David Kirsh

In the course of daily life we solve problems often enough that there is a special term to characterize the activity and the right to expect a scientific theory to explain its dynamics. The classical view in psychology is that to solve a problem a subject must frame it by creating an internal representation of the problem’s structure, usually called a problem space. This space is an internally generable representation that is mathematically identical to a graph structure with nodes and links. The nodes can be annotated with useful information, and the whole representation can be distributed over internal and external structures such as symbolic notations on paper or diagrams. If the representation is distributed across internal and external structures the subject must be able to keep track of activity in the distributed structure. Problem solving proceeds as the subject works from an initial state in mentally supported space, actively constructing possible solution paths, evaluating them and heuristically choosing the best. Control of this exploratory process is not well understood, as it is not always systematic, but various heuristic search algorithms have been proposed and some experimental support has been provided for them.

A complementary strategy can be defined as any organizing activity which recruits external elements to reduce cognitive loads. Typical organizing activities include pointing, arranging the position and orientation of nearby objects, writing things down, manipulating counters, rulers or other artifacts that can encode the state of a process or simplify perception. To illustrate the idea of a complementary strategy, a simple experiment was performed in which subjects were asked to determine the dollar value of collections of coins. In the no-hands condition, subjects were not allowed to touch the coin images or to move their hands in any way. In the hands condition, they were allowed to use their hands and fingers however they liked. Significant improvements in time and number of errors were observed when S's used their hands over when they did not. To explain these facts, a brief account of some commonly observed complementary strategies is presented, and an account of their potential benefits to perception, memory and attention.

A complementary strategy can be defined as any organizing activity which recruits external elements to reduce cognitive loads. Typical organizing activities include pointing, arranging the position and orientation of nearby objects, writing things down, manipulating counters, rulers or other artifacts that can encode the state of a process or simplify perception. To illustrate the idea of a complementary strategy, a simple experiment was performed in which subjects were asked to determine the dollar value of collections of coins. In the no-hands condition, subjects were not allowed to touch the coin images or to move their hands in any way. In the hands condition, they were allowed to use their hands and fingers however they liked. Significant improvements in time and number of errors were observed when S's used their hands over when they did not. To explain these facts, a brief account of some commonly observed complementary strategies is presented, and an account of their potential benefits to perception, memory and attention.

We present data and argument to show that in Tetris—a real-time interactive video game—certain cognitive and perceptual problems are more quickly, easily, and reliably solved by performing actions in the world rather than by performing computational actions in the head alone. We have found that some translations and rotations are best understood as being used to implement a plan, or to implement a reaction. To substantiate our position we have implemented a computational laboratory that lets us record keystrokes and game situations, as well as allows us to dynamically create situations. Using the data of over 30 subjects playing 6 games, tachistoscopic tests of some of these subjects, and results from our own successful efforts at building expert systems to play Tetris, we show why knowing how to use one’s environment to enhance speed and robustness are important components in skilled play.

This article addresses three main questions: What causes cognitive overload in the workplace? What analytical framework should be used to understand how agents interact with their work environments? How can environments be restructured to improve the cognitive workflow of agents? Four primary causes of overload are identified: too much tasking and interruption, and inadequate workplace infrastructure to help reduce the need for planning, monitoring, reminding, reclassifying information, etc… The first step in reducing the cognitive impact of these causes is to enrich classical frameworks for understanding work environments, such as Newell and Simon’s notion of a task environment, by recognizing that our actual workplace is a superposition of many specific environments – activity spaces – which we slip between. Each has its own cost structure arising from the tools and resources available, including the cognitive strategies and interpretational frameworks of individual agents. These cognitive factors are significant, affecting how easy or difficult it is to perform an action, such as finding a specific paper in a “mess” desk. A few simple examples show how work environments can be redesigned and how restructuring can alter the cost structure of activity spaces.

A startling amount of intelligent activity can be controlled without reasoning or thought. By tuning the perceptual system to task relevant properties a creature can cope with relatively sophisticated environments without concepts. There is a limit, however, to how far a creature without concepts can go. Rod Brooks, like many ecologically oriented scientists, argues that the vast majority of intelligent behaviour is concept-free. To evaluate this position I consider what special benefits accrue to concept-using creatures. Concepts are either necessary for certain types of perception, learning, and control, or they make those processes computationally simpler. Once a creature has concepts its capacities are vastly multiplied.

It is sometimes argued that if PDP networks can be trained to make correct judgements of grammaticality we have an existence proof that there is enough information in the stimulus to permit learning grammar by inductive means alone. This seems inconsistent superficially with Gold's theorem and at a deeper level with the fact that networks are designed on the basis of assumptions about the domain of the function to be learned. To clarify the issue I consider what we should learn from Gold's theorem, then go on to inquire into what it means to say that knowledge is domain specific. I first try sharpening the intuitive notion of domain specific knowledge by reviewing the alleged difference between processing limitatons due to shartage of resources vs shortages of knowledge. After rejecting different formulations of this idea, I suggest that a model is language specific if it transparently refer to entities and facts about language as opposed to entities and facts of more general mathematical domains. This is a useful but not necessary condition. I then suggest that a theory is domain specific if it belongs to a model family which is attuned in a law-like way to domain regularities. This leads to a comparison of PDP and parameter setting models of language learning. I conclude with a novel version of the poverty of stimulus argument.

The influence of imagery on perception depends on the content of the mental image. Sixty-three students responded to the location of the 2 hands of a clock while visualizing the correct or an incorrect clock. Reaction time was shorter with valid cueing. Could this have resulted from visual acquisition strategies such as planning visual saccades or shifting covert attention? No. in this study, a crucial control condition made participants look at rather than visualize the cue. Acquisition strategies should have affected equally both types of cueing, but we observed that the effect of the visual cue was smaller and limited to a particular subcase in which one expects visual acquisition strategies. Thus, what matters is the similarity of the content of the mental image with the visual scene. In addition, an interaction involving the hand used for responding supports the notion that composite imagery is lateralized.

We designed an experiment to explore the learning effectiveness of three different ways of practicing dance movements. To our surprise we found that partial modeling, called marking in the dance world, is a better method than practicing the complete phrase, called practicing full-out; and both marking and full-out are better methods than practicing by repeated mental simulation. We suggest that marking is a form of practicing a dance phrase aspect-by-aspect. Our results also suggest that prior work on learning by observation and learning by mental practice may not scale up to complex movements.

A complementary strategy can be defined as any organizing activity which recruits external elements to reduce cognitive loads. Typical organizing activities include pointing, arranging the position and orientation of nearby objects, writing things down, manipulating counters, rulers or other artifacts that can encode the state of a process or simplify perception. To illustrate the idea of a complementary strategy, a simple experiment was performed in which subjects were asked to determine the dollar value of collections of coins. In the no-hands condition, subjects were not allowed to touch the coin images or to move their hands in any way. In the hands condition, they were allowed to use their hands and fingers however they liked. Significant improvements in time and number of errors were observed when S's used their hands over when they did not. To explain these facts, a brief account of some commonly observed complementary strategies is presented, and an account of their potential benefits to perception, memory and attention.

The goal of three-dimensional visualization is to present information in such a way that the viewer suspends disbelief and uses the screen imagery the same way as he or she would use an identical, real 3D scene. To do this effectively, programmers employ a variety of 3D depth cues. Our own anecdotal experience says that shadows and stereopsis are two of the best for visualization. The nice thing is that both of these are possible to do in interactive programs. They sacrifice a certain amount of interactive speed, but they are possible.