Sensorimotor experience enhances automatic imitation of robotic action

Recent research in cognitive neuroscience has found that observation of human actions activates the ‘mirror system’ and provokes automatic imitation to a greater extent than observation of non-biological movements. The present study investigated whether this human bias depends primarily on phylogenetic or ontogenetic factors by examining the effects of sensorimotor experience on automatic imitation of non-biological robotic, stimuli. Automatic imitation of human and robotic action stimuli was assessed before and after training. During these test sessions, participants were required to execute a pre-specified response (e.g. to open their hand) while observing a human or robotic hand making a compatible (opening) or incompatible (closing) movement. During training, participants executed opening and closing hand actions while observing compatible (group CT) or incompatible movements (group IT) of a robotic hand. Compatible, but not incompatible, training increased automatic imitation of robotic stimuli (speed of responding on compatible trials, compared with incompatible trials) and abolished the human bias observed at pre-test. These findings suggest that the development of the mirror system depends on sensorimotor experience, and that, in our species, it is biased in favour of human action stimuli because these are more abundant than non-biological action stimuli in typical developmental environments.

Robotic movement elicits automatic imitation

Recent behavioural and neuroimaging studies have found that observation of human movement, but not of robotic movement, gives rise to visuomotor priming. This implies that the 'mirror neuron' or 'action observation–execution matching' system in the premotor and parietal cortices is entirely unresponsive to robotic movement. The present study investigated this hypothesis using an 'automatic imitation' stimulus–response compatibility procedure. Participants were required to perform a prespecified movement (e.g. opening their hand) on presentation of a human or robotic hand in the terminal posture of a compatible movement (opened) or an incompatible movement (closed). Both the human and the robotic stimuli elicited automatic imitation; the prespecified action was initiated faster when it was cued by the compatible movement stimulus than when it was cued by the incompatible movement stimulus. However, even when the human and robotic stimuli were of comparable size, colour and brightness, the human hand had a stronger effect on performance. These results suggest that effector shape is sufficient to allow the action observation–matching system to distinguish human from robotic movement. They also indicate, as one would expect if this system develops through learning, that to varying degrees both human and robotic action can be 'simulated' by the premotor and parietal cortices.

Automatic prediction of functional site regions in low-resolution protein structures

World-wide structural genomics initiatives are rapidly accumulating structures for which limited functional information is available. Additionally, state-of-the art structural prediction programs are now capable of generating at least low resolution structural models of target proteins. Accurate detection and classification of functional sites within both solved and modelled protein structures therefore represents an important challenge. We present a fully automatic site detection method, FuncSite, that uses neural network classifiers to predict the location and type of functionally important sites in protein structures. The method is designed primarily to require only backbone residue positions without the need for specific side-chain atoms to be present. In order to highlight effective site detection in low resolution structural models FuncSite was used to screen model proteins generated using mGenTHREADER on a set of newly released structures. We found effective metal site detection even for moderate quality protein models illustrating the robustness of the method.

Rapid protein domain assignment from amino acid sequence using predicted secondary structure

The elucidation of the domain content of a given protein sequence in the absence of determined structure or significant sequence homology to known domains is an important problem in structural biology. Here we address how successfully the delineation of continuous domains can be accomplished in the absence of sequence homology using simple baseline methods, an existing prediction algorithm (Domain Guess by Size), and a newly developed method (DomSSEA). The study was undertaken with a view to measuring the usefulness of these prediction methods in terms of their application to fully automatic domain assignment. Thus, the sensitivity of each domain assignment method was measured by calculating the number of correctly assigned top scoring predictions. We have implemented a new continuous domain identification method using the alignment of predicted secondary structures of target sequences against observed secondary structures of chains with known domain boundaries as assigned by Class Architecture Topology Homology (CATH). Taking top predictions only, the success rate of the method in correctly assigning domain number to the representative chain set is 73.3%. The top prediction for domain number and location of domain boundaries was correct for 24% of the multidomain set (±20 residues). These results have been put into context in relation to the results obtained from the other prediction methods assessed

How reward modulates mimicry: EMG evidence of greater automatic facial mimicry of more rewarding faces

Spontaneous mimicry is a marker of empathy. Conditions characterized by reduced spontaneous mimicry (e.g., autism) also display deficits in sensitivity to social rewards. We tested if spontaneous mimicry of socially rewarding stimuli (happy faces) depends on the reward value of stimuli in 32 typical participants. An evaluative conditioning paradigm was used to associate different reward values with neutral target faces. Subsequently, electromyographic activity over the Zygomaticus Major was measured whilst participants watched video clips of the faces making happy expressions. Higher Zygomaticus Major activity was found in response to happy faces conditioned with high reward versus low reward. Moreover, autistic traits in the general population modulated the extent of spontaneous mimicry of happy faces. This suggests a link between reward and spontaneous mimicry and provides a possible underlying mechanism for the reduced response to social rewards seen in autism.

Automatic keyphrase extraction: a comparison of methods

There are many published methods available for creating keyphrases for documents. Previous work in the field has shown that in a significant proportion of cases author selected keyphrases are not appropriate for the document they accompany. This requires the use of such automated methods to improve the use of keyphrases. Often the keyphrases are not updated when the focus of a paper changes or include keyphrases that are more classificatory than explanatory. The published methods are all evaluated using different corpora, typically one relevant to their field of study. This not only makes it difficult to incorporate the useful elements of algorithms in future work but also makes comparing the results of each method inefficient and ineffective. This paper describes the work undertaken to compare five methods across a common baseline of six corpora. The methods chosen were term frequency, inverse document frequency, the C-Value, the NC-Value, and a synonym based approach. These methods were compared to evaluate performance and quality of results, and to provide a future benchmark. It is shown that, with the comparison metric used for this study Term Frequency and Inverse Document Frequency were the best algorithms, with the synonym based approach following them. Further work in the area is required to determine an appropriate (or more appropriate) comparison metric.

Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: a case study

Flood extents caused by fluvial floods in urban and rural areas may be predicted by hydraulic models. Assimilation may be used to correct the model state and improve the estimates of the model parameters or external forcing. One common observation assimilated is the water level at various points along the modelled reach. Distributed water levels may be estimated indirectly along the flood extents in Synthetic Aperture Radar (SAR) images by intersecting the extents with the floodplain topography. It is necessary to select a subset of levels for assimilation because adjacent levels along the flood extent will be strongly correlated. A method for selecting such a subset automatically and in near real-time is described, which would allow the SAR water levels to be used in a forecasting model. The method first selects candidate waterline points in flooded rural areas having low slope. The waterline levels and positions are corrected for the effects of double reflections between the water surface and emergent vegetation at the flood edge. Waterline points are also selected in flooded urban areas away from radar shadow and layover caused by buildings, with levels similar to those in adjacent rural areas. The resulting points are thinned to reduce spatial autocorrelation using a top-down clustering approach. The method was developed using a TerraSAR-X image from a particular case study involving urban and rural flooding. The waterline points extracted proved to be spatially uncorrelated, with levels reasonably similar to those determined manually from aerial photographs, and in good agreement with those of nearby gauges.

ATD: a multiplatform for semiautomatic 3-D detection of kidneys and their pathology in real time

This research presents a novel multi-functional system for medical Imaging-enabled Assistive Diagnosis (IAD). Although the IAD demonstrator has focused on abdominal images and supports the clinical diagnosis of kidneys using CT/MRI imaging, it can be adapted to work on image delineation, annotation and 3D real-size volumetric modelling of other organ structures such as the brain, spine, etc. The IAD provides advanced real-time 3D visualisation and measurements with fully automated functionalities as developed in two stages. In the first stage, via the clinically driven user interface, specialist clinicians use CT/MRI imaging datasets to accurately delineate and annotate the kidneys and their possible abnormalities, thus creating “3D Golden Standard Models”. Based on these models, in the second stage, clinical support staff i.e. medical technicians interactively define model-based rules and parameters for the integrated “Automatic Recognition Framework” to achieve results which are closest to that of the clinicians. These specific rules and parameters are stored in “Templates” and can later be used by any clinician to automatically identify organ structures i.e. kidneys and their possible abnormalities. The system also supports the transmission of these “Templates” to another expert for a second opinion. A 3D model of the body, the organs and their possible pathology with real metrics is also integrated. The automatic functionality was tested on eleven MRI datasets (comprising of 286 images) and the 3D models were validated by comparing them with the metrics from the corresponding “3D Golden Standard Models”. The system provides metrics for the evaluation of the results, in terms of Accuracy, Precision, Sensitivity, Specificity and Dice Similarity Coefficient (DSC) so as to enable benchmarking of its performance. The first IAD prototype has produced promising results as its performance accuracy based on the most widely deployed evaluation metric, DSC, yields 97% for the recognition of kidneys and 96% for their abnormalities; whilst across all the above evaluation metrics its performance ranges between 96% and 100%. Further development of the IAD system is in progress to extend and evaluate its clinical diagnostic support capability through development and integration of additional algorithms to offer fully computer-aided identification of other organs and their abnormalities based on CT/MRI/Ultra-sound Imaging.

A comparison of automated keyphrase extraction techniques and of automatic evaluation vs. human evaluation

Keyphrases are added to documents to help identify the areas of interest they contain. However, in a significant proportion of papers author selected keyphrases are not appropriate for the document they accompany: for instance, they can be classificatory rather than explanatory, or they are not updated when the focus of the paper changes. As such, automated methods for improving the use of keyphrases are needed, and various methods have been published. However, each method was evaluated using a different corpus, typically one relevant to the field of study of the method’s authors. This not only makes it difficult to incorporate the useful elements of algorithms in future work, but also makes comparing the results of each method inefficient and ineffective. This paper describes the work undertaken to compare five methods across a common baseline of corpora. The methods chosen were Term Frequency, Inverse Document Frequency, the C-Value, the NC-Value, and a Synonym based approach. These methods were analysed to evaluate performance and quality of results, and to provide a future benchmark. It is shown that Term Frequency and Inverse Document Frequency were the best algorithms, with the Synonym approach following them. Following these findings, a study was undertaken into the value of using human evaluators to judge the outputs. The Synonym method was compared to the original author keyphrases of the Reuters’ News Corpus. The findings show that authors of Reuters’ news articles provide good keyphrases but that more often than not they do not provide any keyphrases.

Neuromantic : from semi-manual to semi-automatic reconstruction of neuron morphology

The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.

Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

Many modern statistical applications involve inference for complex stochastic models, where it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models. It replaces calculation of the likelihood by a step which involves simulating artificial data for different parameter values, and comparing summary statistics of the simulated data with summary statistics of the observed data. Here we show how to construct appropriate summary statistics for ABC in a semi-automatic manner. We aim for summary statistics which will enable inference about certain parameters of interest to be as accurate as possible. Theoretical results show that optimal summary statistics are the posterior means of the parameters. Although these cannot be calculated analytically, we use an extra stage of simulation to estimate how the posterior means vary as a function of the data; and we then use these estimates of our summary statistics within ABC. Empirical results show that our approach is a robust method for choosing summary statistics that can result in substantially more accurate ABC analyses than the ad hoc choices of summary statistics that have been proposed in the literature. We also demonstrate advantages over two alternative methods of simulation-based inference.