Robot in the mirror: toward an embodied computational model of mirror self-recognition

Self-recognition or self-awareness is a capacity attributed typically only to humans and few other species. The definitions of these concepts vary and little is known about the mechanisms behind them. However, there is a Turing test-like benchmark: the mirror self-recognition, which consists in covertly putting a mark on the face of the tested subject, placing her in front of a mirror, and observing the reactions. In this work, first, we provide a mechanistic decomposition, or process model, of what components are required to pass this test. Based on these, we provide suggestions for empirical research. In particular, in our view, the way the infants or animals reach for the mark should be studied in detail. Second, we develop a model to enable the humanoid robot Nao to pass the test. The core of our technical contribution is learning the appearance representation and visual novelty detection by means of learning the generative model of the face with deep auto-encoders and exploiting the prediction error. The mark is identified as a salient region on the face and reaching action is triggered, relying on a previously learned mapping to arm joint angles. The architecture is tested on two robots with a completely different face.Comment: To appear in KI - K\"unstliche Intelligenz - German Journal of Artificial Intelligence - Springe

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

Biological motion processing in autism spectrum disorders: a behavioural and fMRI investigation

There has been much controversy as to whether people with Autism Spectrum Disorders (ASDs) have a specific impairment in processing biological motion, with some studies suggesting there is an impairment (Blake, et. al. 2003; Klin et. al. 2003, Klin & Jones, 2008, Klin et. al. 2009) and others finding that people with ASDs show intact abilities to detect biological motion and categorise actions, but are impaired in emotion categorisation (Moore et. al. 1997; Hubert et. al. 2007, Parron et. al. 2008). Recent studies have found that although behavioural measures of biological motion processing show no differences, adults with ASDs show different patterns of brain activation to controls in response to intact point-light displays (PLDs), with the STS, MT+ and ITG regions showing reduced activity in this population (Herrington et. al. 2007; Parron et. al. 2009). The current thesis aimed to clarify the nature of these difficulties and to try to elucidate the brain regions used to process configural information from PLDs using novel techniques and stimuli. The first set of experiments were designed to behaviourally test people with ASDs ability to detect biological motion in noise, to categorise actions and to categorise affect from PLDs. Despite finding differences in the two groups in detection of biological motion and affect categorisation in pilot experiments, there were no significant differences between the groups in the main experiments. However, the ASD group showed slightly poorer performance at detecting biological motion and significantly more variability in the action categorisation tasks, suggesting that there may have been an underlying difference between the two groups. Furthermore, an analysis of the pattern of errors tentatively suggested that the ASD group may be using different strategies to categorise affect than controls, particularly for negative affects. We then devised a novel technique for manipulating the amount of configural information available in a PLD without the need to add different degrees of background noise and used this technique to assess the contribution of configural cues in a direction discrimination task behaviourally and neurally. The results confirmed that in typically developed individuals configural cues significantly improved the participants’ ability to correctly determine the direction of locomotion of a point light walker. Furthermore, the fMRI task found that regions of the inferotemporal, parietal and frontal regions were sensitive to the amount of configural information present in the displays that corresponded to increases in individual participants’ behavioural performance. Lastly, we used the same technique, though with a more powerful fMRI design, to assess the behavioural and neural differences between people with ASDs and controls in response to displays containing different degrees of configural information. We found that both groups were comparable in their ability to discriminate the direction of locomotion from PLDs. However, the brain regions used to process this information were found to be substantially different. In displays in which the configural information enabled participants to accurately judge the direction of locomotion, the control group utilised a similar group of regions as found in the previous experiment. The ASD group showed a pattern of activation suggesting that they predominantly used regions in the temporal and occipital cortex, and more specifically a region in the fusiform gyrus. The results of Granger Causality Mapping analysis, which allows for the mapping of directional to and from seeded regions, confirmed that whereas the control group utilised a network of regions starting from the ITG and connecting to parietal and occipital regions, the ASD group seemed to utilise two separate networks, processing form information in the fusiform gyrus and motion information separately in middle-temporal regions. The results are discussed in terms of a potential dysfunction of the ITG region in early childhood and two different models of biological motion processing that have been proposed in the recent literature. In TD individuals the model of Giese & Poggio (2003) may be more applicable, in that it proposes the integration of static form cues with motion signals in areas such as the STS. However, a dysfunctional ITG or dysfunctional connections from the ITG to more dorsal regions would disrupt the integration of form and motion processing and force the brain to place additional processing demands on form processing regions in the fusiform gyrus. This would be more in line with the model proposed by Lange and Lappe (2006) in which information can be derived from biological motion in noise without recourse to the actual motion information, through a process of temporal analysis of static postures. Both systems though, may be intact in TD individuals and may share processing requirements depending on the task. Furthermore, it is hypothesised that a dysfunctional ITG may force the brain to place additional demands on regions in the fusiform gyrus and this neural rewiring may be the cause of the developmental delay seen in processing biological motion in people with ASDs (Annaz et. al. 2009). Future studies should examine the roles of the ITG and fusiform area in more detail, both in TD people and in people with ASDs, and determine the specific nature of these neural differences and there behavioural implications for both groups

A study on dynamical properties of the force exerted by lamellipodia and filopodia using optical tweezers

During neuronal differentiation, lamellipodia and filopodia explore the environment in search for the correct path to the axon's final destination. Although the motion of lamellipodia and filopodia has been characterized to an extent, little is known about the force they exert. In this study, we used optical tweezers to measure the force exerted by filopodia and lamellipodia of dorsal root ganglia (DRG) neurons

Brain Mechanisms Underlying Human Communication

Human communication has been described as involving the coding-decoding of a conventional symbol system, which could be supported by parts of the human motor system (i.e. the “mirror neurons system”). However, this view does not explain how these conventions could develop in the first place. Here we target the neglected but crucial issue of how people organize their non-verbal behavior to communicate a given intention without pre-established conventions. We have measured behavioral and brain responses in pairs of subjects during communicative exchanges occurring in a real, interactive, on-line social context. In two fMRI studies, we found robust evidence that planning new communicative actions (by a sender) and recognizing the communicative intention of the same actions (by a receiver) relied on spatially overlapping portions of their brains (the right posterior superior temporal sulcus). The response of this region was lateralized to the right hemisphere, modulated by the ambiguity in meaning of the communicative acts, but not by their sensorimotor complexity. These results indicate that the sender of a communicative signal uses his own intention recognition system to make a prediction of the intention recognition performed by the receiver. This finding supports the notion that our communicative abilities are distinct from both sensorimotor processes and language abilities