Cognitive Science

1. Overview and History of
Cognitive Science

2. How do minds work?
• What would an answer to this question look like?
• What is a mind?
• What is intelligence?
• How do brains work?
• Neurons
• Brain structure
• What’s the difference between the brain and the
mind?

3. Cognition
• Cognition – from Latin base cognitio – “know
together”
• The collection of mental processes and activities used
in perceiving, learning, remembering, thinking, and
understanding
• and the act of using those processes

4. Ways of thinking about learning
• Who learns?
• brain vs. genome
• individual vs. group
• What is learned?
• facts vs. skills vs. rules vs. ..
• information vs. physiology
• Where does knowledge come from?
• experience vs. reason vs. analogy vs. chance
• How does learning work?

5. Cognitive Processes
• Learning and Memory
• Thinking and Reasoning (Planning, Decision
Making, Problem Solving ...)
• Analogy and metaphor
• Language
• Vision-Perception
• Social Cognition
• Emotions
• Dreaming and Consciousness

6. So What IS Cognitive Science?
• Some possible definitions:
• “The interdisciplinary study of mind and intelligence”
• “Study of cognitive processes involved in the acquisition,
representation and use of human knowledge”
• “Scientific study of the mind, the brain, and intelligent
behaviour, whether in humans, animals, machines or the
abstract”

7. Disciplines in Cognitive Science
• Computer Science- Artificial Intelligence
• Neuroscience
• Psychology – Cognitive Psychology
• Philosophy
• Linguistics
• Anthropology, Education

8. Methods of Cognitive Science
• Computational Modeling (artificial intelligence,
computational neuroscience)
• Experimentation (psychology, linguistics,
neuroscience)
• Introspection, Argumentation, Formal Logic and
Mathematical Modeling (philosophy, linguistics)
• Ethnography (cognitive anthropology)

9. Paradigms of Cognitive Science
• Computational Representational Understanding of
Mind
• Mind = mental representation + computational
processes
• Computational Theory of Mind
• Duplicating mind by implementing the right program
• Cognitivism, Functionalism
• Symbolicism – Connectionism- Dynamicism -
Hybrid approaches

11. Intelligence vs. Cognition
• The goal of cognitive science
• develop a theory of Intelligent Systems?
• The goal of artificial intelligence
• Creation of intelligent artifacts?

12. Modeling for Study of Cognition
• Strong AI (duplicating a mind by implementing the
right program) vs Weak AI (machines that act as if
they are intelligent)
• AI as the study of human intelligence using
computer as a tool vs AI as the study of machine
intelligence as artificial intelligence
• Artificial Intelligence and Cognitive Science: a
history of interaction

13. AI and Cognitive Science
"AI can have two purposes. One is to use the power of
computers to augment human thinking, just as we use
motors to augment human or horse power.
Robotics and expert systems are major branches of that.
The other is to use a computer's artificial intelligence to
understand how humans think. In a humanoid way.
If you test your programs not merely by what they can
accomplish, but how they accomplish it, they you're really
doing cognitive science; you're using AI to understand the
human mind."

14. Representation and Computation
• Central hypothesis of cognitive science
• thinking can best be understood in terms of
representational structures in the mind and
computational procedures that operate on those
structures.
• much disagreement about the nature of the
representations and computations that constitute
thinking

15. Information-Processing Metaphor
• Mind has mental representations analogous to computer
data structures, and computational procedures similar to
computational algorithms.
• Symbolic View: mind contains such mental representations
as logical propositions, rules, concepts, images, and
analogies, and that it uses mental procedures such as
deduction, search, matching, rotating, and retrieval.
• Connectionist View: mental representations use neurons
and their connections as mechanisms for data structures,
and neuron firing and spreading activation as the
algorithms – i.e., cognition can be explained by using
artificial neural networks

16. Levels of analysis (Marr):
• Three kinds of questions
• computation
• what is the problem?
• inputs, outputs
• what is being computed or maximized?
• algorithm
• what are the methods?
• Data representation, “process”
• implementation
• what are the mechanisms?
• springs or neurons

18. History of Cognitive Science
• The study of mind remained the province of
philosophy until the 19th century, when
experimental psychology developed.
• Philosophy: rationalism (Plato, Descartes, Kant) vs empiricism
(Aristotle, Locke, Hume, Mill)
• Cartesian Dualism – the mind-body problem
• experimental psychology became dominated by
behaviorism (e.g., J. B. Watson)
• psychology should restrict itself to examining the
relation between observable stimuli and observable
behavioral responses
• denied the existence of consciousness and mental
representations

19. Behaviourism and Cognitive
Science

20. History of Cognitive Science
• George Miller (1950’s)
• showed that the capacity of human thinking is limited,
with short-term memory, for example, limited to
around seven items
• proposed that memory limitations can be overcome by
recoding information into chunks, mental
representations that require mental procedures for
encoding and decoding the information.

21. History of Cognitive Science
• Cognitive Psychology
• First textbook by Neisser in 1967
• Advances in memory models (60s)
• Artificial Intelligence
• Alan Turing – Turing machines, Turing Test
• Newell and Simon – Logic Theorist, GPS
• McCarthy – Frame problem
• Minsky – The Chinese room

22. History of Cognitive Science
Neuroscience:
• Brain structure and function related (Gall,
Spurzheim)
• Localization of function: Wernicke, Broca
• Measurement of rates of electrical neural
impulses: Helmholtz
• Complexity of the human cortex: Lashley, Penfield
• Neural Network Modeling in 1950s: Pitts and
McCulloch, Hebb, Rosenblatt

23. History of Cognitive Science
• Linguistics:
• Saussure- late 19th century, on structure of language
• Chomsky: language as a generative system
• rejected behaviorist assumptions about language as a learned
habit and proposed instead to explain language
comprehension in terms of mental grammars consisting of
rules.

25. • When people try to explain that
artificial intelligence is already here
since a long time in some form, they
often refer to the algorithms that
power Google’s search technology.
Or an avalanche of apps on mobile
devices. Strictly speaking, these
algorithms are not the same as AI
though.
•

26. • Artificial intelligence (AI) is already present in
plenty of applications, from search algorithms
and tools you use every day to bionic limbs for
the disabled.
• Cognitive computing is a term used by IBM.
• Computers aren’t really cognitive, however.
• What are AI and cognitive computing and how
are various forms of AI used and developing?
•

27. • Artificial intelligence is here for a long
time in many forms and ways.
• In recent years significant progress
has been made in some areas of AI.
• This doesn’t mean that AI, in general,
is evolving as fast, just those fields.
• And some of them are increasingly
used for different domains of digital
transformation.

28. • Instead of talking about artificial intelligence (AI), some describe the
current wave of AI innovation and acceleration with – admittedly
somewhat differently positioned – terms and concepts such as
cognitive computing.
• Others focus on several real-life applications of artificial intelligence
that often start with words such as “smart” (omnipresent in anything
related to the Internet of Things and AI), “intelligent,” “predictive” and,
indeed, “cognitive,” depending on the exact application – and vendor.
•

29. • There are many reasons why several
vendors doubt using the term artificial
intelligence for AI solutions/innovations
and often package them in another
term (trust us, we’ve been there).
• Artificial intelligence (AI) is a term that has
somewhat of a negative connotation in general
perception but also in the perception of
technology leaders and firms.
•

30. • One major issue is that artificial
intelligence – which is really a broad
concept/reality, covering many
technologies and realities – has become
like a thing, just like ‘the cloud’ or ‘the
Internet of Things’, we talk about and
also seem to need to have an
opinion/feeling about, with thanks to,
among others, popular culture.

31. AI vs Superintelligence?
• Superintelligence is a hypothetical agent that
possesses intelligence far surpassing that of
the brightest and most gifted human minds.
• "Superintelligence" may also refer to a property of
problem-solving systems (e.g., superintelligent
language translators or engineering assistants)
whether or not these high-level intellectual
competencies are embodied in agents that act in
the world.
• A superintelligence may or may not be created by
an intelligence explosion and associated with
a technological singularity.

32. AI vs Superintelligence?
•
University of Oxford philosopher Nick
Bostrom defines superintelligence as "any
intellect that greatly exceeds the cognitive
performance of humans in virtually all domains
of interest“.
• The program Fritz falls short of
superintelligence—even though it is much
better than humans at chess—because Fritz
cannot outperform humans in other tasks.

33. • Following Hutter and Legg, Bostrom treats
superintelligence as general dominance at
goal-oriented behavior, leaving open whether
an artificial or human superintelligence would
possess capacities such as intentionality (cf.
the Chinese room argument) or first-person
consciousness (cf. the hard problem of
consciousness).

34. • AI is such a broad concept leads to
misunderstandings about what it exactly
means.
• Some people are really speaking about
machine learning when they talk about AI or
about deep learning or about text mining,
the list goes on.
• Others essentially talk about analytics and
in doomsday movie scenarios everything
gets mixed, including robotics and
superintelligence.
• And in most cases we really talk about some
form of AI.

35. Artificial intelligence is many things – research by Narrative
Science shows various areas in the broader ecosystem of AI
– image: Narrative Science via InformationWeek

36. • think about speech recognition, for instance.
Or identification technologies, product
recommendations and even the electronic
games we play. And of course there are
many examples, depending on industry or
function. Marketing, for instance, uses a
bunch of platforms with forms of AI: from
the sentiment analysis in social platforms to
the predictive capabilities in data-driven
marketing solutions.
• Uber driver in the neighborhood and an
Airbnb place to stay – powered by AI.