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 using the world to improve cognition. These actions are not used to implement a plan, or to implement a reaction; they are used to (...) change the world in order to simplify the problem-solving task. Thus, we distinguish pragmatic actions ñ actions performed to bring one physically closer to a goal - from epistemic actions - actions performed to uncover information that is hidden or hard to compute mentally. To illustrate the need for epistemic actions, we first develop a standard information-processing model of Tetris-cognition, and show that it cannot explain performance data from human players of the game - even when we relax the assumption of fully sequential processing. Standard models disregard many actions taken by players because they appear unmotivated or superfluous. However, we describe many such actions that are actually taken by players that are far from superfluous, and that play valuable roles in improving human performance. We argue that traditional accounts are limited because they regard action as having a single function: to change the world. By recognizing a second function of action - an epistemic function - we can explain many of the actions that a traditional model cannot. Although, our argument is supported by numerous examples specifically from Tetris, we outline how the one category of epistemic action can be incorporated into theories of action more generally. (shrink)

An experiment was performed to test the hypothesis that people sometimes take physical actions to make themselves more effective problem solvers. The task was to generate all possible words that could be formed from seven Scrabble letters. In one condition, participants could use their hands to manipulate the letters, and in another condition, they could not. Results show that more words were generated with physical manipulation than without. However, an interaction was obtained between the physical manipulation conditions and the specific (...) letter sets chosen, indicating that physical manipulation helps more for generating words in some circumstances than in others. Overall, our findings can be explained in terms of an interactive search process in which external, physical activity effectively complements internal, cognitive activity. Within this framework, the interaction can be explained in terms of the relative difficulty of generating words from the letters given in the different sets. (shrink)

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. (shrink)

To discover how to couple reflection with reaction we have been studying how people play the computer game Tetris. Our basic intuition is that the job of the reasoned is to monitor the environment and the agent’s behavior over time to discover trends or deviations from the agent’s normative policy and tune the priorities of the Attentional system accordingly.

Cognitive organisms have three rather different techniques for intelligently regulating their intake of environmental information. In order of the time needed to uncover information they are: 1. control of attention: within an image produced by a given sensor certain elements can be selected for additional processing; 2. control of gaze: the orientation and resolution (center of foveation) of the sensor can be regulated to create a new image; 3. control of activity: certain non-perceptual actions can be performed to increase the (...) probability of unearthing salient information that currently is unavailable, hard to detect, or hard to compute. (shrink)