Intro to Cog Sci 2nd midterm notes (Fall 2013)

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University of California - Berkeley
Cognitive Science
Terry Regier

Cognitive Science Midterm 2 notes 11/3/2013 2:08:00 PM Symbolic versus Imagistic representations  Symbolic: language-like, prepositional; ie Frege, Chomsky, Newell and Simon;  Imagistic: experience-like, associative o Shepard & Metzler 1971: is this picture a rotation of the figure  time to confirm “same figure” is a linear function of angle of rotation  interpretation= people mentally rotate one figure onto the other= imagistic mental representations o Kosslyn 1973: focus/memorize picture  focus on one end  asked if picture contains certain items  time to confirm dependent on how close/far the item is from the point of focus  interpretation = same as Shepard & Metzler = imagistic mental representations (“scanning” picture) Language  Uniquely human  Classic challenge to behaviorism (whitehead)  Symbolic system  Defining feature of human cognition  ELIZA- chatterbot, applied pattern-matching to reply to human statements  SHRLDU-natural language understanding in micro-world (Links language “understanding”, symbolically represented, with observable action in the world) o Language linked to action o “All language use can be thought of as a way of activating procedures within the hearer.” Terry Winograd, 1973 o 1. Syntactic analysis: Grammatical analysis of input sentences o 2. Semantic analysis: Determine what the sentence means. o 3. Perception and inference: Consult the “world” for answers to questions posed. o **All implemented as procedures  Language understanding as dependent on multiple interacting processes Marr’s 3 levels framework  Contribution to cognitive science generally  Marr Prize given annually to best student paper at the Cognitive Science Society conference  LEVELS o Computational theory: the FUNCTIONAL GOAL of the visual system is to determine the shape of objects in the world (goal: to fly) o Representation and algorithm: ??? (represented through: curved wings, aerodynamics) o Hardware implementation: neurons somehow (how exactly: feathers, steel, balsa wood, etc)  Examples o Afterimages can be easily explained at the implementational neural level: photoreceptor adaptation. o The ambiguity of a Necker cube suggests an explanation involving competing 3D interpretations.  Children as rational scientists: goal is to correctly understand the world around them Marr’s model of vision  Contribution to vision science  Sees image: 1) primal sketch 2) 2.5D sketch 3) 3D sketch  Continuum from imagistic to symbolic  Hierarchal structure in perception  Says what representations and processes the visual system uses but not the functional goal nor how it is implemented physically Theories of the mind often driven by new technology  Many theories assume the mind is a machine  Possibility of non-human understanders  Two extremes o Solipsism (no mind exists except mine) ------ anthropomorphism (attribute human abilities to anything)  Metaphors for the mind have historically reflected the technology of the age o Water technology of antiquity is reflected in the Greek pneumatic concept of the soul, e.g. Hippocrates. (ie Humors: substances that balance each other out in a healthy human’s body) o Clockwork of enlightenment is reflected in the concept of “mechanical man”. (ie Mechanical Turk, digesting duck) o “The reception of light, sounds, odors, tastes, warmth, and other like qualities into the exterior organs of sensation; the impression of the corresponding ideas upon a common sensorium and on the imagination; the retention or imprint of these ideas in the memory; ... and finally, the external motions of all the members of the body ... I wish that you would consider all of these as following altogether naturally in this machine from the disposition of its organs alone, neither more nor less than do the movements of a clock or other automaton from that of its counterweight and wheels...” (1664) Descartes o Hodgkin & Huxley’s (1952) model of neuron action potential propagation drew on “telegrapher’s equation”, e.g. the transatlantic undersea cable. o Tabula Rasa- blank slate; Blank Paper- paper; brain as computer Physical Symbol System hypothesis  Newell and Simon (1975): A physical symbol system has the necessary and sufficient means for general intelligent action. o ie problem-solving!  Physical Symbol Systems o 1. Symbols are physical patterns. o 2. Symbols can be combined to form complex symbol structures. o 3. Contains processes for manipulating complex symbol structures. o 4. The processes for generating and transforming complex symbol structures can themselves be represented by symbols and symbol structures.  Syntax: the identification and manipulation of such symbols based purely on their shape.  Semantics: the meaning and changes in meaning that these syntactic manipulations are meant to correspond to.  Search space: Space of possible problem solutions – must search through this space to find optimal solution.  **Intelligence as search through a symbol structure Turing test  Alan Turing: The world's first and most influential computational scientist.  Imitation game: human or machine? o Criterion: can convince you it’s a human o Linguistic in nature Chinese room argument  John Searle  Turing test is an inadequate criterion  Searle (who speaks no Chinese) receives input in Chinese and through syntactic symbol manipulation based on symbol shape, produces appropriate output in Chinese  Still has no understanding of Chinese  ARGUMENTS/REPLIES o Systems reply: John doesn’t understand Chinese but the system as a whole does  Searle: Even with rules memorized and after becoming the system, still no understanding of Chinese o Robot reply: true that room doesn’t understand Chinese but not because it’s symbol-based…instead it is because it has no embodied experience to link its symbols to  Searle: Even if grounded, they’re still just meaningless symbols o **symbol grounding problem: problem of how words and thoughts become meaningful to speakers and thinkers while the problem of intentionality refers to how words and thoughts connect up in the world Brain organization  Parts of brain: o Forebrain o Midbrain o Hindbrain  Lobes of cerebral hemisphere: o Frontal- motor control, speech, planning o Parietal- spatial, sensory integration o Temporal- objects, auditory o Occipital- vision  The case of Phineas Gage o “The equilibrium ... between his intellectual faculties and animal propensities, seems to have been destroyed. He is ... capricious and vacillating, devising many plans of future operations, which are no sooner arranged than they are abandoned ...” –Doctor report o Destroyed part of frontal lobe responsible for planning and decision making  Two hemispheres o Some cerebral asymmetries  Right handed people have more areas in their left hemisphere associated with language. o But most functions are in both hemispheres o Contralateral organization  Information from left visual field is processed in the right hemisphere, and vice versa. Two cortical vision systems:  Two streams hypothesis o Ungerleider and Mishkin monkey experiments o Identification versus localization  Interpretation: parietal cortex involved in spatial recognition and receives most of the information from visual cortex in same hemisphere  So damage to right parietal cortex leads to left hemineglect  Dorsal- “where” pathway  Ventral- “what” pathway  Place cells in hippocampus: a neural correlate of cognitive maps. o From rats running around a triangle track spatial firing of place cells Words in the brain: serial versus parallel processing models  Serial model of word processing  Visual input  auditory form  semantic processing (meaning)  articulation (speaking) o ie passively view words  passively listen to words  generate a related verb  speak a visually presented word  Image people’s brains while they participate in tasks that tap into these hypothesized processes  Problem: some tasks tap into more than one “functional box” o Solution: subtraction method” = subtract the image of one task from that of another, to zoom in on a functional box.  Interpretation: actually a parallel process in which there is overlap in between functional boxes -Peterson Accidents, surgery, imaging as sources of knowledge  Accidents can yield knowledge (although unsystematic and uncertain) concerning the functional organization of the brain o ie Phineas Gage case  If damage to area A is linked to a deficit in function F, what can we conclude?  3. F may be carried out directly by A – or by another area that is downstream from A.  Systematic surgical intervention can help disambiguate. o ie Ungerleider and Mishkin’s monkey experiments  Imagery o Positron emission tomography (PET)- technique that produces an image of blood flow in the body, which correlates with functional processes. Neural computation, connectionism, as an alternate view to computation in cognition  “Classical” symbolic view where: o The mind can be thought of in terms of symbol structures, and syntactic operations on them. o Physical symbol system hypothesis. o Strong innate component o Examples: propositions, logical inference, scripts, schemas, symbolic cognitive architectures... o Proponents: Fodor, Chomsky, Pylyshyn, Simon & Newell  Non-symbolic representations, massively parallel processing, multiple soft constraints o Hypothesis of how one thought leads to another  Spreading activation: Activation spreads along links of a semantic network, activating other concepts.  Similar to how neural networks work  But in a semantic network, each concept is a node, whereas in a neural network, a concept is often a pattern of activation across nodes.  Connectionists argue that: o continuous distributed representations are the key to understanding human cognition o general-purpose learning mechanisms can be used to acquire these representations  Attractions of Connectionism o A simple, unified account of how cognition works and where knowledge comes from.  Powerful, domain-general learning mechanisms o Ability to explain much of the “fuzziness” of human reasoning (dealing with almost-dogs). o The increased neural plausibility of artificial neural networks (as opposed to logic).  Criticisms of Connectionism o Although fu
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