Manual asymmetries in the preparation and control of goal-directed movements

The primary purpose of this experiment was to determine if left hand reaction time advantages in manual aiming result from a right hemisphere attentional advantage or an early right hemisphere role in movement preparation. Right-handed participants were required to either make rapid goal-directed movements to small targets or simply lift their hand upon target illumination. The amount of advance information about the target for a particular trial was manipulated by precuing a subset of potential targets prior to the reaction time interval. When participants were required to make aiming movements to targets in left space, the left hand enjoyed a reaction advantage that was not present for aiming in right space: or simple finger lifts. This advantage was independent of the amount or type of advance information provided by the precue. This finding supports the movement planning hypothesis. With respect to movement execution, participants completed their aiming movements more quickly when aiming with their right hand, particularly in right space. This right hand advantage in right space was due to the time required to decelerate the movement and to make feedback-based adjustments late in the movement trajectory. (C) 2001 Academic Press.

Human parietal and primary motor cortical interactions are selectively modulated during the transport and grip formation of goal-directed hand actions

Posterior parietal cortex (PPC) constitutes a critical cortical node in the sensorimotor system in which goal-directed actions are computed. This information then must be transferred into commands suitable for hand movements to the primary motor cortex (M1). Complexity arises because reach-to-grasp actions not only require directing the hand towards the object (transport component), but also preshaping the hand according to the features of the object (grip component). Yet, the functional influence that specific PPC regions exert over ipsilateral M1 during the planning of different hand movements remains unclear in humans. Here we manipulated transport and grip components of goal-directed hand movements and exploited paired-pulse transcranial magnetic stimulation (ppTMS) to probe the functional interactions between M1 and two different PPC regions, namely superior parieto-occipital cortex (SPOC) and the anterior region of the intraparietal sulcus (aIPS), in the left hemisphere. We show that when the extension of the arm is required to contact a target object, SPOC selectively facilitates motor evoked potentials, suggesting that SPOC-M1 interactions are functionally specific to arm transport. In contrast, a different pathway, linking the aIPS and ipsilateral M1, shows enhanced functional connections during the sensorimotor planning of grip. These results support recent human neuroimaging findings arguing for specialized human parietal regions for the planning of arm transport and hand grip during goal-directed actions. Importantly, they provide new insight into the causal influences these different parietal regions exert over ipsilateral motor cortex for specific types of planned hand movements

Efficient goal directed navigation using RatSLAM

RatSLAM is a system for vision based Simultaneous Localization and Mapping (SLAM) that has been shown to be capable of building stable representations of real world environments. In this paper we describe a method for using RatSLAM representations as the basis for navigation to designated goal locations. The method uses a new component, goal memory, to learn the temporal gradient between places. Paths are recalled or inferred from the goal memory by following the temporal gradient from the robot’s current position to the goal location. Experimental results have been gathered in a combined office and laboratory environment using a Pioneer robot. The experiments show that the robot can perform vision based SLAM on-line and in real time, and then use those representations immediately to navigate directly to designated goal locations.

The role of desire in understanding intentions to drink responsibly : an application of the model of goal-directed behaviour

This study models young people's moderate drinking decision-making using the Model of Goal-Directed Behaviour (MGB), thus presenting insights into young people's desires and intentions to drink responsibly. Testing the applicability of the MGB to quantitatively analyse responsible drinking, the explanatory sphere of the MGB is extended. An online survey resulted in 1522 completed questionnaires from respondents aged between 18 and 25 years. Collected data were analysed with structural equation modelling (SEM) using SPSS AMOS21 (IBM, New York, NY, USA) software. The key finding of this study is that an individual's desire to drink moderately is the most important predictor of young people's responsible drinking intentions. Our use of MGB provides further evidence that there is a strong distinction between consumer desires and intentions.

A hierarchical model of goal directed navigation selects trajectories in a visual environment

We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model’s flexibility in representing large real world environments using odometry information obtained from challenging video sequences. We acquire the visual data from a camera mounted on a small tele-operated vehicle. The camera has a panoramic field of view with its focal point approximately 5 cm above the ground level, similar to what would be expected from a rat’s point of view. Using established algorithms for calculating perceptual speed from the apparent rate of visual change over time, we generate raw dead reckoning information which loses spatial fidelity over time due to error accumulation. We rectify the loss of fidelity by exploiting the loop-closure detection ability of a biologically inspired, robot navigation model termed RatSLAM. The rectified motion information serves as a velocity input to the HiLAM to encode the environment in the form of grid cell and place cell maps. Finally, we show goal directed path planning results of HiLAM in two different environments, an indoor square maze used in rodent experiments and an outdoor arena more than two orders of magnitude larger than the indoor maze. Together these results bridge for the first time the gap between higher fidelity bio-inspired navigation models (HiLAM) and more abstracted but highly functional bio-inspired robotic mapping systems (RatSLAM), and move from simulated environments into real-world studies in rodent-sized arenas and beyond.

Principles of goal-directed spatial robot navigation in biomimetic models

Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in ‘real-world’ environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.

Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration

In this paper we present for the first time a complete symbolic navigation system that performs goal-directed exploration to unfamiliar environments on a physical robot. We introduce a novel construct called the abstract map to link provided symbolic spatial information with observed symbolic information and actual places in the real world. Symbolic information is observed using a text recognition system that has been developed specifically for the application of reading door labels. In the study described in this paper, the robot was provided with a floor plan and a destination. The destination was specified by a room number, used both in the floor plan and on the door to the room. The robot autonomously navigated to the destination using its text recognition, abstract map, mapping, and path planning systems. The robot used the symbolic navigation system to determine an efficient path to the destination, and reached the goal in two different real-world environments. Simulation results show that the system reduces the time required to navigate to a goal when compared to random exploration.

Constructing abstract maps from spatial descriptions for goal-directed exploration

This paper describes ongoing work on a system using spatial descriptions to construct abstract maps that can be used for goal-directed exploration in an unfamiliar office environment. Abstract maps contain membership, connectivity, and spatial layout information extracted from symbolic spatial information. In goal-directed exploration, the robot would then link this information with observed symbolic information and its grounded world representation. We demonstrate the ability of the system to extract and represent membership, connectivity, and spatial layout information from spatial descriptions of an office environment. In the planned study, the robot will navigate to the goal location using the abstract map to inform the best direction to explore in.