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人类是如何感知和认识这个世界一直是个巨大的谜，这里是我学习认知科学时的一些笔记。
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Cerebral cortex - Wikipedia, the free encyclopedia
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Cognitive revolution - Wikipedia, the free encyclopedia
- The "cognitive revolution" is the name for an intellectual movement in the 1950s that began what are known collectively as the cognitive sciences.
- 认知革命的定义：an all-out effort to establish meaning as the central concept of psychology […]. It was not a revolution against behaviorism with the aim of transforming behaviorism into a better way of pursuing psychology by adding a little mentalism to it. […] Its aim was to discover and to describe formally the meanings that human beings created out of their encounters with the world, and then to propose hypotheses about what meaning-making processes were implicated."
- 认知科学的五条假设（原则）
- The mental world can be grounded in the physical world by the concepts of information, computation, and feedback.
- The mind cannot be a blank slate because blank slates don't do anything.
- An infinite range of behavior can be generated by finite combinatorial programs in the mind
- Universal mental mechanisms can underlie superficial variation across culture
- The mind is a complex system composed of many interacting parts.
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Behaviorism (Stanford Encyclopedia of Philosophy)
来自Stanford Encyclopedia of Philosophy。
行为主义是已经过气的心理学分支，被认知革命革了命。- behave is what organisms do.
- Loosely speaking, behaviorism is an attitude. Strictly speaking, behaviorism is a doctrine.
- For such a person, there is no knowable difference between two states of mind unless there is a demonstrable difference in the behavior associated with each state.
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Connectionism (Stanford Encyclopedia of Philosophy)
Stanford Encyclopedia of Philosophy
- 定义Connectionism is a movement in cognitive science which hopes to explain human intellectual abilities using artificial neural networks (also known as ‘neural networks’ or ‘neural nets’). Neural networks are simplified models of the brain composed of large numbers of units (the analogs of neurons) together with weights that measure the strength of connections between the units. These weights model the effects of the synapses that link one neuron to another. Experiments on models of this kind have demonstrated an ability to learn such skills as face recognition, reading, and the detection of simple grammatical structure.
- Connectionists presume that cognitive functioning can be explained by collections of units that operate in this way. Since it is assumed that all the units calculate pretty much the same simple activation function, human intellectual accomplishments must depend primarily on the settings of the weights between the units.
- feed forward net.
- backpropagation
- Training nets to model aspects of human intelligence is a fine art. Success with backpropagation and other connectionist learning methods may depend on quite subtle adjustment of the algorithm and the training set.
- Connectionist learning techniques such as backpropagation are far from explaining this kind of ‘one shot’ learning
- One of the most attractive of these efforts is Sejnowski and Rosenberg's 1987 work on a net that can read English text called NETtalk.
- Nets may be good at making associations and matching patterns, but they have fundamental limitations in mastering general rules such as the formation of the regular past tense.
- For example, Marcus (1998, 2001) argues that Elman's nets are not able to generalize this performance to sentences formed from a novel vocabulary. This, he claims, is a sign that connectionist models merely associate instances, and are unable to truly master abstract rules.
- Over the centuries, philosophers have struggled to understand how our concepts are defined.
- Connectionism promises to explain flexibility and insight found in human intelligence using methods that cannot be easily expressed in the form of exception free principles (Horgan and Tienson 1989, 1990), thus avoiding the brittleness that arises from standard forms of symbolic representation.
- The last forty years have been dominated by the classical view that (at least higher) human cognition is analogous to symbolic computation in digital computers.
- The connectionist claims, on the other hand, that information is stored non-symbolically in the weights, or connection strengths, between the units of a neural net.
- implementational connectionists seek an accommodation between the two paradigms
- They hold that the brain's net implements a symbolic processor. True, the mind is a neural net; but it is also a symbolic processor at a higher and more abstract level of description
- Cool, that's what I belive too! - post by ericwangqing
- They complain that classical theory does a poor job of explaining graceful degradation of function, holistic representation of data, spontaneous generalization, appreciation of context, and many other features of human intelligence which are captured in their models
- Such radical connectionists claim that symbolic processing was a bad guess about how the mind works
- The failure of classical programming to match the flexibility and efficiency of human cognition is by their lights a symptom of the need for a new paradigm in cognitive science. So radical connectionists would eliminate symbolic processing from cognitive science forever.
- distributed representation seems both novel and difficult to understand
- The sub-symbolic nature of distributed representation provides a novel way to conceive of information processing in the brain. If we model the activity of each neuron with a number, then the activity of the whole brain can be given by a giant vector (or list) of numbers, one for each neuron.
- So the brain amounts to a vector processor, and the problem of psychology is transformed into questions about which operations on vectors account for the different aspects of human cognition.
- This suggests that neural network models serve as counterexamples to the idea that the language of thought is a prerequisite for human cognition. However, the matter is still a topic of lively debate
- In a series of papers Horgan and Tienson (1989, 1990) have championed a view called representations without rules. According to this view classicists are right to think that human brains (and good connectionist models of them) contain explanatorily robust representations; but they are wrong to think that those representations enter in to hard and fast rules like the steps of a computer program.
- Fodor and Pylyshyn's often cited paper (1988) launches a debate of this kind. They identify a feature of human intelligence called systematicity which they feel connectionists cannot explain. The systematicity of language refers to the fact that the ability to produce/understand/think some sentences is intrinsically connected to the ability to produce/understand/think others of related structure.
- Since connectionism does not guarantee systematicity, it does not explain why systematicity is found so pervasively in human cognition.
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Mental Representation (Stanford Encyclopedia of Philosophy)
Stanford Encyclopedia of Philosophy
- The notion of a "mental representation" is, arguably, in the first instance a theoretical construct of cognitive science.
- Contemporary philosophers of mind have typically supposed (or at least hoped) that the mind can be naturalized
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- In computability theory the Church–Turing thesis (also known as Church's thesis, Church's conjecture and Turing's thesis) is a combined hypothesis about the nature of effectively calculable (computable) functions by recursion (Church's Thesis), by mechanical device equivalent to a Turing machine (Turing's Thesis) or by use of Church's λ-calculus:
- Every effectively calculable function (effectively decidable predicate) is general[1] recursive
- The three computational processes (recursion, λ-calculus, and Turing machine) were shown to be equivalent by Alonzo Church, Stephen Kleene and J.B. Rosser (1934-6)[2] and by Alan Turing (1936-7)[3]
- effectively calculable
- effectively computable
- Thus, as they stand, neither thesis can be proven
- Informally the Church–Turing thesis states that if an algorithm (a procedure that terminates) exists then there is an equivalent Turing machine or applicable λ-function[15] for that algorithm.
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Nature versus nurture - Wikipedia, the free encyclopedia
- The nature versus nurture debates concern the relative importance of an individual's innate qualities ("nature", i.e. nativism, or philosophical empiricism, innatism) versus personal experiences ("nurture") in determining or causing individual differences in physical and behavioral traits.
- The famous psychologist Donald Hebb is said to have once answered a journalist's question of "which, nature or nurture, contributes more to personality?" by asking in response, "which contributes more to the area of a rectangle, its length or its width?"
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Tabula rasa - Wikipedia, the free encyclopedia
- Tabula rasa (Latin: blank slate) refers to the epistemological thesis that individual human beings are born with no built-in mental content, in a word, "blank", and that their entire resource of knowledge is built up gradually from their experiences and sensory perceptions of the outside world.
- In computer science, tabula rasa refers to the development of autonomous agents which are provided with a mechanism to reason and plan toward their goal, but no "built-in" knowledge-base of their environment. They are thus truly a "blank slate".
- Scientists recognize that the entire cerebral cortex is indeed preprogrammed and organized in order to process sensory input, motor control, emotions, and natural responses.[5] This preprogrammed part of the brain then learns and refines its ability to perform its many tasks.
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