has been likened to high-speed stroboscopic photography of a drop of water forming and falling from a spigot or the famous photograph of a drop of milk splashing into a shallow dish of milk. The finer temporal grain reveals phenomena that would not be seen at normal speeds, thereby indicating new underlying processes. (See Siegler and Crowley  for an extensive discussion of the method.)
Investigators have examined such issues as a child’s development of concepts, with the goal of identifying when the child first used a new strategy, what the experience was like, what led to its discovery, and how it was generalized beyond its individual use. Research by Alibali and Goldin-Meadow (1993), for instance, suggests that a child’s gestures can be indicators of cognitive change; a mismatch between gesture and speech often indicates a point at which a child is poised to make a transition in understanding. As in the case of reaction-time measures, gestures provide yet another potential window on the mind.
Long used by anthropologists and other social scientists to study cultural practices and social patterns, ethnographic analyses have also proven useful for analyzing cognitive processes. These techniques are aimed at gathering rich information about the day-to-day experiences of a community and its individual members. They have been used to study cognitive performance in many different settings, including classrooms, workplaces, and other environments. In the ethnographic approach, researchers immerse themselves in a particular situation to obtain a sense of its characteristics and its people. They make detailed observations and records of people engaging in normal tasks. They may also use interviews, surveys, videotape recordings, or other methods to elicit qualitative information. This approach has been adapted by cognitive scientists to conduct what Dunbar (1999) calls “in vivo” studies of complex, situated, scientific problem solving in contexts such as worldclass research laboratories.
Many highly effective tools exist for probing and modeling a person’s knowledge and for examining the contents and contexts of learning. Some of these methods, such as tracking of eye movements and computational modeling, rely on sophisticated technology, while others, such as close observation of what problem solvers say and do over meaningful periods of time, are outgrowths of more traditional and lower-technology modes of research. Although several of these techniques have been designed for use in laboratory studies with one person at a time, they could potentially be