Useful research on human judgment and decision making requires both rigor and relevance. Our approach blends rigor and relevance by performing experiments in a "synthetic task environment", which is a laboratory context that simulates the cognitive challenges of "naturalistic decision making" (Zsambok & Klein, 1997).

One synthetic task environment that we use is a new family of card games called TRACS: Tool for Research on Adaptive Cognitive Strategies (Burns, 2001; 2004; 2005a). TRACS is a played with a special deck of two-sided sides cards, where the backs provide clues to the fronts. 

With respect to rigor, the back of each card constrains the identity of the front, unlike standard playing cards with non-informative backs. This constraint makes the games more tractable to mathematical analyses of optimal solutions, which are needed to benchmark cognitive performance. With respect to relevance, the TRACS cards and rules are designed to simulate the probabilistic and dynamic challenges of naturalistic decision making in military, medical and other practical domains. For example, in one version of the game the backs of the cards (called tracks) are analogous to the radar images that a military commander must diagnose (e.g., Friend or Foe?) to make decisions and take actions.

The TRACS games can be played online in the TRACS Arcade and TRACS Casino. More background on the art and science of the games, and details on the cards and rules, are available at www.tracsgame.com and in Burns, 2004; 2005a. All of the games use the same deck of two-sided cards (see below).

Initial experiments (Burns, 2002a; 2002b; 2005b) were performed with the simplest game (played solitaire) called Straight TRACS. This is a matching game, kind of like walking through a mine field where you have to turn over one of two tracks (left or right) at each step, trying to match a given tread (Red or Blue) for that step. The object is to minimize the number of "strikes" (mismatches) on a trip through the deck. The challenge is to count cards and update odds to make the best choice on each turn. 

Experiments on TRACS (Burns 2002a; 2002b; 2005b) show that people can only count up to 3... 

That is, 3 cards of one type when trying to count several different types of cards at once, even when players are tested after much practice in the game. So why can people count cards so much better in Bridge, Blackjack and other card games? The difference between Bridge and TRACS is that there are multiple (identical) copies of each card in the TRACS deck. So, while a Bridge player can count higher than 3 spades (or clubs or hearts or diamonds), the spades are all different (Ace, King, Queen, etc.) and Bridge players are really only counting only up to one of each thing (card). In the case of Blackjack (Mezrich, 2002), the card-counting strategy is simply to count how many more low cards (2, 3, 4, 5, 6) have been seen than high cards (10, J, Q, K, A) while ignoring the mid cards (7, 8, 9). In other words, Blackjack players are really only counting one thing at a time.

From the perspective of Natural Computation (Richards, 1988), mental limits like this are not just arbitrary constraints but rather reflect the natural structure of the world in which mankind evolved. In fact, there are computational arguments that suggest a "limitation" of 3 in one context (like seeing dots or counting cards) may actually be an "optimization" in other contexts (like hierarchical memory organization; see MacGregor, 1987).

The findings from TRACS experiments (see plot above) are consistent with the heuristic strategy of "anchoring and adjustment" found in previous research on human judgment and decision making (Kahneman, Slovic & Tversky, 1982). But research on TRACS has made two new contributions. First, TRACS was used to measure human performance in a continuously "dynamic" task (card game), as opposed to the primarily "static" tasks (questions) studied in previous research. Second, models of human performance in TRACS were implemented as computer simulations in order to provide more powerful explanations and predictions of anchoring and adjustment behavior.

For example, one model uses "fuzzy functions" and simple summing to simulate the cognitive processes of memory activation and evidence accumulation. The computer model (gray line in plot above) provides a good match to the cognitive data (black line in plot above), especially compared to a simulated agent (dotted line in plot above) that plays perfectly. The details are documented in 1, 2, 3, More: An Accumulator Algorithm for Anchoring and Adjustment (Burns, 2003).

Burns, K. (2001). TRACS: A Tool for Research on Adaptive Cognitive Strategies. At www.tracsgame.com.

Burns, K. (2002a). On Straight TRACS: A Baseline Bias from Mental Models. Proceedings of the 24th Annual Conference of the Cognitive Science Society. 

Burns, K. (2002b). Dealing with TRACS: The Game of Confidence and Consequence. Proceedings of the American Association for Artificial Intelligence, Symposium on Chance Discovery. 

Burns, K. (2003). 1, 2, 3, More: An Accumulator Algorithm for Anchoring and Adjustment. Proceedings of the 6th International Conference on Computational Intelligence and Natural Computing.

Burns, K. (2004). Making TRACS: The Diagrammatic Design of a Double-Sided Deck. Proceedings of the 3rd International Conference on the Theory and Application of Diagrams.

Burns, K. (2005a). On TRACS: Dealing with a Deck of Double-sided Cards. Proceedings of the IEEE Symposium on Computational Intelligence and Games.

Burns, K. (2005b). Dealing with TRACS: A Game of Chance and Choice. In Akinori, A., & Ohsawa, Y. (Eds.). Readings in Chance Discovery (Advanced Knowledge International).

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Dehaene, S. (1997). The Number Sense: How the Mind Creates Mathematics (Oxford University Press).

Kahneman, D., Slovic, P. & Tversky, A. (1982). Judgment Under Uncertainty: Heuristics and Biases (Cambridge University Press).

MacGregor, J. N. (1987). Short-term Memory Capacity: Limitation or Optimization? Psychological Review, 94(1), 107-108.

Mezrich, B. (2002). Bringing Down the House: The Inside Story of Six MIT Students Who Took Vegas for Millions (Free Press). 

Miller, G. (1956). The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. Psychological Review, 63, 81-97. 

Richards, W. (1988). Natural Computation (MIT Press).

Zsambok, C. E., & Klein, G. (1997). Naturalistic Decision Making (Lawrence Erlbaum).