Intelligence, Cognition, and Adaptive Behavior. Steffen Wischmann, Intelligence, cognition, and adaptive behavior. CNS lecture no. 2,

Transcription

1 Intelligence, Cognition, and Adaptive Behavior

2 The Interdisciplinary Nature of Computational Neuroscience

3 Intelligence, Cognition, and Adaptive Behavior Is something missing?

4 Warm up What are the main characteristics of intelligence and cognition? Any suggestions?

5 What is intelligence? Mainly assumed characteristics: thinking logic reasoning problem solving What is about drinking a glass of water? recognizing friends? preparing a meal?

6 What is intelligence? Is a bacteria intelligent? Are ants intelligent? Ant colonies? Mice? Cats? Babies? Machines?

7 What is intelligence? Intelligence is a descriptive term. Descriptive terms are rather arbitrary (and usually relative to your perspective). Are there common denominators?

8 Intelligence What is it for? Our brain hasn't evolved to solve mathematical proofs! It controls our behavior to ensure our survival. Natural intelligence is always manifested in behavior. We have to understand behavior and its underlying mechanisms to understand intelligence. To behave a body is needed -> embodied intelligence.

9 Intelligence Definitions Are there general definitions about intelligence? (unfortunately not) Let's have a look in the history: Journal of Educational Psychology (1921) asked 14 experts for definitions of intelligence, and it got 14 different answers: abstract thinking (Terman) ability of learning to adjust yourself to the environment (Colvin) a biological mechanism by which the effects of a complexity of stimuli are brought together and given a somewhat unified effect in behavior (Peterson) the capacity to acquire capacity (Woodrow) the capacity to learn or to profit by experience (Dearborn)

10 Intelligence Commonsense notions Thinking and problem solving Learning and memory Language Intuition and creativity Consciousness Emotions Surviving in a complex world Perceptual and motor abilities

11 Intelligence Commonsense notions Thinking and problem solving: We never know whether another agent (human, animal, robot) is thinking or not! We can only speculate about it. Learning and memory: Just memorizing facts out of context is pointless! Memory for useful knowledge is important. Transfer of knowledge, not merely storing it. Learning per se is not intelligent, but learning to learn!

12 Intelligence Commonsense notions Language: Often seen as the hall mark of intelligence. And humans are outstanding in that. Combines good learning and memory capacity. Abilities many animals have, too! Intuition and creativity: go beyond thinking; requires engagement of emotions intuition: come to conclusions without a tracable chain of logical thoughts creativity: relative to a particular society's value often seen as the highest form of human intelligence combination of consciousness thought and unconsciousness processes curiosity? (animals have that)

13 Intelligence Commonsense notions Consciousness hard to define what it is all about required by thinking, language, and creativity essential for intelligence??? Emotions richness of emotions are claimed to depend on intelligence emotional intelligence?

14 Intelligence Commonsense notions Surviving in a complex world is fulfilled by every living being in the world for instance: social behavior (from bacteria to humans) use of tools (birds, primates, etc.) Perceptual and motor abilities: mistakenly often not assumed to be essential for intelligence What's about: recognizing kin (e.g. faces)? walking on two legs, playing tennis, constructing a spider web?

15 Intelligence Can we measure intelligence?

16 Intelligence How useful is an IQ test to measure intelligence? In fact, it is problematic to reduce a complex issue as intelligence to a single number! For instance, Gardner (1985) proposed a theory of multiple intelligence or multiple competences: linguistic intelligence, musical intelligence, spatial intelligence, bodily-kinesthetic intelligence, personal (emotional) intelligence. most of those competences can: not be measured with standard tests, not simply mapped onto one dimension.

17 Intelligence A matter of brain size? 508 meters high 282 times the average height of a man 2,50 meters high / deep 250 times the average length of an ant

18 Intelligence A matter of brain size? Ant brain: 250,000 neurons So, 40,000 ants have the same number of neurons as a single human brain

19 Intelligence A matter of brain size? Myxomycota (slime molds) No brain and no neurons at all!

20 What are the origins of intelligence?

21 Intelligence The nature-nurture debate Is intelligence largely innate, genetically predetermined? If so, it has to be coded in the genes! BUT: genes interact with their environment on all levels "There is virtually no interesting aspect of development that is strictly 'genetic'" (Elman et al., 1996) Solution to the question about the origin of intelligence presumably lies in the interaction between genetic factors (nature) and environmental factors (nurture). This leads to a new scientific quest: How does development actually works? In which precise way do genetic and environmental factors interact?

22 Is there a common denominator for intelligence?

23 Intelligence The diversity-compliance trade-off What are the mechanisms enabling organisms to adapt to, cope with, environmental changes? The dual meaning of adaptivity: complying with existing rules (compliance: conservative component) rules of information, social rules, rules grammar, laws of nature and physics generating new behavior (diversity: innovative component) This characterization applies to all levels: a mathematician's abstract thinking abilities a child's conversation to its parents an animal's escape from a predator Note and keep in mind: Terms like adaptation, behavior, and generation of behavioral diversity imply the existence of a body interacting with an environment!

24 Intelligence The diversity-compliance trade-off Known from psychology (Piaget, 1952) as Assimilation & Accommodation:

25 Intelligence Is it all about information processing?

26 Intelligence The information processing metaphor Intelligence as an input-processing-output cycle (open-loop systems!) Focuses mainly on: thinking abstract reasoning abstract problem solving Wait a second, what have we said about intelligence?

27 Intelligence What is it for? Our brain hasn't evolved to solve mathematical proofs! It controls our behavior to ensure our survival. Natural intelligence is always manifested in behavior. We have to understand behavior and its underlying mechanisms to understand intelligence. To behave a body is needed -> embodied intelligence.

28 Intelligence The information processing metaphor Intelligence as an input-processing-output cycle (open-loop systems!) with this view of intelligence it proved that it is extremely difficult to get machines do even the simplest jobs, like: moving around picking up objects bringing them to designated locations What's the matter with machines? They are an excellent indicator for our current understanding of natural phenomena. For instance, nowadays we can build an artificial heart because we fully understood its function and the mechanisms which underlie that functional behavior. Have you ever seen a machine, where you thought "Wow that machine behaves almost as intelligent as a baby, a cat, an ant, a bacteria"? Honestly!

29 What is intelligence? Is a bacteria intelligent? Are ants intelligent? Ant colonies? Mice? Cats? Babies? Machines?

30 Intelligence The information processing metaphor Intelligence as an input-processing-output cycle (open-loop systems!) with this view of intelligence it proved extremely difficult to get machines do even the simplest job, like: moving around picking up objects bringing them to designated locations Brook's (1986) subsumption architecture: intelligent behavior by using many loosely coupled process, functioning asynchronous and in parallel minimal internal processing is required, if we consider an intelligent systems as tightly coupled to the environment (closed-loop systems!) in this perspective, an agent has a body, sensors, motors it is embodied and autonomous!

31 We'll skip Brook's (1986) approach, even though it was a radically new perspective on intelligent behavior at this time. Instead, we'll take a look to the history in science, probably there already is something similar interesting? But before, let's talk about embodiment, autonomy, and situatedness.

32 Autonomy Loose definition: independence from human (external) control (robotics). More technical: A non-autonomous system: some parameters or constraints are independent functions of time. e.g., systems driven by some external factor Otherwise it is autonomous

33 Biological autonomy Autonomous systems are organizationally closed: "That is, their organization is characterized by processes such that (1) the processes are related as a network, so that they recursively depend on each other in the generation and realization of the processes themselves, and (2) they constitute the system as a unity recognizable in the space (domain) in which the processes exist." (F. Varela, 1979) We'll come back to that later when we talk about Autopoiesis.

34 Embodiment and Situatedness Embodiment in short: The behavior of an agent arises from the physiology of its brain and body with which it can perceive and act in its environment. "In everyday life you usually remember your "place" largely because the external world is there to remind you what you have or haven't done. For instance, you can check up whether you have already added the vanilla essence by sniffing or tasting the mixture, or perhaps by referring to the pencil and paper representation of the culinary task that you have drawn up for this mnemonic purpose. A computation system that solves its problems "in its head" rather than by perceiving and acting in the real world, or pencil and paper models of it, has to have all its memory aids in the form of internal representations." (M. Boden, 1977)

35 Embodiment and Situatedness Embodiment in short: The behavior of an agent arises from the physiology of its brain and body with which it can perceive and act in its environment. Situatedness: closely related to embodiment a situated agent can acquire information about the world and its current state through its sensors and own actions. the agent's world is its ecological niche! important for the perspective issue of the frame-of-reference problem

36 Looking back Now to the history in science. Something interesting there?

37 Cybernetics Cybernetics started in the early 1940s! and brought together different fields of research: mathematics (Wiener, von Neumann) neuroscience (Rosenblueth, McCulloch) social science (Bateson, Mead) engineering (Shannon) psychology (Frank, Lewin)...

38 Cybernetics Greek: Κυβερνήτης (kybernetes, steersman, governor, pilot) Aimed at unifying principles. Introduced, on abstract levels, concepts as: control, feedback, signal information logic networks... If you think you have a great idea, go in the library and check the literature on cybernetics first. You may wonder... Are you searching for interesting questions nobody asks? Check the cybernetics literature first!

39 Cybernetics Norbert Wiener mathematician published 1948: Cybernetics or Control and Communication in the Animal and Machine. "The best model of a cat is another or, preferably, the same cat.".

40 Cybernetics Norbert Wiener introduced the idea of feedback sensors nervous system actuators sensors nervous system actuators feedback

41 Cybernetics Norbert Wiener sensing and acting are tightly coupled controversial to the standard view of the time (and it is not quite standard even today) sensors nervous system actuators feedback

42 Cybernetics Grey Walter neurophysiologist and roboticist nowadays famous for his tortoise like robots Elsie and Elmer

43 Cybernetics Grey Walter's "Machina Speculatrix" Elsie, and its circuit. two sensors, two motors a complete physical agent that exploits interactions with the environment.

44 Cybernetics Grey Walter behavior integration: avoid obstacles and tropism to light 1950s!!!

45 Cybernetics Grey Walter's Elsie Autonomy: recharged its own battery 1950s!!!

46 Cybernetics Grey Walter's Elsie Decision making / action selection mechanisms: choose between alternatives 1950s!!!

47 Cybernetics Grey Walter's Elsie narcissism 1950s!!!

48 Cybernetics Grey Walter's Elsie and Elmer love and hunger 1950s!!!

49 Cybernetics It's children artificial intelligence robotics control theory complex systems neural networks, cellular automaton, genetic algorithms self-organization order from noise ultra-stability autopoiesis information theory Most of those are essential parts of modern trends of research like, for instance, the dynamical systems approach to behavior and cognition.

50 The frame of reference problem

51 The frame-of-reference problem It's probably the most FUNDAMENTAL PROBLEM!!! Whenever you want to understand an intelligent / adaptive system (natural or artifical) never, really never, underestimate the frame-of-reference problem! It concerns the relation between the observer (that's probably you), the designer / modeler (maybe you as well), the artifact, the environment, and the observed agent.

52 The frame-of-reference problem The main aspects (Clancey, 1991): Perspective: observer's vs. agent's perspective descriptions of behavior from an observer's perspective must not be taken as the internal mechanism underlying the described behavior Behavior vs. mechanism: behavior always results from system-environment interactions. not explainable by the internal mechanism only. Complexity: the observable complexity of a particular behavior does not necessarily reflect the complexity of the underlying mechanisms.

53 The frame-of-reference problem An example

54 The frame-of-reference problem Herbert Simon's (1969) ant on the beach: starting point nest

55 The frame-of-reference problem: Perspective Observer's perspective. starting point puddles pebbles rocks nest

56 The frame-of-reference problem: Perspective Ant's perspective is different. because of an entirely different embodiement (sensors, brain, body) starting point puddles pebbles rocks nest

57 The frame-of-reference problem: Behavior vs. mechanism Possible mechanism maybe thought by an observer: the complex path is stored in the brain, and then guide the ant to the nest starting point nest

58 The frame-of-reference problem: Behavior vs. mechanism The actual mechanism is much simpler: "if obstacle sensor to the right is active, turn left" and vice versa starting point nest

59 The frame-of-reference problem: Complexity The seemingly complex trajectory results from simple behavioral mechanisms. starting point nest

60 The frame-of-reference problem: Complexity The seemingly complex trajectory results from simple behavioral mechanisms. the complexity of the environment is a prerequisite for the ant's behavioral complexity. We need to take internal mechanisms, the environment, and the interactions between them into account. Behavior cannot be explained by the internal rules alone.

61 The frame-of-reference problem: Complexity Let's make the ant bigger.

62 The frame-of-reference problem: Complexity Let's make the ant bigger. How would the trajectory look like, now?

63 The frame-of-reference problem: Complexity The seemingly complex trajectory results from simple behavioral mechanisms. the complexity of the environment is a prerequisite for the ant's behavioral complexity. We need to take internal mechanisms, the environment and their interactions into account Behavior cannot be explained by the internal rules alone. That is a crucial aspect and it has to be carefully considered in building up models of biological systems!!!

64 The frame-of-reference problem Is that something new? to be honest: unfortunately to many people it still is but, actually, it is not new! Guess what: Look to the good old cybernetics! Remember Elsie and Elmer?

65 The frame-of-reference problem hungry? irresolute? in love? 1950s!!! narcissistic?

66 Now, a bit more concrete examples from the biology.

67 Place cells in mice for more details see work of Eric Kandels group at Columbia University

68 Place cells in mice for more details see work of Eric Kandels group at Columbia University

69 Place cells in mice for more details see work of Eric Kandels group at Columbia University

70 Perceptual development of kittens Held and Hein, 1967.

71 Perceptual development of kittens Held and Hein, visual development depends not only on the movement of the body relative to the environment, but also on self-actuated movement. The behavior, how the kitten perceives the world and react to it, does heavily depend on previous sensory-motor experiences..

72 Sensory motor experience How about you, how do you perceive the world around you? We are in the realm of science. Thus, let's do an experiment (or two)!.

73 Aplysia

74 Aplysia

75 Aplysia

76 Aplysia for more details see work of Eric Kandels group at Columbia University

77 Aplysia for more details see work of Eric Kandels group at Columbia University

78 Aplysia a perfect system for a reductionist approach every individual shows the identical nervous system (NS) structure (at least the same amount of neurons) BUT: the strength and structure of single synaptic connections is determined by environmental contingencies! Usually the environment is not static! It is dynamic, and unfortunately mostly nonlinear. Thus, the behavior of a single individual cannot solely be explained by the general nervous system structure. Environmental interactions permanently change the NS's structure. As listening to this lecture (hopefully) changes something in the structure of your brain!... and of course in mine as well!

79 Reductionism, structural changes and couplings, autopoiesis. If you like, forget everything said before (except the frame of reference problem, of course), but take home what comes now!

80 Some remarks to reductionisms Scientific reductionism: There is no clear definition, but let's take that one: When As reduce to Bs, As are nothing but Bs (e.g., brains and neurons) If I can explain how every single subcomponent of a system works, I can explain the behavior of the system as a whole. Where does it come from? Descartes, 1637, Discourses. animals are like machines

81 Some remarks to reductionisms Scientific reductionism: There is no clear definition, but let's take that one: When As reduce to Bs, As are nothing but Bs (e.g., brains and neurons) If I can explain how every single subcomponent of a system works, I can explain the behavior of the system as a whole. Is it like that? Think about the wetness of water: Think about your grandmother: it clearly is a property of water but it clearly is not a property of a H20 molecule! sure, in your brain specific neurons fire when you do that but it is not the property of a single neuron that let you make up a mental picture of your grandmother! There is no mystical "above and beyond" thing! Nevertheless, both cannot be explained solely by the properties of the system's subcomponents.

82 Some remarks to reductionisms However, we have to be careful: One can explain the wetness of water, if one knows everything about the molecules and how they interact with each other and their environment. that's easy (nowadays)! One could explain the mechanisms underlying your generation of a mental picture of your grandmother, if one knows: everything about all of your neurons, how your neurons interact with each other, and how that is influenced by your (past, current) interaction with the environment. that's much more difficult, and far away from being disentangled (on every level!)

83 Some remarks to reductionisms So, where is the problem? I can explain the wetness of water because I know the underlying physical laws acting on its components, the molecules. Surely true, but here comes the real problem: the water by itself cannot change the interaction between the molecules and their interactions with the environment. And that's exactly why it is: not a living being not intelligent not adaptive not able to act cognitively THAT IS THE CRUCIAL DIFFERENCE!

84 We are not like machines! A popular analogy of many 'behavioralists': If you want to entirely understand a car engine, you can not do so, if you don't put it in a car and see how the car behaves when you change certain aspects of the engine. Surely true, but here comes the important warning: We, and every organism, are not like a mechanic duck or a car!

85 We are not like machines! You, me, and every living being 'is able' to change the way of interactions on almost every level: the sensitivity of sensory elements how your neurons are connected how you learn certain things, forget other things how you play tennis or piano how you judge the lecturer from lecture to lecture how you socialize with other living entities On the one hand, that is the foundation of intelligence and cognition! On the other hand, this makes the whole thing difficult to understand and probably impossible to do so in a straight forward way. However, actually there is nothing mystical about it, it's 'just' tremendously complex!

86 A taste of complexity E.g., part of the metabolic system of E. coli. When there is cognition, then it is here! and somewhere over there is cognition

87 A taste of complexity The bad news are: Whenever we strive to fully understand intelligent or cognitive behavior of any biological system (humans as well as bacteria), we have to understand the system of investigation as a whole, which, unfortunately, dynamically changes its own intrinsic structure and the way how it interacts with its environment. And even worse, the environment is full of such permanently changing entities (for instance, your seat neighbors or me). Two approaches are currently debated: 1. the building block approach (bottom up) figure out the function of each subcomponent subsequently and then how they macroscopically work together (e.g., "hierarchical reductionism", Dawkins, 1986) 2. a lot of work currently seems to be the most promising and most accepted method. finding a unified theory would be really nice, but where to start?

88 A very short introduction to autopoiesis! Searching for a unified theory. Looking back.

89 Autopoiesis from the Greek: auto - αυτό for self- and poiesis - ποίησις for creation or production Humberto Maturana Francisco Varela

90 Autopoiesis (Maturana, Varela) An autopietic system is organized as "a network of processes of production (transformation and destruction) of components that produces the components which..." "...through their interactions and transformations continuously regenerate and realise the network of processes (relations) that produced them; and" "... constitute it [...] as a concrete unity in the space in which they (the components) exist by specifying the topological domain of its realisation as such a network." input determines molecular components produces bounded system generates reaction network Adaptation = Viability output

91 Autopoiesis (Maturana, Varela) Applied to the nervous system: motors CNS relative activity sensors coupling with the environment neural dynamics Neuronal activity depends only on the structure of the whole nervous system and its current state of activity sensory inputs only modulate (e.g. by induced structural changes) neuronal activity the nervous system interacts with the rest of the body (e.g. by hormones, muscle activation)

92 Autopoiesis (Maturana, Varela) Applied to social behavior: Structural coupling: organism and environment mutually perturb each other without loosing autopoiesis (autonomy) Coordination: Due to the interactions coherent behavior arises, which is not immediately obvious from the interactive patterns. Structural congruence: Perturbations induce plastic changes if chances for further interactions reduce, coordination will disrupt if these chances enhance, repeated coordination will follow. after some time, coordination is facilitated because the structures of both organisms have changed towards a congruent state

93 Take home message(s) Intelligence must always be seen with respect to a particular ecological niche. Intelligence is always manifested in behavior! Intelligent and adaptive behavior can be characterized by how efficient it solves the compliance-diversity trade-off. Cognition = Life (!?) Don't forget to look in the cybernetics literature from time to time. Always think about the frame-of-reference problem! A living organism is fundamentally different to every machine that exists today! (Autopoiesis)

94 Further reading Ashby, W. (1960), Design for a brain. Chapman. Clancey, W.J. (1991), The frame of reference problem in the design of intelligent machines. In: Architectures for intelligence. Camazine, S. et al. (2001), Biological self-organization. Princeton university press. Clark, A. (1996) Being there: Putting brain, body, and world together again. MIT press. Haken, H. (1982), Synergetik. Springer. Kandel, E. et al. (2000), Principles of neural science. McGraw-Hill medical. Kelso, J. (1995), Dynamic patterns: The self-organization of brain and behavior. MIT press. Maturana, H. and Varela, F. (1992), The tree of knowledge. Shambala. Pfeifer, R. and Scheier, C. (1999), Understanding intelligence. MIT press. Searle, J. (2004), Mind: A brief introduction. Oxford University press. Simon, H. A. (1969), The science of the artificial. MIT Press. Varela, F. (1979), Principles of biological autonomy. North-Holland. Walter, G. (1963), The living brain. W. W. Norton. Wiener, N. (1948), Cybernetics or Control and Communication in the Animal and Machine. MIT press.

95 Adaptivity What is an adaptive systems? A system, natural or artificial, that changes due to perturbations (e.g. changes in the environment). It aims at maintaining a invariant (e.g. survival). It do so by altering its properties (behavior, structure) or by modifying its environment.