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Tessa Baradon. Kari Dunn Buron. Nadine Burke Harris. Zero to Three. Zeshan Qureshi.

8 Stages of Development by Erik Erikson

Kay Al-Ghani. David Marsten. Russell A.

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Uta Frith. Joan Lovett. Maggie Johnson. Sally Ozonoff. Carol Gray. Angela Scarpa. Alan Carr.

1. Introduction

Robert J. Thomas Paul. Thomas Boyce. Stanley I.

Martin L. Vimala McClure. Margaret E. Benita A. Eugene C. Todd Florin. Daniel S. Ajay Sharma. Douglas A. Malcolm MacMillan. Hellgard Rauh. Philip J. The shift from egocentric to allocentric representation is noted to be slow, and could be related to a number of factors including identification of visual landmarks, rotation of the torso, and crawling Newcombe and Huttenlocher, ; Vasilyeva and Lourenco, , and that it could be impaired by cognitive load Kaufman et al.

7.2 Infancy and Childhood: Exploring and Learning

Our vision system is not capable of identifying relationships between objects, nor does the robot perform any relocation of the body until the torso develops late in the experiment. Therefore, we currently restrict our model to the early egocentric and proprioceptive representation of space. Motor babbling generates candidate data for learning this sensorimotor coordination.

The discovered associations between stimuli properties and motor acts represent important information that will support further competencies. For example, in controlling the eyeball to move to fixate on a target it is necessary to know the relationship between the target distance from the center of the retina and the strength of the motor signals required to move the eye to this point.

As targets vary their location in the retinal periphery so the required motor command also varies. We use a mapping method as a framework for sensorimotor coordination Lee et al. A mapping consists of two 2D arrays or maps , representing sensory or motor variables, connected together by a set of explicit links that join points or small regions, known as fields , in each array. Although three dimensions might seem appropriate for representing spatial events, we take inspiration from neuroscience, which shows that most areas of the brain are organized in topographical two-dimensional layers 3 Mallot et al.

This remarkable structural consistency suggests some potential advantage or efficacy in such two-dimensional arrangements Kaas, Fields are analogous to receptive fields in the brain, and identify regions of equivalence. Any stimulus falling within a field produces an output. A single stimulus may activate a number of fields if it occurs in an area of overlap between fields. Further studies of the map structure and how it relates to neural sheets in the brain is presented in Earland et al.

For the saccade example, a 2D map of the retina is connected to a 2D array of motor values corresponding to the two degrees of freedom provided by the two axes of movement of the eyeball pan and tilt. The connections representing the mapping between the two arrays are established from sensory-motor pairs that are produced during learning. Eventually the maps are fully populated and linked, but even before then they can be used to drive saccades if entries have been created for the current target location.

Mappings provide us with a method of connecting multiple sensor and motor systems that are directly related. This is sufficient for simple control of independent motor systems, such as by generating eye-motor commands to fixate on a particular stimulus. However, more complex and interdependent combinations of sensor and motor systems require additional circuitry and mechanisms in order to provide the required functionality.

For example, audio-visual localization requires the correlation of audible stimuli in head-centered coordinates, with visual stimuli in eye-centered coordinates. The system has the added complexity that the eye is free to rotate within the head, making a direct mapping between audio and visual stimuli impossible. Just as in the brain, careful organization and structuring of these mechanisms and mappings is required.

To control coupled sensorimotor systems, such as the eye and head during gaze shifts, we take inspiration from the relevant biological literature Guitton and Volle, ; Goossens and van Opstal, ; Girard and Berthoz, ; Freedman, ; Gandhi and Katnani, Our aim is to reproduce the mechanisms at a functional level, and connect them to form an appropriate abstraction of their biological counterparts. We do not endeavor to create accurate neurophysiological models but rather to create plausible models at a functional level based on well-established hypotheses.

Consequently we use mappings to transform between sensorimotor domains, and incorporate standard robotic sensors and actuators, and low-level motor control techniques in place of their biological equivalents. The final design issue concerns the requirement to remember learned skills.

Cognitive Development During Childhood

The system as described above has a rich sensorimotor model of the immediate events being experienced but has limited memory of these experiences. Until this point the mappings have acted as the sole memory component, storing both short term sensory, and long term coordination information. On the other hand, the coordinations between motor and sensory subsystems are stored as connections and thus represent long term memories with scope for plastic variation. These are also mainly spatially encoded experiences and so represent, for example, how to reach and touch an object seen at a specific location.

What is not represented is any sensorimotor experience that has temporally dependent aspects. For example, consider a sequence of actions such as: reach to object, grasp object, move to another location, release object. This can be seen as a single compound action move object consisting of four temporally ordered actions.

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For this reason we introduced a long term associative memory mechanism that supports: the memory of successful basic action patterns; the associative matching of current sensorimotor experience to the stored action patterns; the generalization of several similar experiences into single parameterized patterns; and the composition of action chains for the temporal execution of more powerful action sequences. A concomitant feature of such requirements is that the patterns in long term memory should be useful as predictors of action outcome—a function that is unavailable without action memory.

Inspired by Piaget's notions of schemas Piaget and Cook, we implemented a schema memory system that stores action representations as triples consisting of: the pre-conditions that existed before the action; the action performed; and the resulting post-conditions see Sheldon, for further details. The schema memory provides long term memory in order to prevent repeated attention on past stimuli, and to match previous actions to new events. This formalism has been used by others, e. There are four significant results from this longitudinal experiment.

ISBN 13: 9780444893284

The first result concerns the emergence of a series of distinct qualitative stages in the robot's behavior over the duration of the experiment. The results described here used maturity levels for constraint release that previous experiments suggested as reasonable. This gave competence for reaching to be performed with an end-point accuracy of 1 cm, which is sufficient for the iCub to grasp 6—8 cm objects.

Further data on the effects of staged constraint release can be found in Shaw et al. This shows the use of the eyes, head, torso, and arm joints to gaze to a novel target, bring it into reach, and place the hand at its location. At around 4 s into the experiment a novel target appears and the robot initiates a gaze shift.

This is produced using the eye and head motor movements mapped to the location of the stimulus in the retina map. The eye is the first system to move, and fixates on the target at around 6 s. The head then begins to contribute to the gaze shift, and the eye counter-rotates to keep the target fixated the mapping resolution and dynamics of the system result in jerky head movements and some fluctuation of the gaze direction.

The gaze shift completes at 14 s and is followed by a separate vergence movement to determine the distance to the target this has a small effect on the gaze direction, which is based on readings from the dominant eye. Full fixation occurs around 19 s. Next the robot selects a torso movement to position the target within the reach space. This takes place between 31 and 35 s and is accompanied by compensatory eye and head movements, which complete at 48 s.