People frequently gesture when problem-solving, particularly on tasks that require spatial transformation. Gesture often facilitates task performance by interacting with internal mental representations, but how this process works is not well understood. We investigated this question by exploring the case of mental abacus, a technique in which users not only imagine moving beads on an abacus to compute sums, but also produce movements in gestures that accompany the calculations. Because the content of MA is transparent and readily manipulated, the task (...) offers a unique window onto how gestures interface with mental representations. We find that the size and number of MA gestures reflect the length and difficulty of math problems. Also, by selectively interfering with aspects of gesture, we find that participants perform significantly worse on MA under motor interference, but that perceptual feedback is not critical for success on the task. We conclude that premotor processes involved in the planning of gestures are critical to mental representation in MA. (shrink)

Previous accounts of how people develop expertise have focused on how deliberate practice transforms the cognitive and perceptual representations and processes that give rise to expertise. However, the likelihood of developing expertise with a particular tool may also depend on the degree to which that tool fits pre‐existing perceptual and cognitive abilities. The present studies explored whether the abacus—a descendent of the first human computing devices—may have evolved to exploit general biases in human visual attention, or whether developing expertise with (...) the abacus requires learning special strategies for allocating visual attention to the abacus. To address this question, we administered a series of visual search tasks to abacus experts and subjects who had little to no abacus experience, in which search targets and distractors were overlaid atop abacus “beads.” Across three studies, we found that both experts and naïve subjects were faster to detect targets in semantically relevant components of the abacus, suggesting that abacus training is not required to exhibit attentional biases toward these components of the abacus. This finding suggests that the attentional biases that scaffold numerical processing of the abacus may emerge from general properties of visual attention that are exploited by the design of the abacus itself. (shrink)