Timing sensory integration for robot simulation of autistic behavior

UTISM is the most common condition in a group of developmental disorders known as the autism spectrum disorders (ASDs) [42]. It is characterized by impaired social interaction, problems with verbal and nonverbal communication, and unusual, repetitive, behaviors and interests. Movement disturbance symptoms in individuals with autism have long not been considered an important symptom. During the last decade, Leary and Hill [4] have offered a radical perspective on this subject. After thorough analysis of the bibliography on movement impairments in autism they outlined how deficits in movement preparation and execution could lead to many of the behaviors exhibited by individuals with autism. Difficulties in planning and executing simple discrete movements, can lead to problems in learning to coordinate diverse muscle groups into a unitary movement pattern. Moreover, when a person is unable to respond to another’s action in a timely fashion they will miss the positive reinforcement associated with interpersonal interaction. Behavioral evidence of human perception and action indicates that organisms make use of multisensory stimulation. Under normal circumstances, multisensory stimulation leads to enhanced perceptions of, and facilitated responses to, objects in the environment (e.g. [5][6][7]). But literature shows that imprecise grasping or other motor or executive dysfunctions observed in autistic patients are caused by a disturbance in a dynamic mechanism that involves multisensory processing and integration. This can be caused by discrepancies between stimuli that are normally concordant. In these circumstances, multisensory stimulation actually leads to inaccurate perceptions and responses, regarding location, identity, and timing. Temporal binding for instance is identified as a dynamic mechanism that is disrupted and likely implicated in the perceptual as well as higher order deficits observed in autism (Brock et al. [8]). In other studies, atypical processing is specifically associated with enhanced sensory processing or discrimination in various modalities [9][10][11]. Some studies argue for a broader neurological problem such as an executive function deficit in the coordination of sources of information from different modalities [12][13][14]. All these works suggest that the dynamic aspects of integration of multisensory input influences the forming of coherent perception, planning, and coordination of action. Even more concrete, many studies assume that simple motor planning is intact but that the use of externally guided visual feedback is diminished, affecting the quality of motor performance, postural stability, and the lack of effective sequencing of actions [15][16][17][18]. Thus, perceptually challenging tasks that require smooth integration of visual with vestibular-proprioceptive information, for example, may be particularly difficult to perform and could result in poor quality of motor performance on complex tasks. We test this assumption by simulating the dynamic mechanism of temporal multisensory integration in order to investigate how the atypical forming of coherent perception might influence coordination of action, and compare the results with experimental studies by typical and autistic patients. Temporal multisensory integration has previously been discussed in the context of autism in [8] [19] in attempts to obtain a clear understanding of the underlying biological mechanism of interaction and to simulate it in [20][21] in the robotics setting in [22][23], and implicitly in many other robotics studies. Masterton and Biederman [15] in particular have shown that a proper interplay between integration of distal (visual) and proximal (proprioceptive) cues is essential by grasping. We simulate the integration of these cues on a mobile robot in a two-dimensional plane. A very important point is that the integration process that causes the movement behavior is approximated by a dynamic mechanism. Proper modeling of dynamic (or temporal) integration mechanisms requires a dynamic neural model. The mainstream connectionist methods such as self-organizing or supervised feed-forward networks, and Hopfield type recurrent networks, produce static outputs because their internal dynamics lack feedback loops, and their input space is static. Therefore they are suitable for modeling static behaviors. We are interested in a neural system that can spontaneously exhibit several dynamic behaviors derived from the interaction between changing input and complex inner dynamics. However, for the sake of controllability and computational expense, we have chosen the model that requires the least complexity. Schöner and colleagues [2[2][23][24] have adapted the dynamic neural field model of Amari [1] for controlling mobile robots and robot-manipulators. It produces smooth behavioral trajectories satisfying more than one external variable. In this model the Timing sensory integration for robot simulation of autistic behavior