Cognitive and Motor Aspects of Human Tool Use

Among the most complex and fascinating abilities of human beings is their skill in manufacturing and using various kinds of tools. It might therefore seem somewhat surprising that the scientific study of human tool use has for a long time been neglected in fields like cognitive psychology, movement science, or philosophy (cf. Baber, 2003; Preston, 1998). In recent years, this situation has started to change and a growing number of scientific studies have been devoted to the study of human tool use (e.g., Kunde, Müsseler, & Heuer, 2007; Massen & Sattler, 2010; Osiurak, Jarry, & Le Gall, 2010; Sülzenbrück & Heuer, 2009). The present topical issue of the Zeitschrift für Psychologie takes up this development and presents a number of empirical and theoretical contributions from the field of experimental psychology that focus on cognitive and motor aspects of human tool use.

One reason for the longstanding lack of scientific engagement in the topic of tool use is presumably its enormous complexity. Tool use is complex because the movements of the acting person not only depend on the environmental goal at which the movement is directed, but also on the tool and its properties. The relationship between the movements of the effector operating the tool (which is usually the hand) and the associated movements of the tool can be extremely complex. It is usually described with reference to two kinds of transformations. The first transformation is the kinematic transformation and describes how the spatio-temporal characteristics of the body movement are transformed into associated spatio-temporal characteristics of the tool movement. For instance, when a pair of scissors is used, the movement of the fingers is transformed into a movement of the two blades, the amplitude of which is not necessarily the same as that of the fingers’ movements. The second kind of transformation is the dynamic transformation. It describes how the forces an actor exerts on a tool are transformed into forces that the tool exerts on the environment. For instance, a nutcracker transforms the force that is exerted upon its arms into a (usually higher) force that acts upon the nut located in between the nutcracker’s arms. When learning to use a tool, human beings have to adapt to both kinematic and dynamic transformations. Rieger (2012) discusses two possibilities of how the results of this adaptation process could be represented in the motor system. However, tool use requires more than just learning kinematic and dynamic transformations: Tool users also have to select (or even manufacture) an appropriate tool for the task at hand, an ability that is often considered to be functionally distinct from the sensorimotor skill of appropriately using the tool (Johnson-Frey, 2004).

Studying tool use is thus not a simple undertaking and researchers have begun to investigate the acquisition of tool transformations in simplified experimental settings. For instance, the acquisition of kinematic transformations has often been studied by varying the relationship between the movement of a computer mouse and the associated movement of a cursor on a computer screen in certain aspects such as direction and/or gain (e.g., Bock & Burghoff, 1997; Krakauer, Pine, Ghilardi, & Ghez, 2000). Sülzenbrück (2012) reviews the results of this literature, with a special focus on the role of visual feedback that is presented to participants during learning. The empirical study by Ladwig, Sutter, and Müsseler (2012) focuses on the relative roles of proprioceptive and visual feedback in the control of kinematic transformations.

Tool transformations inherent in the relationship between movements involved in using some input device and those of a cursor on a computer screen differ in one important aspect from tool transformations implemented by mechanical everyday tools. This difference is the transparency of the tool transformation to the user: While the transformations implemented by mechanical tools like hammers or pliers are immediately evident from the appearance and mechanical structure of the tool, the arbitrary transformation implemented by an input device (e.g., a computer mouse transformation) is nontransparent and cannot be derived from the device’s physical appearance. To investigate whether this difference has consequences for processes of learning and control, tool transformations also have to be studied in the context of transparent mechanical tools. This special issue takes a significant step toward this research aim by presenting a number of studies that investigate tool use with mechanical, transparent tools like levers, hammers, scissors, or stones. Biryukova and Bril (2012) argue that the tradition of Bernstein, who began to biomechanically analyze movements with mechanical tools as early as 1926, needs to be revived. The empirical study by Janczyk, Pfister, and Kunde (2012) investigates the control of kinematic tool transformations implemented by mechanical levers. In particular, it focuses on the performance costs associated with incompatible relationships between the direction of tool movements and hand movements or between stimulus movements and tool movements.

Two studies investigate performance with dynamic tool transformations. The paper by Fitzpatrick, Wagman, and Schmidt (2012) focuses on the question of how inertial properties of hammers change participants’ movement characteristics when hammering pegs into a pegboard. Vernooij, Mouton, and Bongers (2012) study how participants learn to hit a ground plate with a stone when a certain angle of blow and a certain force are required.

Two further contributions deal with the question of how tools or tool transformations are selected. The study by Herbort (2012) investigates how participants grasp a lever at different distances from the joint (and thus select different tool transformations) in order to trade off speed and accuracy of the tool movement. Elsner and Schellhas (2012) study how children learn to select an appropriate tool for a task at hand, either by prior experience or by observation of an adult.

We hope that the present special issue not only provides a useful overview of current research developments in the field of human tool use but will also stimulate further research in this area. We also hope to have assembled a selection of papers that demonstrates the large variety of methods currently employed to approach the diverse research questions in the field. We thank all authors who submitted their papers in response to our call for papers and we especially thank the reviewers who helped to improve the papers in the course of the review process.