1,721,058 research outputs found
Digital storytelling teaching robotics basics
No full textDigital Storytelling (DST) is a powerful tool for teaching complex concepts. DSTs are typically used in the humanities but several papers have shown that they are also a wonderful tool for the sciences because they are more involving, contextualized and can easily lead to deeper understanding. In the classical use of DST the story is the content, while the digital medium is the way of telling it. Our approach is slightly different: the story is not the content but a glue for the main contents, while the digital medium remains the way to tell the story. We propose the use of DST as a means to teach surgeons the basics of robotic surgery, by using a story that should be involving for them, i.e. a surgical operation, within which we will illustrate specific concepts on robotics in surgery
How pain and body representations transform each other: A narrative review
No full textPain, as a multidimensional and subjective experience, intertwines with various aspects of body representation, involving sensory, affective and motivational components. This review explores the bidirectional relationship between pain and body representations, emphasizing the impact of the sense of ownership on pain perception, the transformative impact of pain on motor imagery, the effects associated with vicarious pain perception on body representations and the role of pain in the maintenance of body representations in specific clinical conditions. Literature indicates complex interactions between pain and body representations, with the sense of ownership inducing analgesic effects in some cases and hyperalgesia in others, contingent upon factors such as the appearance of the affected limb. Pain sensations inform the body on which actions might be executed without harm, and which are potentially dangerous. This information impacts on motor imagery too, showing reduced motor imagery and increased reaction times in tasks where motor imagery involves the painful body parts. Finally, contrary to the conventional view, according to which pain impairs body representation, evidence suggests that pain can serve as an informative somatosensory index, preserving or even enhancing the representation of the absent or affected body parts. This bidirectional relationship highlights the dynamic and multifaceted nature of the interplay between pain and body representations, offering insights into the adaptive nature of the central nervous system in response to perceived bodily states
Electrophysiological correlates of action monitoring in brain-damaged patients: A systematic review
No full textAction monitoring is crucial to the successful execution of an action and understanding the actions of others. It is often impaired due to brain lesions, in particular after stroke. This systematic review aims to map the literature on the neurophysiological correlates of action monitoring in patients with brain lesions. Eighteen studies were identified and divided into two groups: studies on monitoring of one's own actions and studies on monitoring of the actions of others. The first group included EEG studies on monitoring of self-performed erroneous and correct actions. Impaired error detection (decreased error-related negativity) was observed in patients with lesions in the performance-monitoring network, as compared to healthy controls. Less consistent results were shown for error positivity and behavioral error monitoring performance. The second group of studies on monitoring of others' actions reported decreased mu frequency suppression, impaired readiness potential in the affected hemisphere and decreased EEG indices of error observation (observed error positivity and theta power) in stroke patients. As a whole, these results indicate distinct patterns of impaired neurophysiological activity related to monitoring one's own versus others' actions in patients with brain lesions. EEG recordings of this dissociation in the same patients might be a useful index of motor recovery, and therefore, potentially also beneficial in rehabilitation protocols
Scrivere report statistici con R e Sweave
No full textIn questo articolo si cercherà di fornire alcune informazioni introduttive sull’uso di Sweave, uno strumento che combina codice LATEX con codice R, un linguaggio di programmazione per il calcolo statistico che negli ultimi anni sta guadagnando sempre più consensi, al punto tale da diventare il linguaggio di riferimento della comunità statistica
Bayesian Multilevel Single Case Models using 'Stan'. A new tool to study single cases in Neuropsychology
No full textSingle case studies continue to play an important role in neuropsychological research. However, the range of statistical tools specifically designed for single cases is still limited. The current gold standard is the Crawford's t-test, but it is crucial to note that this is limited to simple designs and it is not possible to make inferences relevant to support for the null hypothesis with it. The Bayesian Multilevel Single Case models (BMSC) provide a novel tool that grants the flexibility of linear mixed model designs. BMSC is also able to support both null and alternative hypotheses in complex experimental designs using the Bayesian framework. We compared the BMSC and Crawford's t-test in a simulation study involving a case of no-dissociation and a case of simple dissociation between a single case patient and a series of control groups of different sizes (N=5, 15, or 30). We then showed how BMSC is useful in complex designs by means of an example using real data. The BMSC proved to be more reliable than the Crawford's test, in terms of first-type errors and more precise estimating the parameters. Notably, the BMSC model provides a comprehensive vision of the whole experimental design, interpolating a single model. It follows the recent trend which involves a shift in attention from p-values to other inferential indices and estimates
Reliability and Feasibility of Linear Mixed Models in Fully Crossed Experimental Designs
Full text linkThe use of linear mixed models (LMMs) is increasing in psychology and neuroscience research In this article, we focus on the implementation of LMMs in fully crossed experimental designs. A key aspect of LMMs is choosing a random-effects structure according to the experimental needs. To date, opposite suggestions are present in the literature, spanning from keeping all random effects (maximal models), which produces several singularity and convergence issues, to removing random effects until the best fit is found, with the risk of inflating Type I error (reduced models). However, defining the random structure to fit a nonsingular and convergent model is not straightforward. Moreover, the lack of a standard approach may lead the researcher to make decisions that potentially inflate Type I errors. After reviewing LMMs, we introduce a step-by-step approach to avoid convergence and singularity issues and control for Type I error inflation during model reduction of fully crossed experimental designs. Specifically, we propose the use of complex random intercepts (CRIs) when maximal models are overparametrized. CRIs are multiple random intercepts that represent the residual variance of categorical fixed effects within a given grouping factor. We validated CRIs and the proposed procedure by extensive simulations and a real-case application. We demonstrate that CRIs can produce reliable results and require less computational resources. Moreover, we outline a few criteria and recommendations on how and when scholars should reduce overparametrized models. Overall, the proposed procedure provides clear solutions to avoid overinflated results using LMMs in psychology and neuroscience
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