Adele Abrahamsen

University of California, San Diego

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William Bechtel

University of California, San Diego

Benjamin Sheredos

University of California, San Diego

Daniel C. Burnston

Tulane University

George Graham

South Shore Hospital

Gao Yin

Qiqihar Medical University

Peter Bradley

Urologische Klinik München

Thomas S. Brinsmead

Commonwealth Scientific and Industrial Research Organisation

Sean S. Downey

The Ohio State University

ÁM

Álvaro Moreno

University of the Basque Country

David Cockburn

University of Wales Trinity Saint David

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University of California, San Diego

Stanford University

Indiana University Bloomington

Washington University in St. Louis

The Ohio State University

University of the Basque Country

New York University

City University of New York

University of Newcastle Australia

Princeton University

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Teaching the How of Cognitive Science: The Inquiry Website

Proceedings of the Annual Meeting of the Cognitive Science Society (2003)

Adele Abrahamsen William Bechtel Peter Bradley Carl F. Craver

Cognitive scientists are pretty adept at coordinating multiple methods and perspectives; it is what we do. Teaching other people to do it is harder, especially when the other people are whole classes of undergraduates in one of the new cognitive science majors or specializations. The challenge is to provide students with (a) a sufficient understanding of some of the methods used in the contributing fields, (b) the strengths and weaknesses of these methods, and (c) how they can be coordinated in interdisciplinary research to achieve new understanding. Inquiry is a web-based curriculum for introducing students to the range of research methods employed in cognitive science. This tutorial provides an opportunity to hear about Inquiry and the ideas it embodies, to interact with it in a group setting, and to help and be helped to improve undergraduate education in cognitive science. To promote active understanding of the research methods that are introduced, the course materials are interactive. For example, instead of just providing a definition of cognitive science, students are guided to construct their own characterization after identifying and classifying a variety of phenomena as cognitive or not. They are then challenged to test the adequacy of their characterization in light of other phenomena and characterizations advanced by other students. Such devices as animation, pop-up windows, and a dynamic menu system also increase students' engagement. The materials are organized into semi-independent modules that can be selected and recombined to meet the objectives of particular courses. To provide integration to the different methods, research on memory provides a common theme, but examples from a variety of other domains are offered as well. The core modules for the course are divided into empirical strategies and modeling strategies. The range of empirical strategies addressed includes observational and correlational techniques, causal reasoning, including the use of directed graphs, and various experimental designs. The modeling strategies include mechanistic modeling, mathematical modeling, symbolic modeling, and neural network modeling. The final set of modules (not yet available) focus on the integration of research techniques; cases examined include neuroimaging and memory research on the hippocampus. In addition to the materials for student use, a variety of tools have been designed to enable instructors to utilize these modules and to supplement them with material of their own or found elsewhere on the web. The instructors' site also offers reports of web usage organized by student or by module and a “lab manual” that provides ideas and guidance for in-class projects designed to make the material more concrete. In one of the in-class projects, students are given a complex mechanism (e.g., a Pachinko Machine) and are given the task of understanding how it works and of communicating that understanding in writing or in a diagram. In another class, students watch raw footage of an amnesic patient (K.C.) being questioned by a psychologist and are then asked to diagnose his memory deficit on the basis of the interview. In a more extensive project, each student is asked to construct an interesting experiment in a field of their choice, to envision possible results, and to say something about what each possible result would mean. This tutorial will provide hands-on experience with both the web-based modules for students that have been developed so far and the tools and lab manual designed for instructors. Participants will be invited to incorporate some of these modules and tools into their own courses, and will receive guidance in doing so. Since the design of the materials is ongoing, participants also may provide feedback on how to make the Inquiry site (http://inquiry.wustl.edu) more useful to students and instructors at a broad range of educational institutions.

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Diagrams as Vehicles for Scientific Reasoning

William Bechtel Adele Abrahamsen

We argue that diagrams are not just a communicative tool but play important roles in the reasoning of biologists: in characterizing the phenomenon to be explained, identifying explanatory relations, and developing an account of the responsible mechanism. In the first two tasks diagrams facilitate applying visual processing to the detection of patterns that constitute phenomena or explanatory relations. Diagrams of a mechanism serve to guide reasoning about what parts and operations are needed and how potential parts of the mechanism are related to each other. Further they guide the development of computational models used to determine how the mechanism will behave. We illustrate each of these uses of diagrams with examples from research on circadian rhythms

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Beyond the exclusively propositional era

Synthese (1990)

William Bechtel Adele Abrahamsen

Connectionism

Philosophy of language

Principle of compositionality

10.1007/bf00413663

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Mechanism, Dynamic

Springer eBooks (2013)

William Bechtel Adele Abrahamsen

10.1007/978-1-4419-9863-7_72

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Learning, reward, and cognitive differences

Behavioral and Brain Sciences (1988)

William Bechtel Adele Abrahamsen

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

10.1017/s0140525x00058283

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History and core themes

Cambridge University Press eBooks (2012)

Adele Abrahamsen William Bechtel

10.1017/cbo9781139033916.003

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From Reduction Back to Higher Levels - eScholarship

Proceedings of the Annual Meeting of the Cognitive Science Society (2008)

William Bechtel Adele Abrahamsen

From Reduction Back to Higher Levels William Bechtel (bechtel@mechanism.ucsd.edu) Department of Philosophy-0119, UCSD La Jolla, CA 92093-0119 USA Adele Abrahamsen (aabrahamsen@ucsd.edu) Center for Research in Language-0526, UCSD La Jolla, CA 92093-0526 USA into a more comprehensive account that considers processes at the higher levels that were initially left behind in the re- ductionistic quest. To trace both the downwards and up- wards trajectories in specific cases, we examine two reduc- tionistic research programs targeting behavioral phenomena. In both cases the reductionistic pursuit has been highly suc- cessful—an enormous amount has been learned about the genes and biochemical processes involved. But this success has been very local, and itself points to the need to integrate what has been learned into higher-level accounts. One of these cases involves research on memory consoli- dation, a phenomenon identified and studied by psycholo- gists beginning in the late 19 th century, but investigated pri- marily at the molecular level since the 1970s. The other case, circadian rhythms, also began with behavioral studies (by evolutionary and behavioral biologists), with pursuit of the molecular level added more recently. Molecular biolo- gists working in these domains make use of others’ ongoing investigations at higher levels. This suggests that investiga- tion of the neural processes underlying more prototypically cognitive domains likewise will require complementation by higher-level inquiries such as those pursued by cognitive scientists. On this view, reductionism is not a threat to re- place cognitive accounts; instead, it leads to new informa- tion that can enrich and improve those accounts. The contrasting view of reduction, in which lower-level accounts supplant higher-level ones, is anchored in a 20 th century philosophy of science that emphasizes laws as the explanatory engine. From this perspective, if laws existed that completely characterized how lower-level entities be- haved in all contexts, it is hard to see what a higher-level account could add (Kim, 1998). But it is unclear what these laws would be like, since current laws in physics only char- acterize the behavior of physical objects in highly idealized contexts in which they are isolated from other factors that usually impinge on them (Cartwright, 1999). The applicabil- ity of law-based accounts to the biological and cognitive sciences is dubious as well, since explanations in these sci- ences seldom invoke laws. Rather, they most frequently take the form of identifying the mechanism responsible for a given phenomenon. Philosophers focused on these sciences have recently articulated a new mechanistic philosophy of science that is especially appropriate to these sciences (Bechtel, 2008; Bechtel & Richardson, 1993; Machamer, Darden, & Craver, 2000; Thagard, 2006). From the mechanistic perspective, to explain a phenome- non is to explicate the mechanism responsible for it. In each Abstract In the context of mechanistic explanation, reductionistic re- search pursues a decomposition of complex systems into their component parts and operations. Using research on circadian rhythms and memory consolidation as exemplars, we consider the gains to be made by finding genes and proteins that figure in mechanisms underlying behavioral phenomena. However, we also show that such research is insufficient to explain the initial phenomenon. Accordingly, researchers have increas- ingly recognized the need to consider higher-level organiza- tion and integration with other systems. This illustrates a common need to complement reductionistic inquiry with in- vestigations at higher levels and identifies a trajectory whereby cognitive science can embrace molecular neurosci- ence without surrendering its own contributions. Keywords: reduction, mechanistic explanation, memory con- solidation, circadian rhythms. Introduction The rise of cognitive neuroscience offers both opportunities and challenges to cognitive scientists. In addition to neuro- imaging and other new tools for linking cognitive processes to brain regions, it opens a potential conduit to the thriving fields of cell and molecular neuroscience. What should cog- nitive scientists make of this? To many it brings the threat- ening prospect of accounts of human behavior in terms of genetic and biochemical processes, leaving little room for cognitive scientists’ theoretical and computational models. This is, in fact, how reductionistic research is often por- trayed by both its advocates and critics (see papers in Schouten & Looren de Jong, 2007). The goal of reduction is seen as completely explaining the phenomenon of interest at the lowest possible level (e.g., in terms of genes and bio- chemistry), thereby supplanting and rendering superfluous the kinds of accounts typically offered by cognitive scien- tists or even those of systems neuroscientists (Bickle, 2003). That is, if one can account for and predict all that happens in terms of the lowest-level parts and operations, there is no need for any additional account at a higher level. A researcher invoking psychological processes, for exam- ple, is trying to explain what has already been explained. The psychological narrative is at best epiphenomenal (that is, psychological processes result from the lower-level proc- esses and have no causal efficacy of their own). We will argue that this seriously misrepresents reduction- ist research, which even when most successful does not pro- vide a complete account of the phenomenon of original in- terest. It uncovers crucial components, but these must be fit

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TRACE (psycholinguistics)

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Representing Time in Scientific Diagrams

Cognitive Science (2014)

William Bechtel Daniel C. Burnston Benjamin Sheredos Adele Abrahamsen

Representing Time in Scientific Diagrams William Bechtel (bechtel@ucsd.edu) Daniel Burnston (dburnston@ucsd.edu) Benjamin Sheredos (bsheredos@ucsd.edu) Department of Philosophy and Center for Circadian Biology, University of California, San Diego La Jolla, CA, 92093-0119 USA Adele Abrahamsen (aabrahamsen@ucsd.edu) Center for Research in Language and Center for Circadian Biology, University of California, San Diego, La Jolla, CA 92093 USA Abstract tively for graphical representations of the parts and opera- tions of a mechanism. We refer to these as mechanism dia- grams, and they are of particular interest as they play crucial roles in developing, evaluating, and presenting mechanistic explanations. Biologists often begin by identifying a system that in relevant conditions generates a phenomenon of inter- est and then seek a mechanistic account of how it does so. This involves identifying its parts, determining the opera- tions they perform, and showing how, when organized ap- propriately, the parts and operations generate the phenome- non of interest (Bechtel & Abrahamsen, 2005; Bechtel & Richardson, 1993/2010; Machamer, Darden, & Craver, 2000). This practice is often supported by mechanism dia- grams in which icons or glyphs (Tversky, 2011) specify parts of the mechanism and arrows indicate the operations by which parts affect other parts or are transformed into other types of parts. However, these mechanism diagrams do not stand alone. To relate parts and operations represent- ed in the diagram to a phenomenon, researchers need to represent both how the phenomenon is realized in time and how the mechanism operates in time. We will examine both. Circadian rhythms are approximately 24-hour oscillations generated endogenously within organisms that regulate a host of physiological, behavioral, and cognitive functions. They are found in organisms ranging from bacteria and fun- gi to plants and animals. Much early research focused on the phenomenon of circadian rhythmicity as observed in ani- mals’ fluctuating levels of activity. During the last few dec- ades of the 20 th century, circadian researchers began tracing these rhythms to intracellular molecular mechanisms involv- ing feedback relations between proteins and the genes from which they are transcribed and translated. Challenged to understand how individual cells maintain an approximately 24-hour oscillation and how populations of cells synchronize their activity, circadian rhythm re- searchers have developed a variety of diagram formats. Most straightforward is to map time to one of the two spa- tial dimensions (or hours to one dimension and days to the other), but this comes at the cost of pre-empting a resource and hence limiting what else can be included. If, a circle is used instead to represent a 24-hour duration, that opens up several ways to incorporate other kinds of information. We will display and discuss examples of how these formats dis- play timing either of a phenomenon or of an operation with- Cognitive scientists have shown increased interest in dia- grams in recent years, but most of the focus has been on spa- tial representation, not conventions for representing time. We explore a variety of ways in which time is represented in dia- grams by one research community: scientists investigating circadian rhythms at the behavioral and molecular levels. Di- agrams that relate other variables to time or indicate a mecha- nism’s states across time use one or two spatial dimensions or circles to represent time and sometimes include explicit time markers (e.g., the hours on a clockface). Keywords: Circadian rhythms; diagrams; mechanistic expla- nation; time Introduction A number of cognitive scientists have become interested in the interaction between human reasoning and external visu- alizations. Projects in such areas as knowledge representa- tion, human-computer interaction, and situated cognition have all focused on how information can be represented in a range of distinct formats and used as reasoning tools. Exper- imental and theoretical work on diagrams in particular has made great strides in recent years (Cheng, 2002, 2011; Gooding, 2010; Hegarty, 2004, 2011; Nersessian, 2008; Tversky, 2011). Still, significant challenges remain in un- derstanding visualization. Our focus is on how diagrams support reasoning in complex empirical domains (Sheredos, Burnston, Abrahamsen, & Bechtel, 2013). A critical chal- lenge researchers face in developing diagrams is how to represent multiple aspects of a problem space. For instance, while two-dimensional diagrams readily support spatial rea- soning tasks, many tasks require reasoning about time, and representing time and integrating both spatial and temporal information pose special challenges. Our strategy in this paper is to examine published dia- grams from a field in empirical science that has dedicated significant attention to ways of representing events in time: chronobiology, the study of circadian and other biological rhythms. What is learned here has broader implications. The term diagram is used in both inclusive and restricted senses. In its inclusive sense, indicated by the etymology of the word, diagrams are visuospatial representations. All the figures in a scientific paper, including line graphs, typically count as diagrams. Sometimes the term is used more restric-

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Cognitive and Linguistic Development

Adele Abrahamsen

Aisha, age 24 months, sees her older sister trip over a toy and squeals "Kiki fell!" At 30 months her doll falls behind the couch, and she asks "Where dolly falled?" At 48 months her computer mouse falls behind a stack of computer manuals under the desk, and she asks "Where did that silly mouse fall?" Both her world and her sentences are getting more complex as she gets older, but there is one oddity: the past-tense verb fell shows a U-shaped developmental pattern. Aisha gets it right at 24 months, wrong at 30 months, and right again at 48 months. Why?

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10.1002/9781405164535.ch6

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From reduction back to higher levels

William Bechtel Adele Abrahamsen

In the context of mechanistic explanation, reductionistic research pursues a decomposition of complex systems into their component parts and operations. Using research on circadian rhythms and memory consolidation as exemplars, we consider the gains to be made by finding genes and proteins that figure in mechanisms underlying behavioral phenomena. However, we also show that such research is insufficient to explain the initial phenomenon. Accordingly, researchers have increasingly recognized the need to consider higher-level organization and integration with other systems. This illustrates a common need to complement reductionistic inquiry with investigations at higher levels and identifies a trajectory whereby cognitive science can embrace molecular neuroscience without surrendering its own contributions.

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Component (thermodynamics)

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Citations (15)