Use of functional near-infrared spectroscopy to evaluate cognitive change when using healthcare simulation tools

This is an accepted manuscript of an article published by BMJ on 01/11/2020, available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8936993/ The accepted version of the publication may differ from the final published version.Background The use of brain imaging techniques in healthcare simulation is relatively rare. However, the use of mobile, wireless technique, such as functional nearinfrared spectroscopy (fNIRS), is becoming a useful tool for assessing the unique demands of simulation learning. For this study, this imaging technique was used to evaluate cognitive load during simulation learning events. Methods This study took place in relation to six simulation activities, paired for similarity, and evaluated comparative cognitive change between the three task pairs. The three paired tasks were: receiving a (1) face-toface and (2) video patient handover; observing a simulated scene in (1) two dimensions and (2) 360° field of vision; and on a simulated patient (1) taking a pulse and (2) taking a pulse and respiratory rate simultaneously. The total number of participants was n=12. Results In this study, fNIRS was sensitive to variations in task difficulty in common simulation tools and scenarios, showing an increase in oxygenated haemoglobin concentration and a decrease in deoxygenated haemoglobin concentration, as tasks increased in cognitive load. Conclusion Overall, findings confirmed the usefulness of neurohaemoglobin concentration markers as an evaluation tool of cognitive change in healthcare simulation. Study findings suggested that cognitive load increases in more complex cognitive tasks in simulation learning events. Task performance that increased in complexity therefore affected cognitive markers, with increase in mental effort required

Socially-distributed cognition and cognitive architectures: towards an ACT-R-based cognitive social simulation capability

ACT-R is one of the most widely used cognitive architectures, and it has been used to model hundreds of phenomena described in the cognitive psychology literature. In spite of this, there are relatively few studies that have attempted to apply ACT-R to situations involving social interaction. This is an important omission since the social aspects of cognition have been a growing area of interest in the cognitive science community, and an understanding of the dynamics of collective cognition is of particular importance in many organizational settings. In order to support the computational modeling and simulation of socially-distributed cognitive processes, a simulation capability based on the ACT-R architecture is described. This capability features a number of extensions to the core ACT-R architecture that are intended to support social interaction and collaborative problem solving. The core features of a number of supporting applications and services are also described. These applications/services support the execution, monitoring and analysis of simulation experiments. Finally, a system designed to record human behavioral data in a collective problem-solving task is described. This system is being used to undertake a range of experiments with teams of human subjects, and it will ultimately support the development of high fidelity ACT-R cognitive models. Such models can be used in conjunction with the ACT-R simulation capability to test hypotheses concerning the interaction between cognitive, social and technological factors in tasks involving socially-distributed information processing

Simulation of complex environments:the Fuzzy Cognitive Agent

The world is becoming increasingly competitive by the action of liberalised national and global markets. In parallel these markets have become increasingly complex making it difficult for participants to optimise their trading actions. In response, many differing computer simulation techniques have been investigated to develop either a deeper understanding of these evolving markets or to create effective system support tools. In this paper we report our efforts to develop a novel simulation platform using fuzzy cognitive agents (FCA). Our approach encapsulates fuzzy cognitive maps (FCM) generated on the Matlab Simulink platform within commercially available agent software. We firstly present our implementation of Matlab Simulink FCMs and then show how such FCMs can be integrated within a conceptual FCA architecture. Finally we report on our efforts to realise an FCA by the integration of a Matlab Simulink based FCM with the Jack Intelligent Agent Toolkit

Cognitive Conflict Strategy and Simulation Practicum to Overcome Student Misconception on Light Topics

One way to reduce misconceptions can be overcome by cognitive conflict learning strategies with the help of simulation practicum instead of actual practicum. This study aims to determine whether there are differences in students' misconceptions before and after learning with cognitive conflict strategies as an effort to reduce misconceptions on light material. Research sample of 31 students. Data on the degree of misconception before the study was 0, 36 and after doing research was 0.17. The t-paired test results for the mean percentage of students' misconceptions on light material before and after learning differed at a significance level of 0.05. While, the results of N-Gain calculations to student achievement increase in overcoming misconceptions on light material were 0.3, that means the average students' achievement in dealing with misconceptions are in the medium category and cognitive conflict strategies combined with simulation practicum have a strong effect on reducing students' misconceptions on light material with a range of 2.91. Based on the results of the study it can be concluded that cognitive conflict strategies combined with simulation practicum can be used to reduce misconceptions that lead to increased student learning achievement. Further research is needed to explore students' misconceptions on other physics topics and can measure student misconceptions at each meeting so that students are more organized and developed in learning

Mindreading in individuals with an empathizing versus systemizing cognitive style An fMRI study

Our fMRI study compares the neural correlates of face-based mindreading in healthy individuals with an empathizing (n=12) versus systemizing cognitive style (n=12). The empathizing group consists of individuals that score high on empathizing and low on systemizing, while the systemizing group consists of individuals with an opposite cognitive pattern. We hypothesize that the empathizing group will show stronger simulation-type neural activity (e.g., in mirror neuron areas, medial prefrontal cortex, anterior cingulate cortex) or simulation-related neural activity (e.g., in areas involved in perspective taking and experiential processing) compared to the systemizing group. As hypothesized, our study reveals that the empathizing group shows significantly stronger activity in mirror neuron areas of the brain, such as the left inferior frontal gyrus and inferior parietal lobe, and in temporal areas involved in perspective taking and autobiographical memory. Moreover, the empathizing group, but not the systemizing group, shows activity in the medial prefrontal cortex and anterior cingulate cortex which have been related to simulation-type neural activity in the brain and are central to mindreading. Also, the systemizing group shows significantly stronger activity in the left parahippocampal gyrus. In conclusion, both the empathizing and systemizing individuals show simulation-type and simulation-related neural activity during face-based mindreading. However, more neural activity indicative of simulation-based processing is seen in the empathizing individuals, while more neural activity indicative of non-simulation-based processing is seen in the systemizing individuals

Aiming for Cognitive Equivalence – Mental Models as a Tertium Comparationis for Translation and Empirical Semantics

This paper introduces my concept of cognitive equivalence (cf. Mandelblit, 1997), an attempt to reconcile elements of Nida’s dynamic equivalence with recent innovations in cognitive linguistics and cognitive psychology, and building on the current focus on translators’ mental processes in translation studies (see e.g. Göpferich et al., 2009, Lewandowska-Tomaszczyk, 2010; Halverson, 2014). My approach shares its general impetus with Lewandowska-Tomaszczyk’s concept of re-conceptualization, but is independently derived from findings in cognitive linguistics and simulation theory (see e.g. Langacker, 2008; Feldman, 2006; Barsalou, 1999; Zwaan, 2004). Against this background, I propose a model of translation processing focused on the internal simulation of reader reception and the calibration of these simulations to achieve similarity between ST and TT impact. The concept of cognitive equivalence is exemplarily tested by exploring a conceptual / lexical field (MALE BALDNESS) through the way that English, German and Japanese lexical items in this field are linked to matching visual-conceptual representations by native speaker informants. The visual data gathered via this empirical method can be used to effectively triangulate the linguistic items involved, enabling an extra-linguistic comparison across languages. Results show that there is a reassuring level of interinformant agreement within languages, but that the conceptual domain for BALDNESS is linguistically structured in systematically different ways across languages. The findings are interpreted as strengthening the call for a cognition-focused, embodied approach to translation

Rules and principles in cognitive diagnoses

Cognitive simulation is concerned with constructing process models of human cognitive behavior. Our work on the ACM system (Automated Cognitive Modeler) is an attempt to automate this process. The basic assumption is that all goal-oriented cognitive behavior involves search through some problem space. Within this framework, the task of cognitive diagnosis is to identify the problem space in which the subject is operating, identify solution paths used by the subject, and find conditions on the operators that explain those solution paths and that predict the subject's behavior on new problems. The work presented in this paper uses techniques from machine learning to automate the tasks of finding solution paths and operator conditions. We apply this method to the domain of multi-column subtraction and present results that demonstrate ACM's ability to model incorrect subtraction strategies. Finally, we discuss the difference between procedural bugs and misconceptions, proposing that errors due to misconceptions can be viewed as violations of principles for the task domain

Active Learning: Effects of Core Training Design Elements on Self-Regulatory Processes, Learning, and Adaptability

This research describes a comprehensive examination of the cognitive, motivational, and emotional processes underlying active learning approaches, their effects on learning and transfer, and the core training design elements (exploration, training frame, emotion-control) and individual differences (cognitive ability, trait goal orientation, trait anxiety) that shape these processes. Participants (N = 350) were trained to operate a complex computer-based simulation. Exploratory learning and error-encouragement framing had a positive effect on adaptive transfer performance and interacted with cognitive ability and dispositional goal orientation to influence trainees’ metacognition and state goal orientation. Trainees who received the emotion-control strategy had lower levels of state anxiety. Implications for developing an integrated theory of active learning, learner-centered design, and research extensions are discussed

TACOP: A Cognitive Agent for a Naval Training Simulation Environment

The full version of this paper appeared in: Doesburg, W. A. van, Heuvelink, A., and Broek, E. L. van den (2005). TACOP: A cognitive agent for a naval training simulation environment. In M. Pechoucek, D. Steiner, and S. Thompson (Eds.), Proceedings of the Industry Track of the Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-05), p.34-41. July 25-29, Utrecht, The Netherlands