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The visible face of intention: why kinematics matters.

Ansuini C, Cavallo A, Bertone C, Becchio C - Front Psychol (2014)

Bottom Line: We argue that intentions become "visible" in the surface flow of agents' motions.Consequently, the ability to understand others' intentions cannot be divorced from the capability to detect essential kinematics.This hypothesis has far reaching implications for how we know other minds and predict others' behavior.

View Article: PubMed Central - PubMed

Affiliation: Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology Genova, Italy.

ABSTRACT
A key component of social understanding is the ability to read intentions from movements. But how do we discern intentions in others' actions? What kind of intention information is actually available in the features of others' movements? Based on the assumption that intentions are hidden away in the other person's mind, standard theories of social cognition have mainly focused on the contribution of higher level processes. Here, we delineate an alternative approach to the problem of intention-from-movement understanding. We argue that intentions become "visible" in the surface flow of agents' motions. Consequently, the ability to understand others' intentions cannot be divorced from the capability to detect essential kinematics. This hypothesis has far reaching implications for how we know other minds and predict others' behavior.

No MeSH data available.


Related in: MedlinePlus

Techniques used to quantify the influence of intention on movement kinematics. (A) Example of experimental set-up employed in action execution studies. The participant sits at a table with his hand resting in a starting position, which is kept constant across participants. The task is to reach and grasp the object (i.e., a bottle) either to lift it or to place it inside a box. An optoelectronic system (Vicon Motion Systems Ltd., UK) equipped with nine infra-red cameras is used to quantify reach-to-grasp movements. This system relies on passive markers (retro-reflective material on a plastic sphere) placed on points of interest over participant’s hand. An infra-red light is transmitted toward the work space area and the rays are reflected back off the markers to a series of “cameras” that record their positions. These positions are then referred to a coordinate system, the origin of which is either in 2-D or 3-D coordinates, i.e., two or three mutually orthogonally axes, each passing through the origin. (B) A computer-generated stick figure representing the position of the markers placed over arm and hand joints during a reach-to-grasp movement toward the bottle. After collecting raw data, it is possible to identify and track the marker’s trajectories almost in real time by means of tracking procedures.
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Figure 1: Techniques used to quantify the influence of intention on movement kinematics. (A) Example of experimental set-up employed in action execution studies. The participant sits at a table with his hand resting in a starting position, which is kept constant across participants. The task is to reach and grasp the object (i.e., a bottle) either to lift it or to place it inside a box. An optoelectronic system (Vicon Motion Systems Ltd., UK) equipped with nine infra-red cameras is used to quantify reach-to-grasp movements. This system relies on passive markers (retro-reflective material on a plastic sphere) placed on points of interest over participant’s hand. An infra-red light is transmitted toward the work space area and the rays are reflected back off the markers to a series of “cameras” that record their positions. These positions are then referred to a coordinate system, the origin of which is either in 2-D or 3-D coordinates, i.e., two or three mutually orthogonally axes, each passing through the origin. (B) A computer-generated stick figure representing the position of the markers placed over arm and hand joints during a reach-to-grasp movement toward the bottle. After collecting raw data, it is possible to identify and track the marker’s trajectories almost in real time by means of tracking procedures.

Mentions: Research on hand kinematics has proven insightful in revealing how specific kinematic landmarks modulate with respect to object properties, including object size, shape, texture, fragility, and weight. As recently reviewed, all these factors influence the kinematics of grasping (Castiello, 2005). The way an object is grasped, however, does not only depend exclusively on the properties of the object, but it is also influenced by the agent’s intention. This was first demonstrated by Marteniuk et al. (1987) by asking participants to grasp a disk and either fit it carefully or throw it. The deceleration time was longer for fitting than for throwing (see Table 1). Since this seminal work, a plethora of studies have investigated how intentions influence the execution of reach-to-grasp movements (e.g., Ansuini et al., 2006, 2008; Armbrüster and Spijkers, 2006). The logic of these studies has been to “manipulate” the intention while keeping the object to be grasped (i.e., goal) as well as the situational requirements (i.e., context) constant (see Figure 1). If within the same context, the same object is handled differently depending on the agent’s intention, this would indicate that the intention influences the grasping kinematics.


The visible face of intention: why kinematics matters.

Ansuini C, Cavallo A, Bertone C, Becchio C - Front Psychol (2014)

Techniques used to quantify the influence of intention on movement kinematics. (A) Example of experimental set-up employed in action execution studies. The participant sits at a table with his hand resting in a starting position, which is kept constant across participants. The task is to reach and grasp the object (i.e., a bottle) either to lift it or to place it inside a box. An optoelectronic system (Vicon Motion Systems Ltd., UK) equipped with nine infra-red cameras is used to quantify reach-to-grasp movements. This system relies on passive markers (retro-reflective material on a plastic sphere) placed on points of interest over participant’s hand. An infra-red light is transmitted toward the work space area and the rays are reflected back off the markers to a series of “cameras” that record their positions. These positions are then referred to a coordinate system, the origin of which is either in 2-D or 3-D coordinates, i.e., two or three mutually orthogonally axes, each passing through the origin. (B) A computer-generated stick figure representing the position of the markers placed over arm and hand joints during a reach-to-grasp movement toward the bottle. After collecting raw data, it is possible to identify and track the marker’s trajectories almost in real time by means of tracking procedures.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4109428&req=5

Figure 1: Techniques used to quantify the influence of intention on movement kinematics. (A) Example of experimental set-up employed in action execution studies. The participant sits at a table with his hand resting in a starting position, which is kept constant across participants. The task is to reach and grasp the object (i.e., a bottle) either to lift it or to place it inside a box. An optoelectronic system (Vicon Motion Systems Ltd., UK) equipped with nine infra-red cameras is used to quantify reach-to-grasp movements. This system relies on passive markers (retro-reflective material on a plastic sphere) placed on points of interest over participant’s hand. An infra-red light is transmitted toward the work space area and the rays are reflected back off the markers to a series of “cameras” that record their positions. These positions are then referred to a coordinate system, the origin of which is either in 2-D or 3-D coordinates, i.e., two or three mutually orthogonally axes, each passing through the origin. (B) A computer-generated stick figure representing the position of the markers placed over arm and hand joints during a reach-to-grasp movement toward the bottle. After collecting raw data, it is possible to identify and track the marker’s trajectories almost in real time by means of tracking procedures.
Mentions: Research on hand kinematics has proven insightful in revealing how specific kinematic landmarks modulate with respect to object properties, including object size, shape, texture, fragility, and weight. As recently reviewed, all these factors influence the kinematics of grasping (Castiello, 2005). The way an object is grasped, however, does not only depend exclusively on the properties of the object, but it is also influenced by the agent’s intention. This was first demonstrated by Marteniuk et al. (1987) by asking participants to grasp a disk and either fit it carefully or throw it. The deceleration time was longer for fitting than for throwing (see Table 1). Since this seminal work, a plethora of studies have investigated how intentions influence the execution of reach-to-grasp movements (e.g., Ansuini et al., 2006, 2008; Armbrüster and Spijkers, 2006). The logic of these studies has been to “manipulate” the intention while keeping the object to be grasped (i.e., goal) as well as the situational requirements (i.e., context) constant (see Figure 1). If within the same context, the same object is handled differently depending on the agent’s intention, this would indicate that the intention influences the grasping kinematics.

Bottom Line: We argue that intentions become "visible" in the surface flow of agents' motions.Consequently, the ability to understand others' intentions cannot be divorced from the capability to detect essential kinematics.This hypothesis has far reaching implications for how we know other minds and predict others' behavior.

View Article: PubMed Central - PubMed

Affiliation: Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology Genova, Italy.

ABSTRACT
A key component of social understanding is the ability to read intentions from movements. But how do we discern intentions in others' actions? What kind of intention information is actually available in the features of others' movements? Based on the assumption that intentions are hidden away in the other person's mind, standard theories of social cognition have mainly focused on the contribution of higher level processes. Here, we delineate an alternative approach to the problem of intention-from-movement understanding. We argue that intentions become "visible" in the surface flow of agents' motions. Consequently, the ability to understand others' intentions cannot be divorced from the capability to detect essential kinematics. This hypothesis has far reaching implications for how we know other minds and predict others' behavior.

No MeSH data available.


Related in: MedlinePlus