Projectible predicates in analogue and simulated systems

We investigate the relationship between two approaches to modeling physical systems. On the first approach, simplifying assumptions are made about the level of detail we choose to represent in a computational simulation with an eye toward tractability. On the second approach simpler, analogue physical systems are considered that have more or less well-defined connections to systems of interest that are themselves too difficult to probe experimentally. Our interest here is in the connections between the artifacts of modeling that appear in these two approaches. We begin by outlining an important respect in which the two are essentially dissimilar and then propose a method whereby overcoming that dissimilarity by hand results in usefully analogous behavior. We claim that progress can be made if we think of artifacts as clues to the projectible predicates proper to the models themselves. Our degree of control over the connection between interesting analogue physical systems and their targets arises from determining the projectible predicates in the analogue system through a combination of theory and experiment. To obtain a similar degree of control over the connection between large-scale, distributed simulations of complex systems and their targets we must similarly determine the projectible predicates of the simulations themselves. In general theory will be too intractable to be of use, and so we advocate an experimental program for determining these predicates.

the object of the natural history which I propose is...to give light to the discovery of causes and supply a suckling philosophy with its first food. Francis Bacon, The Great Instauration

     

Need fulfillment and motivation in physical education predict trajectories of change in leisure-time physical activity in early adolescence

ObjectivesWe examined (1) how psychological need fulfillment and motivation in physical education and leisure-time physical activity change during early adolescence, and (2) the degree to which need fulfillment and motivation predict trajectories of change in physical activity.DesignLongitudinal survey.MethodsStudents (N = 134, ages 10–13 years) completed surveys assessing perceived competence, autonomy, relatedness, intrinsic motivation, and identified, introjected, and external regulations in physical education, and leisure time physical activity each semester in school for 3 years.ResultsUnconditional growth models showed an average increase in physical activity. Competence also increased, while autonomy and relatedness, and identified and introjected regulation decreased. Conditional models showed that students with higher levels of autonomy, relatedness, intrinsic motivation, and identified regulation had higher levels of physical activity at baseline and throughout the study. Students with lower levels of autonomy, relatedness, intrinsic motivation, and identified regulation experienced significantly greater increases in physical activity, but these effects were very small.ConclusionsNeed fulfillment and motivation variables positively predict physical activity. While youth with lower levels of need fulfillment and motivation have lower levels of physical activity, they also tend to increase physical activity levels more across early adolescence. These associations highlight how physical education experiences in early adolescence may influence change in physical activity, and suggest efforts to foster need fulfillment and autonomous regulation in physical education may promote physical activity.     

iMinerva: A Mathematical Model of Distributional Statistical Learning

Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non‐adjacent relations. On the surface, these different aspects share few commonalities. Despite this, we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain‐general characteristics of memory.     

Numerical discrimination is mediated by neural coding variation

One foundation of numerical cognition is that discrimination accuracy depends on the proportional difference between compared values, closely following the Weber–Fechner discrimination law. Performance in non-symbolic numerical discrimination is used to calculate individual Weber fraction, a measure of relative acuity of the approximate number system (ANS). Individual Weber fraction is linked to symbolic arithmetic skills and long-term educational and economic outcomes. The present findings suggest that numerical discrimination performance depends on both the proportional difference and absolute value, deviating from the Weber–Fechner law. The effect of absolute value is predicted via computational model based on the neural correlates of numerical perception. Specifically, that the neural coding “noise” varies across corresponding numerosities. A computational model using firing rate variation based on neural data demonstrates a significant interaction between ratio difference and absolute value in predicting numerical discriminability. We find that both behavioral and computational data show an interaction between ratio difference and absolute value on numerical discrimination accuracy. These results further suggest a reexamination of the mechanisms involved in non-symbolic numerical discrimination, how researchers may measure individual performance, and what outcomes performance may predict.     

The Neurodynamics of Cognition: A Tutorial on Computational Cognitive Neuroscience

Computational Cognitive Neuroscience (CCN) is a new field that lies at the intersection of computational neuroscience, machine learning, and neural network theory (i.e., connectionism). The ideal CCN model should not make any assumptions that are known to contradict the current neuroscience literature and at the same time provide good accounts of behavior and at least some neuroscience data (e.g., single-neuron activity, fMRI data). Furthermore, once set, the architecture of the CCN network and the models of each individual unit should remain fixed throughout all applications. Because of the greater weight they place on biological accuracy, CCN models differ substantially from traditional neural network models in how each individual unit is modeled, how learning is modeled, and how behavior is generated from the network. A variety of CCN solutions to these three problems are described. A real example of this approach is described, and some advantages and limitations of the CCN approach are discussed.     

Dislocations in basic-nickel decagonal Al–Ni–Co single quasicrystals

We present a study of dislocations in decagonal Al₇₀Ni₂₁Co₉ quasicrystals by means of diffraction contrast analysis as well as convergent-beam electron diffraction in the transmission electron microscope. The nickel-rich Al–Ni–Co quasicrystals show diffraction patterns characteristic of the basic-nickel decagonal phase exhibiting almost no diffuse scattering. We succeeded in growing this phase in the form of large single quasicrystals. The two-beam bright-field images show a homogeneous background and no striation contrast as reported for other Al–Ni–Co decagonal phases. We have, for the first time in a two-dimensional quasicrystal, observed the weak contrast-extinction condition.     

不同类型美感体验的自主反应特点

不同美感体验类型有何特异的生理反应？美感的生理反应是否类似于积极情绪？本研究以音乐和图片两类材料为刺激,以心率、皮肤电为生理反应指标,测量了不同审美状态下的自主生理反应情况。结果发现：不论是听觉还是视觉通道,优美、壮美、悲剧、喜剧4种审美风格的刺激都使被试心率和皮肤电下降;不同的美感形态所引起的反应不同,喜剧和壮美风格刺激诱发的自主生理反应变化相对于其他两种风格显得更明显,喜剧风格效果尤为突出;不同美感体验类型特异生理反应具有跨通道的一致性;四种美感体验类型的生理反应都不同于消极情绪,而类似积极情绪,从生理反应视角证明了美感体验递属于积极情绪。     

A Bayesian framework for word segmentation: Exploring the effects of context

Since the experiments of Saffran et al. [Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning in 8-month-old infants. Science, 274, 1926-1928], there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words - in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what’s that, do you, in the house) misidentified as individual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered.