the 

agent 

– The system constructs its reality (world) as a result of its operation 

in that world 

– Perception provides sensory data to enable effective action, but as 

a consequence of the system’s actions 

– Cognition and perception are functionally-dependent on the 

richness of the action interface [Granlund] 



• Cognition is the complement of perception [Sandini] 

– Perception deal with the immediate 

– Cognition deals with longer time frames 

• Primate model of cognitive learning is anticipative skill construction 

(not knowledge acquisition) 

• The root of intelligence is the ability to guide action and, at the same 

time, improve that ability 

• Embodied as physical systems capable of physical interaction with the 

world 



‘Cognitive systems need to acquire information about the external 

world through learning or association’ 

[Granlund’02] 




• Self-organization 

– “Self-organizing systems are physical and biological systems in which 

pattern and structure at the global level arises solely from interactions 

among the lower-level components of the system.” 

– “The rules specifying interactions among the system’s components are 

executed only using local information, without reference to the global 

pattern.” 

• Emergence 

– A process by which a system of interacting elements acquires qualitatively 

new pattern and structure that cannot be understood simply as the 

superposition of the individual contributions. 

Camazine 2006 


• Self-organization 


– Short-range Activator 

• autocatalysis: promote its own productions 

• Increase the production of an inhibitor (antagonist) 

– Inhibitor 

• Diffuses rapidly 

– Result: 

• Local increase in activation 

• Long-range antagonistic inhibition which keeps the self-enhancing 

reaction localized 

Camazine 2006 



Local autocatalysis 

Non-local negative feedback 

Meinhardt 95 



Camazine 2006 


Connectionist Systems 


Connectionist Systems 

• Rely on 

– Parallel processing 

– Non-symbolic distributed activation patterns in networks 

– Not logical rules 

• Neural networks are the most common instantiations 

– Dynamical systems that capture statistical regularities or 

associations 


Dynamical Systems 


COGNITION 


Cognitivist 

Hybrid 

Emergent 





Dynamical 

Systems 

Connectionist 

Systems 

Enactive 

Systems 


• Dynamical Systems 

Dynamical Systems 

– A dynamical system is an open dissipative non-linear non-equilibrium 

system 

– System: large number of interacting components & large number of 

degrees of freedom 

– Dissipative: diffuse energy – phase space decreased in volume with 

time (⇨ preferential sub-spaces) 

– Non-equilibrium: unable to maintain structure or function without 

external sources of energy, material, information (hence, open) 

– Non-linearity: dissipation is not uniform – small number of system’s 

degrees of freedom contribute to behaviour 

• Order parameters / collective variables 

Kelso ‘95: Dynamic Pattern – The Self-Organization of Brain and Behaviour 


Dynamical System 

q = N(q, p, n) 

time derivative 

state vector 

noise 

control parameters 

From [Shoner Kelso 88] 


Enactive Systems 


Enaction 

• Orthodoxy (cognitivist) 

– World as the system experiences it is independent of the cognitive 

system (knower) 

• Enactive view 

– Known and knower ‘stand in relation to each other as mutual 

specification: they arise together’ 


Enaction 

• Five key elements to enactive systems 

– Autonomy 

– Embodiment 

– Emergence 

– Experience 

– Sense-making 


Enaction 

• Autonomy 

– Self-maintenance 

– Homeostasis 

– Not controlled by outside agencies 

– Stands apart from its environment 


Enaction 

• Embodiment 

– Exists as a physical entity 

– Directly interacts with its environment 

• Structural coupling 

• Mutual perturbation 

– Constitutive part of the cognitive process 


Enaction 

• Emergence 

– Cognitive behaviour arises from dynamic interplay between 

component parts 

– Internal dynamics 

• Maintains autonomy 

• Condition the system’s experiences through their embodiment in a 

specific structure 


Enaction 

• Experience 

– History of interaction with the world 

– Interactions don’t control 

– Interaction do trigger changes in system state 

– Changes are structurally-determined 

• Phylogeny 



Enaction 

• Sense-making 

– Knowledge is generated by the system itself 

– Captures some regularity or lawfulness in the interactions 

– The ‘sense’ is dependent on the way interaction can take place 

• Perception & Action 

– Modify its own state (CNS) to enhance 

• Predictive capacity 

• Action capabilities 

– Development: generative autonomous self-modification 


Development 

Progressive ontogenetic acquisition of anticipatory capabilities 

– Cognition cannot short-circuit ontogeny 

– Necessarily the product of a process of embodied development 

– Initially dealing with immediate events 

t 

– Increasingly acquiring a predictive capability 

t 

Cognition and perception are functionally-dependent 

on the richness of the action interface 


Co-determination / Structural Coupling 

Autonomony-preserving mutual interaction 

Perturbation of the system is only effected by the environment 

[Note: this ideogram and similar ones to follow were introduced in Maturana and Varela 1987] 


Cognitive system: operationally-closed system with a nervous system 

Nervous system facilitates a highly-plastic mapping between 

sensor and motor surfaces 

Perturbation by both environment and system (of receptors & NS) 


Enaction 

NERVOUS SYSTEM 

(a) Facilitates huge increase in the number of possible 

sensor-motor patterns (that result from structural coupling 

with the environment) 

(b) Creates new dimensions (DoF) of structural coupling 

by facilitating association of internal states with the 

interactions in which the system is engaged 


t 

t 

Anticipation / Planning / Explanation / Prediction 


INTERACTION 

A shared activity in which the actions of each agent 

Influence the actions of the other agents in the same interaction 

Resulting in a mutually-constructed pattern of shared behaviour 

[Ogden et al.] 


Meaning emerges through shared consensual 

experience mediated by interaction 

Interaction is a shared activity in which the actions of each agent 

influence the actions of the other agents engaged in the same 

interaction, resulting in a mutually-constructed patterns of shared 

behaviour 

Ogden, Dautenhahn, Stribling 2002 

Copyright © 2012 David Vernon 

Bond of Union 

M. C. Escher, 1956

COGNITION & SENSE-MAKING 

Cognition is a process whereby the issues that are important 

for the continued existence of the cognitive entity 

are brought forth: co-determined by the entity as it interacts 

with the environment in which it is embedded 


THE SPACE OF PERCEPTUAL POSSIBILITIES 

Is predicated not on an objective environment, 

but on the space of possible actions 

that the system can engage in whilst still 

maintaining the consistency of its coupling with the environment 



Enaction 

• Autopoiesis 

– Autopoiesis is a special type of self-organization 

– Requires self-specification and self-generation 

(autopoiesis means literally `self-production') 

– An autopoietic system is a special type of homeostatic 

system (i.e. self-regulating system) 

• regulation applies not to some system parameter 

• but to the organization of the system itself. 


Enaction 


– Thus, its organization is constituted by relations between 

components that by their interaction and transformation 

continuously regenerate the network of processes that 

produce them 

– That is, the relations among components -- a network of 

processes of production --- produce the components 

themselves 

– A significant aspect of autopoiesis is that its function is to 

`create and maintain the unity that distinguishes it from the 

medium in which it exists'. 


Enaction 


– Self-production 

– System emerges as a coherent systemic entity, distinct from 

its environment, as a consequence of processes of selforganization 

– Three orders of system … 


Enaction 


– First-order autopoietic systems correspond to cellular 

entities that achieve physical identity through structural 

coupling with the environment: 

Environmental perturbations trigger structural changes that 

permit it to continue operating 


Enaction 


– Second-order systems correspond to meta-cellular entities 

that exhibit autopoiesis through operational closure in their 

organization 

They specify a network of dynamic processes that don’t 

leave the network (organizationally closed) 

Engage in structural coupling with the environment, this time 

with a nervous system ( -> much enlarged space of internal 

states & much enlarge space of interaction) 

– Cognitive systems (at agent level) 


Enaction 


– Third-order systems exhibit coupling between second-order 

(cognitive) systems 

– Common ontogenetic drift between reciprocally-coupled 

systems 

– Shared epistemology / language 

• Reflects but does not abstract the common medium in which 

they are coupled 

– 3 types of behaviour 

• Instinctive behaviour (based on autopoietic self-organization) 

• Ontogenic behaviour (based on its development) 

• Communication / social behaviour 


Enaction 


– Linguistic behaviours: intersection of ontogenic and 

communication behaviours 

– Facilitates creation of a common understanding of a shared 

world (in which they are coupled) 

– Language is an emergent consequence of the third-order 

structural coupling of a socially-cohesive group of cognitive 

entities 


Enaction 


– Consciousness (mind) emerges when language and 

linguistic behaviour is applied recursively to the agent itself 

– Consciousness is an emergent property of social coupling 

– Doesn’t have any independent existence (and you can’t 

model it!) 


Enaction 

• Third-order systems: coupling between second-order 

(cognitive) systems 

– Recurrent (common) ontogenic drift from reciprocal coupling 

– Instinctive behaviour based on second-order organization 

(phylogenic evolution) 

– Ontogenetic behaviour, development qua learning over its lifetime 

– Communicative behaviour: third order structural coupling (social 

coupling) 

– Linguistic behaviours: establishment of a shared epistemology 


The Hierarchy of Enactive Systems 

Same approach: operational closure 

Emergence of Language: 

Explicit deliberative self-reflective reasoning 

Rationalization of 

interactions & perceptions: 

2 

Intelligent 

(adaptive & anticipatory) 

behaviour 1 

3 

Societies of 

Cognitive Entities 

Viable Organisms 

Emergence of Cognition 

Cellular Organization 

Physical Instantiation 

Structural coupling with 

the environment 

Multilevel Autopoeitic / Cognitive Systems 


Enactive Cognition 

MEANING 

Emerges through shared consensual experience 

mediated by interaction 






– Necessarily embodied 

– Operating in synchronous real-time 

Machine representations reflect 

the machine’s epistemology 



Need to achieve synthetic cognition without falling back into using 

cognitivist preconceptions basing the ‘design’ on representations 

derived by external observers (us) 


Bickhard’s Self-Maintenant & 

Recursive Self-Maintenant Systems 


Emergent Models 

• “The grounds for cognition are adaptive far-fromequilibrium 

autonomy – recursively self-maintenant 

autonomy – not symbol processing nor connectionist 

input processing.” 



• Autonomy: the property of a system to contribute to 

its own persistence 

• Different grades of contribution -> different levels of 

autonomy 


• Self-maintenant Systems 


– Make active contributions to its own persistence 

– Do not contribute to the maintenance of the conditions for 

persistence 



• Recursive self-maintenant systems 

– Do contribute actively to the conditions for persistence 

– Can deploy different processes of self-maintenance 

depending on environmental conditions 

– “they shift their self-maintenant processes so as to maintain 

self-maintenance as the environment shifts” !!! 



• Far from equilibrium stability 

– Non-thermodynamic equilibrium 

– Requires that the system does NOT go to thermodynamic 

equilibrium 

– Completely dependent on their continued existence on continued 

contributions from external factors 

– Do require environmental interaction 

– Necessarily open processes (which exhibit closed organization) 



• Self-maintenant and recursive self-maintenant systems are both 

far-from-equilibrium systems 

• Recursive self-maintenant systems do yield the emergence of 

representations 

– Function emerges in self-maintenant systems 

– Representation emerges in a particular type of function 

(“indications of potential interactions”) in recursively self-maintenant 

systems 



Why development in humanoid form 

is important for cognition 


Emergent 

Systems 

Cognition is the process whereby 

an autonomous system becomes 

viable and effective in its 

environment 


Emergent 

Systems 

through moderation of mutual systemenvironment 

Self- 

Organization 

The system is continually 

re-constituting itself in real-time 

to maintain its operational identity 


Emergent 

Systems 

Self- 

Organization 

Codetermination 

The system and environment are co-determined 

through structural coupling and 

contingent self-organization 

Co-determination: 

Agent is specified by its environment and the 

cognitive process determines what is real or 

meaningful for the agent 

Agent constructs its reality as a result of its 

operation in that world. 


Emergent 

Systems 

Self- 

Organization 


Maturana's and Varela's idea of structural coupling of level one 

autopoietic systems (MaturanaVarela87) 

Kelso's circular causality of action and perception (Kelso95) 

Organizational principles inherent in Bickhard's self-maintenant 

systems (Bickhard00) 


Emergent 

Systems 

Self- 

Organization 


This implies a particular phylogeny: 

Phylogeny 

1. To effect the homeostatic self-organization 

2. To effect the structural coupling 


Emergent 

Systems 

Self- 

Organization 


Phylogeny 

Neuroscience 

Developmental 

Psychology 

Recent studies in natural cognition 

guide the identification of this phylogeny 


Emergent 

Systems 

Self- 

Organization 


Development 

Phylogeny 

Development is the cognitive process of 

establishing (discovering) the possible space of 

mutually-consistent couplings 

Bickhard's concept of recursive self-maintenance (Bickhard00) 

Maturana's and Varela's level two and level three autopoietic systems (MaturanaVarela87) 


Emergent 

Systems 

Self- 

Organization 


Development 

Phylogeny 

Ontogeny 

Development implies ontogenic development: 

the cognitive system develops its own 

epistemology: its own system-specific history- and 

context-dependent knowledge of its world 


Emergent 

Systems 

Self- 

Organization 


Development 

Phylogeny 

Ontogeny 

Neuroscience 

Developmental 

Psychology 


Neural Nets 

Dynamical 

Systems 

Emergent 

Systems 

Self- 

Organization 


Development 

Phylogeny 

Ontogeny 


Emergent 

Systems 

Self- 

Organization 

Embodiment 


Development 

Phylogeny 

Ontogeny 

Emergent systems also implies embodiment … 

But what type of embodiment? 

It depends on the needs of the structural coupling 


Emergent 

Systems 

Self- 

Organization 

Embodiment 


Development 

Phylogeny 

Ontogeny 

Structural Coupling 

The system-environment perturbations must be rich enough 

to drive the ontogenic development but not destructive of the self-organization 

No guarantee that the cognitive behaviour will be consistent with human preconceptions 


Emergent 

Systems 

Self- 

Organization 

Embodiment 


Development 

Phylogeny 

Ontogeny 

Humanoid 

Meaning emerges through a shared consensual experience, 

mediated by interaction (mutually-constructed shared pattern of behaviour) 

Therefore, we require a humanoid embodiment to effect mutually-consistent meanings 


Otherwise, this is what we’ll get … 


Emergent 

Systems 

Self- 

Organization 

Embodiment 


Development 

Phylogeny 

Ontogeny 

Humanoid 


Emergent 

Systems 

Self- 

Organization 

Embodiment 


Development 

Phylogeny 

Ontogeny 

Humanoid 

Morphology is a constitutive component of both co-determination and development 

Consequently, a plastic morphology is important: the embodiment shouldn’t be static 


Emergent 

Systems 

Self- 

Organization 

Embodiment 


Development 

Phylogeny 

Ontogeny 

Humanoid 

Cognitive 

Development 

Increasing complexity 

in action space 

Increasing degree 

of Prospection 




Hybrid Approaches 


Hybrid Models 

• Combination of cognitivist & emergent approaches 

• Don’t use explicit programmer based knowledge 

• Can use symbolic representations (& representational 

invariances) but these are populated by the system itself as it 

learns 

• Still emphasize perception-action coupling 

• Often with action-dependent perception 



Hybrid Models 

 

 

 


RECAP 


Differences between Cognitivist & Emergent Paradigms 

Differences between Cognitivist 

& Emergent Paradigms 

1. Computational operation 

2. Representational framework 

3. Semantic grounding 

4. Temporal constraints 

5. Inter-agent epistemology 

6. Embodiment 

7. Perception 

8. Action 

9. Anticipation 

10.Adaptation 

11.Motivation 

12.Autonomy 

13.Cognition 

14.Philosophical foundation 

[Vernon, Von Hofsten, Fadiga 2010] 



What is Cognition? 

Cognitivism 

Information processing: rule-based 

manipulation of symbols 

Connectionism 

Emergence of global states in a network of 

simple components 

Dynamical 

Systems 

A history of activity that brings forth change and 

activity 

Enactive 

Systems 

Effective action: history of structural coupling 

which enacts (brings forth) a world 

Adapted from [Thelen & Smith 94] and [Varela 88] 


How Does it Work? 

Cognitivism 

Through any device that can manipulate 

symbols 

Connectionism 

Through local rules and changes in the 

connectivity of the elements 

Dynamical 

Systems 

Through self-organizing processes of 

interconnected sensorimotor subnetworks 

Enactive 

Systems 

Through a network of interconnected elements 

capable of structural changes undergoing an 

uninterrupted history 


What does a good cognitive system do? 

Cognitivism 

Represents the stable truths about the real 

world, and solves problems posed to it 

Connectionism 

Develops emergent properties that yield stable 

solutions to tasks 

Dynamical 

Systems 

Becomes an active and adaptive part of an 

ongoing and continually changing world 

Enactive 

Systems 

Becomes part of an existing on-going world of 

meaning (in ontogeny) or shapes a new one (in 

phylogeny) 


[From Varela 92] 


Which Paradigm is Correct? 

• Paradigms are not equally mature 

• Dynamical systems 

– Arguments are compelling BUT .. 

– Not yet clear how to get higher-level cognition 

• Cognitivist systems 

– More advanced 

– Not many achievements in generalization 

– More brittle (in principle) 

• Enactive (& Dynamical) 

– SHOULD be much less brittle (mutual specification through co-development) 

– But limited cognition at present 

• Hybrid systems 

– Best of both worlds? 

– But unclear how one can really combine antagonistic philosophies 


Which Paradigm is Correct? 

• No on one has actually designed a complete cognitive system 

• Still lots of disagreement on the right approach to take 


Recommended Reading 

Vernon, D. “Artificial Cognitive Systems – A Primer for Students”, 

Chapter 2. 

Vernon, D., Metta. G., and Sandini, G. “A Survey of Artificial 

Cognitive Systems: Implications for the Autonomous 

Development of Mental Capabilities in Computational Agents”, 

IEEE Transactions on Evolutionary Computation, special issue 

on Autonomous Mental Development, Vol. 11, No. 2, pp. 151- 

180 (2007). 

Vernon, D., von Hofsten, C., and Fadiga, L. A Roadmap for Cognitive 

Development in Humanoid Robots, Cognitive Systems 

Monographs (COSMOS), Springer, ISBN 978-3-642-16903-8 

(2010); Chapter 5.

Lecture 2: Paradigms of Cognitive Science - David Vernon

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