# Summary from Common Cog Blog [Common Cog Summary](https://commoncog.com/blog/how-note-taking-can-help-you-become-an-expert/) >**Core syllogism** >1)  Learning is the active construction of conceptual understanding. >2)  Training must support the learner in overcoming reductive explanation. _JM-don't oversimplify_ >3)  Reductive explanation reinforces and preserves itself through misconception networks and through knowledge shields. _JM-our minds are lazy and create heuristics to do the job quickly and more efficiently at the expense of accuracy_ >4) Advanced Learning is the ability to flexibly apply knowledge to cases within the domain. > _Therefore_, instruction by incremental complexification will not be conducive of advanced learning. > _Therefore_, advanced learning is promoted by emphasizing the interconnectedness of multiple cases and concepts along multiple dimensions, and the use of multiple, highly organized representations. > > **Empirical ground** >- Studies of learning of topics that have conceptual complexity (medical students). >- Demonstrations of knowledge shields and dimensions of difficulty. >- Demonstrations that learners tend to oversimplify ([[Reductive Bias]]) by the spurious reduction of complexity. _JM-traders looking for exacting references, following price, etc._ >- Studies of the value of using multiple analogies. >- Demonstrations that learners tend to regularise that which is irregular, which leads to failure to transfer knowledge to new cases. _JM-traders do what has worked most recently_ >- Demonstrations that learners tend to de-contextualize concepts, which leads to failure to transfer knowledge to new cases. _JM-no day in the markets is ever the same it is all about context_ >- Demonstrations that learners tend to take the role of passive recipient versus active participants. _JM-looking for an exact or definite answer_ >- Hypothesis that learners tend to rely too much on generic abstractions, which can be too far removed from the specific instances experienced to be apparently applicable to new cases, i.e., failure to transfer knowledge to new cases. _JM-buy the dip_ >- Conceptual complexity and case-to-case irregularity pose problems for traditional theories and modes of instruction. _JM-people look for shortcuts because the truth is too hard_ >- Instruction that simplifies and then complicates incrementally can detract from advanced knowledge acquisition by facilitating the formation of reductive understanding and knowledge shields. >- Instruction that emphasizes recall memory will not contribute to inferential understanding and advanced knowledge acquisition (transfer). > >**Additional propositions in the theory** >- Advanced knowledge acquisition (apprentice-journeyman-expert) depends on the ability to achieve deeper understanding and apply it flexibly. >- Barriers to advanced learning include [[Complexity]], interactions, context-dependence, and illstructuredness (inconsistent patterns of concepts-in-combination). >- Cognitive flexibility includes the ability to mobilize small, pre-compiled knowledge structures, and this “adaptive schema assembly” involves integration and updating, rather than just recall. >- Active “assembly of knowledge” from different conceptual and case sources is more important in learning (for domains of complexity and ill-structuredness) than retrieval of knowledge structures. >- Misconceptions compound into networks of misconceptions. Misconceptions of fundamental concepts can cohere in systematic ways, making each misconception easier to believe and harder to change. >- Representations with high interconnectedness will tend to serve as “misconception-disabling correct knowledge.” >- Cognitive flexibility is the ability to represent knowledge from different conceptual and case perspectives and construct from those an adaptive knowledge ensemble tailored to the needs of the problem at hand. # The Goals of Advanced Knowledge Acquisition >the learner must attain a deeper understanding of content material, reason with it, and apply it flexibility in diverse contacts (p.3) >The methods of education in introductory and advanced learning seem, in many ways, to be at odds. For example, compartmentalizing knowledge, presenting clear instances (and not the many pertinent exceptions), and employing reproductive memory criteria are often in conflict with the realities of advanced learning knowledge which is intertwined and dependent, has significant context-dependent variations, and requires the ability to respond flexibly to "messy" application situations. (p.3) # Deficiencies in Advanced Knowledge Acquisition >Important aspects of conceptual complexity must now be mastered (superficial familiarity with key concepts is no longer sufficient); and the ability to apply knowledge from formal instruction to real-world cases is certainly something that is expected of those studying to be physicians. (p.3) >Prefigurative "world views" that underlie learners' understanding processes also cause problems; for example, the presupposition that the world works in such a way that "parts add up to wholes" leads students to decompose complex processes into components that are treated (mistakenly) as independent. (p.3-4) ## Reductive biases: The pervasive role of oversimplification in the development of misconceptions [[Reductive Bias]] ### Oversimplification of complex and irregular structure [[Availability Bias]] ![[Reductive Bias#^67fc4a]] >Superficial similarities among related phenomena are treated as unifying characteristics. Interacting components are treated as independent. Incomplete Conceptual accounts are presented (or accepted by the learner) as being comprehensive. Instances that are referred to as belonging to the same generic category are treated in a uniform manner despite their being highly diverse. The irregular is treated as regular, the nonroutine as routine, the disorderly as orderly, the continuous as discrete, the dynamic as static, the multidimensional as unidimensional. (This first reductive bias is the most general one, encompassing many of the specific ones listed below.) (p.4) ### Overreliance on a single basis for mental representation [[Mental Models]] | [[The Map is Not the Territory]] >A single, encompassing representational logic is applied to complex concepts and phenomena that are inadequately covered by that logic. For example: Understanding of a new concept is reduced to the features of a (partially) analogous concept. New, highly divergent examples are understood by exclusive reference to a single prototype. A single schema or theory is proffered and preferred, despite the fact that its coverage is significantly incomplete. Complexly multifaceted content has its understanding narrowed to just those aspects covered by a single organizational scheme. And so on. (p.4) ### Overreliance on "top down" processing >Understanding and decision making in knowledge application situations (i.e., cases) rely too exclusively on generic abstractions (i.e., concepts, theories, etc.); detailed knowledge of case structure is not used enough (i.e., knowledge of "how cases go," as well as reasoning from specific case precedents). (p.4) ### Context-independent conceptual representation >The contexts in which a concept is relevant are treated as having overly uniform characteristics. This promote s the representation of conceptual knowledge in a manner too abstract for effective application (i.e., without sufficient regard for the specifics of application in context). Concepts are insufficiently tailored to their uses; concepts are not recognized as relevant when, in fact, they are; and concepts are mistakenly judged to be relevant in contexts where they are not. (p.5) ### Overreliance on precompiled knowledge structures >Fired protocols or rigidly prepackaged schemas are presented to learners and used by them as recipes for what to do in new cases. ### Rigid compartmentalization of knowledge components >Components of knowledge that are in fact interdependent are treated as being separable from each other. Learners develop mistaken beliefs in the independence of the components. Relatedly, where knowledge components do function independently, it may nevertheless be the case that conveying relationships between their conceptual structures would aid understanding; these connections are not drawn. When components are interrelated, there is a tendency to use just one linkage scheme, thereby underrepresenting the richness of interconnection in the system and promoting narrow, doctrinaire viewpoints (see the problem of single representations). (p.5) It is here where [[Artificial Intelligence]] offers the ability to fully capture the interconnectedness of knowledge components. Simply relying on the single knowledge component of the lack of excess would ignore the message that time was sending. It is very difficult to know which component of the auction process (lack of excess or time showing rejection) to weight in this scenario and the market experts seem to know which knowledge component to weigh better than less-experienced traders. This yields the uncanny feeling that they have some otherworldly power to divine what the market is doing. ### Passive transmission of knowledge >Knowledge is preemptively encoded under a scheme determined by external authority (e.g., a textbook) or a scheme which facilitates delivery and use. Knowledge is "handed" to the learner. The preemptive encoding is passively received by the learner, and useful benefits that result from personalized knowledge representations, derivable from active exploration and involvement in the subject area, do not develop. When active, participatory learning is encouraged, adequate support for the management of increased indeterminacy and cognitive load is not provided (e.g., mentor guidance, memory aids, etc.). (p.5) # Cognitive Flexibility Theory: Themes of Advanced Knowledge Acquisition [[Knowledge]] >The themes are, in a sense, conditions for developing mastery of complexity and knowledge transferability. ## Avoidance of Oversimplification and Overregularization >Where the problem is so often a presumption of simplicity and regularity, the remedy is to take special measures to demonstrate complexities and irregularities. This is what Jim Dalton does so well in pointing out the [[Complexity]] of the markets. >Where conceptual error frequently occurs from atomistic decomposition of complexly interacting information, followed by misguided attempts at "additive" reassembly of the decomposed elements, the remedy is to take pains to highlight component interactions, to clearly demonstrate the intricate patterns of conceptual combination. (p.6) This is a potential issue with [[First Principles Thinking]]. >Cognitive flexibility involves the selective use of knowledge to adaptively fit the needs of understanding and [[Decision-Making]] in a particular situation; the potential for maximally adaptive knowledge assembly depends on **having available as full a representation of [[complexity]] to draw upon as possible.** (p.6) ## Multiple Representations >Cognitive flexibility is dependent upon having a diversified repertoire of ways of thinking about a conceptual topic. >[...] >The complexity of cases requires that they be represented from multiple theoretical/conceptual perspectives--if cases are treated narrowly by characterizing them using a too limited subset of their relevant perspectives, the ability to process future cases will be limited. First, there will be an assumption that cases are simpler than they in fact are, and attempts to deal with new e4es will prematurely conclude after they are only partially analyzed. Second, there will be insufficient preparedness to deal with the specific patterns of interaction of theoretical/conceptual perspectives within cases. Third, to the extent that performance in future cases will require reasoning from sets of precedent cases (which is always a greater need in ill-structured domains), the likelihood of having case representations available in prior knowledge which are maximally apt in their relation to some new case is lessened to the extent that cases are narrowly represented in memory.(p.6-7) >As we have said, our studies of medical students have indicated that one of the most serious contributors to the problems of advanced knowledge acquisition is the use of a single knowledge representation. Complex concepts can rarely be adequately represented using a single schema, theoretical perspective, line of exposition, and so on. Nevertheless, in practice, complex concepts frequently are represented in some single fashion, with substantial consequences. (p.7) ## Centrality of Cases >The more ill-structured the domain, the poorer the guidance for knowledge application that "top-down" structures will generally provide. >(p.9) ## Conceptual Knowledge as Knowledge-in-Use >Again, in an ill-structured domain the meaning of a concept is intimately connected to its patterns of use. When the uses (instances, cases) of the same concept have a complex and irregular distribution (i.e., the domain is ill-structured), adequate prepackaged prescriptions for proper activation of the concept cannot be provided (i.e., concept instantiation is non-routine). Instead, greater weight (than in a well-structured domain) must be given to activating concepts in a new case by examination of family resemblances across the features of past cases that have been called (labeled as instances of) that concept.(p.10) ## Schema Assembly (from Rigidity to Flexibility) >In complex and ill-structured domains, one cannot have a prepackaged schema for everything! >[...] >For any particular case, many small precompiled knowledge structures will need to be used. And there will be relatively little repetition of patterns across case-specific assemblies of these smaller pieces of precompiled knowledge. Accordingly, in knowledge acquisition for cognitive flexibility, the "storage of fixed knowledge is devalued in favor of the mobilization of potential knowledge" (p.10) ## Non-compartmentalization of Concepts and Cases (Multiple Interconnectedness) >Because of the complex and irregular way that abstract conceptual features weave through cases/examples in ill-structured domains, knowledge cannot be neatly compartmentalized. In order to enable the situation-dependent, adaptive schema assembly from disparate knowledge sources that characterizes cognitive flexibility, those multiple sources must be highly interconnected. (p.10) ## Active Participation, Tutorial Guidance, and Adjunct Support for the Management of Complexity >In an ill-structured domain, knowledge cannot just be handed to the learner. A priori codifications of knowledge are likely to misrepresent. (That is part of what ill-structuredness means.) Hence the importance, increasingly widely recognized today, of active learner involvement in knowledge acquisition, accompanied by opportunistic guidance by expert mentors (which can be incorporated in a computer program--it does not have to be live, ione-to-one guidance). Furthermore, aids must be provided to help the learner manage the added complexity that comes with ill-structure.(p.13) Integrating knowledge bases, Artificial Intelligence, and [[Relational inductive biases, deep learning, and graph networks|graph networks]] to model the extremely complex interconnectedness of ill-structured domains can provide a powerful tool to aid the [[learning]] process. # Recapitulation: A Shift from Single to Multiple Representations and from Generic Schema Retrieval to Situation-Specific Knowledge Assembly >In general, we argue that the goals of advanced knowledge acquisition in complex and ill-structured domains can best be attained (and the problems we have identified avoided) by the development of mental representations that support cognitive flexibility. Central to the cultivation of cognitive flexibility are approaches to learning, instruction, and knowledge representation that: (a) allow an important role for multiple representations; (b) view learning as the multidirectional and multiperspectival "criss-crossing" of cases and concepts that make up complex domains' "landscapes" (with resulting interconnectedness along multiple dimensions); and (c) foster the ability to assemble diverse knowledge sources to adaptively fit the needs of a particular knowledge application situation (rather than the search for a precompiled schema that fits the situation). We suggest that theory-based computer hypertext systems can implement the goals and strategies of Cognitive Flexibility Theory, engendering multiple cognitive representations that capture the real-world complexities of the kinds of cases to which abstract conceptual knowledge must be applied.(p.13) Explore Further: [Cognitive Flexibility Theory: Advanced Knowledge Acquisition in Ill-Structured Domains](https://eric.ed.gov/?id=ED302821) Tags: #models Your support for Cedars Hill Group is greatly appreciated <form action="https://www.paypal.com/donate" method="post" target="_top"> <input type="hidden" name="hosted_button_id" value="74PGN8ZXHQVHS" /> <input type="image" src="https://www.paypalobjects.com/en_US/i/btn/btn_donate_LG.gif" border="0" name="submit" title="PayPal - The safer, easier way to pay online!" alt="Donate with PayPal button" /> <img alt="" border="0" src="https://www.paypal.com/en_US/i/scr/pixel.gif" width="1" height="1" /> </form>