The relationship between mental toughness and subjective mental illness recovery

Recently there has been an increasing interest in subjective mental illness recovery, and it has been recognised that there is an important role for characteristics such as self-efficacy and locus of control. Mental toughness describes a set of characteristics important for dealing with stress and pressure, and is comprised of challenge, commitment, control of emotion, control of life, confidence in abilities, and interpersonal confidence. The current study explored relationships between mental toughness and subjective mental illness recovery. Seventy-seven participants (nineteen males and fifty-eight females, the majority of which were young adults aged 18–21 years) who had previously recovered from, or were currently recovering from a mental illness (self-reported) completed questionnaire measures of mental toughness and subjective mental illness recovery. Mental toughness had a positive significant relationship to recovery, with particular roles for commitment and confidence in abilities. The findings are discussed in terms of implications for recovery-oriented practice, intervention, and measurement. Further research should examine the role of mental toughness in different aspects of subjective recovery, and also explore relationships with objective measures of recovery.     

汉语阅读中词切分研究进展

词切分指读者在文本阅读过程中将词从句子或语篇中切分出来以促进文本阅读与加工的过程。本文对近20 年来汉语词切分研究成果总结发现:词间空格、词素特性、词意识、词汇预期均可作为词切分的依据; 汉语阅读存在词n和词n+1两种切分,两种切分过程中字词加工均遵循交互激活假设,切分词n过程中字词加工的交互激活特点表现为字词同等相互影响,切分词n+1过程中字词加工的交互激活特点更多表现为词汇加工影响字加工; 重叠歧义词切分遵循多重激活假设,其中被激活的单词之间的竞争机制符合竞争假设,左侧词汇在这一竞争中具有优势,此外,重叠歧义词的切分受到词频,语境的影响。     

Visual perspective as a two-dimensional construct in episodic future thought

Visual perspective (first-person vs. third-person) is a salient characteristic of memory and mental imagery with important cognitive and behavioural consequences. Most work on visual perspective treats it as a unidimensional construct. However, third-person perspective can have opposite effects on emotion and motivation, sometimes intensifying these and other times acting as a distancing mechanism, as in PTSD. For this reason among others, we propose that visual perspective in memory and mental imagery is best understood as varying along two dimensions: first, the degree to which first-person perspective predominates in the episodic imagery, and second, the degree to which the self is visually salient from a third-person perspective. We show that, in episodic future thinking, these are anticorrelated but non-redundant. These results further our basic understanding of the potent but divergent effects visual perspective has on emotion and motivation, both in everyday life and in psychiatric conditions.     

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.     

Exploration of the effects of task-related fatigue on eye-motion features and its value in improving driver fatigue-related technology

Mental fatigue has been lacked attention in developing eye-tracking fatigue detection system for drivers. However, it has great influence on eye movement which could account for the poor validity of current fatigue detectors only focusing on sleep-related fatigue. This work sought to investigate the influence of two types of task-related mental fatigue on eye movement by examining 8 saccade-based, 3 blink-based, and 1 pupil-based metrics. We propose that two types of task-related fatigue caused by cognitive overload and prolonged underload will induce different physiological responses to eye-motion features. Twenty participants completed a vigilance task before and after a 1-h driving with a secondary task in a virtual simulation environment, while forty participants, divided equally into two groups, finished the same task before and after a 1-h and 1.5-h monotonous driving. T-test was applied to analyse the eye-motion, subjective and vigilance data during vigilance task. We found that overload driving made drivers vigilance ability decrease. The eye metrics showed different changes in underload and overload scenario. The blink duration, the mean velocity of saccade and saccade duration increased after 1-h overload driving, while the pupil diameter decreased. However, none of those changes were observed in 1.5-h underload driving, but saccade duration had a significant increase. The fatigue response to heavy demands over short periods of driving is different from the lighter demands over long periods in terms of eye-motion metrics. Considering mental fatigue in designing an eye-tracking fatigue detection system could possibly improve its accuracy.     

Trust and intention to use autonomous vehicles: Manufacturer focus and passenger control

Autonomous Vehicle (AV) research has focused on public acceptance of and intention to use AVs, with trust emerging as important to these variables. Research on AV trust has centered on trust in vehicle performance. However, trust evaluation may include AV manufacturers and developers, and regulation pertaining to AVs; thus, we expand our measurement of trust to include beliefs based on manufacturers and regulation. In this experiment, we manipulate information regarding manufacturer focus (i.e., an emphasis on standards, regulations, and research (SRR) versus speed to market) to determine its effect on trust. When information focused on SRR, we found higher levels of trust in AV performance and in manufacturers, compared to when it focused on speed to market. In addition, information regarding passenger control (i.e., the ability to take over for the vehicle and to determine privacy settings) was manipulated to yield either high or low passenger control conditions. Behavioral Intention (BI) to use AVs was lowest when speed to market was emphasized and when passenger control was low. Furthermore, trust was tested as a mediator between perceived risk and BI. Trust in AV performance partially mediated the relationship between perceived AV performance risk and BI. In addition, trust in AV regulation partially mediated the relationship between AV privacy/security risk and BI. Researchers should continue to examine trust beyond the AV itself to encompass trust in manufacturers and regulations. Those designing and marketing AVs should carefully consider decisions that influence manufacturer/developer reputation and passenger control due to their effect on intention to use AVs.     

Evaluating driver comprehension of the roadway environment to retain accountability of safety during driving automation

Driver comprehension is a substantial component of situation awareness that involves the ability of an individual to understand the significance of an object, traffic sign, or hazard while driving. An increase in crashes related to autonomous driving systems has raised a concern regarding the safety of other roadway users due to the diminishing accountability resulting from a general lack of understanding of the limitations or disregard of the safety protocols by users. To keep drivers vigilant when engaged in partial automated systems, a methodology to monitor real-time driver comprehension was proposed. A driving simulator study consisting of 90 participants, equally split between males and females, was executed to establish driver comprehension in six different variations of driving difficulty. Joint probability density functions were created by considering percent time spent gazing, answers to probe questions, and driving performance. Based on these density functions, five levels of comprehension were devised and assigned thresholds. Overall, as task difficulty increased, a non-linear deterioration in driving speed along with an increase in total gaze duration was observed before comprehension was attained. A two-step validation protocol was also proposed to ensure similar levels of comprehension to non-automated driving from the human driver, when engaged in early forms of automation. The proposed real-time driver comprehension monitoring constitutes a first step toward developing a methodology to reinstate the accountability of safety of other roadway users when engaged in driver-in-the-loop automation systems.     

An expanded model for perceptual visual single object recognition system using expectation priming following neuroscientific evidence

Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems have not yet surpassed human vision, especially in its universality of application. We argue that modeling the recognition process in an exclusive feedforward manner hinders those systems’ performance. To bridge that performance gap between them and human vision, we present a brief review of neuroscientific data, which suggests that considering an agent’s internal influences (from cognitive systems that peripherally interact with visual-perceptual processes) recognition can be improved. Then, we propose a model for visual object recognition which uses these systems’ information, such as affection, for generating expectation to prime the object recognition system, thus reducing its execution times. Later, an implementation of the model is described. Finally, we present and discuss an experiment and its results.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.