History of Theory Discussion

Hi there, for this assignment you will be writing a research paper on the topic ”History of Theory”. There will be a YouTube video on this topic lecture linked below along with the instructions for the research paper which will give you most of the information that you need in along with the readings also linked in order to get started. Please make sure all work is original and unique (no plagiarism, you should use the lectures and readings for this paper, and outside articles are available online for the information you can’t find. )

YOUTUBE:

READING:

https://aeon.co/essays/ten-questions-about-the-har…

INSTRUCTIONS:

State how you believe the topic ”History of Theory”  can benefit humanity long-term. Find onepaper that supports this assertion. Find two other papers that provides background information about this topic asa foundation for your assertion. This paper is meant to be relatively brief and apply concepts we’ve covered inclass. You can focus on one concept or combine concepts from various lectures.  01-Friedenberg-4747.qxd
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Introduction: Exploring Inner Space
“The sciences have developed in an order the reverse of what might
have been expected. What was most remote from ourselves was first
brought under the domain of law, and then, gradually, what was
nearer: first the heavens, next the earth, then animal and vegetable
life, then the human body, and last of all (as yet very imperfectly)
the human mind.”
—Bertrand Russell, 1935
A Brave New World
We are in the midst of a revolution. For centuries science has made great
strides in our understanding of the external observable world. Physics revealed
the motion of the planets, chemistry discovered the fundamental elements of
matter, biology has told us how to understand and treat disease. But during
much of this time, there were still many unanswered questions about something
perhaps even more important to us. That something is the human mind.
What makes mind so difficult to study is that, unlike the phenomena
described above, it is not something we can easily observe, measure, or manipulate. In addition, the mind is the most complex entity in the known universe.
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To give you a sense of this complexity consider the following. The human
brain is estimated to contain ten billion to one hundred billion individual nerve
cells or neurons. Each of these neurons can have as many as ten thousand connections to other neurons. This vast web is the basis of mind, and gives rise to
all of the equally amazing and difficult-to-understand mental phenomena such
as perception, memory, and language.
The past several decades have seen the introduction of new technologies and
methodologies for studying this intriguing organ. We have learned more about
the mind in the past half-century than in all the time that came before that.
This period of rapid discovery has coincided with an increase in the number of
different disciplines—many of them entirely new—that study mind. Since then,
a coordinated effort among the practitioners of these disciplines has come to
pass. This interdisciplinary approach has since become known as cognitive
science. Unlike the science that came before, which was focused on the world
of external, observable phenomena, or “outer space,” this new endeavor turns
its full attention now to the discovery of our fascinating mental world, or
“inner space.”
What Is Cognitive Science?
Cognitive science can be roughly summed up as the scientific interdisciplinary
study of the mind. Its primary methodology is the scientific method, although
as we will see, many other methodologies also contribute. A hallmark of
cognitive science is its interdisciplinary approach. It results from the efforts
of researchers working in a wide array of fields. These include philosophy,
psychology, linguistics, artificial intelligence, robotics, and neuroscience. Each
field brings with it a unique set of tools and perspectives. One major goal of
this book is to show that when it comes to studying something as complex as
the mind, no single perspective is adequate. Instead, intercommunication and
cooperation among the practitioners of these disciplines tell us much more.
The term cognitive science refers not so much to the sum of all these disciplines but to their intersection or converging work on specific problems. In this
sense, cognitive science is not a unified field of study like each of the disciplines
themselves, but a collaborative effort among researchers working in the various fields. The glue that holds cognitive science together is the topic of mind
and, for the most part, the use of scientific methods. In the concluding chapter,
we talk more about the issue of how unified cognitive science really is.
In order to really understand what cognitive science is all about we need to
know what its theoretical perspective on the mind is. This perspective centers
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Introduction: Exploring Inner Space
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on the idea of computation, which may alternatively be called information
processing. Cognitive scientists view the mind as an information processor.
Information processors must both represent and transform information. That
is, a mind, according to this perspective, must incorporate some form of mental representation and processes that act on and manipulate that information.
We will discuss these two ideas in greater detail later in this chapter.
Cognitive science is often credited with being influenced by the rise of
the computer. Computers are of course information processors. Think for a
minute about a personal computer. It performs a variety of informationprocessing tasks. Information gets into the computer via input devices, such as
a keyboard or modem. That information can then be stored on the computer,
for example, on a hard drive or other disk. The information can then be
processed using software such as a text editor. The results of this processing
may next serve as output, either to a monitor or printer. In like fashion, we
may think of people performing similar tasks. Information is “input” into our
minds through perception—what we see or hear. It is stored in our memories
and processed in the form of thought. Our thoughts can then serve as the basis
of “outputs,” such as language or physical behavior.
Of course this analogy between the human mind and computers is at a very
high level of abstraction. The actual physical way in which data is stored on a
computer bears little resemblance to human memory formation. But both systems are characterized by computation. In fact, it is not going too far to say
that cognitive scientists view the mind as a machine or mechanism whose
workings they are trying to understand.
Representation
As mentioned before, representation is fundamental to cognitive science. But
what is a representation? Before listing the characteristics of a representation,
it is helpful to describe briefly four categories of representation. A concept
stands for a single entity or group of entities. Single words are good examples
of concepts. The word “apple” denotes the concept of that particular type of
fruit. Propositions are statements about the world and can be illustrated with
sentences. The sentence “Mary has black hair” is a proposition that is itself
made up of concepts. Rules are yet another form of representation that can
specify the relationships between propositions. For example, the rule “If it is
raining, I will bring my umbrella,” makes the second proposition contingent
on the first. There are also analog representations. An analogy helps us to
make comparisons between two similar situations. We will discuss all four of
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these representations in greater detail in the In Depth section at the end of this
chapter.
There are four crucial aspects of any representation (Hartshorne, Weiss
& Burks, 1931–1958). First, a “representation bearer” such as a human or a
computer must realize a representation. Second, a representation must have
content—meaning it stands for one or more objects. The thing or things in the
external world that a representation stands for are called referents. A representation must also be “grounded.” That is, there must be some way in which
the representation and its referent come to be related. Fourth, a representation
must be interpretable by some interpreter, either the representation bearer him
or herself, or somebody else. These and other characteristics of representations
are discussed next.
The fact that a representation stands for something else means it is symbolic. We are all familiar with symbols. We know for instance that the symbol
“$” is used to stand for money. The symbol itself is not the actual money, but
instead is a surrogate that refers to its referent, which is actual money. In the
case of mental representation, we say there is some symbolic entity “in the
head” that stands for real money. Figure 1.1 shows some aspects of a mental
representation of money. Mental representations can stand for many different
types of things and are by no means limited to simple conceptual ideas such as
“money.” Research suggests that there are more complex mental representations that can stand for rules, for example, knowing how to drive a car, and
analogies, which may enable us to solve certain problems or notice similarities
(Thagard, 2000). See the In Depth section for a more detailed discussion of
these other forms of mental representation.
Human mental representations, especially linguistic ones, are said to be
semantic, which is to say they have meaning. Exactly what constitutes meaning and how a representation can come to be meaningful are topics of debate.
According to one view, a representation’s meaning is derived from the relationship between the representation and what it is about. The term that
describes this relation is intentionality. Intentionality means “directed upon an
object.” Mental states and events are intentional. They refer to some actual
thing or things in the world. If you think about your brother, then the thought
of your brother is directed toward him, not toward your sister, a cloud, or
some other object.
Intentionality is considered to have at least two properties. The first is isomorphism, or similarity of structure between a representation and its referent.
This similarity means one can map different aspects of a representation onto
its referent. Analog visual images, discussed further below, are good examples
of this property. This is because they are believed to preserve the spatial
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Introduction: Exploring Inner Space
The Mind
$
Representation
(Symbolic)
Intentionality
Referent
(Nonsymbolic)
The World
Figure 1.1
Different aspects of the symbolic representation of money
characteristics of the referent. A visual image of a cruise ship, for instance,
would have greater horizontal than vertical extent because these boats are
much longer than they are tall. The researcher Stephen Kosslyn has shown that
it takes longer to “scan” a visual image across a dimension where distances
between points in the object are greater and relatively less time across a dimension where such distances are shorter. The section on visual imagery contains
more on the methods and results of this experiment and others that demonstrate the isomorphic characteristics of images.
A second characteristic of intentionality has to do with the relationship
between inputs and outputs to the world. An intentional representation must
be triggered by its referent or things related to it. Consequently, activation of
a representation (i.e., thinking about it) should cause behaviors or actions that
are somehow related to the referent. For example, if your friend Sally told you
about a cruise she took around the Caribbean last December, an image of a
cruise ship would probably pop to mind. This might then cause you to ask her
if the food on board was good. Sally’s mention of the cruise was the stimulus
input that activated the internal representation of the ship in your mind. Once
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A digital representation of time
Figure 1.2
An analog representation of time
Digital and analog clocks represent time in fundamentally different ways
it was activated, it caused the behavior of asking about the food. This relation
between inputs and outputs is known as an appropriate causal relation.
Digital Representations
Skip to next page
In a digital representation, sometimes also known as a symbolic representation, information is coded in a discrete way with set values. A digital clock for
example, represents time discretely (see Figure 1.2). It displays a separate
number for each hour, minute, or year. There are distinct advantages to digital
representations. They specify values exactly. The symbols used in digital representations, such as numbers, can be operated on by a more general set of
processes than analog structures. In mathematics, a wide range of operators
such as addition, division, or squaring can be applied to digital number representations. The results of these operations are new numbers that can themselves be transformed by additional operations.
Language can serve as an example of a digital mental representation, and in
fact verbal concepts seem to be the system of human symbolic representation
that is most commonly used. The basic elements of written language are letters.
These are discrete symbols that are combined according to a set of rules. The
combinations, or words, have meaning and are themselves combined into
other higher-order units, sentences, which also have semantic content. The
rules by which these word elements are combined and transformed in language
are called syntax. Syntax constitutes the set of permissible operations on the word
elements. It is the elements themselves that are the mental representations. In
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The Cognitive Approach I:
History,Vision, and Attention
“I sometimes worry about my short attention span, but not for very
long.”
—Strange de Jim, 1974
Some History First:The Rise of Cognitive Psychology
Early pioneering work in what would come to be known as cognitive psychology began in the 1950s. Later, in 1967, Ulric Neisser published the first textbook on this subject. Cognitive psychology is the newest of the major disciplines
in the overall field of psychology and is currently its most influential. The
impact of the cognitive approach can be measured in terms of how it has
affected other psychological disciplines. There are now cognitive theories in
social, developmental, and clinical psychology.
To really understand the cognitive perspective, though, we need to backtrack to our prior discussion of behaviorism. You may recall from the previous chapter that for many decades behaviorism was the dominant movement
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in psychology. Behaviorists avoided the study of mental phenomena because
they believed that they were too difficult to define or measure. They stuck
instead to external, observable behaviors, which were more amenable to
scientific scrutiny.
Cognitive psychology can be seen as a backlash or counter-revolution
against the behaviorist movement. This becomes evident when one examines
the basic assumptions of the cognitive movement (Ashcraft, 2002). Whereas
the behaviorists avoided studying the mental world and in some cases may
have denied its existence, the cognitivists firmly acknowledged the existence of
mental processes and focused their investigatory attentions on them. Whereas
behaviorism saw the mind as a passive organ that operated according to simple
rules of conditioning, cognitive psychology saw the mind as active, as selecting
information from the environment, relating it to prior knowledge, and acting
on the results of such processing.
Let us look more closely at some of the reasons for the so-called cognitive
revolution. There were three main reasons for the rapid growth of this new
perspective. The first was the failure of behaviorism to account for findings in
areas such as language acquisition. The second was the invention of new measuring devices to examine mental activity. The third was the rise of the computer and the widespread use of the metaphor of mind-as-computer.
B. F. Skinner outlined the behaviorist take on language in 1957 in his book
Verbal Behavior. He believed a child learned language through reward. If a
baby said “mama” in front of its mother, the mother would get excited, smile,
and talk back to the child—all forms of positive reinforcement. This would
reward the child for having made that utterance, which he or she would then
do more often. This process would be repeated, with the child uttering more
complex words and sentences, being rewarded each time for correct pronunciation or syntax.
The linguist Noam Chomsky soon critiqued this theory of language acquisition (Chomsky, 1959). Chomsky argued that the behaviorists gave us no
good account of why children suddenly use language. He asked: Why should
a child utter a new sound or word? The child can’t be reinforced until after he
or she has already said it. Yet all children around the world spontaneously
utter the basic phonetic elements of language. This suggests that there is an
innate mechanism for generating language and that this mechanism may not,
as the behaviorists would have us believe, be under environmental control.
Another problem is that children often combine the “pieces” of language in
new ways to create new meanings. A child, after having learned the words
“cat” and “sofa,” might say “cat on sofa,” even though he or she had never
been reinforced for arranging the two words in exactly this way in a sentence.
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The tremendous flexibility demonstrated by children in creating new meanings
couldn’t be accounted for by a system of reward.
A second reason for the rise of cognitive psychology was the development
of new measuring tools. During the behaviorist era, there were no ways of
“peering inside the head.” That is, one could not directly measure mental
processes. After the start of the cognitive movement, new technologies that
provided a more accurate picture of mental processes as they were occurring
emerged. The new devices included positron emission tomography (PET), computerized axial tomography (CAT), and magnetic resonance imaging (MRI).
These techniques are described further in the neuroscience chapter.
But perhaps the thing that contributed most significantly to the decline of
behaviorism was the increased use of computers. Prior to the 1960s computers
were constructed using vacuum tubes. Because of the size and number of
vacuum tubes needed to perform computations, computers were quite large. In
some cases, entire rooms were required to house them. The transistor was
invented in 1947, but was not widely applied to computer design until years
later. The transistor performed the same computational function as a large
number of vacuum tubes, but was much smaller. This miniaturization allowed
for the construction of correspondingly smaller and cheaper computers. The
widespread presence of computers spurred psychologists to begin thinking
more about them. Psychologists realized that the mind, like a computer, could
be viewed as a device that represented and transformed information. The
mind-as-computer metaphor was born. Computers thus accelerated the adoption of the information processing view, not only in psychology, but also more
generally, in other cognitive science fields.
The Cognitive Approach: Mind as an Information Processor
So now that we know how cognitive psychology came about, what exactly
is it? Cognitive psychology is the study of knowledge representation and use
in human beings. It is concerned with understanding how people represent,
process, and store information. According to Ulric Neisser, “cognitive psychology” refers to all processes whereby the sensory input is transformed,
reduced, elaborated, stored, recovered, and used” (Neisser, 1967). The many
verbs used in the preceding sentence give us a sense of the many possible information processing activities of the human mind.
Cognitive psychology differs from other approaches in cognitive science in
that its focus is on human information processing (as opposed to animal or
machine modes of information processing). Like the practitioners of many
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other disciplines in psychology, cognitive psychologists adopt the scientific
method as their primary tool of investigation. Hypotheses are tested by analyzing data that has been obtained from controlled experiments. However,
cognitive psychology also supplements experimentation with modeling and
computer simulation. A specific information-processing model of a mental
ability can be run on a computer. The results can then be compared against
data from human experiments. This is often a synergistic and iterative procedure. Parameters of the computer model can be refined so as to provide a better
fit between the computer model and the empirical data. Aspects of the simulation can also yield insights that cause researchers to go back and design new
experiments.
A defining characteristic of the cognitive approach is the way it represents
human information processing. These processes are often conceptualized using a
process model. In a diagram of a process model, boxes are used to designate each
stage or step in an information processing sequence. Arrows that point toward
or away from the boxes represent the flow of information among the stages.
Many of the figures in this chapter show a process model that depicts a particular theory of human computation. Feel free to examine a few of them now.
Process models, in the classical view of information processing, carry two
assumptions. First, they are assumed to be sequential, meaning that information that lies within one stage is processed before it is output to the next.
Information cannot be processed simultaneously in multiple stages. Second,
excluding inputs and outputs, processing that occurs within one stage is independent of processing that occurs within other stages. These assumptions were
later challenged by the connectionist view of information processing, which
adopts a radically different architecture as the basis of cognition.
Process models are a very important part of the cognitive perspective. They
are a powerful conceptual tool for understanding human information
processes. In fact, the remainder of this chapter and the next chapter are
devoted to describing the major processing models that underlie various
domains in cognition. These domains include, but are not limited to, perception, attention, memory, imagery, and problem solving.
Modularity of Mind
An assumption of many cognitive theories is modularity of mind. According to
this idea, the mind is made up of innate, functionally independent modules.
The boxes in the process models that are described throughout this chapter and
in the next chapter can in many cases be viewed as modules. The evolved
psychological mechanisms that are discussed in the evolutionary chapter can
History of Theory
Brendan Conway-Smith
Housekeeping
Questions and answer forum on Brightspace.
Attending class is essential to getting a good grade.
We are seeding main concepts – we’ll return to them repeatedly.
– levels of cognition
– information processes
– cognitive architecture
Review of last week
– main concepts
Concepts from last week?
Why study cognition?
• Intelligence is the most unique
phenomena in the universe.
• The defining attribute of our species.
• We can use our own cognition better.
• The future will almost entirely depend
on human and artificial intelligence.
Why did the brain evolve?
– For the same reason all things evolve …. its adaptive benefit.
– How does the brain fulfill its beneficial function?
By representing reality
we can change it beneficially
Clothes
Shelter
Fire
Cooking
Medicine
Tools
How does the brain fulfill its beneficial function?
It builds mental models – theories – about reality, and acts on them.
What is a theory?
At first glance – a mental model of some part of reality. Cognition takes percepts
and builds concepts.
A history of theories
600 BCE
Change in how we theorize
The ‘first philosopher’ (Thales, 600 BCE)
posited that the causes of nature are not
supernatural but natural.
From supernatural theories to natural theories
Theories of what caused things were originally thought to be of supernatural
causality (e.g.: the cause of infection or mental illness was thought to have been
caused been a demon, a witch, or curse.)
Theories then moved to natural causality: scientific theories.
For the first time in history,
human intelligence is finally
becoming able to ‘see’ itself,
using scientific theories from
many different disciplines.
“The sciences have developed in the reverse of what might have been
expected. What was most remote from ourselves was first brought under
the domain of law, and then, gradually, what was nearer: first the heavens,
next the earth, then animal and vegetable life, then the human body, and
last of all… the human mind.”
—Bertrand Russell, 1935
What is a scientific theory?
A scientific theory is an explanation of some aspect of the natural
world that has been substantiated through repeated experiments
and testing.
A theory we discussed last week?
What is a scientific theory?
A scientific theory is an explanation of some aspect of the natural
world that has been substantiated through repeated experiments
and testing.
A theory we discussed last week?
– light
– gravity
– evolution
– memory
Theory of memory
bike
Declarative
Procedural
Modern understanding
Information theories
• Information-processing explanations
• Hardware/software analogy
350 BCE
Aristotle
Aristotelian philosophy
The universe’s design is orderly (not chaotic).
All things exist with a rational design.
All things have a rational function.
– Acorns become Oak trees, not Maple trees
The universe is not the product of human thought – it must be discovered
by human thought. The universe is mind-independent.
It is proper for humans to understand the universe. The human mind is capable of
understanding the universe, using rationality (reasoning from evidence).
Humans’ defining characteristic is rationality.
From natural reasoning to science
Aristotle was the first to systematically study and catalogue the
rules of correct logical reasoning.
Logic was important because it dominated all Western thought,
including scientific thought until the 19th century.
Aristotle’s logical system was a powerful way to teach reasoning
skills in numerous academic disciplines. Reasoning from evidence
instead of from supernaturalism or received tradition is how
knowledge has progressed in every field (medicine, technology,
biology, astronomy, etc.).
What does philosophy contribute?
1.
Philosophy can teach us how to direct our thoughts to arrive more directly
at truth.
2.
Philosophy generates testable hypotheses.
3.
Philosophy integrates a wide range of empirical results into a unified
theoretical picture of a certain phenomenon.
4.
Philosophy contributes to conceptual clarity in the sciences.
Conceptual Clarity
Philosophy contributes to conceptual clarity in other ways:
• It asserts how knowledge should be arrived at.
• Keeping careful track of how concepts are being used and applied.
• Evaluating their use according to certain standards (e.g., parsimony, accuracy,
consistency).
• Identifying cases in which the same concept is mistakenly applied to distinct
referents (e.g., consciousness, attention).
Aristotle
Many believed out thoughts should follow otherworldly sources.
Aristotle and others believed we should observe reality and
reason according to it. We should be willing to change our
opinions if the evidence changes (intellectual humility).
Aristotle believed the brain only served as a sort of radiator whose
function was to “cool the passions of the heart”. He explained the
human brain’s large size to be the result of humans needing large
brains to cool their warm hearts.
Mind is the product of the brain
Seeing the first neurons
1832 – Johannes Purkinje examined sections of
nervous tissue. First neurons to be identified: Purkinje
cells found in the cerebellum.
History of Bad Theories
Scientists are influenced by the
prevailing ideas of their times
29
J Phillippe Rushton
VIDEO: ”Victorian Pseudoscience” (4:00 mins)
The beginning of modern psychology
In the 19th century, Wilhelm Wundt launched a new research enterprise
leading to modern psychology. His view was that psychology needed to
focus largely on the study of conscious mental events – feelings,
thoughts, perceptions and recollections.
How? By way of introspection, or ‘looking within’ to observe the
content of our mental lives. One major point was that unconscious
thoughts can influence our lives and actions. Introspective data was
difficult to study objectively – as the introspective ability itself needed
to be explained. This was thought unacceptable as a means of
generating and evaluating hypotheses.
Behaviorism
In response to introspection and its problems, behaviorists asserted that scientists
needed objective that everyone could observe. Solution: an organism’s behaviors are
observable. Likewise, stimuli in the world are in the same ‘objective’ category –
measurable, recordable, physical events. You can record how the pattern of behavior
changes over time in response to ‘reward’ or ‘punishment.’
In contrast — beliefs, desires, goals, and intentions (the cognitivist view) cannot be
directly observed, or recorded. They were invisible internal entities. So they were
thought to be impossible to study scientifically.
Behaviourism
“Psychology as the behaviourist views it is a purely objective experimental
branch of natural science. Its theoretical goal is the prediction and control
of behavior. Introspection forms no part of its methods, nor is the
scientific value of its data dependent upon the readiness with which
they lend themselves to interpretation in terms of consciousness. The
behaviourist, in his efforts to get a unitary scheme of animal response,
recognizes no dividing line between man and brute.”
(Watson, 1913)
Operant Conditioning
Adding feedback
Removing feedback
Encouraging behavior
Positive reinforcement
Negative reinforcement
Diminishing behavior
Punishment
(positive punishment)
Extinction
(negative punishment)
Making a behavior more or less likely to happen in the presence of a
stimulus depending on reward, punishment, or taking away a reward or
aversive stimulus.
Skinner Box
Kinds of Operant Conditioning
Positive reinforcement (the strongest)
Someone smiles at you when you hold the door for him or her.
Negative reinforcement
A baby screams until you give them candy. Then she stops.
Positive punishment
You get burned by touching the hood of a car in the sun.
Negative punishment
Your parents cut off your allowance because you lied.
Problem with behaviorism
By the late 1950s psychologists realized that many behaviors could not be explained in
behaviorist terms.
Example: ”Pass the salt” depends on contextual information.
Asking “what did she say?” – depends on knowledge of who, when,
and in what context.
The ways people act are guided by how they understand or interpret the situation, and not
by the objective situation itself. Understanding people’s invisible mental entities (beliefs,
desires, and intentions) are essential to understanding and predicting behavior.
This is part of how thinking changes – the old paradigm shows signs of cracks –
contradictions that can not be explained by the old way of thinking.
What triggered the cognitive revolution?
One contribution came from within the behaviorist movement itself.
Most argued that learning could be understood as simply a change in behavior. Tolman
(1886-1959) argued that learning involved something more abstract – the acquisition of new
knowledge. His experiments showed that rats learned the layout of a maze without
reinforcement (‘cognitive maps’).
COGNITIVE MAPS
Navigation in rats
A revolution in psychological theories
In their famous experiments, Tolman and Honzik
(1930) built a maze to study how rats could learn in
relation to reinforcement.
The experiment demonstrated that, rather than working
solely on a behaviourist stimulus-response principles,
rats learned from processing information.
Maze Learning in Rats
Tolman & Honzik had 3 groups of rats:
1. Rewarded each time they ran a maze successfully
(rewarded group)
2. Never rewarded (unrewarded group)
3. Not rewarded for 10 days and then
rewarded (unrewarded-rewarded group)
What would Skinner predict about the performance of unrewarded rats?
According to Skinner, should unrewarded rats be able to learn?
Tolman & Honzik Findings
▰ Unrewarded rats wandered around aimlessly
▰ Rewarded rats learned to run the maze quickly
▰ Both unrewarded and rewarded rats on the 11th day learn to run the maze.
▰ Why is this a problem for behaviorism? The information the rats gained about the
maze mattered. These ‘invisible entities’ that had ‘no scientific value’ turned out to
matter a lot – it effected predictions and experimental outcomes.
Scientists then attempted to
connect the rats’ information
processing activity to their neural
source.
Video: neural evidence for cognitive maps (1:30)
LANGUAGE
Skinner vs Chomsky
Skinner
▰ Children learn language by
reinforcement
▰ Correct utterances are
positively reinforced
Chomsky
▰ Published a scathing review of Skinner’s
book
▰ Stimulus is not enough (“Poverty
of stimulus” assertion)
Poverty of Stimulus
▰ Poverty of stimulus: Information required to learn language is not present in the
environment. Stimulus is not enough. There must be innate mechanism in the human brain.
▰ The rules of any language are too “rich” to learn by imitation from the environment, e.g.,
from others.
▰ Children learn to say many phrases without any reinforced response.
– learning is too quick to explain via behaviorism
– we need information processing explanations.
Chomsky showed that there are
neural mechanisms in the brain
(hardware) that are dedicated to
processing language information.
There are neural mechanisms
dedicated to processing language.
COMPUTATION
Beginnings of computational theory of mind
In the 1950s a new approach to psychological explanation came about by way of
electric information processing. It became clear that computers were capable of
things previous attributed only to living things – information storage and retrieval
(memory), and problem solving.
Beginnings of computational theory of mind
Mechanics helped us understand
the body
Computational helped us understand
the nature of the mind
More than one type of thing can think
In the philosophy of mind, multiple realizability is the idea that the same mental states can be
implemented by different physical properties (e.g. memory). This defends a version of
functionalism (particularly machine-state functionalism).
Some phenomena are ‘substrate-independent’ = money, computation, music.
“Can Machines
Think?”
(Turing 1950)
Questions?
History of Theory
Brendan Conway-Smith
Why did the brain evolve?
To allow us to represent reality and change it beneficially.
How does the brain fulfill its beneficial function?
It builds mental models – theories – of reality, and acts on them.
What is a scientific theory?
What is a scientific theory?
A scientific theory is an explanation of some aspect of the natural
world that has been substantiated through repeated experiments
and testing.
Information-processing theories of cognition
• Computational metaphor
• Hardware/software analogy
Early philosophy
The human mind is capable of understanding the universe.
It is proper for humans to understand the universe.
We should not look to the supernatural to tell us about reality,
but we should direct our thinking rationally.
Reasoning from evidence is how knowledge has progressed in every field
(medicine, technology, biology, astronomy, etc.).
The word “idea” comes from Greek ἰδέα meaning “to see.”
The history of ideas
350 BCE
2022
Skinner Box
A revolution in psychological theories
In their famous experiments, Tolman and Honzik
(1930) built a maze to study how rats could learn in
relation to reinforcement.
The experiment demonstrated that, rather than working
solely on a behaviourist stimulus-response principles,
rats learned from processing information.
Scientists attempted to connect
information processing in the brain with
their neural correlates.
The discovery of grid cells (hardware) was
achieved using an empirical method.
Grid cells
Grid cells were discovered in 2005 by Edvard Moser, May-Britt Moser.
A grid cell is a place-modulated neuron. Its firings can encode one’s location in a twodimensional environment. Grid cells form an essential part of the brain’s coordinate
system. Understanding the origin and properties of grid cells is an important challenge
for anybody wanting to know how brain circuits compute.
A key property of a grid cell representations is its apparently universal nature
(Hafting et al., 2005).
The discovery of neural hardware can
also be achieved using a rationalist
approach. Armchair reasoning can be
used to discover what must be so.
The Language Acquisition Device (LAD)
In the 1960s, Noam Chomsky’s revolutionized the field of
language acquisition. The LAD is an intrinsic mental capacity
that enables an infant to acquire and produce language.
It is part of the nativist theory of language. This theory
asserts that humans are born with the instinct or “innate
facility” for acquiring language (hardware).
The main argument given in favor of the LAD was the
argument from the poverty of the stimulus.
Poverty of Stimulus
Information required to learn language is not present in the environment.
Stimulus is not enough. There must be innate mechanism in the human brain.
Chomsky argued that unless children have significant innate knowledge of grammar, they would
not be able to learn language as quickly as they do. Children learn to say many phrases without
any reinforcement. Their learning is too quick to explain via behaviorism. So… we need
explanations of how our brain’s hardware and software function.
This can be considered a ‘paradigm shift.’
Cognitive science is a ‘paradigm shift’
A paradigm shift is a major change in the concepts of how something works.
A concept by Thomas Kuhn (The Structure of Scientific Revolutions, 1962). It is a fundamental
change in the basic concepts and experimental practices of a scientific discipline. The concept is
also used to describe a change in a fundamental model or perception.
Paradigm shifts happen when the old, dominant paradigm is seen to be incompatible with new
phenomena. As errors build up, a ‘shift’ occurs with the adoption of a new theory or paradigm.
Example:
Theory of the world: a shift from geocentrism to heliocentrism.
Theory of the mind: a shift from behaviorism to cognitivism.
Behaviorism was similar to geocentrism – behavior was at the center
A paradigm shift occurred in how humans saw
the physical world. The old, dominant
paradigm (the earth being at the center of our
system) was incompatible with new
phenomena. As errors build up, more and
more complexities needed to be proposed to
fix the old system. Finally, a ‘shift’ occurs with
the adoption of a new theory: Heliocentrism
Cognitive science – the new paradigm
Instead of behavior, mental entities are the centre of our scientific system. Thoughts,
beliefs, intentions, and desires are seen as either real or indispensably useful. Either way,
they give us extraordinary predictive power when it comes to human actions. Our actions –
say the cognitivists – are the consequence of thoughts.
The Intentional Stance – Daniel Dennett
Imagine if aliens were to observe humans, to investigate and predict ‘why’ humans do
things. Now imagine they were unaware that we had thoughts, beliefs, or intentions. How
much predictive value would there be in the aliens mapping every atom in our bodies to
investigate why Billy went to the bank? Image their explanations, using only atoms and
physics. How much simpler would it be if they knew that Billy believed that he had money at
the bank, and that he intended to withdraw it?
Beginnings of Computational Theory of Mind (CTM)
In the 1950s, a new approach to psychological explanation came about by way of electric
information processing. It became clear that computers were capable of things previous
attributed only to living things – information storage, retrieval (memory), and problem solving.
The origins of CTM are even older.
Thomas Hobbes: “Reason . . . is nothing but Reckoning (that is, adding and subtracting).”
Ada Lovelace: Was the first to propose a calculating machine that could could go beyond
math and do thinking operations.
Ada Lovelace – The first computer programmer
1833 – Babbage made a calculating machine called
the Analytical Engine. This inspired Ada Lovelace who
was an English mathematician and writer.
She was the first to recognise that the machine had
applications beyond pure calculation, and to have
published the first algorithm intended to be carried
out by this machine.
One example she wrote —how to calculate Bernoulli
numbers—is the first computer program.
Beginnings of computational theory of mind
The first mechanics helped us
understand the body.
The first computers helped us
understand the nature of the mind.
It is as if humans need external metaphors to help us understand ourselves.
“Can Machines
Think?”
(Turing 1950)
Alan Turing was an English mathematician,
computer scientist, and codebreaker during
WWII.
Multiple realizability
Turning hypothesized that ‘thinking’ could also be instantiated in non-biological
device. A ‘thinking’ machine, through information transformation, could display
many of the phenomena as humans brains (memory storage and retrieval,
decision making, and problem solving.
More than one type of thing can think
In the philosophy of mind, multiple realizability is the idea that the same mental states can
be implemented by different physical properties (e.g. memory). This defends a version of
functionalism (particularly machine-state functionalism).
Some phenomena are ‘substrate-independent’ = money, computation, music.
Thoughts are like music – they can exist in different mediums
Information processing depends on internal representations (symbols in various formats – sounds,
images, perceptions, etc.).
Examples of internal representations are thoughts, beliefs, desires, perceptions, etc.
The central hypothesis of cognitive science is that thinking is best understood as representations in
the mind and computations that operate on those structures. Or
“operations acting on representations.”
”Operations acting on representations”
Operations
Representations
Representations
Operations
– Representations
– Operations
The Turing Machine
First described by Alan Turing (1936), it is a simple abstract computational device
intended to help investigate the principles of computation, and the limitations of
what can be computed.
The Turing Test
“May not machines carry out something which ought to be described as
thinking but which is very different from what a man does? This objection is
a very strong one, but at least we can say that if, nevertheless, a machine
can be constructed to play the imitation game satisfactorily, we need not be
troubled by this objection.” (Turing 1950)
The Turing Machine
A device with an infinite tape.
A simple processor.
A read/write head.
• It can write symbols to the tape (typically 0’s or 1’s)
• Erase symbols from the tape
• Move over one space to the right
• Move over one space to the left
• Actions are governed by a set of instructions called the machine table
The Turing Test
Turing (1950):
“I believe that in about fifty years’ time it will be possible, to program computers […]to
make them play the imitation game so well that an average interrogator will not have
more than 70 per cent chance of making the right identification after five minutes of
questioning. The original question, “Can machines think?” I believe to be too meaningless
to deserve discussion. Nevertheless I believe that at the end of the century the use of
words and general educated opinion will have altered so much that one will be able to
speak of machines thinking without expecting to be contradicted.”
The Turing Test
A.k.a. “The imitation game” (Turing 1950)
A (machine) and B (person) are in one room.
C (interrogator) is in another room.
Interrogator asks questions of A and B and attempts to determine,
on the basis of their answers, which of the two is a machine.
The machine passes if it fools the interrogator as often as a human would.
That’s all – see you next time!