These findings demonstrate, to our knowledge for the first time, that face recognition memory in patients with Alzheimer is improved when differential outcomes are used and draw attention to the potential of this procedure as a therapeutic technique.

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These findings demonstrate, for the first time, that DOP can help elderly people overcome their memory limitations, and they draw attention to the potential of this procedure as a therapeutic technique.

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Memory for medical recommendations is a prerequisite for good adherence to treatment, and therefore to ameliorate the negative effects of the disease, a problem that mainly affects people with memory deficits. We conducted a simulated study to test the utility of a procedure (the differential outcomes procedure, DOP) that may improve adherence to treatment by increasing the patient’s learning and retention of medical recommendations regarding medication. The DOP requires the structure of a conditional discriminative learning task in which correct choice responses to specific stimulus–stimulus associations are reinforced with a particular reinforcer or outcome. In two experiments, participants had to learn and retain in their memory the pills that were associated with particular disorders. To assess whether the DOP improved long-term retention of the learned disorder/pill associations, participants were asked to perform two recognition memory tests, 1 h and 1 week after completing the learning phase. The results showed that compared with the standard non-differential outcomes procedure, the DOP produced better learning and long-term retention of the previously learned associations. These findings suggest that the DOP can be used as a useful complementary technique in intervention programs targeted at increasing adherence to clinical recommendations.

It has recently been reported that the differential outcomes procedure (DOP) might be one of the therapeutical techniques focused at promoting autonomy in the elderly to deal with their medical issues. Molina et al. (2015) found that a group of healthy young adults improved their learning and long-term retention of six disorder/pill associations when each relationship to be learned was associated with a particular reinforcer (the differential outcomes condition) compared to when they were randomly administered (the non-differential outcomes condition). In the present study, we extend these findings to older adults who usually show difficulties to remember to take their medications as prescribed. Participants were asked to learn the association between three pills and the specific time at the day when they had to take each medication. Two memory tests were also performed 1 h and 1 week after completing the training phase. Results showed a faster learning of the task and long-term retention of the previously learned associations (pill/time of day) when differential outcomes were used. Furthermore, the older adults’ performance in the learning and memory phases did not differ from that of the younger adults in the DOP condition. These findings demonstrate that this procedure can help elderly people to ameliorate not only their learning, but also their long-term memory difficulties, suggesting the potential for the DOP to promote adherence to treatment in this population.

Abstract. This paper addresses classification of 3D point cloud data from natural environments based on voxels. The proposed model uses multi-layer perceptrons to classify voxels based on a statistic geometric analysis of the spatial distribution of inner points. Geometric features such as tubular structures or flat surfaces are identified regardless of their orientation, which is useful for unstructured or natural environments. Furthermore, the combination of voxels and neural networks pursues faster computation than alternative strategies. The model has been successfully tested with 3D laser scans from natural environments.Keywords: Multi-layer perceptron · 3D classification · Mobile robot · Voxel map IntroductionKnowledge of geometric features in three-dimensional (3D) scenes is useful for object recognition in challenging applications in natural and unstructured environments, such as robotics for search and rescue (SAR) and planetary exploration [1,2]. In these applications, scenes are usually obtained through laser scanners [3] and stereo vision [4] as large and complex point clouds. Object recognition from point clouds usually involves three main steps: segmentation, feature extraction, and classification. Three main approaches have been adopted for object recognition in point clouds. The first approach avoids point-based computations by reducing the scene to a 2D representation which can be processed with standard artificial vision algorithms. The objects may be classified based on local and global statistics features of each object from a range image [5] or from a 2D deviation map when classification is based on texture analysis [6]. The classification can be also performed with image with depth data (RGB-D) by fusing results from separate 2D and 3D segmentation and feature extraction processes [7].