a computational perspective on mind and brain · martin butz, [email protected] a...

Martin Butz, [email protected]

A Computational Perspective On Mind and Brain1. From Body and Soul to the Embodied, Anticipatory Mind

Institute of Computer Science & Institute of Psychology

2 | Martin Butz | A Computational Perspective on Mind and Brain

Goal

• Understand how the Mind Comes Into Being

- Functional / Computational Perspective

David Marr: 3 Levels of Understanding- „Computational“- „Algorithmic“- „Hardware“

All three levels and their interactions! This is the functional & computational perspective!

- Abstraction

- Modeling

- Building it, i.e., cognitive systems / cognitive architectures


What …

• … is intelligence ?

• … is understanding ?

• … does a word „mean“ ?

• … do we actually „see“ in our world?

• … do we do - „consciously“ - sub-consciously- automatically ?


True Intelligence

• To understand what is actually happening in our world- Typically primarily on a human-relevant level. Intuitive Physics Folk Psychology (social competence)

• To be able to manipulate objects and use tools.

• To interact with the world flexibly and adaptivelygiven the current circumstances.

• Doing all this for the purpose of …

- satisfying “bodily needs” ? ? ?

- content ? ? ?

- true happiness ? ? ?

https://www.biomotionlab.ca/Demos/BMLwalker.html


BookHow the Mind Comes into Being:Introducing Cognitive Science from a Functional and Computational Perspective

December 2016 | Paperback

Martin V. Butz and Esther F. Kutter

How is it that we can think highly abstract thoughts, seemingly fully detached from the actual, physical reality? It is puzzling how our mind controls our body, and vice versa, how our body shapes our mind.

This book offers an interdisciplinary introduction to embodied cognitive science, addressing the question of how the mind comes into being, while actively interacting with and learning from the environment by means of the own body.


CURRENT MAJOR PROBLEMS IN COGNITIVE SCIENCE

“Frame Problem““Symbol & GrammarGrounding”“Binding Problem“ & “Mind-Body Problem”


Problem 1: The Frame ProblemMore Info: http://plato.stanford.edu/entries/frame-problem/

• Central problem: How do we know what is actually relevant (for accomplishing

something).

• Which effects of a behavior / a manipulation are actually relevant?

- Generally, an myriad, mere infinite number of effects are caused by any behavior !

• Prediction problem: what to predict / consider as consequences) ? • Relevancy problem: what is actually relevant for accomplishing a task / an

action / a goal ? • Qualification problem: did we really consider all relevant circumstances /

potential situations )


The Frame Problem

• R1“robot”

• R1D1“robot deducer”(considers changes)

• R2D1“robotrelevant-deducer”Considers relevancies

• R2D2Always knows „inituitively“ what to consider.

Daniel DennettAmerican philosopher(“philosophy of mind”)

by Isabelle Follath


Problem 2: The „Symbol Grounding“ Problem

• How do symbols (e.g. words) encode the world?


The „Symbol Grounding“ ProblemHow do symbols encode about the world?

• Even more pressing: - The grammar grounding problem.

• Poverty of stimulus argument: - Grammar is so complex … - … but as children,

we learn it nearly effortless Develop an intuitive grammatical understanding

- How?

• Noam Chomsky’s answer: Must be innate!

• Embodied cognitive science: - We develop a grammar-ready, language-ready cognitive system from our

sensorimotor interactions with and experiences about our environment.

- Neural attractors (encoding e.g. an object, a particular behavior etc.) are linked to symbols.

- Environmental interactions are associated with grammatical structures / contain grammatical structures – reflecting the systematics of our world.


Problem 3: The Binding Problem

• Navon figures (many small or one big letter, cf. simultanagnosia) ?

• Multiple perspectives(von M.C. Escher)

• Nuns or Voltaire (von S. Dali)


Walker or dots ?


Binding Problem II

How does our brain bind multiple sources of stimulus information into one percept or „Gestalt“? Gold

BlackGoldBlack

readsilent

Flora

GiuseppeArcimboldo*1526-1593

Simultanagnosia(literally failing to see the forest for all the trees)


Problem 4: The Mind-Body Problem

• How does animmaterial, immortal soul control a material, mortal body?


Plato‘s Question: What is Knowledge?(424/423 BC – 348/347)

• Mind and Body- Plato believes that the mind does

not reflect the organization of the body (somewhat in disagreement with embodiment). (in „Phaedo“).

- He favors the belief of an immortal, immaterial soul.

http://lssacademy.com/2008/01/14/shadows-or-reality/

Platon

http://lssacademy.com/2008/01/14/shadows-or-reality/


The Cave Allegory (in “The Republic”).

• SOCRATES: [...] Imagine human beings living in an underground, cavelike dwelling, [...]. They have been there since childhood, with their necks and legs fettered, so that they are fixed in the same place, able to see only in front of them, because their fetter prevents them from turning their heads around. Light is provided by a fire burning far above and behind them. Between the prisoners and the fire, there is an elevated road stretching. Imagine that along this road a low wall has been built-like the screen in front of people that is provided by puppeteers, and above which they show their puppets.

• [...] Also imagine, then, that there are people alongside the wall carrying multifarious artifacts that project above it statues of people and other animals, made of stone, wood, and every material. And as you would expect, some of the carriers are talking and some are silent.

• GLAUCON: It is a strange image you are describing, and strange prisoners.

• SOCRATES: They are like us. I mean, in the first place, do you think these prisoners have ever seen anything of themselves and one another besides the shadows that the fire casts on the wall of the cave in front of them?

- [...] What about the things carried along the wall? Isn’t the same true where they are concerned?

- [...] All in all, then, what the prisoners would take for true reality is nothing other than the shadows of those artifacts.


Descartes: Mind-Body „Problem“

• Descartes (1596 – 1650)- "Je pense, donc je suis" (Discours

de la méthode pour bien conduiresa raison, et chercher la verité dansles sciences, 1637)

- "Dubito, ergo cogito, ergo sum" (Principles of Philosophy (Principia philosophiae), 1644)

Sets the state for philosophical developments over the next centuries

Particularly sets the stage for the „Mind-Body Problem“(Dualism):How can an immaterial soul control a material body ?

http://www.optics.arizona.edu/Nofziger/UNVR195a/Class3/Descartes3.jpg


The Homunculus Problem(Cartesian Theatre - Daniel Dennett, 1991)

• The perspective emphasizes the problem of „re-representations“

- Including symbolic representations

• Homunculus (i.e. the soul) observes sensory inputs and issues motor commands.

• How may this explain (the functionality of) our mind, our feelings, our consciousness !?


Alan Turing (1912–1954)The TURING TEST

“I propose to consider the question, "Can machines think?“This should begin with definitions of the meaning of the terms "machine" and "think."

The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous.If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd.

Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game‘.“ (p. 433)

Turing (1950) Computing machinery and intelligence. Mind 59, 433-460.

Alan Turing


The „Chinese Room“ Problem(John Searle, 1980) http://www.iep.utm.edu/chineser

• Chinese room:- Room with a huge data base of Chinese symbols

- A huge set of rules (in English), how to manipulate the symbols.

- Human, who uses the rules to manipulate the symbols (producing „answers“ to incoming „questions“ via the symbol manipulations)

- External person probes the „Chinese Room“ Is there a thinking, intelligent person in the room? Can this person speak / understand Chinese?

Implications− Human in the room does not

understand Chinese. − Symbols remain meaningless, despite

the appearance to the external

Argument for “weak AI” systems:Seemingly intelligent, human-like behavior does not imply that there is a sentient human being behind it.

http://www.iep.utm.edu/chineser


Searle‘s Dualismus

• Problem of the Chinese Room: “SEMATICS” are missing

- Note the close relation to the „Symbol Grounding“ Problem

- There are no meaningful encodings, states, representations …

- Neither the room nor the human inside are truly „intelligent“.

• John Searle‘s (1980) Argumentation:

- „Brains cause minds“

- „Syntax does not suffice for semantics“

- There is more to the brain than symbols and syntax.

• His arguments are

in favor of- “Weak AI” (intelligent machines have no semantics)

against - “Strong AI” (seemingly intelligent machines are like humans)

John Searle


Consequence: The „Embodiment Turn“(Grafiken: Pfeifer & Bongard (2006). How the Body Shapes the Way We Think: A New View of Intelligence.)

• Cognitive intelligence needs a body!

- Semantics cannot emerge from symbols and rules alone.

- It are the sensorimotor interactions generated by the body, with which the environment is experienced, and from which semantics develops!!!


The Development of Semantics(from an embodiment perspective)

• Environment, body, and brain continuously interact with each other.

• Intelligence develops from these interactions, that is, from the gathered sensorimotor experiences.

• These experiences are thus inevitably pre-structured by the bodily morphology and the properties of the environment (including physics, biology, environment, social and cultural factors etc.) – they essentially reflect reality.

• Cognitive development results from the analysis of these experiences, driven by self-motivation.

• Self-motivation is inevitably evolutionarily grounded.

• To understand how this actually works, scientific research on Embodied Cognition developed.

• Main implications with respect to the four problems are: Symbols become semantically meaningful by associating them with sensorimotor-grounded

experiences (and suitably abstractec and generalized encodings thereof). (Symbol grounding)

Entities and behaviors are flexibly bound into units of thought. (Binding problem) The analysis of embodied sensorimotor experiences directly encodes beliefs about

relevancies. (Frame problem) Grammar reflects the semantics found in our world. (Grammar grounding) The mind-body problem disappears.


EMERGENCE

Morphological ComputationsEmergent BehaviorsEmergent Attractors


Phenomenon: Emergent Behavior

• Emergent behavior is behavior that was no programmed explicitly or directly, but rather emerges from control-body-sensory-motor-environmental interactions.

• Types of emergent behaviors: - Individual behavioral phenomena via body (i.e. sensory-motor) -environment

interactions. Passive walker Attractors in four-legged motion Insect eyes, flying and landing Frog

- Globally collective behavior, where individual beahavior is determined by simple sensory-motor couplings Swiss robots Ants & Pheromones

- Ontogenetische Phänomene wie Entwicklung des Selbst, Bewusstsein, der Sprache, Laufen lernen etc.

- Evolutionäre Entwicklung der Körpermorphologie, Entwicklungsprädispositionen(„Biases“)


Behavior-Oriented Artificial Intelligence / Cognition

• Marvin Minsky (*1927)- Society of Mind, 1987Modular, interactive approach to intelligence with small, processing

experts.

• Rodney A. Brooks (*1954)- Behavior-oriented artificial intelligence- Intelligence without representation / without reason- Subsumption architecture (more complex, interdependen behavioral

modules)

• Rolf Pfeifer- Understanding Intelligence, 1999- How the Body Shapes the Way We Think: A New View of Intelligence,

2007 (mit J. Bongard)


Importance of Bodily Morphology

• The morphology of the body is rather intelligent (shaped by evolution!)Self-stabilizing (e.g. agonist / antagonist)Morphological computations (automatic sensory normalization, e.g.

when entering a dark room)

• Sensor and motor system is strongly interactive- Body is a dynamic, complex, interactive system- Sensory-motor interactions result in morphological computations and

yield useful morphological attractors. Attractors essentially encode dynamic, stable patterns. Such a pattern can be encoded into a “symbolizable” attractor state


Passive Walker(http://ruina.tam.cornell.edu/research/topics/robots/index.php)

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http://ruina.tam.cornell.edu/research/topics/robots/index.php


Morphological Algorithms

Figure 4.1 in Pfeifer & Bongard (2006): Morphological computation. (a) Sprawl robot exploiting the material properties of its legs for rapid locomotion. The elasticity in the linear joint provided by the air pressure system allows for automatic adaptivity of locomotion over uneven ground, thus reducing the need for computation. (b) An animal exploiting the material properties of its legs (the elasticity of its muscle-tendon system) thus also reducing computation. (c) A robot built from stiff materials must apply complex control to adjust to uneven ground and will therefore be very slow.


Morphological Attractors

• Imagine an attractor surface

• Example: different types of four-legged motion:

- Walking- Trotting- Running- Jumping- Skipping


Beispiel Morphologische Berechnung: Billige, einfache, flexible Bewegung I

• Robot „Stumpy“ (Iida et al., 2002, Paul et al., 2002)http://www.ifi.unizh.ch/ailab/people/iida/stumpy/

http://www.ifi.unizh.ch/ailab/people/iida/stumpy/


Beispiel Morphologische Berechnung: Einfache, flexible Bewegung II

• Robot „Puppy“http://www.ifi.unizh.ch/ailab/teaching/semi2004/

http://www.ifi.unizh.ch/ailab/teaching/semi2004/


Example of a Biological System that uses Morphological Computations

• Task: Fly past an object / above a ground at a safe distance.

• Classical AI approach: complete computation

- Compute object / ground encodings. - Determine the exact distance to object / ground. - Compute desired heading direction dependent on distance.

- … how can an insect compute all this ?

Isn‘t this way too complicated ?

Is it possible to encode this in an evolutionary manner in neurons ?

Does an insect learn this ?


Morphological computationsby the Eyes of an Insect

• Systematic morphology of insect eyes

• Optical flow is approximately constant when passing an object / the floor

• Facets of the eyes are very suitably distributed

• Modelling approach: - Eyebot by Lichtensteiger &

Eggenberger:Evolutionary algorithm evolved similar eye structure in robots.

• (Lichtensteiger, L. & Eggenberger, P. (1999). Evolving the morphology of a compound eye on a robot. Advanced Mobile Robots, 1999. (Eurobot '99) 1999 Third European Workshop on, 127-134. 10.1109/EURBOT.1999.827631.)


Sensorimotor coupling

• Keep optical flow at a constant rate:- As a result: objects / floor is kept at a certain distance

• Analyze optical flow expansion:- When expansion reaches very high speed, landing is imminent

(project legs towards point of expansion)

• Similar, more advanced coupling in the frog: Food = small quickly, irregularly moving optical flow (e.g. flies) Danger = large, dark, expanding optical flow.


Braitenberg VehiclesValentino Braitenberg (1984) Vehicles: Experiments in Synthetic Psychology

• Direct coupling between sensory signals to motor activities.

• Consequence: - Move towards light

source- Avoid light source


Example: Ants

• Probabilistically: - Put down pheromones- Follow pheromones

• Carry food back to nest with homing algorithm.

• Shortest path receives more pheromones faster.

• Result: shortest paths to food source develop in an emergent manner.


MIND THE “FRAME OF REFERENCE PROBLEM”!

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„Frame of Reference Problem“

• Example: Simon‘s Ants at a beach:- Ant moves about on the beach

• Different perspectives: - Observer perspective: Ants follows a complex path avoiding obstacles / seemingly wanting to go

somewhere. → automatic (false) inference of a complex (planning) mechanism.

- Perspective of the ant Follows simple sensory-motor couplings and few heuristics.

→ Ants does not really plan at all!


Robots are Pushing the Blocks Together (duration 20min)


“Swiss Robots” (Didabot)ftp://ftp.ifi.unizh.ch/pub/institute/ailab/techreports/html/95.09/index.html

Observer Thinks:Robots are Pushing the Blocks Together

Robot Thinks: Nothing!

Robot Control: • Braitenberg-Coupling

between distance sensors and wheels.


Human Interpretation of „Reality“

• Based on Fritz Heider & Marianne Simmel (1944, experimentellen Untersuchung von Attributionen bei der Beobachtung der Bewegung einfacher geometrischer)

• http://www.youtube.com/watch?v=cYAMPsALf-E&feature=player_embedded

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http://www.youtube.com/watch?v=cYAMPsALf-E&feature=player_embedded


Frame of Reference Problem -Consequence

• Beware of your interpretation of reality!

• We humans are complex, anticipatory, actively interpreting systems!

• These interpretations depend on our (physical, biological, bodily, social, & cultural) experiences of reality!


So what about the Mind ?

• Reflexes / sensorimotor couplings are not enough!

• Nonetheless, cognition (particularly predictions & anticipations) must develop from sensorimotor experiences.

Human

Cognition

ReflexesSensors

Motors


Tomorrow –Development of Our Minds

a computational perspective on mind and brain · martin butz, [email protected] a...

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