An Approach for Determining the Mix between Human and Automated Service Providers

With the growing popularity of self‐service technologies (SST), businesses have to determine how to combine traditional human service with SST technologies. These mix problems have not received adequate attention in the research literature. This paper offers a decision model for solving mix problems between human service channels and SSTs. The decision problem cannot be determined as a traditional max or min problem and it is fuzzy. In fuzzy problems, the decision maker (DM) is considered as the focal point with the relevant knowledge and preferences and therefore, the objective is to maximize the DM's preferences. This paper develops a decision support system for eliciting a DM's value function when the service channels are considered to be substitutes, but when the rate of substitution may change and then uses a linear‐fractional model to fit the DM's data and optimize the DM's preferences. A service mix problem for a grocery store with human service providers and SSTs is used to demonstrate how the decision model can be employed to analyze and solve service mix decision making problems. Copyright © 2014 John Wiley & Sons, Ltd.     

Intolerance of Uncertainty and Perfectionistic Beliefs About Parenting as Cognitive Mechanisms of Symptom Change During Cognitive Behavior Therapy for Perinatal Anxiety

ObjectiveA recent randomized controlled trial of group cognitive behavior therapy (CBGT) for perinatal anxiety showed that CBGT is effective in reducing anxiety and depression in pregnant and postpartum women. In secondary analyses, the role of potential mechanisms of symptom change was examined, including intolerance of uncertainty (IU), self-oriented parenting perfectionism (SOPP) and societal-prescribed parenting perfectionism (SPPP).MethodThe sample included 75 women (M_age = 31.99, SD = 3.57; 37.3% pregnant, 62.7% postpartum) who sought treatment for anxiety and completed the 6-week CBGT or 6-week waitlist within the larger trial. Measures of anxiety (State-Trait Inventory for Cognitive and Somatic Anxiety; STICSA), depression (Edinburgh Postnatal Depression Scale; EPDS), and the proposed mediators (IU, SOPP, SPPP) were completed at baseline and 6-weeks post-baseline.ResultsTwo moderated mediation models were evaluated to identify potential mediators of the effect of condition (CBGT, waitlist) on anxiety (STICSA; Model 1) or depressive symptoms (EPDS; Model 2). In Model 1, changes in IU partially mediated the effect of condition on anxiety (STICSA) for both pregnant and postpartum women. Changes in SOPP and SPPP were partial mediators for postpartum women only. Change in depression (EPDS) was also a partial mediator for pregnant women in this model. In Model 2, none of the cognitive variables mediated the effect of condition on depressive symptoms (EPDS). However, change in anxiety (STICSA) was a significant mediator of the effect of condition on depression (EPDS) and only among pregnant women.ConclusionsThe results provide support for IU, SOPP and SPPP as mechanisms of change during CBGT and identify differences in important mechanisms among pregnant and postpartum women.     

Delivered as Described: A Successful Case of Cognitive Processing Therapy With an Older Woman Veteran With PTSD

Cognitive processing therapy (CPT) is a first-line, evidence-based treatment for posttraumatic stress disorder (PTSD). Little is known, however, about the use of CPT for older adults. As the United States population continues to grow and age, an understanding of the utility of CPT for older adults is vital. We present a case study describing the assessment and cognitive treatment of a 74-year-old woman veteran with PTSD secondary to military sexual trauma. CPT was associated with decreased PTSD symptoms as measured before and after treatment. Factors contributing to the veteran’s response, as well as contextual and environmental factors, are discussed. The case demonstrates that CPT may be effective for older adults without major modification.     

How is the visual working memory load of driver influenced by information density of traffic signs?

The rapid development of urban roads has resulted in an increase in the amount of information that needs to be displayed on traffic signs. The overloading of information on traffic signs may increase drivers’ visual cognition burden and cause driving errors. It could be an important factor affecting driving safety and result in accidents. To enable drivers to cognize traffic signs, the information density that they could endure under reasonable workload levels should be determined. In this study, we designed and implemented trials to analyze drivers’ visual working memory load (VWML) caused by different information densities (including stimulus intensity (SI) and interval time of stimulus (IS)) of traffic signs. Twenty-four paid participants (12 females) with a mean age of 23.38 years (SD = 1.38) participated in laboratory simulation trials based on the memory retrieval approach. Subjective ratings (rating scale mental effort (RSME)), and performance measurement based on reaction time and accuracy of information-matching task were measured to reflect drivers’ VWML at different information densities. The results show that information density is an important factor affecting drivers’ VWML. The following are the significant observations of the study: (1) The subjective ratings on mental effort increased with increase in the SI. Meanwhile, it first decreased and then increased with increase in the IS. (2) The response accuracy results show that a high accuracy (≥0.7) was achieved by drivers in an IS of 15 s when the SI was at most 14.16 bit/m². Correspondingly, the drivers underwent less mental workload as a result of their better short-term memory characteristic. (3) The mean reaction time was over 1800 ms when the SI was over 14.16 bit/m². This indicated a reduction in the information-processing capability of the drivers. The results indicate that cognition of information of unreasonable density on traffic signs would increase drivers’ VWML. This would, in turn, result in an increase in their reaction time and decrease in accuracy. The threshold of information density was also determined through the experiment. It would satisfy the information demand of drivers and prevent information redundancy. The results contribute to an understanding of drivers’ VWML while cognizing traffic signs with different information densities and provide safety, effectiveness, and information continuity considerations for the design and setting of traffic signs.     

Bio-inspired cognitive architecture of episodic memory

Memory and learning are essential functions in human beings as they allow us to acquire and store in the brain representations of thoughts, experiences, and behaviors, which are required for problem-solving in our daily life and mainly for survival. Episodic memory is a type of memory that provides the ability to re-experience events in one’s life, and it is associated with their conscious recollection. Since episodic memory can represent our experiences about the environment, similar to a mental journey, it is desired in systems that attempt to create human-like behavior. Currently, the main problem is that state of the art proposals do not consider neuroscientific evidence like memory dynamics for forgetting or bottom-up inputs, and most of them do not consider episodic memory as a different memory but as part of general declarative memory. We consider these omissions to limit the generation of human-like behavior. In this work, we propose a bio-inspired cognitive architecture of episodic memory. Neuroscientific evidence provides us with the brain structures associated with this type of memory, the connections, and the operations these areas perform. We hypothesize that virtual entities endowed with our episodic memory cognitive architecture will plan and make decisions in a more human-like fashion. To test the capabilities of the proposal, we endowed a virtual creature with a distributed and concurrent implementation of our architecture, and it was given two tasks. The first task validated the functions of the memory independently, and in the second task, the creature used episodic memory to solve a planning problem. From the results of these experiments, we validate our proposal and show that it is possible to create a system that behaves as the human brain does.     

Where to from here? On the future development of autonomous vehicles from a cognitive systems perspective

Self-driving cars not only solve the problem of navigating safely from location A to location B; they also have to deal with an abundance of (sometimes unpredictable) factors, such as traffic rules, weather conditions, and interactions with humans. Over the last decades, different approaches have been proposed to design intelligent driving systems for self-driving cars that can deal with an uncontrolled environment. Some of them are derived from computationalist paradigms, formulating mathematical models that define the driving agent, while other approaches take inspiration from biological cognition. However, despite the extensive work in the field of self-driving cars, many open questions remain. Here, we discuss the different approaches for implementing driving systems for self-driving cars, as well as the computational paradigms from which they originate. In doing so, we highlight two key messages: First, further progress in the field might depend on adapting new paradigms as opposed to pushing technical innovations in those currently used. Specifically, we discuss how paradigms from cognitive systems research can be a source of inspiration for further development in modelling driving systems, highlighting emergent approaches as a possible starting point. Second, self-driving cars can themselves be considered cognitive systems in a meaningful sense, and are therefore a relevant, yet underutilized resource in the study of cognitive mechanisms. Overall, we argue for a stronger synergy between the fields of cognitive systems and self-driving vehicles.     

Bio-inspired computational object classification model for object recognition

Human beings can effortlessly perceive stimuli through their sensory systems to learn, understand, recognize and act on our environment or context. Over the years, efforts have been made to enable cybernetic entities to be close to performing human perception tasks; and in general, to bring artificial intelligence closer to human intelligence.Neuroscience and other cognitive sciences provide evidence and explanations of the functioning of certain aspects of visual perception in the human brain. Visual perception is a complex process, and its has been divided into several parts. Object classification is one of those parts; it is necessary for carrying out the declarative interpretation of the environment. This article deals with the object classification problem.In this article, we propose a computational model of visual classification of objects based on neuroscience, it consists of two modular systems: a visual processing system, in charge of the extraction of characteristics; and a perception sub-system, which performs the classification of objects based on the features extracted by the visual processing system.With the results obtained, a set of aspects are analyzed using similarity and dissimilarity matrices. Also based on the neuroscientific evidence and the results obtained from this research, some aspects are suggested for consideration to improve the work in the future and bring us closer to performing the task of visual classification as humans do.     

The mediational role of executive functions for the relationship between motor ability and academic performance in pediatric cancer survivors

IntroductionPediatric Cancer Survivors (PCS) bear a high risk for late effects within motor abilities (MAs) and executive functions (EFs). In typically developing children, these domains are interrelated and predictors of academic performance. The current study investigated (i) whether MAs and EFs are also interrelated in PCS, and (ii) whether EFs mediate the relation between MAs and academic performance.Methods78 PCS (7–16 years; M = 11.23; SD = 2.49) participated in this study. Three MAs were assessed: coordination and strength (using the German Motor Test) and endurance (using a cycle ergometer test). EFs were assessed: inhibition and cognitive flexibility (Color-Word Interference Test) and working memory (Block Recall test); and academic performance by questionnaire asking for children’s grade point average.ResultsPearson correlations revealed associations of coordination and strength with EFs and associations of all three MAs with academic performance. A multiple regression model revealed that among the three MAs, coordination was the only significant predictor of EFs (β = 0.42, p = .001). Lastly, mediation analyses revealed that the association of MAs with academic performance was mediated by EFs (indirect effect: β = 0.167, p = .003). Regarding individual motor abilities, this was only true for coordination and strength, but not for endurance.ConclusionResults show that MAs and EFs are interrelated in PCS and that EFs mediate the relationship between coordination and strength with academic performance. This may be important for the design of future physical activity interventions to improve MAs, EFs and academic performance.     

The role of mental rotation in TetrisTM gameplay: An ACT-R computational cognitive model

The mental rotation ability is an essential spatial reasoning skill in human cognition and has proven to be an essential predictor of mathematical and STEM skills, critical and computational thinking. Despite its importance, little is known about when and how mental rotation processes are activated in games explicitly targeting spatial reasoning tasks. In particular, the relationship between spatial abilities and Tetris^TM has been analysed several times in the literature. However, these analyses have shown contrasting results between the effectiveness of Tetris-based training activities to improve mental rotation skills. In this work, we studied whether, and under what conditions, such ability is used in the Tetris^TM game by explicitly modelling mental rotation via an ACT-R based cognitive model controlling a virtual agent. The obtained results show meaningful insights into the activation of mental rotation during game dynamics. The study suggests the necessity to adapt game dynamics in order to force the activation of this process and, therefore, can be of inspiration to design learning activities based on Tetris^TM or re-design the game itself to improve its educational effectiveness.     

An integrated model of eye movements and visual encoding

Recent computational models of cognition have made good progress in accounting for the visual processes needed to encode external stimuli. However, these models typically incorporate simplified models of visual processing that assume a constant encoding time for all visual objects and do not distinguish between eye movements and shifts of attention. This paper presents a domain-independent computational model, EMMA, that provides a more rigorous account of eye movements and visual encoding and their interaction with a cognitive processor. The visual-encoding component of the model describes the effects of frequency and foveal eccentricity when encoding visual objects as internal representations. The eye-movement component describes the temporal and spatial characteristics of eye movements as they arise from shifts of visual attention. When integrated with a cognitive model, EMMA generates quantitative predictions concerning when and where the eyes move, thus serving to relate higher-level cognitive processes and attention shifts with lower-level eye-movement behavior. The paper evaluates EMMA in three illustrative domains — equation solving, reading, and visual search — and demonstrates how the model accounts for aspects of behavior that simpler models of cognitive and visual processing fail to explain.