It is terribly ironic that someone from MIT would claim that the brain operating continuously was a longstanding and obvious overriding assumption. MIT (historically their AI lab, and more recently their Brain & Cognitive Sciences Department and their Linguistics Department) is where much of the fervor and argumentation for the mind being a finite state automaton came from. Personally, I couldn't agree more that neurophysiological data make it obvious that the brain functions essentially continuously, but if you read a contemporary cognitive psychology textbook you'll see a very different "information-processing approach" as the dominant perspective.
"One question I'm particularly interested in is how we convert from the continuous domain of our senses to the making a binary descision move mouse over picture A or picture B. "
Excellent question.
Directly related to it, there are a number of dynamical types working on what is called "symbolic dynamics." I recommend you look up work by J. P. Crutchfield, P. beim Graben, C. Shalizi, E. Bollt, and Ben Goertzel. Happy reading.
Actually, in our article in Proceedings of the National Academy, we describe a localist attractor network simulation that explicitly simulates our human data and mathematically implements the temporally dynamic trajectory through a multi-dimensional space. However, it must be said that neural network simulations are often less transparent than the partial differential equations and phase-space manifolds from dynamical systems theory. Therefore, regarding your claim that "the actual math of the actual operations is beyond the abilities of anyone making the claims" is probably best refuted by books by Dr. Lawrence Ward (Dynamical Cognitive Science) and Dr. Scott Kelso (Dynamical Patterns), and scientific journal articles by people like Dr. Michael Turvey and Dr. Guy Van Orden. In those sources, you should find enough mathematical explication to satisfy your concerns....There are a lot of cognitive scientists in the world, DynaSoar, and believe it or not, some of them do actually work with some sophisticated mathematics.
It is, however, quite informative to see the distribution of reactions. Rather ironically, about half of the people say that our claim that the mind works like a dynamical system (not like a digital computer) is obvious and trivial, whereas the other half argue vehemently that digital computing theory is perfectly consistent with our results and the mind as computer metaphor is still alive and strong. But you're right. I'm better off to step back and let them fight it out amongst themselves.
Some examples of people who continue to argue for sequential processing and/or discrete representation and/or modular cognitive architectures are Jerry Fodor, Zenon Pylyshyn, and Eric Dietrich (Philosophy of Mind), John Anderson and Art Markman (Cognitive Psychology), Doug Lenat (Artificial Intelligence), Steven Pinker and Elizabeth Spelke (Developmental Psychology), Leda Cosmides and Nancy Kanwisher (Cognitive Neuroscience)... the list goes on.
At the theoretical level, my argument is that analog (or nearly analog) computing provides a much better simulation of the mind than digital computing theory. At the pragmatic level, my argument hinges on decades of research on Artificial Intelligence being motivated by traditional tree-search algorithms, production systems, and other discrete serial processing systems. In speech recognition, however, hidden markov models have more recently been the popular method for automated word recognition, and they do indeed perform in a way that is describable as representing multiple potential words at once. Therefore, probabilistic algorithms and neural networks (programmed on digitial computers) are indeed useful and informative ways to build simulations of various human mental processes.
If they didn't remember what the objects were, and had to scan them again to know, then you would expect them to do that in the control condition as well (i.e., when instructed to "click the candy" and there was a candy and a jacket [in place of the candle]). However, they only made those eye movements to the similarly-named competitor objects, not to irrelevant competitor objects. Correspondingly, the mouse movements (in the recent article) only show the graded curvature toward similar-named objects, not toward irrelevant objects.
p.s. I promise these scientific articles in journals like Science and Proceedings of the National Academy of Sciences really do undergo quite extensive scientific review for the kinds of concerns you are raising.
The reason one might expect mouse movements to go intially all the way to a competitor object is because when my colleagues and I recorded people's eye movements in previous research, that's exactly what they did. The mouse movements show much more clearly (than previous work) that the competition from the similar-named object is continuous rather than discrete.
Slashdot Mirror
It is terribly ironic that someone from MIT would claim that the brain operating continuously was a longstanding and obvious overriding assumption. MIT (historically their AI lab, and more recently their Brain & Cognitive Sciences Department and their Linguistics Department) is where much of the fervor and argumentation for the mind being a finite state automaton came from. Personally, I couldn't agree more that neurophysiological data make it obvious that the brain functions essentially continuously, but if you read a contemporary cognitive psychology textbook you'll see a very different "information-processing approach" as the dominant perspective.
"One question I'm particularly interested in is how we convert from the continuous domain of our senses to the making a binary descision move mouse over picture A or picture B. " Excellent question. Directly related to it, there are a number of dynamical types working on what is called "symbolic dynamics." I recommend you look up work by J. P. Crutchfield, P. beim Graben, C. Shalizi, E. Bollt, and Ben Goertzel. Happy reading.
Actually, in our article in Proceedings of the National Academy, we describe a localist attractor network simulation that explicitly simulates our human data and mathematically implements the temporally dynamic trajectory through a multi-dimensional space. However, it must be said that neural network simulations are often less transparent than the partial differential equations and phase-space manifolds from dynamical systems theory. Therefore, regarding your claim that "the actual math of the actual operations is beyond the abilities of anyone making the claims" is probably best refuted by books by Dr. Lawrence Ward (Dynamical Cognitive Science) and Dr. Scott Kelso (Dynamical Patterns), and scientific journal articles by people like Dr. Michael Turvey and Dr. Guy Van Orden. In those sources, you should find enough mathematical explication to satisfy your concerns. ...There are a lot of cognitive scientists in the world, DynaSoar, and believe it or not, some of them do actually work with some sophisticated mathematics.
It is, however, quite informative to see the distribution of reactions. Rather ironically, about half of the people say that our claim that the mind works like a dynamical system (not like a digital computer) is obvious and trivial, whereas the other half argue vehemently that digital computing theory is perfectly consistent with our results and the mind as computer metaphor is still alive and strong. But you're right. I'm better off to step back and let them fight it out amongst themselves.
Some examples of people who continue to argue for sequential processing and/or discrete representation and/or modular cognitive architectures are Jerry Fodor, Zenon Pylyshyn, and Eric Dietrich (Philosophy of Mind), John Anderson and Art Markman (Cognitive Psychology), Doug Lenat (Artificial Intelligence), Steven Pinker and Elizabeth Spelke (Developmental Psychology), Leda Cosmides and Nancy Kanwisher (Cognitive Neuroscience)... the list goes on.
At the theoretical level, my argument is that analog (or nearly analog) computing provides a much better simulation of the mind than digital computing theory. At the pragmatic level, my argument hinges on decades of research on Artificial Intelligence being motivated by traditional tree-search algorithms, production systems, and other discrete serial processing systems. In speech recognition, however, hidden markov models have more recently been the popular method for automated word recognition, and they do indeed perform in a way that is describable as representing multiple potential words at once. Therefore, probabilistic algorithms and neural networks (programmed on digitial computers) are indeed useful and informative ways to build simulations of various human mental processes.
I couldn't have said it better myself.
That was beautiful, Lemuridae. I'm glad someone is getting the point of my experiment.
If they didn't remember what the objects were, and had to scan them again to know, then you would expect them to do that in the control condition as well (i.e., when instructed to "click the candy" and there was a candy and a jacket [in place of the candle]). However, they only made those eye movements to the similarly-named competitor objects, not to irrelevant competitor objects. Correspondingly, the mouse movements (in the recent article) only show the graded curvature toward similar-named objects, not toward irrelevant objects. p.s. I promise these scientific articles in journals like Science and Proceedings of the National Academy of Sciences really do undergo quite extensive scientific review for the kinds of concerns you are raising.
The reason one might expect mouse movements to go intially all the way to a competitor object is because when my colleagues and I recorded people's eye movements in previous research, that's exactly what they did. The mouse movements show much more clearly (than previous work) that the competition from the similar-named object is continuous rather than discrete.