Several studies compared performance on mathematical types of problems across different contexts. Scribner (1984, 1986) studied the strategies used by milk processing plant workers to fill orders. Workers who assemble orders for cases of various quantities (e.g., gallons, quarts, or pints) and products (e.g., whole milk, two percent milk, or buttermilk) are called assemblers. Rather than employing typical mathematical algorithms learned in the classroom, Scribner found that experienced assemblers used complex strategies for combining partially filled cases in a manner that minimized the number of moves required to complete an order. Although the assemblers were the least educated workers in the plant, they were able to calculate in their heads quantities expressed in different base number systems, and they routinely outperformed the more highly educated white collar workers who substituted when assemblers were absent. Scribner found that the order-filling performance of the assemblers was unrelated to measures of school performance, including cognitive test scores, arithmetic test scores, and grades.

Another series of studies of everyday mathematics involved shoppers in California grocery stores who sought to buy at the cheapest cost when the same products were available in different-sized containers (Lave, Murtaugh, and de la Roche, 1984; Murtaugh, 1985). (These studies were performed before cost per unit quantity information was routinely posted). For example, oatmeal may come in two sizes, 10 ounces for $.98 for 24 ounces for $2.29. One might adopt the strategy of always buying the largest size, assuming that the larger size is always the most economical. However, the researchers (and savvy shoppers) learned that the larger size did not represent the least cost per unit quantity for about a third of the items purchased. The findings of these studies were that effective shoppers used mental shortcuts to get an easily obtained answer, accurate enough to determine which size to buy. A common strategy, for example, was mentally to change the size and price of an item to make it more comparable with the other size available. For example, one might mentally double the smaller size, thereby comparing 20 ounces at $1.96 versus 24 ounces at $2.29. The difference of 4 ounces for about 35 cents, or about 9 cents per ounce, seems to favor the 24-ounce size, given that the smaller size of 10 ounces for $.98 is about 10 cents per ounce. These mathematical shortcuts yield approximations that are as useful as the actual values of 9.80 and 9.33 cents per ounce for the smaller and larger sizes, respectively, and are much more easily computed in the absence of a calculator. When the shoppers were given a mental-arithmetic test, no relation was found between test performance and accuracy in picking the best values (Lave et al., 1984; Murtaugh, 1985).

Ceci and colleagues (Ceci and Liker,1986, 1988; see also Ceci and Ruiz, 1991) studied expert racetrack handicappers. Ceci and Liker (1986) found that expert handicappers used a highly complex algorithm for predicting post time odds that involved interactions among seven kinds of information. By applying the complex algorithm, handicappers adjusted times posted for each quarter mile on a previous outing by factors such as whether the horse was attempting to pass other horses, and if so, the speed of the other horses passed and where the attempted passes took place. By adjusting posted times for these factors, a better measure of a horse's speed is obtained. It could be argued that the use of complex interactions to predict a horse's speed would require considerable cognitive skill (at least as it is traditionally measured). However, Ceci and Liker reported that the successful use of these interactions by handicappers was unrelated to their overall cognitive ability.