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Quote by Roger Schank

“There are endless books about what every third grader must know that use the idea that factual knowledge is the basis of the ability to read as their justification. Unfortunately, the writers of these tracts have misunderstood the cognitive science behind those statements. It is difficult to read things when you don't understand what they are about, but it does not follow from that thatthe solution is to ram that knowledge down kids' throats and then have them read. It is much more clever to have them read about what they know and to gradually increase their knowledge through stories that cause them to have to learn more in order to make the stories understandable to them.”

Quote by Roger Schank

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Author

Roger Schank
Roger Schank

Roger Schank (born 1946) is an American cognitive scientist, artificial intelligence researcher, and educational innovator. He is best known for his pioneering work in natural language processing, case-based reasoning, and story-centered learning theory. A founder of the Yale Artificial Intelligence Project, Schank developed influential theories including conceptual dependency, script theory, and dynamic memory models. His research emphasizes the narrative nature of human thinking and has been applied to create AI-based educational systems. He has founded several edtech companies and authored numerous books on learning and cognition. more

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“In summary, a good teacher does the following: - never tells a student anything that the teacher thinks is true - never allows himself to be the ultimate judge of his own students' success - teacher practice first, theory second (if he must teach theory at all) - does not come up with lists of knowledge that every student must know - doesn't teach anything unless he can easily explain the use of learning it - assigns no homework, unless that homework is to produce something - groups students according to their interests and abilities, not their ages - ensures that any reward to a student is intrinsic - teaches students things they may actually need to know after they leave school - helps students come up with their own explanations when they have made a mistake - never assumes that a student is listening to what he is saying - never assumes that students will do what he asks them to do if what he asked does not relate to a goal they truly hold - never allows pleasing the teacher to be the goal of the student - understands that students won't do what he tells them if they don't understand what is being asked of them - earns the respect of students by demonstrating abilities - motivate students to do better, and does not help them to do better - understands that his job is to get students to do something - understands that experience, not teachers, changes belief systems - confuses students - does not expect credit for good teaching”

EducationLearningTeachingTeachersCognitive ScienceGood Teachers
Author:Roger Schank
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“The brain works in a holistic, cooperative way that makes our basest desire or most abject fear as expressive of who we are as abstract thinking of the highest order. That means that we are all equal part snakes, monkeys, and spacemen.”

BrainBrainy QuotesNeuroscienceCognitionCognitive Science
Book:The Sniper Mind: Eliminate Fear, Deal with Uncertainty, and Make Better Decisions
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“In game theory, as in applications of other technologies that use RPT [Revealed Preference Theory], the purpose of the machinery is to tell us what happens when patterns of behavior instantiate some particular strategic vector, payoff matrix, and distribution of information—for example, a PD [Prisoner's Dilemma]—that we’re empirically motivated to regard as a correct model of a target situation. The motivational history that produced this vector in a given case is irrelevant to which game is instantiated, or to the location of its equilibrium or equilibria. As Binmore (1994, pp. 95–256) emphasizes at length, if, in the case of any putative PD, there is any available story that would rationalize cooperation by either player, then it follows as a matter of logic that the modeler has assigned at least one of them the wrong utility function (or has mistakenly assumed perfect information, or has failed to detect a commitment action) and so made a mistake in taking their game as an instance of the (one-shot) PD. Perhaps she has not observed enough of their behavior to have inferred an accurate model of the agents they instantiate. The game theorist’s solution algorithms, in themselves, are not empirical hypotheses about anything. Applications of them will be only as good, for purposes of either normative strategic advice or empirical explanation, as the empirical model of the players constructed from the intentional stance is accurate. It is a much-cited fact from the experimental economics literature that when people are brought into laboratories and set into situations contrived to induce PDs, substantial numbers cooperate. What follows from this, by proper use of RPT, not in discredit of it, is that the experimental setup has failed to induce a PD after all. The players’ behavior indicates that their preferences have been misrepresented in the specification of their game as a PD. A game is a mathematical representation of a situation, and the operation of solving a game is an exercise in deductive reasoning. Like any deductive argument, it adds no new empirical information not already contained in the premises. However, it can be of explanatory value in revealing structural relations among facts that we otherwise might not have noticed.”

EconomicsRationalityPhilosophy Of ScienceCognitive ScienceEconomic TheoryGame TheoryMicroexplanation
Author:Don Ross
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“While AI technology has reached important levels of performances in narrow settings, the missing part concerns exactly the study of how to create artificial companions (embodied and disembodied) able to integrate different skills in order to help humans in their everyday activities. Similarly, computational cognitive science is interested in individuating how the brain and the mind works as integrated systems. This renewed convergence is, in my view, a necessity driven by the fact that modern and future AI and CogSci research will be again disciplines interested in the same topic: namely the discovery of the mechanisms enabling multitasking intelligence. In order to advance the scientific knowledge in their respective field, in fact, they need to evolve and become sciences (of the artificial) studying the mysteries of "integrated intelligence". Time seems mature for a renewed collaboration.”

Artificial IntelligenceCognitive Science
Book:Cognitive Design for Artificial Minds
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