四字谜问题解决中原型启发的时间抵消与位置效应

根据顿悟的原型启发理论,通过两个实验考察原型启发的时间抵消和位置效应。实验1采用2（字谜类型）×2（原型呈现时间）混合设计,结果发现：松组块字谜的正确率显著高于紧组块字谜;原型谜面消失的正确率显著大于谜面存在的正确率。实验2采用2（原型位置）×2（字谜类型）×2（原型呈现时间）混合设计,结果不仅证明了实验1,且原型位置、字谜类型、原型呈现时间的交互作用显著,原型在靶字谜之前时,紧组块字谜上谜面消失的正确率显著大于谜面存在的正确率;原型在靶字谜之后时,松组块字谜上谜面消失的正确率显著大于谜面存在的正确率。表明字谜顿悟问题解决中存在原型的时间抵消和位置效应,二者连同任务难度共同影响问题解决。     

How professional pilots perceive interactions of working conditions,rosters, stress,sleep problems,fatigue and mental health. A qualitative content analysis

IntroductionSo far, no qualitative content analysis (QCA) has analysed pilots’ experiences and perceptions regarding weaknesses of fatigue risk management (FRM), flight time limitations (FTL), rosters, fatigue-severity, sleep problems, and how they relate to pilots’ mental health and well-being.ObjectiveThis research analyses pilots’ described perceptions of stress, fatigue, aviation safety and how regulations like FTL can affect their health and well-being.MethodIn total, 119 international pilots described their perceptions of FTL, rosters, aviation safety, and how they relate to fatigue and health. The QCA was conducted to analyse interactions of working conditions, stressors, fatigue, sleep problems and mental health of EASA-based and Australian pilots.ResultsAlthough pilots were rostered for only 60.8% to 62.5% of the legally allowed duty and flight hours/month, 78.6% reported severe or very high fatigue, 22.8% significant depression, 12.3% significant anxiety symptoms, 10.5% reported significant depression and anxiety symptoms. Pilots uttered severe concerns about FTL, sleep restrictions associated with early starts, minimum rest, etc. Pilots also expressed distinct fears regarding more fatigue-related crashes, and how adverse working conditions, work-related and psychosocial stress could impair their health.ConclusionsThis QCA provided valuable insights into interactions of working conditions, fatigue, sleep restrictions, physical and mental health. Progressive health impairment due to lack of sleep and accumulated fatigue promote burnout, mental and physical health problems, which not only threaten flight safety, but also sustainability of aviation.     

Identification of neurons specifically activated after recall of context fear conditioning

The learning of new information and recall of that information presumably involves modification of and access to shared circuitry in the brain. However, learning and recall may involve the activation of distinct parts of that circuitry, according to the quite distinct functional differences between these two processes. Previously we examined neuronal activation following learning of context fear conditioning. Using the Fos-Tau-LacZ (FTL) transgenic mouse to label activated neurons, we identified a number of distinct populations of neurons in amygdala and hypothalamus which showed learning specific activation. These populations of neurons showed much less activation following recall. Here we ask what populations of neurons might be specifically activated following recall. We trained mice in context fear conditioning, and then looked at FTL activation following recall of context fear. We identified a number of populations of neurons which showed recall specific activation in nucleus accumbens shell, the anterio-medial bed nucleus of stria terminalis, the anterior commissural nucleus and the periventricular hypothalamic nucleus. These were all different populations of neurons compared with those activated following context fear learning. These different functional activation patterns occurring between learning and recall may reflect the different brain functions occurring between these two memory related processes.     

Precipitate strengthening in nanostructured metallic material composites

Nanostructured metallic material (NMM) composites are a new class of materials that exhibit high structural stability, mechanical strength, high ductility, toughness and resistance to fracture and fatigue; these properties suggest that these materials can play a leading role in the future micromechanical devices. However, before those materials are put into service in any significant applications, many important fundamental issues remain to be understood. Among them, is the question of the strengthening of NMM using second phase particles and if the addition of precipitates will strengthen the structures in the same manner as in bulk crystalline solids. This issue is addressed in this work by performing molecular dynamics simulations on NMM with precipitates of various sizes and comparing the results with the same structure without precipitates. In this view, Cu/Nb bilayer thin films with spherical Nb particles inside the Cu layer were examined using molecular dynamics simulations and show a significant improvement on their mechanical behavior, compared to similar structures without particles. Furthermore, an analytical model is developed that explains the strengthening behavior of an NMM that has precipitates inside one layer. The theoretical results show a qualitative agreement with the finding of the atomistic simulations.     

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.     

Effector dynamics of rhythmic wrist activity and its implications for (modeling) bimanual coordination

To examine the role of the effector dynamics of the wrist in the production of rhythmic motor activity, we estimated the phase shifts between the EMG and the task-related output for a rhythmic isometric torque production task and an oscillatory movement, and found a substantial difference (45-52 degrees) between the two. For both tasks, the relation between EMG and task-related output (torque or displacement) was adequately reproduced with a physiologically motivated musculoskeletal model. The model simulations demonstrated the importance of the contribution of passive structures to the overall dynamics and provided an account for the observed phase shifts in the dynamic task. Additional simulations of the musculoskeletal model with added load suggested that particular changes in the phase relation between EMG and movement may follow largely from the intrinsic muscle dynamics, rather than being the result of adaptations in the neural control of joint stiffness. The implications of these results are discussed in relation to (models of) interlimb coordination in rhythmic tasks.     

Diffusion dynamics of laser-ablated noble-metal nanoparticles in liquids

The diffusion dynamics in water of Brownian particles constituted by noble-metal nanoclusters has been treated by a transport model across a limiting surface that separates two layers of the same fluid, having different values of density and temperature. This model, developed by Parrondo and adapted by Streater, has recently been shown to be useful in several situations like ours by the work of Grillo et al. By this approach, which introduces a one-dimensional vertical adiabatic potential produced by density gradients governing the net current of particles across the limiting surface, we are able to explain the mass and thermal energy transport, which rapidly restores the equilibrium conditions when they are perturbed by laser ablation of a noble-metal target vertically submerged in water. Experimentally, the laser ablation of noble-metal targets has been performed by irradiating pure metal sheets submerged in water with a pulsed Nd:YAG laser at 500 mJ cm^?2 fluency for 5 min and pulses of duration on the nanosecond timescale.     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.     

Mining event-related brain dynamics

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.     

Long-range channelling in low energy ion implantation into silicon

Abstract

Low energy (50 eV) implantation of boron and silicon into crystalline Si through the {110} and {100} faces is studied by a molecular dynamics simulation and the results compared with a binary-collision crystalline computer model and SIMS data. It is found that long-range channelling of the B particles takes place and that their ranges far exceed those predicted by transport theory in random media or Monte-Carlo computer models. It is found that channelling of B occurs only in the 〈110〉 direction. The Si atoms which are displaced by more than the nearest-neighbour spacing always originate from near to the surface as a result of direct knock-ons from ions which do not channel. These displacements show a distinct angular dependence because of the crystalline nature of the solid. For the Si implantation, the attractive forces between the incoming ion and the crystal atoms play an important role in limiting the ion range.     

Infinity Machines and Creation Ex Nihilo

In this paper a simple model in particle dynamics of a well-known supertask is constructed (the supertask was introduced by Max Black some years ago). As a consequence, a new and simple result about creation ex nihilo of particles can be proved compatible with classical dynamics. This result cannot be avoided by imposing boundary conditions at spatial infinity, and therefore is really new in the literature. It follows that there is no reason why even a world of rigid spheres should be eternal, as has been erroneously assumed, especially since the time of Newton. This revised version was published online in August 2006 with corrections to the Cover Date.     

From Theorizing Radicalization to Surveillance Practices: Muslims in the Cross Hairs of Scrutiny

There are several psychological analyses of the processes of radicalization resulting in terrorism. However, we know little about how those in authority (e.g., the police) conceptualize the psychological dynamics to radicalization. Accordingly, we present a detailed account of an official U.K. counterterrorism intervention, the Workshop to Raise Awareness of Prevent, designed to enlist frontline professionals in identifying and referring those at risk of radicalization. Specifically, we report data gathered during an observation of this intervention delivered by the police in Scotland. This provides insight into the psychological model of radicalization being disseminated in the United Kingdom, and we evaluate the merits of this model in the light of current psychological theory. First, we consider how this model may overlook certain social dynamics relevant to understanding radicalization. Second, we discuss how this neglect limits consideration of how the surveillance warranted by the official model may lead Muslims to disengage from majority group members. Our analysis points to how political psychology's analysis of social identities and citizenship can inform public policy and practice.     

The response dynamics of preferential choice

The ubiquity of psychological process models requires an increased degree of sophistication in the methods and metrics that we use to evaluate them. We contribute to this venture by capitalizing on recent work in cognitive science analyzing response dynamics, which shows that the bearing information processing dynamics have on intended action is also revealed in the motor system. This decidedly “embodied” view suggests that researchers are missing out on potential dependent variables with which to evaluate their models—those associated with the motor response that produces a choice. The current work develops a method for collecting and analyzing such data in the domain of decision making. We first validate this method using widely normed stimuli from the International Affective Picture System (Experiment 1), and demonstrate that curvature in response trajectories provides a metric of the competition between choice options. We next extend the method to risky decision making (Experiment 2) and develop predictions for three popular classes of process model. The data provided by response dynamics demonstrate that choices contrary to the maxim of risk seeking in losses and risk aversion in gains may be the product of at least one “online” preference reversal, and can thus begin to discriminate amongst the candidate models. Finally, we incorporate attentional data collected via eye-tracking (Experiment 3) to develop a formal computational model of joint information sampling and preference accumulation. In sum, we validate response dynamics for use in preferential choice tasks and demonstrate the unique conclusions afforded by response dynamics over and above traditional methods.     

A simple 1+ dimensional model of rowing mimics observed forces and motions

We have devised a simple, yet predictive model of the mechanics of both sculling and sweep rowing that reasonably mimics observed kinematic and force data. Our physical model is largely based upon the model proposed by Alexander [Alexander, F. H. (1925). The theory of rowing. In Proceedings of the University of Durham Philosophical Society (pp. 160-179).]. The model's primary features include: one dimensional momentum balance, a point mass rower, infinitely stiff oars with inertia and non-infinitesimal stroke angles, and quadratic relationships between force and velocity for the boat and oar blade. Using an inverse dynamics approach, we are able to construct reasonable fits to force and kinematic data of real rowing. We show that the model is able to reasonably well predict boat velocity even when we do not fit for it. A sensitivity analysis shows that the quality of fit is more sensitive to the boat and oar drag coefficients than to other physical parameters. Allowing oar slip (C(D) < infinity) proves to be a necessary model ingredient but, for example, allowing for oar flexibility does not improve the quality of fit. The model seems to have the key terms and a minimum of superfluous terms for investigations of rowing.     

Heating up or cooling up the brain? MEG evidence that phrasal verbs are lexical units

There is a considerable linguistic debate on whether phrasal verbs (e.g., turn up, break down) are processed as two separate words connected by a syntactic rule or whether they form a single lexical unit. Moreover, views differ on whether meaning (transparency vs. opacity) plays a role in determining their syntactically-connected or lexical status. As linguistic arguments could not settle these issues, we used neurophysiological brain imaging to address them. Applying a multi-feature Mismatch Negativity (MMN) design with subjects instructed to ignore speech stimuli, we recorded magnetic brain responses to particles (up, down) auditorily presented as infrequent "deviant" stimuli in the context of frequently occurring verb "standard" stimuli. Already at latencies below 200ms, magnetic brain responses were larger to particles appearing in existing phrasal verbs (e.g. rise up) than to particles appearing in non-existing combinations (e.g. ?fall up), regardless of whether particles carried a literal or metaphorical sense (e.g. rise up, heat up). Previous research found an enhanced MMN response to morphemes in existing (as opposed to non-existing) words but a reduced MMN to words in grammatically acceptable (as opposed to unacceptable) combinations. The increased brain activation to particles in real phrasal verbs reported here is consistent with the lexical enhancement but inconsistent with the syntactic reduction of the MMN, thus providing neurophysiological support that a congruent verb-particle sequence is not assembled syntactically but rather accessed as a single lexical chunk.     

A Dynamical Approach to Gestural Patterning in Speech Production

In this article, we attempt to reconcile the linguistic hypothesis that speech involves an underlying sequencing of abstract, discrete, context-independent units, with the empirical observation of continuous, context-dependent interleaving of articulatory movements. To this end, we first review a previously proposed task-dynamic model for the coordination and control of the speech articulators. We then describe an extension of this model in which invariant speech units (gestural primitives) are identified with context-independent sets of parameters in a dynamical system having two functionally distinct but interacting levels. The intergestural level is defined according to a set of activation coordinates; the interarticulator level is defined according to both model articulator and tractvariable coordinates. In the framework of this extended model, coproduction effects in speech are described in terms of the blending dynamics defined among a set of temporally overlapping active units; the relative timing of speech gestures is formulated in terms of the serial dynamics that shape the temporal patterning of onsets and offsets in unit activations. Implications of this approach for certain phonological issues are discussed, and a range of relevant experimental data on speech and limb motor control is reviewed.     

Multi-agent models of social dynamics in children

A series of computational models are presented which address the question of how peer relations change over time. We examine data from a standardized metric (CDC) that places school children in one of five categories: Popular, Rejected, Neglected, Controversial, and Average, and how such classifications change over time. A simple random model is shown to not match the empirical data, while a computational model and an ACT-R model are shown to match equally well, even though they are highly architecturally distinct. To test these models’ ability to give useful predictions in other domains, we introduced variation among the individuals in the models. For both models, we observed equivalent behavior that was consistent with the empirically known effects of Hostile Attribution Bias, variations in social skill, and shyness among others. This indicates that both models are capturing inherent underlying regularities of the social dynamics of peer relations in children. The relationship between these models and its implications are discussed.     

Learning as change of coordination dynamics: theory and experiment

Learning of coordination patterns was investigated theoretically from the point of view of a dynamic theory of biological coordination and with reference to recent experiments on the learning of relative timing patterns. The theory is based on theoretical and experimental work showing that coordinated movement is characterized not only by the actually performed pattern of coordination but by an entire dynamics of coordination. Theoretically, such dynamics are captured as equations of motion of relevant collective variables. Experimentally, signatures of these underlying dynamics can be found in the temporal stability of coordination patterns, which can be assessed through various stability measures as well as through processes of pattern change. We argue that not only intrinsic coordination tendencies, but also specific behavioral requirements, be they perceived, memorized, or intended, must be expressed in terms of such dynamics. The concept of behavioral information captures such requirements as part of the coordination dynamics. We expound two hypotheses on the nature of learning in this framework. First, we assume that at each point during the learning process the system is governed by a well-defined coordination dynamics. This equation of motion evolves with learning so as to acquire an attractor solution near the to-be-learned pattern. Second, we hypothesize that this change of the coordination dynamics, captured by the time course of memorized behavioral information, can itself be ascribed to an additional layer of dynamics, the slower learning dynamics. Testable consequences of these views are discussed in the light of recent experimental findings on the learning of a relative phase in rhythmic movement: (a) Learning affects dynamic properties of performed coordination patterns, in particular, their stability; (b) the change of the coordination dynamics due to learning leads to specific changes of behavior also under conditions other than the learned condition, namely, to systematic deviation toward the learned patterns; (c) learning may lead to instabilities in the coordination behavior if initial and learned performance differ sufficiently; and (d) the dynamic properties of the performed coordination patterns are distinct on the two time scales of learning and of performance.