Direct and indirect effects of mood on risk decision making in safety-critical workers

The study aimed to examine the direct influence of specific moods (fatigue, anxiety, happiness) on risk in safety-critical decision making. It further aimed to explore indirect effects, specifically, the potential mediating effects of information processing assessed using a goodness-of-simulation task. Trait fatigue and anxiety were associated with an increase in risk taking on the Safety-Critical Personal Risk Inventory (S-CPRI), however the effect of fatigue was partialled out by anxiety. Trait happiness, in contrast was related to less risky decision making. Findings concerning the ability to simulate suggest that better simulators made less risky decisions. Anxious workers were generally less able to simulate. It is suggested that in this safety-critical environment happiness had a direct effect on risk decision making while the effect of trait anxiety was mediated by goodness-of-simulation

Influence of Context on Item Parameters in Forced-Choice Personality Assessments

A fundamental assumption in computerized adaptive testing (CAT) is that item parameters are invariant with respect to context – items surrounding the administered item. This assumption, however, may not hold in forced-choice (FC) assessments, where explicit comparisons are made between items included in the same block. We empirically examined the influence of context on item parameters by comparing parameter estimates from two FC instruments. The first instrument was compiled of blocks of three items, whereas in the second, the context was manipulated by adding one item to each block, resulting in blocks of four. The item parameter estimates were highly similar. However, a small number of significant deviations were observed, confirming the importance of context when designing adaptive FC assessments. Two patterns of such deviations were identified, and methods to reduce their occurrences in a FC CAT setting were proposed. It was shown that with a small proportion of violations of the parameter invariance assumption, score estimation remained stable

NPCs as People, Too: The Extreme AI Personality Engine

PK Dick once asked “Do Androids Dream of Electric Sheep?” In video games, a similar question could be asked of non-player characters: Do NPCs have dreams? Can they live and change as humans do? Can NPCs have personalities, and can these develop through interactions with players, other NPCs, and the world around them? Despite advances in personality AI for games, most NPCs are still undeveloped and undeveloping, reacting with flat affect and predictable routines that make them far less than human— in fact, they become little more than bits of the scenery that give out parcels of information. This need not be the case. Extreme AI, a psychology-based personality engine, creates adaptive NPC personalities. Originally developed as part of the thesis “NPCs as People: Using Databases and Behaviour Trees to Give Non-Player Characters Personality,” Extreme AI is now a fully functioning personality engine using all thirty facets of the Five Factor model of personality and an AI system that is live throughout gameplay. This paper discusses the research leading to Extreme AI; develops the ideas found in that thesis; discusses the development of other personality engines; and provides examples of Extreme AI’s use in two game demos

EEG Fractal Dimension Measurement before and after Human Auditory Stimulation

The aim of this work is to investigate the change of fractal dimension Df with the help of Higuchi Fractal Dimension measure (HFD) in Event-Related Potentials (ERP) of human EEG time series, obtained as a result of oddball paradigm usage and auditory stimulation with instruction for passive listening and counting tasks, depending on gender, personality type and task condition. In our study 77 healthy volunteers have been participated and 38 of them have been selected after a personality classification with Eysenck’s personality questionnaire (EPQ).The achieved results showed specific functional meaning of ERP HFD change depending on the individual personality type and gender

Generalized Network Psychometrics: Combining Network and Latent Variable Models

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of Structural Equation Modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework Latent Network Modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance-covariance structure of indicators is modeled as a network. We term this generalization Residual Network Modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package lvnet, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms performs adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.Comment: Published in Psychometrik

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

The networked seceder model: Group formation in social and economic systems

The seceder model illustrates how the desire to be different than the average can lead to formation of groups in a population. We turn the original, agent based, seceder model into a model of network evolution. We find that the structural characteristics our model closely matches empirical social networks. Statistics for the dynamics of group formation are also given. Extensions of the model to networks of companies are also discussed

“Some like it hot”:spectators who score high on the personality trait openness enjoy the excitement of hearing dancers breathing without music

Music is an integral part of dance. Over the last 10 years, however, dance stimuli (without music) have been repeatedly used to study action observation processes, increasing our understanding of the influence of observer’s physical abilities on action perception. Moreover, beyond trained skills and empathy traits, very little has been investigated on how other observer or spectators’ properties modulate action observation and action preference. Since strong correlations have been shown between music and personality traits, here we aim to investigate how personality traits shape the appreciation of dance when this is presented with three different music/sounds. Therefore, we investigated the relationship between personality traits and the subjective esthetic experience of 52 spectators watching a 24 min lasting contemporary dance performance projected on a big screen containing three movement phrases performed to three different sound scores: classical music (i.e., Bach), an electronic sound-score, and a section without music but where the breathing of the performers was audible. We found that first, spectators rated the experience of watching dance without music significantly different from with music. Second, we found that the higher spectators scored on the Big Five personality factor openness, the more they liked the no-music section. Third, spectators’ physical experience with dance was not linked to their appreciation but was significantly related to high average extravert scores. For the first time, we showed that spectators’ reported entrainment to watching dance movements without music is strongly related to their personality and thus may need to be considered when using dance as a means to investigate action observation processes and esthetic preferences