Complex Communication Dynamics: Exploring the Structure of an Academic Talk

Communication is a multimodal phenomenon. The cognitive mechanisms supporting it are still understudied. We explored a natural dataset of academic lectures to determine how communication modalities are used and coordinated during the presentation of complex information. Using automated and semi‐automated techniques, we extracted and analyzed, from the videos of 30 speakers, measures capturing the dynamics of their body movement, their slide change rate, and various aspects of their speech (speech rate, articulation rate, fundamental frequency, and intensity). There were consistent but statistically subtle patterns in the use of speech rate, articulation rate, intensity, and body motion across the presentation. Principal component analysis also revealed patterns of system‐like covariation among modalities. These findings, although tentative, do suggest that the cognitive system is integrating body, slides, and speech in a coordinated manner during natural language use. Further research is needed to clarify the specific coordination patterns that occur between the different modalities.     

与立体视觉相关的视诱发电位研究

在微机上编制能产生动态随机点立体图（ＤＲＤＳ）,亮暗类棋盘格和随机点背景三种图形刺激的软件．记录了３４例正常人在这三种刺激下的ＶＢＰ．其结果为：（１）ＤＲＤＳ刺激下的ＶＥＰＮ１波潜伏期为２６５±２５ｍｓ,基波能量占总交流能量的６３．２±１８．９％．（２）亮暗类棋盘格刺激下的ＶＥＰＮ１波的潜伏期为１９０±２２ｍｓ,基波能量占总交流能量的３９．２±１９．６％．（３）随机点背景刺激下的ＶＥＰ波则为一些杂乱的小波．结果提示：动态ＲＤＳ刺激下的ＶＥＰ除在潜伏期方面具有特异性外,并在谐波的能量分布上也具有特异性,且客观性更强．根据这些特征有助于对立体视觉功能进行客观性评价．     

Dynamics Versus Development in Numerosity Estimation: A Computational Model Accurately Predicts a Developmental Reversal

Perceptual judgments result from a dynamic process, but little is known about the dynamics of number-line estimation. A recent study proposed a computational model that combined a model of trial-to-trial changes with a model for the internal scaling of discrete numbers. Here, we tested a surprising prediction of the model—a situation in which children's estimates of numerosity would be better than those of adults. Consistent with the model simulations, task contexts led to a clear developmental reversal: children made more adult-like, linear estimates when to-be-estimated numbers were descending over trials (i.e., backward condition), whereas adults became more like children with logarithmic estimates when numbers were ascending (i.e., forward condition). In addition, adults’ estimates were subject to inter-trial differences regardless of stimulus order. In contrast, children were not able to use the trial-to-trial dynamics unless stimuli varied systematically, indicating the limited cognitive capacity for dynamic updates. Together, the model adequately predicts both developmental and trial-to-trial changes in number-line tasks.     

Dynamic multi-criteria decision making

Most medium-and long-term decision making in industry and government can be viewed as dynamic multi-criteria decision making (MCDM), in which the decision makers are free to alter the emphasis placed on each objective in the light of developing circumstances. In this paper the problem of time-dependent weights in MCDM is discussed and an analysis of empirical data associated with dynamic decision making is presented.     

The TROCA Project: An autonomous transportation robot controlled by a cognitive architecture

Autonomous mobile robots emerged as an important kind of transportation system in warehouses and factories. In this work, we present the use of MECA cognitive architecture in the development of an artificial mind for an autonomous robot responsible for multiple tasks, including transportation of packages along a factory floor, environment exploration, warehouse inventory, its internal energy management, self-monitoring and dealing with human operators and other robots. The present text provides a detailed specification for the architecture and its software implementation. Future work will present the simulation results under different configurations, together with a detailed analysis of the architecture performance and its generalization for autonomous robot control.     

Network change point detection in resting-state functional connectivity dynamics of mild cognitive impairment patients

Background/Objective: This study aims to characterize the differences on the short-term temporal network dynamics of the undirected and weighted whole-brain functional connectivity between healthy aging individuals and people with mild cognitive impairment (MCI). The Network Change Point Detection algorithm was applied to identify the significant change points in the resting-state fMRI register, and we analyzed the fluctuations in the topological properties of the sub-networks between significant change points. Method: Ten MCI patients matched by gender and age in 1:1 ratio to healthy controls screened during patient recruitment. A neuropsychological evaluation was done to both groups as well as functional magnetic images were obtained with a Philips 3.0T. All the images were preprocessed and statistically analyzed through dynamic point estimation tools. Results: No statistically significant differences were found between groups in the number of significant change points in the functional connectivity networks. However, an interaction effect of age and state was detected on the intra-participant variability of the network strength. Conclusions: The progression of states was associated to higher variability in the patient's group. Additionally, higher performance in the prospective and retrospective memory scale was associated with higher median network strength.     

Fabricated Illness: Working within the Family System to Find a Pathway to Health

The term medically unexplained symptoms refers to a clinical presentation where the child's symptoms and impairment cannot be explained by any known organic pathology, and may include conversion disorders, somatoform pain disorders, factitious disorder, and factitious disorder by proxy. In this case study, we present our treatment of a 9‐year‐old girl with a 2‐year history of medically unexplained abdominal cramping and vaginal discharge. During the 9 months that we worked with this family, we were never able to clarify in our own minds the source of the child's symptoms—that is, who was responsible for their induction or who was the instigator or maintainer of the exaggerated symptoms. Nor did we come to fully understand the function of the symptoms in the family system. Our case report does not answer either of these questions. Instead, we describe how we worked with the family despite the ongoing ambiguities as to why the symptoms were occurring and who was inducing them. The functional outcome was disappearance of symptoms, return to full school attendance, and improved parenting behavior.     

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male’s emotion dynamics over time, but not in the female’s—which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.     

Transference occurs across group boundaries

Transference occurs when a perceiver's representation of a significant other is activated and applied to a new target person (Andersen & Chen, 2002). Extending past research, it was hypothesized that transference occurs even when a target person possesses a core feature—namely, group membership status—that is inapplicable to the relevant significant-other representation. Supporting this, transference led perceivers to make representation-consistent memory and evaluation judgments about a target—regardless of whether the target's political (experiment 1) or ethnic (experiment 2) group membership was the same or different from that of the relevant significant other. Moreover, in experiment 2, perceivers undergoing transference involving a positively evaluated significant other behaved more positively toward the target even when the target was from an ethnic out-group. The results represent initial evidence of transference processes occurring across group boundaries. Implications for transference and the reduction of out-group bias are discussed.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.