Nice answer! Carol Phone mail
> On Feb 8, 2016, at 10:15 PM, Mike Palij <[email protected]> wrote: > >> On Mon, 08 Feb 2016 12:57:00 -0800, Stuart McKelvie wrote: >> Dear Tipsters, >> >> I like D. O. Hebb's distinction between sensation and >> perception as a way of distinguishing bottom-up and >> top-down processing. > > One thing to keep in mind is that the "bottom-up" versus > "top-down" distinction originates in computer science > or what we now refer to as artificial intelligence. Alan > Turing proposed the bottom-up approach in a 1947 > lecture and 1948 paper as operating in a simple > neural network model (the first real neural network > model, Rosenblatt and perceptrons notwithstanding). > Hebb's (1949) "Organization of Behavior" does not > mention Turing work but what is being described is > sensation ad Stuart describes below. > > Chris Green can confirm if Hebb's distinction is in > fact a classical distinction going back to the 19th > century. Today, the distinction is fuzzy given that > we know that stimuli can activate knowledge structures > and schemas, so it become a single process of > sensation to cognition with perception as an intermediate > step for simple stimuli. But I could be wrong. > >> Hebb defines sensation as activity in the sense organ >> and corresponding sensory receiving areas of the brain. >> You can easily illustrate this with a diagram, say for the >> visual system. > > More importantly, stimulus information processing causes > the system to learn recurring patterns of stimulation which > causes the neural network to recognize symbols over time. > This would be a purely bottom-up system and can be described > mathematically -- it implies that such a neural network can be > implemented in biological as well as nonbiological systems. > For more background on these points see the following > reference: > Copeland, B. J., & Proudfoot, D.. (1996). On Alan Turing's > Anticipation of Connectionism. Synthese, 108(3), 361-377. > > For a superficial statement on Turing's contribution, see the > following entry on AlanTuring.net: > http://www.alanturing.net/turing_archive/pages/reference%20articles/what_is_AI/What%20is%20AI09.html > See also: > www.cs.virginia.edu%2F~robins%2FAlan_Turing%27s_Forgotten_Ideas.pdf&usg=AFQjCNHjHHgmZ7dZi_snhg8l6mpb27WHcQ&sig2=kBGlsXOMwl6ABdKkHEu7Zg > > The Turing 1948 paper is titled "Intelligent machinery". > >> Perception is then what occurs when this information is sent >> on to other parts of the brain and interpreted in the light of context >> and past experience (top-down processing). > > In Turing's framework, a list or a representation is stored in memory > and in top-down processing, activating this representation guides > processing. In a mixed system, bottom-up processing matches > its output to the representation as a confirmation that it's processing > was correct. The interaction of bottom-up and top-down processing > is somewhat redundant because once the input network learns to > recognized a pattern, it should function stablely if the stimulus doesn't > vary. Building in top-down processing allows one to deal with > such variability where the stored representation is a prototypical > representation and has a category containing variations of the > prototype. > > Gibson, I believe would argue that an internal representation is not > necessary because all of the information is present in the stimulus > and a trained neural network extract the information and could execute > and action based on this output. In a perceptual system this might > work but even Neisser had accept that mental representations existed > in an ecological approach to cognition because it was necessary for > other mental operations. > > Re: Annette's question of human use of template models of pattern > recognition: take a look at Selfridge & Neisser (1960) Pattern > Recognition by Machine, Scientific American where they present > Pandenmonium, a neural network perceptron pattern recognition > system. They discuss the template model and its problems and > why it fails at character recognition. However, Hubel and Wiesel > feature detectors can be thought of as microtemplates (e.g., lines > of different orientation, curves, etc.) which are combined in the > visual neural network involved in bottom-up processing. To take it > one step further, Biederman's geons can be thought of as abstract > templates which are used as components to form objects that > ultimately are confirmed by matching to existing representations > of objects in world. Geon being simple geometric forms can be > built into the nervous system (like feature detectors) because they > represent the basic geometry in the real world and define the shap > of objects therein or can be derived through training the neural > network for objects (but this would take much longer than activating > built-in geons which would confer an evolutionary advantage). > > -Mike Palij > New York University > [email protected] > > > --- > You are currently subscribed to tips as: [email protected]. > To unsubscribe click here: > http://fsulist.frostburg.edu/u?id=177920.a45340211ac7929163a0216244443341&n=T&l=tips&o=48104 > or send a blank email to > leave-48104-177920.a45340211ac7929163a0216244443...@fsulist.frostburg.edu --- You are currently subscribed to tips as: [email protected]. 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