Social network composition and attitude strength: Exploring the dynamics within newly formed social networks

Recent evidence suggests that people who are embedded within attitudinally congruent social networks have stronger attitudes than those embedded in attitudinally diverse networks. The current research examines the causal processes responsible for this relation. Two studies capitalized on naturally occurring experiments whereby college students are quasi-randomly assigned to social contexts containing varying levels of attitudinal diversity. Replicating past research, individuals in attitudinally diverse social networks exhibited less resistance to attitude change and less attitude stability than those in more attitudinally congruent networks. Although there was evidence of attitude projection and selective network construction, neither of these processes could account for the relation between social network composition and attitude strength. These findings corroborate and extend previous results, reinforcing the notion that the social context in which people are embedded has important implications for the durability of their attitudes.     

Similarity and rules United: similarity- and rule-based processing in a single neural network

A central controversy in cognitive science concerns the roles of rules versus similarity. To gain some leverage on this problem, we propose that rule- versus similarity-based processes can be characterized as extremes in a multidimensional space that is composed of at least two dimensions: the number of features (Pothos, 2005) and the physical presence of features. The transition of similarity- to rule-based processing is conceptualized as a transition in this space. To illustrate this, we show how a neural network model uses input features (and in this sense produces similarity-based responses) when it has a low learning rate or in the early phases of training, but it switches to using self-generated, more abstract features (and in this sense produces rule-based responses) when it has a higher learning rate or is in the later phases of training. Relations with categorization and the psychology of learning are pointed out.     

内隐记忆中通道效应的认知与神经机制

通道转换是使内隐和外显记忆任务之间产生功能性分离经常操纵的典型变量之一,被看作是内隐记忆主要依赖刺激知觉住处加工的有利语气。该文回顾了近些年通道效应实验和理论研究的有关文献。对产生通道效应的认知机制和神经解剖基础进行了总结和分析,并提出了有待于进一步验证的问题。     

Understanding Cognition Through Large-Scale Cortical Networks

An emerging body of evidence from a number of fields is beginning to reveal general neural principles underlying cognition. The characteristic adaptability of cognitive function is seen to derive from large-scale networks in the cerebral cortex that are able to repeatedly change the state of coordination among their constituent areas on a subsecond time scale. Experimental and theoretical studies suggest that large-scale network dynamics operate in a metastable regime in which the interdependence of cortical areas is balanced between integrating and segregating activities. Cortical areas, through their coordination dynamics, are thought to rapidly resolve a large number of mutually imposed constraints, leading to consistent local states and a globally coherent state of cognition.     

Connecting the Many Levels and Facets of Cognitive Aging

Basic cognitive mechanisms, such as the abilities to briefly maintain, focus, and process information, decline with age. Related fields of cognitive aging research have been advancing rapidly, but mostly independently, at the biological, information processing, and behavioral levels. To facilitate integration, this article reviews research on cognitive aging at the different levels, and describes a recent integrative theory postulating that aging-related deficiencies in neurotransmission cause increased noise in information processing and less distinctive cortical representation, which in turn lead to cognitive deficits. Aging-related attenuation of catecholaminergic modulation can be modeled by lowering a neural network parameter to reduce the signal-to-noise ratio of information processing. The performance of such models is consistent with benchmark phenomena observed in humans, ranging from age differences in learning rate, asymptotic performance, and interference susceptibility to intra- and interindividual variability and ability dedifferentiation. Although the details of the conjectured sequence of effects linking neuromodulation to cognitive aging deficits await further empirical validation, cross-level theorizing of the kind illustrated here could foster the coevolution of related fields through cross-level data synthesis and hypothesis testing.     

Chaos breeds autonomy: connectionist design between bias and baby-sitting

In connectionism and its offshoots, models acquire functionality through externally controlled learning schedules. This undermines the claim of these models to autonomy. Providing these models with intrinsic biases is not a solution, as it makes their function dependent on design assumptions. Between these two alternatives, there is room for approaches based on spontaneous self-organization. Structural reorganization in adaptation to spontaneous activity is a well-known phenomenon in neural development. It is proposed here as a way to prepare connectionist models for learning and enhance the autonomy of these models.     

Neurophysiology and neuroanatomy of smooth pursuit in humans

Smooth pursuit eye movements enable us to focus our eyes on moving objects by utilizing well-established mechanisms of visual motion processing, sensorimotor transformation and cognition. Novel smooth pursuit tasks and quantitative measurement techniques can help unravel the different smooth pursuit components and complex neural systems involved in its control. The maintenance of smooth pursuit is driven by a combination of the prediction of target velocity and visual feedback about performance quality, thus a combination of retinal and extraretinal information that has to be integrated in various networks. Different models of smooth pursuit with specific in- and output parameters have been developed for a better understanding of the underlying neurophysiological mechanisms and to make quantitative predictions that can be tested in experiments. Functional brain imaging and neurophysiological studies have defined motion sensitive visual area V5, frontal (FEF) and supplementary (SEF) eye fields as core cortical smooth pursuit regions. In addition, a dense neural network is involved in the adjustment of an optimal smooth pursuit response by integrating also extraretinal information. These networks facilitate interaction of the smooth pursuit system with multiple other visual and non-visual sensorimotor systems on the cortical and subcortical level. Future studies with fMRI advanced techniques (e.g., event-related fMRI) promise to provide an insight into how smooth pursuit eye movements are linked to specific brain activation. Applying this approach to neurological and also mental illness can reveal distinct disturbances within neural networks being present in these disorders and also the impact of medication on this circuitry.     

Lexical categories at the edge of the word

Language acquisition may be one of the most difficult tasks that children face during development. They have to segment words from fluent speech, figure out the meanings of these words, and discover the syntactic constraints for joining them together into meaningful sentences. Over the past couple of decades, computational modeling has emerged as a new paradigm for gaining insights into the mechanisms by which children may accomplish these feats. Unfortunately, many of these models assume a computational complexity and linguistic knowledge likely to be beyond the abilities of developing young children. This article shows that, using simple statistical procedures, significant correlations exist between the beginnings and endings of a word and its lexical category in English, Dutch, French, and Japanese. Therefore, phonetic information can contribute to individuating higher level structural properties of these languages. This article also presents a simple 2-layer connectionist model that, once trained with an initial small sample of words labeled for lexical category, can infer the lexical category of a large proportion of novel words using only word-edge phonological information, namely the first and last phoneme of a word. The results suggest that simple procedures combined with phonetic information perceptually available to children provide solid scaffolding for emerging lexical categories in language development.     

Organizational network perceptions versus reality: A small world after all?

Given the complexity of organizing and keeping track of even a small organizational network, boundedly rational people may have learned to use small world principles in perceiving friendship networks: arrange people in dense clusters, and connect the clusters with short paths. Analysis of 116 perceived friendship networks from four different organizations showed that these perceived networks exhibited greater small world properties than the actual friendship networks. Further, people perceived more friendship clustering than actually existed, and attributed more popularity and brokerage to the perceivedly-popular than to the actually-popular.     

A nonlinear dynamic artificial neural network model of memory

Nonlinearity and dynamics in psychology are found in various domains such as neuroscience, cognitive science, human development, etc. However, the models that have been proposed are mostly of a computational nature and ignore dynamics. In those models that do include dynamic properties, only fixed points are used to store and retrieve information, leaving many principles of nonlinear dynamic systems (NDS) aside; for instance, chaos is often perceived as a nuisance. This paper considers a nonlinear dynamic artificial neural network (NDANN) that implements NDS principles while also complying with general neuroscience constraints. After a theoretical presentation, simulation results will show that the model can exhibit multi-valued, fixed-point, region-constrained attractors and aperiodic (including chaotic) behaviors. Because the capabilities of NDANN include the modeling of spatiotemporal chaotic activities, it may be an efficient tool to help bridge the gap between biological memory neural models and behavioral memory models.