You can't play straight TRACS and win : memory updates in a dynamic Task environment

You Can’t Play Straight TRACS and Win: Memory Updates in a Dynamic Task Environment Hansj¨org Neth Chris R. Sims (nethh@rpi.edu) (simsc@rpi.edu) (vekslv@rpi.edu) Cognitive Science Department Rensselaer Polytechnic Institute Vladislav D. Veksler Abstract Wayne D. Gray (grayw@rpi.edu) Table 1: Baseline distribution of cards in the deck. The back of every card shows only its shape, whereas the front shows both its shape and color. To investigate people’s ability to update memory in a dy- namic task environment we use the experimental card game TRACS™ (Burns, 2001). In many card games card count- ing is a component of optimal performance. However, for TRACS, Burns (2002a) reported that players exhibited a base- line bias: rather than basing their choices on the actual num- ber of cards remaining in the deck, they chose cards based on the initial composition of the deck. Both a task analysis and computer simulation show that a perfectly executed memory update strategy has minimal value in the original game, sug- gesting that a baseline strategy is a rational adaptation to the demands of the original game. We then redesign the game to maximize the difference in performance between baseline and update strategies. An empirical study with the new game shows that players perform much better than could be achieved by a baseline strategy. Hence, we conclude that people will adopt a memory update strategy when the benefits outweigh the costs. Shape: Color: N red red N red blue blue blue Initial deck: bilities of the actor (Simon, 1990). To capture functional relationships of complex tasks while abstracting away from domain specific details we advocate the use of synthetic task environments, or microworlds (Gray, 2002). If the properties of the synthetic task environments are known and manipula- ble, the scope and limits of human rationality can be assessed. Moreover, the effects of environmental changes are tractable. Introduction Straight TRACS Optimal performance in dynamic environments requires that we base our decisions on the current state of the world, not on past states. Radar operators must act on the basis of continu- ously changing variables such as plane altitude and heading. Drivers constantly need to monitor the current speed limit, posted road signs and the traffic behind and in front of them. Failure to mentally update these types of information can lead to dangerous decisions and catastrophic behavior. Even our chances to win at card games like Blackjack or Bridge are closely tied to our ability to count cards and update memory. Previous research suggests that human ability to monitor and adjust to change is limited and dependent on various fac- tors. Yntema (1963) found that people are better at tracking a small number of variables with a large range of values each, than a large number of variables with a small set of possi- ble values each. In addition, reducing the frequency of up- date can improve performance. Other manipulations, such as increased predictability of a sequence, provide little or no advantage in remembering the current state of the environ- ment. Venturino (1997) distinguished the memory capacity for static information from that for dynamically changing in- formation and showed that the latter is highly limited, par- ticularly when the to-be-remembered attributes are similar. Hess, Detweiler and Ellis (1999) added that update perfor- mance is improved when spatial invariants constrain where different data values are presented on a visual display. In general, human rational behavior is constrained by the structure of task environments and the computational capa- TRACS™ is a ‘Tool for Research on Adaptive Cognitive Strategies,’ designed and developed by Kevin Burns (2001, 2004). Being both entertaining card game and experimental research tool, TRACS provides a microworld which promises to bridge the gap between mathematical rigor and real-world relevance. We will limit our discussion to Straight TRACS, which is the simplest version of an entire family of games. 1 TRACS is played with a deck of 24 cards. The back of each card shows one of three shapes—circle, triangle, or square— filled in with black. The front of each card shows both its shape, and one of two colors (red or blue). Table 1 shows the initial deck distribution for each of the six possible card types. This baseline information is always available to the player. As hands are played the number of cards remaining in the deck decreases, and the odds for each shape change accordingly. At the start of a game, three cards are dealt in a row. The middle card is dealt face up (showing both its shape and color), while the left and right cards are dealt face down, showing their shape not their color. The task for the player is to choose the card, either left or right, most likely to match the color of the middle card. The chosen card is then turned over, revealing its color. If the chosen card matches the color of the middle card, a hit is credited to the player’s score. A mismatch is scored as a miss. The two face up cards (the mid- dle and the chosen card) are then removed from the game. On Online versions are available at www.tracsgame. com.