Idiographic network analysis of discrete mood states prior to treatment

Idiographic network models based on time‐series data have received recent attention for their ability to model relationships among symptoms and behaviours as they unfold in time within a single individual (cf. Epskamp, Borsboom, & Fried, 2018; Fisher, Medaglia, & Jeronimus, 2018). Rather than examine the correlational relationships between variables in a sample of individuals, an idiographic network examines correlations within a single person, averaged over many time points. Because the approach averages over time, the data must be stationary (i.e. relatively consistent over time). If individuals experience varying states over time—different mixtures of symptoms and behaviours in one moment or another—then averaging over categorically different moments may undermine model accuracy. Fisher and Bosley (2019) address these concerns via the application of Gaussian finite mixture modelling to identify latent classes of time points in intraindividual time‐series data from a sample of adults with major depressive disorder and/or generalised anxiety disorder (n = 45). The present paper outlines an extension of this work, wherein network analysis is used to model within‐class covariation of symptoms. To illustrate this approach, network models were constructed for each intraindividual class identified by Fisher and Bosley (137 networks across the 45 participants, mean classes/person = ~3, range = 2–4 classes/person). We examine the relative consistency in symptom organisation between each individual's multiple mood state networks and assess emergent group‐level patterns. We highlight opportunities for enhanced treatment personalisation and review nomothetic patterns relevant to transdiagnostic conceptualisations of psychopathology. We address opportunities for integrating this approach into clinical practice and outline potential shortcomings.     

Dimensions of fatigue in different working populations

Perceived fatigue related to work has often been measured in one dimension. The main purpose of the present study was to validate a proposed five-factor model of perceived fatigue in a new sample. 597 persons, employed in five occupations with different types of work loads, rated their fatigue after work. The ratings were subjected to analyses of linear structural equation models. The results suggest a slightly revised model for perceived fatigue, still with the five dimensions: Lack of energy, Physical exertion, Physical discomfort, Lack of motivation and Sleepiness. As expected, the rating profiles describing fatigue states differed between the five occupations. On the basis of these results, a revised version of the Swedish Occupational Fatigue Inventory (SOFI) is presented.     

验证性因素分析:问卷题数及小样本应用策略

验证性因素分析在心理、教育等研究应用日广。本文引用三个模拟数据研究,提出一些在分析时的应用策略,包括用较多题目及强制同一因子的负荷相等。样本容量一般愈大愈好,但当资源等条件有限制时,上述两个技巧,可协助解决叠代收敛问题,并能更准确估计各重要参数。此外,一些方法如：每因子只得两题目、当样本只得约１００人时每因子用少於四题目、将题目合并以其平均数为指标,均可能导致不收敛、高标准误及偏差估计量等问题,不值得推荐。     

Independent race of colour and word can predict the Stroop effect

Mainstream theories of the Stroop effect suggest that faster colour classification on congruent trials (say, the word RED printed in red colour) relative to incongruent trials (GREEN in red) is due to channel interaction. Namely, information from the irrelevant word channel perturbs processing of the print colour, causing in turn slower processing of incongruent displays. In this note, I advance a new model in which colour and word are processed in parallel and completely independent channels. The Stroop effect is then the outcome of signal redundancy in congruent displays, where both colour and word contribute to the same response. Numerical computations show that the model can produce the Stroop effect (along with high accuracy rates) for a subset of parameter values. Thus, it provides a proof of existence for a separate channel theory, and a challenge to many existing theories.     

Manager–salesperson congruence in customer orientation and job outcomes: The bright and dark sides of leadership in aligning values

To create customer-oriented organizations, managers are often asked to promote a values-based vision. Yet, many managers struggle with transferring their values to employees making strategic value changes difficult. Despite this challenge, research has yet to demonstrate how managers effectively align values within the sales force, or the impact alignment has on job outcomes. Therefore, we develop and empirically test a conceptual framework to examine the role of transformational leadership in aligning salesperson customer orientation (CO) values. We find that transformational leadership is a strong mechanism in creating perceived value congruence, yet may have a surprising dark side. Results suggest that transformational managers achieve congruence by raising or, contrary to conventional wisdom, lowering salesperson CO values to meet the perceived values of the manager. Response surface modelling results support the importance of perceived manager values. Customer-oriented salespeople have higher job satisfaction and sales performance when they perceive their manager to also have high CO. When values are misaligned, job satisfaction increases more for low CO salespeople as perceptions of manager CO increase. Exploratory findings show that performance was higher under situations of perfect alignment but also under severe misalignment suggesting that values generate performance under complementary or supplementary conditions.     

Levels of analysis and sources of data in sales research: a multilevel-multisource review

Sales researchers are increasingly adopting a multilevel-multisource (MLMS) approach to answer many key questions involving sales managers, salespeople and customers. MLMS research involves the acquisition and analysis of data collected from two or more sources pertaining to multiple hierarchical levels and presents a number of opportunities and challenges for sales researchers to consider. The authors highlight the benefits and the drawbacks of MLMS research and describe various approaches for implementing an MLMS collection and analysis. Additionally, a review of the MLMS studies conducted in the sales domain is provided which summarizes and delineates relationships tested in the extant literature. Based on this review, the authors advance a number of underdeveloped areas of research where MLMS approaches can be applied to further the understanding of the dynamic conditions that typify sales research.     

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data).     

Heuristics versus direct calculation,and age-related differences in multiplication: an evidence accumulation account of plausibility decisions in arithmetic

We offer a new theoretical angle for cognitive arithmetic, which is that evidence accumulation may play a role in problem plausibility decisions. We build upon previous studies that have considered such a hypothesis, and here formally evaluate the paradigm. We develop the finding that performance differences, due to variations in strategy use and aging effects, can indeed be reasonably explained through these accumulation-to-bound cognitive models. Results suggest that these models may be effectively used to learn more about the underlying cognitive processes. In this study, we modelled young (18–24) and older (68–82) adults’ solution times in performing arithmetic verification (e.g. whether 8?×?5?=?41 is true/false). The domain-relevant factors in strategy use (problem-verification heuristics) and aging differences (older/younger adult groups) were analyzed by a response process model of the latency data, that is fit by participant and item. Lower thresholds accounted for the faster response times (RTs) for problems solved with heuristics (arithmetic rule-violation checking strategies), as opposed to problems solved by calculation approaches. A more rapid accumulation accounted for faster RTs on problems in which two arithmetic rules were violated (strategy combination) rather than one. Third, higher thresholds (i.e. preferring to have greater certainty before responding) accounted for older adults’ slower speed. These findings are in support of accumulation models being relevant for more complex cognitive tasks, as well as to account for the age-related differences therein.     

A comparison of positive vicarious learning and verbal information for reducing vicariously learned fear

Research with children has demonstrated that both positive vicarious learning (modelling) and positive verbal information can reduce children's acquired fear responses for a particular stimulus. However, this fear reduction appears to be more effective when the intervention pathway matches the initial fear learning pathway. That is, positive verbal information is a more effective intervention than positive modelling when fear is originally acquired via negative verbal information. Research has yet to explore whether fear reduction pathways are also important for fears acquired via vicarious learning. To test this, an experiment compared the effectiveness of positive verbal information and positive vicarious learning interventions for reducing vicariously acquired fears in children (7–9 years). Both vicarious and informational fear reduction interventions were found to be equally effective at reducing vicariously acquired fears, suggesting that acquisition and intervention pathways do not need to match for successful fear reduction. This has significant implications for parents and those working with children because it suggests that providing children with positive information or positive vicarious learning immediately after a negative modelling event may prevent more serious fears developing.     

The natural mathematics of behavior analysis

Models that generate event records have very general scope regarding the dimensions of the target behavior that we measure. From a set of predicted event records, we can generate predictions for any dependent variable that we could compute from the event records of our subjects. In this sense, models that generate event records permit us a freely multivariate analysis. To explore this proposition, we conducted a multivariate examination of Catania's Operant Reserve on single VI schedules in transition using a Markov Chain Monte Carlo scheme for Approximate Bayesian Computation. Although we found systematic deviations between our implementation of Catania's Operant Reserve and our observed data (e.g., mismatches in the shape of the interresponse time distributions), the general approach that we have demonstrated represents an avenue for modelling behavior that transcends the typical constraints of algebraic models.