This book is about a shift in the study of cognition into the idea of practice. Lave’s book is the construction of a complex argument to free the understanding of cognition from the limits imposed by traditional methods of study. As far as this goes, Lave asserts that the use of the laboratory and theory impose certain values into the study of cognition, classifying some uses of cognition as superior to others. Additionally, these approaches also yield incorrect results. For example, the first part of the book challenges the idea of learning transfer, which would be valid if we the functionalist theory of cognition held. Lave demonstrates that learning transfer clearly does not occur in practice.

A lot of ground is covered in this book, and Lave makes some very progressive and compelling arguments. Her work can be considered a criticism of functionalism in all its forms, which would include mental model theory. Having found model theory useful for many purposes, I would posit that practice can be understood in terms of models. This requires a subtle understanding of what a model is: a consistent system of meaning that comes with a way to interpret and translate things in the world into symbols in the model. Lave’s argument could easily be extended to argue that all theories are practices, but I would extend this further and say that theories and practices are both models in turn.

Psychology and Anthropology I

An opening example is how arithmetic is used in everyday life. Problems that ordinary people face are very unlike problems that might be specified in a classroom sort of setting. In the introduction, Lave gives an example of a shopper who is buying apples, and rationalizing how many apples to buy. As presented, arithmetic is a tool that is used in everyday life, not as an explicit approach, but rather as something like duct tape, to be used for estimations than exact calculations. Classical mathematics studies math as an artifact, a whole subject, rather than a tool.

Lave gives a solid critique of functionalist theory. This is characterized by several points (p. 8):

• Passivity of learner: The learner is passive and receives knowledge without having an active role in its learning or use.
• Isolation of skills: Skills may be separated and understood atomically. Separation is unproblematic, and usage is not dependent on context.
• Leads to meritocracy: The use and adeptness at arbitrary skills leads to a value system where proficiency at one skill indicates general superiority.
• Stress on rationality and cognitive life: This is as opposed to usage and practice.
• Cultural uniformity: If the functionalist theory holds, then skills and functions transcend culture, meaning that culture must have uniform basis.

The book is organized into two parts: The first is a critique of the practice of cognitive theory. This challenges the functionalist emphasis on learning transfer, and the artificiality of the laboratory method. The theme of this argument is around the idea of ecological validity, where a theory is ecologically invalid if it fails to be supported in a diversity of settings. The latter half of the book explores and emphasizes the study of practice in of itself.

Missionaries and Cannibals (Indoors)

The section here is trying to understand the role and concept of knowledge transfer, the process by which knowledge from one domain is transferred to another. Presumably this happens analogically. The actual results of experiments about knowledge transfer obtained mixed conclusions. These experiments give students a math problem, which may be solved algorithmically, and give the students other problems which are isomorphic in some way. Students do not make the necessary leaps in these sorts of problems, and generally seem very confused. A functionalist theory would support a sort of reductive understanding and transfer, but this does not hold. Incidentally, this type of transfer is exactly the sort of mechanism by which Newell’s GPS worked.

Lave gives a critique of the method by which students were presented with problems in the learning transfer studies. “A number of problem characteristics are common to all four papers and by extension to the genre more broadly. The puzzles or problems are assumed to be objective and factual. They are constructed ‘off-stage’ by experimenters, for, not by, problem solvers.” (p. 35) Thus, the problems are presented as tests, and come replete with the values of producing a correct solution. These have an overall trend of disempowering the subjects, depriving them of suitable context or situation.

The learning transfer experiments rely on a sense of knowledge as component based or modular. In this point of view, knowledge is put into domains, which function like disconnected islands. However, this point of view, and the problems themselves, are cultural artifacts.

Life After School

To find an alternative to traditional problem solving, Lave conducted a study called the Adult Math Project, which studied how ordinary people use mathematics in everyday life, and more specifically, grocery shopping. This study found that people are remarkably successful at math problems, (framed both on paper, as well as in the store) but they use dramatically different methods than those taught in school.

A problem I found with her study, though, is that it is placing supermarket decisions primarily in terms of cost. This relates back to the fallacious idea of the rational consumer. Supermarket choices may factor in cost, but also have a great deal to do with social and class awareness and attitudes, preferences, and identity.

Psychology and Anthropology II

Lave is attempting to undermine the false dichotomy of scientific and “everyday” thought. This descends from dualism and cultural colonialism. The central theme in this is that science itself is a practice, and that it is culturally constructed.

Inside the Supermarket (Outdoors) and From the Veranda

The goal in this section is to develop a theory of practice. The first part of this is to distinguish between practice and formal knowledge domains, or conceptual spaces. A primary difference is that practice occurs simultaneously within other activities, and may be synchronized with them to some degree. An example of this that Lave gives is knitting and reading. Instead of taking place individually, they are situated within each other and affect each other.

There is a concern over the form of scientific inquiry. This may be seen as a concern over model building. How do we decide what to model, or decide what is important or relevant? This is an authorial judgement, and comes in part with judgement of value. Lave asks, “Further, who is to decide what cognitive phenomena are significant objects of study, and how? Are guidelines to be found in normative models of cognition, in an investigation of the activities of peoples’ lives, in some combination, or in other sources altogether?”

To address this concern, she introduces the idea of ecological validity. Experiments are ecologically valid if they get the same results regardless of situation. This is important for the study of cognitive science, because the way people think within a laboratory setting may be different in some ways than how they think outside. If a cognitive experiment yields one set of results within the laboratory, but totally breaks down outside, then the theory on which that experiment is based must be questioned.

An experiment that Lave critiques a great deal in this section is another supermarket math experience, conducted by Capon and Kuhn. Their experiment is intrinsically biased towards the knowledge domain understanding of math. The experiment was set at a grocery store, but still carried the structure of laboratory problems: answers were right or wrong, and the type of reasoning was intended to be proportional. Instead of asking “what sort of math occurs in grocery shopping?” they tested whether subjects could perform a certain kind of math.

Out of Trees of Knowledge into Fields for Activity

This chapter frames the complexity in problem solving. Instead of problem solving existing at one small and discrete domain, it is situated within a broader context. “People experience ‘problems’ subjectively in the form of dilemmas and, so motivated, ‘problem-solving’ actively often leads to more or less enduring resolutions than precise solutions.” (p. 124)

An example that Lave focuses on is a study of practitioners of Weight Watchers, who incorporate the goals of Weight Watchers into their daily routines of shopping and food preparation. Thus, these activities are recast with a new set of motivations. I would argue that these practices are totally consistent with model theory. Weight Watchers has a model with one underlying principle: the quantization of food. This causes activities involving food to be understood as systems of quantities. These models and motivations are necessarily in conflict with others.

Outdoors: A Social Anthropology of Cognition in Practice

In the opening to her concluding chapter, Lave overviews some of the elements of her study of practice (p. 171):

1. The context of a study is important, (for example, in the supermarket), but the context surrounding the situation is also important. The supermarket is also contextualized in peoples’ lives, in which the shoppers’ dilemmas are construed.
2. Conventional premises and analytic questions must be understood critically. This book can be seen as a project which makes these assumptions to be objects of study.
3. The study focuses on “whole-person activity” rather than attempting to understand cognitive functions in isolation. This places cognition as dependent on on time and setting, within culture.
4. Accepting that activity is situated, then those activities are placed within some point in history and culture, and must be understood in that context. Thus, the dilemmas of the shopper (and hence the form of math and cognition used) in 1950s America may be different from those found in contemporary Beirut.

Lave’s final conclusion has a good review of her project as a whole: “I have tried to move the investigation of ‘cognition’ outdoors in several senses: out of the laboratory, out of the head, out of a confusion with a rationalistic ‘culture,’ out of conflation with conventional ‘knowledge structures,’ and out of the role of order-producing, primary constraints on activity in the world.” (p. 189-190)

I haven’t had the chance to do much reading this week. I really ought to do more, but it has been hard to fit it in. What I have been doing, oddly enough, is a lot of writing. Last Thursday, I gave a presentation overviewing my work on game adaptations of Pride and Prejudice to Janet’s narrative schema group, and it went alright. It was… tricky, though. She was expecting me to be further along than I was, and presenting slightly different material than I was, but there was a very productive discussion afterwards. A few new ideas floated up, but mostly I had the chance to “try out” some of the ideas I have been thinking about for a long time. Now that it is over, I have been writing an overview of the adaptation project. It started out as a small thing, but has quickly evolved into something quite big.

Rest assured, I will put it up when I am done. Whether anyone will find it interesting or not, well…. we’ll have to see.

One of my classes is cross listed with an undergraduate course, this is Nancy Nersessian‘s Cognition and Culture. One fun advantage to having a course with undergraduates is that they make a lot of interesting and occasionally profoundly brilliant observations. Not to say that us graduate students are incapable of insight, but we tend to be very bogged down by our own research objectives.

We have been discussing Jean Lave‘s book, Cognition in Practice, and came to a segment where Lave discusses how mathematics is a cultural artifact, but we view it as universal and supremely valuable. An example of this is that we “beam” the Pythagorean theorem into space, in hopes that, were the signal ever to be discovered by extraterrestrials, it would help communication because mathematics is a universal language, that transcends humanity. I didn’t find a source on this beaming precisely, but it seems like the sort of thing that people might do. Coming from a mathematical background, and moving into the complex and tricky field of cognitive science and cultural studies, I had very torn reactions to this conflict, and only realized how to articulate that reaction after the discussion ended. So, I present it here.

Mathematicians are extremely strange people. I don’t really identify as a mathematician anymore, but I still consider myself close to the culture, so I say this pridefully. The conclusions of mathematics are universal, and they are fundamental, but, and this is where things get difficult, these universal conclusions rely on premises. These premises are necessarily situational, and depend on other cultural factors. Furthermore, the practice of mathematics is also culturally relevant, and lots of mathematicians disagree, not on conclusions (a proof is a proof, after all), but on the relevance, importance, usefulness, and elegance of different practices of math. All of these terms are subjective, and while there are many common impressions of what elegance means, it is far from universal.

Generally issues regarding the practice of math applies to topics that are more sophisticated than the Pythagorean theorem. The Pythagorean theorem has to be universal because of its simplicity, elegance, and universality in almost all kinds of math that we use conventionally, right? Those aliens must use that kind of math too, right? Well, mostly. Even in this case, the situation is ambiguous, and that ambiguity arises from the premises under which the Pythagorean theorem is valid, namely: Euclidean geometry. If you are dealing with some other domain of planar geometry, (most notably, spherical or hyperbolic geometry), then the Pythagorean theorem breaks down. It has analogues (which are quite elegant, I might say), but the existence of these alternative types of geometries, and the ways in which the theorems are modified illustrates that our idyllic Euclidean world is not quite as simple or so complete as it first seemed. Space itself is non-Euclidean, according to both relativity and quantum mechanics. So perhaps the Pythagorean Theorem may actually have something to do with our experience as humans on Earth, and may not be quite so transcendent after all.

For real transcendence, we need Godel’s Incompleteness Theorem. That’ll do it for sure.

I found myself discussing Michael Mateas‘s paper Semiotic Considerations with Audrey a week or two ago, and it occurred to me to put the paper on my reading lists. It is an important work, and also ties in very nicely to some of my focus on models. Reading his essay in detail, now that I have a much more substantial backing in cognitive science and structuralist theory, I came to realize that we are approaching similar regions of focus, but from coming to them from very different perspectives.

Mateas’s actual paper is breathtakingly short, a mere four pages of 18 very explicitly defined points. This deceptively clear organizational structure conceals the real depth at play in the paper. The paper is roughly divided into three regions of focus, and I will explore these and summarize these sections in context. His paper has the form of a poetics of Expressive AI, rather than an aesthetics, or an approach for criticism of such works.

One of my interests here, although I’m not going to have the chance to get into it, is the relationship between model and simulation, as compared to Mateas’s rhetorical and computational machines.

One: The Experiences of AI and Art

Mateas’s paper is on the practice of creating AI based art, which he calls Expressive AI. In Mateas’s perspective, art is fundamentally semiotic in nature. Art involves negotiating flows of meaning or semiosis, which is very situationally dependent. Some examples of situations are “a busy freeway, an office in a large bureaucratic organization, a party, a riot, or, perhaps, an art gallery,” these all are both recipients to artistic intervention, and they turn around and affect the flow of meaning given by the art itself.

Art in this case is a proactive and assertive force, engaging with the world and reconfiguring it. Art is  intrinsically participatory, as semiosis requires active observers and interpreters. Art is dynamic, as it is subject to influence by the context in which it is situated, and necessarily affected by the flows of its participants. Even the term flow communicates dynamism.

Connected with AI practice, expressive AI is the combination of AI and  injects not only the affordances of participating or simulated computer controlled characters, but also a unique intersection of rhetoric and semiotic functions that are unavailable in other forms of art practice. An AI based artwork becomes itself an active participant in semiosis, and may engage in working with flows of meaning. In this sense, the AI based work shares the situation with the human participants or observers.

Because Expressive AI operates at this unique intersection, Mateas argues that we should think of it in a new light. We must use special rhetorical strategies for understanding the relationship between the computational and artistic dimensions of the artwork. We must share a language to address these different domains, which operate on very different symbolic terms, but Expressive AI is interesting precisely because it exists at this intersection. The issue then becomes how to understand and negotiate these two critical components of AI practice.

Two: The Computational and Rhetorical Machines

Computational artifacts, and AI especially, are subject to a set of semiotic properties that are unique to the computational form. Specifically, computation enables automatic symbolic processing. This is the property of the computer heralded by Turing and popularized by Herbert Simon and Alan Newell. In this tradition, symbol systems exist in a domain of pure or abstract reasoning, much like abstract sign systems in the semiotic tradition. What is interesting about symbols and signs, is that, in principle anyway, they are intrinsically arbitrary and meaningless. One sign or symbol may be used to denote or connote anything.

Rhetorical meaning derives from and requires human interpretation. The human observer is necessary for connecting signs to referents, and for extracting meaning from an arbitrary configuration or conglomeration of symbols. Interpretation is necessary for categorizing a program as intelligent in any form. The role of interpretation imposes a new complexity in the otherwise ideal symbolic world, because human observation involves partial observers and the revelation of functional values which may be intrinsically encoded within the symbolic systems.

The technical operation of the AI system involves a computational machine, which is responsible for the processing of symbols. However, under the lens of human interpretation the AI system becomes part of something else, a rhetorical machine, wherein the system is coupled with the world of meanings and referents. “Every system is doubled, consisting of both a computational and rhetorical machine.”

This doubling affects how AI systems are created and interpreted. A developer of an AI based artifact must be aware of the relationship between the rhetorical and computational machines, as technical decisions will ultimately reflect rhetoric. A creator of Expressive AI must construct not only an artifact which engages as a participant in semiosis and flows of meaning making, the creator must also inscribe artistic intentions into the dual machines in order to affect the possible outcomes of interaction with the system. This lays out new artistic affordances and challenges, the scope and breadth of which is not yet clear.

Three: Systems of Code and Execution

All computational programs work using two systems or planes of meaning. The first plane is the space of the written program. This is what is actually authored, and its content is not the artifact, but rather the set of meanings that will enable or allow the artifact. It signifies the space of all possible executions. The second plane is the actual plane of execution, on which the artifact may actually be engaged and may participate in the semiotic processes described above. Mateas calls the first system, the code system, system1; and the second system, the execution system, system2.

The division between these two planes is deeper than their functional dimensions, but extends to the rhetorical strategies for interpreting, understanding, and manipulating the artifact on those planes. The two systems share signs, and we use similar language for discussing them. They are paired with their own matching rhetorical systems, which are different, but interact closely with each other.

Both of these systems have what Mateas calls “iterpretive surpluses.” The system1 has an interpretive surplus for the author, and the system2 has an interpretive for the audience. The author’s surplus comes with a freedom to embed strategies and approaches into the system, and interpret the composition of the system creatively. This leads to (especially in Mateas’s own projects) new terminology for constructing content in the system1. The audience’s surplus is one that resembles more closely the interpretive surplus afforded by interaction with works of art and other artifacts. The domain of execution can (and must) make use of other established media strategies and traditions for expressivity, that can be used for the audience to better glean meaning from the work.

The arrangement so far presents a portrait of Expressive AI as a practice of negotiating and manipulating many flows of meaning, and many complex and interdependent systems. The issue of rhetoric and language is extremely important, and provides a methodology according to which one may author works of Expressive AI. Mateas does not lay down any specific language that he thinks should be used, but rather, examines the role which he thinks rhetoric and language should play in the development of such artifacts. Furthermore, because the systems are so intrinsically connected, he argues against the approach of AI as a means to an end. To achieve a system with certain rhetorical goals, it must employ a computational machine that mirrors that rhetoric.