Prosthesis embodiment and attenuation of prosthetic touch in upper limb amputees – A proof-of-concept study

Sensory attenuation of self-touch, that is, the perceptual reduction of self-generated tactile stimuli, is considered a neurocognitive basis for self-other distinction. However, whether this effect can also be found in upper limb amputees using a prosthesis is unknown. Thirteen participants were asked to touch their foot sole with a) their intact hand (self-touch), b) their prosthesis (prosthesis-touch), or c) let it be touched by another person (other-touch). Intensity of touch was assessed with a questionnaire. In addition, prosthesis embodiment was assessed in nine participants. Self-touch as well as prosthesis-touch was characterized by significant perceptual attenuation compared to other-touch, while self- and prosthesis-touch did not differ. The more embodied the prosthesis was, the more similar was its elicited touch perception to actual self-touch. These findings – although preliminary – suggest that perceptually embodied prostheses can be represented as an actual limb by the users’ sensorimotor system.     

Gradedness of visual awareness depends on attentional scope: Global perception is more graded than local perception

The gradedness or discreteness of our visual awareness has been debated. Here, we investigate the influence of spatial scope of attention on the gradedness of visual awareness. We manipulated scope of attention using hierarchical letter-based tasks (global: broad scope; local: narrow scope). Participants reported the identity of a masked hierarchical letter either at the global level or at the local level. We measured subjective awareness using the perceptual awareness scale ratings and objective performance. The results indicate more graded visual awareness (lesser slope for the awareness rating curve) at the global level compared to the local level. Graded perception was also observed in visibility ratings usage with global level task showing higher usage of the middle PAS ratings. Our results are in line with the prediction of level of processing hypothesis and show that global/local attentional scope and contextual endogenous factors influence the graded nature of our visual awareness.     

Computational models of the “active self” and its disturbances in schizophrenia

The notion that self-disorders are at the root of the emergence of schizophrenia rather than a symptom of the disease, is getting more traction in the cognitive sciences. This is in line with philosophical approaches that consider an enactive self, constituted through action and interaction with the environment. We thereby analyze different definitions of the self and evaluate various computational theories lending to these ideas. Bayesian and predictive processing are promising approaches for computational modeling of the “active self”. We evaluate their implementation and challenges in computational psychiatry and cognitive developmental robotics. We describe how and why embodied robotic systems provide a valuable tool in psychiatry to assess, validate, and simulate mechanisms of self-disorders. Specifically, mechanisms involving sensorimotor learning, prediction, and self-other distinction, can be assessed with artificial agents. This link can provide essential insights to the formation of the self and new avenues in the treatment of psychiatric disorders.     

Safety of micro-mobility: Riders’ psychological factors and risky behaviors of cargo TTWs in China

Cargo two- or three-wheeled vehicles (TTWs), as a new form of micro-mobility, have become a popular mode of urban cargo transportation in China. Cargo TTW riders’ psychological factors and risky behaviors lead to a number of accidents. A questionnaire is designed by comprehensively considering these factors and behaviors of cargo TTW riders that includes eleven risk factors to quantitatively analyze the risky behaviors based on structural equation modeling (SEM). One thousand three hundred nineteen participants reported using cargo TTWs on a questionnaire distributed across the country. The characteristics of riding behavior data are analyzed to verify the three-layer risk theoretical framework of “Psychological factors (Personality traits/specific factors) - Psychological acceptability of risks (confidence/perception/attitude) - Risky behaviors”. The results show that anger has a strong direct effect on riding violations, while normlessness and altruism have a direct effect on riding errors. Workload has a weak but direct effect on risky behaviors, and riding feedback has a weak and mixed effect. In addition, high-risk groups are identified by analysis of variance (ANOVA) with rider population attributes. These quantitative analyses can help guide safety countermeasures to mitigate accidents involving cargo TTWs.     

Correlations of multiple rider behaviors with self-reported attitudes,perspectives on traffic rule strictness and social desirability

Powered Two-Wheeler (PTW) riders constitute a very vulnerable group of road users, while riding a PTW is considerably more dangerous than using any other motor vehicle. Behavioral issues have been identified major moderating factors to PTW crashes, as riders display great variability in their attitudes towards road safety. Τhe aim of this paper is to present a thorough, overarching structure of relationships correlating various unsafe stated PTW rider behaviors (riding after alcohol consumption, speeding, helmet use and texting) with several self-reported attitude parameters and factors regarding rider perspectives on traffic rule strictness and social desirability. A structural equation model (SEM) was developed using data from the ESRA2 survey, which provided a broad sample encompassing 5,958 respondent riders from 32 countries. Numerous statistical relationships were discovered and quantified correlating the four examined unsafe rider behaviors with eight latent unobserved variables. All covariances between unsafe behaviors were found to be positive and statistically significant, indicating that a rider who will engage more frequently in every single one of the four examined unsafe riding behaviors is more likely to also engage in all the others as well.     

Analysing the influence of attitude and habit on bicycle commuting

New transport trends have emerged in developing countries to promote bicycling due to its individual and societal benefits. Although bicycle infrastructure provision has been one of the most utilised strategies to encourage bicycling, it has had a limited immediate impact on the bicycling modal share. Published research indicates the need to incorporate the psychosocial dimension to understand commuters' transport behaviour and explain why bicycling infrastructure usage is lower than expected. This study highlights the processes behind bicycle mode choice decisions, explicitly incorporating pro-bicycle attitudes and habits, while also considering the influence of socioeconomic, bicycle facilities and bicycling experience variables on shaping that attitude. For this purpose, an online survey was designed and sent to students, faculty members and staff of two Chilean universities to collect ad-hoc data. The modelling used an approach based on an integrated choice and latent variable model. The main findings are: attitude in itself is a relevant construct to explain behaviour; bicycling infrastructure contribute to explain bicycle choice but only indirectly through attitude; socioeconomic characteristics, bicycling familiarity and practical issues have both a direct effect on bicycle choice decision and an indirect one by fostering pro-bicycling attitudes. Additionally, results show that the explicit inclusion of habit reduces the contribution of pro-bicycle attitude in explaining bicycle choice. This might be due to interaction and reinforcement between habit and attitude.     

An adaptive network model covering metacognition to control adaptation for multiple mental models

Learning processes can be described by adaptive mental (or neural) network models. If metacognition is used to regulate learning, the adaptation of the mental network becomes itself adaptive as well: second-order adaptation. In this paper, a second-order adaptive mental network model is introduced for metacognitive regulation of learning processes. The focus is on the role of multiple internal mental models, in particular, the case of visualisation to support learning of numerical or symbolic skills. The second-order adaptive network model is illustrated by a case scenario for the role of visualisation to support learning multiplication at the primary school.     

Decision-making bioinspired model for target definition and “satisfactor” selection for physiological needs

Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, among others. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or trivial. However, making the correct decision(s) in a timely manner for these situations is one of the most complex and delicate challenges that human beings face. This is due to the arduous mental process required to be carried out. Providing such behavior to a virtual entity is possible through the use of Cognitive Architectures (CAs). CAs are an approach for modeling human intelligence and behavior. This paper presents an functional bioinspired computational decision-making model to satisfy the physiological needs of hunger and thirst. Our proposal considers as black boxes other cognitive functions that are part of a general CA (named Cuäyöllötl or brain in Nahuatl). In the proposed case study, it is proved that the decision-making process plays an essential role in determining the objective and selecting the object that satisfies the established need.     

Declarative working memory: A bio-inspired cognitive architecture proposal

Memory is considered one of the most important functions since it allows us to code, store and retrieve knowledge. These qualities make it an indispensable function for a virtual creature. In general, memory can be classified based on the durability of the stored data in working memory and long-term memory. Working memory refers to the capacity to maintain temporarily a limited amount of information in mind, which can then be used to support various abilities, including learning, reasoning, planning and decision-making. Unlike short-term memory, working memory is not only a storage site, but it is also a framework of interacting processes that involve the temporary storage and manipulation of information in the service of performing complex cognitive activities. Declarative memory is a type of long-term memory related with the storage of facts and events. This research focuses on the development of a cognitive architecture for the type of working memory that maintains and manipulates declarative information. The construction of the model was grounded in theoretical evidence taken from cognitive sciences such as neuroscience and psychology, which gave us the components and their processes. The model was evaluated through a case study that covers the encoding, storing, and retrieval stages. Our hypothesis is that a virtual creature endowed with our working memory model will provide faster access to the information needed for the ongoing task. Therefore, it improves the planning and decision-making processes.