Omid Reza Taheri's Conference at the Philosophy Department, Pune university
امید رضا طاهری
لیسانس و فوق لیسانس علوم کامپیوتر، پژوهشگر در فلسفه ذهن
و دانشجوی دکترای تخصصی در سیستم ادراک
فرانسه - پاریس
موضوع سمینار: رابطه فلسفه ذهن با مدلهای سیستم ادراکی انسان و هوش مصنوعی.
در حضور جمعی از اساتید دانشگاه در زمینه های مختلف بویژه فلسفه و دانشجویان و پژوهشگران در مقاطع و زمینه های مختلف
محل برگزاری: دانشگاه پونا - هند
Philosophy of Mind and its relation to Cognitive models
The philosophy of mind considers mental phenomena such as sensation, perception, thought, belief, desire, intention , memory, emotion, imagination, and purposeful action.
Philosophers have been interested in the nature of each of these phenomena as well as their relationships to one another and to physical phenomena such as motion.
The relationship of the mind to the body, is commonly seen as the central issue in philosophy of mind.
Attempts to understand the mind and its operation go back at least to the Ancient Greeks, when philosophers such as Plato and Aristotle tried to explain the nature of human knowledge.
In the religion, the nature of mind is connected with various conceptions of the soul and the possibility of life after death.
In many abstract theories of mind there is considerable overlap between philosophy and the science of psychology.
Psychology was a part of philosophy, but later on it became split off and formed a separate branch of knowledge in the 19th century.
While psychology uses scientific experiments to study mental states and events, philosophy in other hand uses reasoned arguments and thought experiments in seeking to understand the concepts that underly mental phenomena.
Within a few decades, however, experimental psychology became dominated by behaviorism, a view that denied the existence of mind.
According to behaviorists such as J. B. Watson, psychology should restrict itself to examining the relation between observable stimuli and observable behavioral responses.
Talk of consciousness and mental representations was banished from respectable scientific discussion. Especially in
The early studies on the operation of the mind established the field of logic. Today the logical approach aim to construct computer programs, with the hope that these programs will be able to create intelligent systems.
Warren McCulloch and Walter Pitts (1943) developed a model of artificial neurons. Today this work is recognized as one of the earliest works in Artificial Intelligence.
A few years later, in 1951,Marvin Minsky and Dean Edmonds built the first neural computer.
In the early 1950’s Claude Shannon, and Alan Turing (1953) developed chess programms, Around 1956, the intellectual landscape began to change dramatically. George Miller summarized numerous studies which showed that the capacity of human thinking is limited, He proposed that memory limitations can be overcome by recoding information into chunks, mental representations that require mental procedures for encoding and decoding the information. At this time, primitive computers had been around for only a few years, but pioneers such as John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon were founding the field of artificial intelligence. In addition, Noam Chomsky rejected behaviorist assumptions about language as a learned habit and proposed instead to explain language comprehension in terms of mental grammars consisting of rules. The six thinkers mentioned in this paragraph can be viewed as the founders of cognitive science.
Cognitive Science and its Method
Cognitive science has unifying theoretical ideas, but we have to appreciate the diversity of outlooks and methods that researchers in different fields bring to the study of mind and intelligence. Although cognitive psychologists today often engage in theorizing and computational modeling, their primary method is experimentation with human participants. People, usually undergraduates satisfying course requirements, are brought into the laboratory so that different kinds of thinking can be studied under controlled conditions. For example, psychologists have experimentally examined the kinds of mistakes people make in deductive reasoning, the ways that people form and apply concepts, the speed of people thinking with mental images, and the performance of people solving problems using analogies. Our conclusions about how the mind works must be based on more than “common sense” and introspection, since these can give a misleading picture of mental operations, many of which are not consciously accessible. Psychological experiments that carefully approach mental operations from diverse directions are therefore crucial for cognitive science to be scientific.
Although theory without experiment is empty, experiment without theory is blind. To address the crucial questions about the nature of mind, the psychological experiments need to be interpretable within a theoretical framework that postulates mental representations and procedures. One of the best ways of developing theoretical frameworks is by forming and testing computational models intended to be analogous to mental operations. To complement psychological experiments on deductive reasoning, concept formation, mental imagery, and analogical problem solving, researchers have developed computational models that simulate aspects of human performance. Designing, building, and experimenting with computational models is the central method of artificial intelligence (AI), the branch of computer science concerned with intelligent systems. Ideally in cognitive science, computational models and psychological experimentation go hand in hand, but much important work in AI has examined the power of different approaches to knowledge representation in relative isolation from experimental psychology.
While some linguists do psychological experiments or develop computational models, most currently use different methods. For linguists in the Chomskian tradition, the main theoretical task is to identify grammatical principles that provide the basic structure of human languages. Identification takes place by noticing subtle differences between grammatical and ungrammatical utterances. In English, for example, the sentences “She hit the ball” and “What do you like?” are grammatical, but “She the hit ball” and “What does you like?” are not. A grammar of English will explain why the former are acceptable but not the latter.
Like cognitive psychologists, neuroscientists often perform controlled experiments, but their observations are very different, since neuroscientists are concerned directly with the nature of the brain. With nonhuman subjects, researchers can insert electrodes and record the firing of individual neurons. With humans for whom this technique would be too invasive, it has become possible in recent years to use magnetic and positron scanning devices to observe what is happening in different parts of the brain while people are doing various mental tasks. For example, brain scans have identified the regions of the brain involved in mental imagery and word interpretation. Additional evidence about brain functioning is gathered by observing the performance of people whose brains have been damaged in identifiable ways. A stroke, for example, in a part of the brain dedicated to language can produce deficits such as the inability to utter sentences. Like cognitive psychology, neuroscience is often theoretical as well as experimental, and theory development is frequently aided by developing computational models of the behavior of groups of neurons.
Cognitive anthropology expands the examination of human thinking to consider how thought works in different cultural settings. The study of mind should obviously not be restricted to how English speakers think but should consider possible differences in modes of thinking across cultures. Cognitive science is becoming increasingly aware of the need to view the operations of mind in particular physical and social environments. For cultural anthropologists, the main method is ethnography, which requires living and interacting with members of a culture to a sufficient extent that their social and cognitive systems become apparent. Cognitive anthropologists have investigated, for example, the similarities and differences across cultures in words for colors.
With a few exceptions, philosophers generally do not perform systematic empirical observations or construct computational models. But philosophy remains important to cognitive science because it deals with fundamental issues that underlie the experimental and computational approach to mind. Abstract questions such as the nature of representation and computation need not be addressed in the everyday practice of psychology or artificial intelligence, but they inevitably arise when researchers think deeply about what they are doing. Philosophy also deals with general questions such as the relation of mind and body and with methodological questions such as the nature of explanations found in cognitive science. In addition, philosophy concerns itself with normative questions about how people should think as well as with descriptive ones about how they do. In addition to the theoretical goal of understanding human thinking, cognitive science can have the practical goal of improving it, which requires normative reflection on what we want thinking to be. Philosophy of mind does not have a distinct method, but should share with the best theoretical work in other fields a concern with empirical results.
In its weakest form, cognitive science is just the sum of the fields
(Fig-1) mentioned: psychology, Computer Science, linguistics, neuroscience, anthropology, and philosophy. Interdisciplinary work becomes much more interesting when there is theoretical and experimental convergence on conclusions about the nature of mind. For example, psychology and artificial intelligence can be combined through computational models of how people behave in experiments. The best way to grasp the complexity of human thinking is to use multiple methods, especially psychological and neurological experiments and computational models. Theoretically, the most fertile approach has been to understand the mind in terms of representation and computation.
Cognitive Representation and Computation
The central hypothesis of cognitive science is that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures. While there is much disagreement about the nature of the representations and computations that constitute thinking, the central hypothesis is general enough to encompass the current range of thinking in cognitive science, including connectionist theories which model thinking using artificial neural networks.
Most work in cognitive science assumes that the mind has mental representations analogous to computer data structures, and computational procedures similar to computational algorithms. Cognitive theorists have proposed that the 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. The dominant mind-computer analogy in cognitive science has taken on a novel twist from the use of another analog, the brain.
Connectionists have proposed novel ideas about representation and computation that use neurons and their connections as inspirations for data structures, and neuron firing and spreading activation as inspirations for algorithms. Cognitive science then works with a complex 3-way analogy among the mind, the brain, and computers. Mind, brain, and computation can each be used to suggest new ideas about the others. There is no single computational model of mind, since different kinds of computers and programming approaches suggest different ways in which the mind might work. The computers that most of us work with today are serial processors, performing one instruction at a time, but the brain and some recently developed computers are parallel processors, capable of doing many operations at once.
Cognitive Theoretical Approaches
Here is a schematic summary of current theories about the nature of the representations and computations that explain how the mind works.
Formal logic
Formal logic provides some powerful tools for looking at the nature of representation and computation. Propositional and predicate calculus serve to express many complex kinds of knowledge, and many inferences can be understood in terms of logical deduction with inferences rules.
It is not certain, however, that logic provides the core ideas about representation and computation needed for cognitive science, since more efficient and psychologically natural methods of computation may be needed to explain human thinking.
Rules
Much of human knowledge is naturally described in terms of symbolic logic, this discovery, combined with advances in electronics and philosophy gave birth to our present computer revolution, computers are essentially symbolic logic machines and being governed of the form negation, conjunction, disjunction, conditional, and biconditional , and many kinds of thinking such as planning can be modeled by rule-based systems.
Computational models based on rules have provided detailed simulations of a wide range of psychological experiments, Rule-based systems have also been of practical importance in suggesting how to improve learning and how to develop intelligent machine systems.
Connectionism within cognitive science – is a theory of information processing. Unlike classical systems which use explicite, often logical , rules arranged in an hierarchy to manipulate symbols in a serial manner , however, connectionist systems rely on parallel processing of sub-symbols,using statistical properties instead of logical rules to transform information.
Connectionists base their models upon the known neurophysiology of the brain and attempt to incorporate those functional properties thought to be required for cognition.
What, then, are the functional properties of the brain that are required for information processing? Connectionists adopt the view that the basic building block of the brain is the neuron. The neuron has six basic functional properties[27].
It is an input device receiving signals from the environment or other neurons. It is an integrative device integrating and manipulating the input. It is a conductive device conducting the integrated information over distances.
It is an output device sending information to other neurons or cells. It is a computational device mapping one type of information into another.
And ,it is a representational device subserving the formation of internal representations.
Consequently ,we would expect to find these functional properties within our artificial neural networks.
The main properties of connectionist models are:
- A set of processing units
- A state of activation
- An output function for each unit.
- An activation rule
- A learning rule
- An environment with in which the system must operate.
Relevance to Philosophy
Some philosophy, in particular naturalistic philosophy of mind, is part of cognitive science. But the interdisciplinary field of cognitive science is relevant to philosophy in several ways. First, the psychological, computational, and other results of cognitive science investigations have important potential applications to traditional philosophical problems in epistemology, metaphysics, and ethics. Second, cognitive science can serve as an object of philosophical critique, particularly concerning the central assumption that thinking is representational and computational. Third and more constructively, cognitive science can be taken as an object of investigation in the philosophy of science, generating reflections on the methodology and presuppositions of the enterprise.
There are many questions raised by Cognitive science that are worthy of investigation by philosophers of Science.
What is the nature of representation?
What role do computational models play in the development of cognitive theories?
What is the relation among apparently competing accounts of mind involving symbolic processing, neural network , and dynamical systems?
Are psychological phenomena subject to reductionist explanations via neuroscience?
Finally, the Cognitive science is related to Philosophy in that, there are many phenomena which can’t be explained through the empirical approach or experimental method, for this type of complexity the philosophers goes through the theoretical and hypothetical methods to reveal and fulfill these gaps.
Sources
- The philosophy of Cognitive Science-Rick Grush -2001 department of philosophy – university of California, San Diago
- Encyclopedia of Encarta-2006-Internet
- Stanford Encyclopedia of Philosophy –Internet
- Principes de psychology Cognitive- Annie Bertrand – Henri Garnier
- What is Mind Design ?- By : John Haugeland-1996
- Introduction to Connectionism – Laura E.Northrup-12-16-2004
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