Cultural schemas are a central cognitive mechanism through which culture affects action. In this article, we develop a theoretical model of cultural schemas that is better able to support empirical ...work, including inferential, sensitizing, and operational uses. We propose a multilevel framework centered on a high-level definition of cultural schemas that is sufficiently broad to capture its major sociological applications but still sufficiently narrow to identify a set of cognitive phenomena with key functional properties in common: cultural schemas are socially shared representations deployable in automatic cognition. We use this conception to elaborate the main theoretical properties of cultural schemas, and to provide clear criteria that distinguish them from other cultural or cognitive elements. We then propose a series of concrete tests empirical scholarship can use to determine if these properties apply. We also demonstrate how this approach can identify potentially faulty theoretical inferences present in existing work. Moving to a lower level of analysis, we elaborate how cultural schemas can be algorithmically conceptualized in terms of their building blocks. This leads us to recommend improvements to methods for measuring cultural schemas. We conclude by outlining questions for a broader research program.
This article introduces the Causal Attitude Network (CAN) model, which conceptualizes attitudes as networks consisting of evaluative reactions and interactions between these reactions. Relevant ...evaluative reactions include beliefs, feelings, and behaviors toward the attitude object. Interactions between these reactions arise through direct causal influences (e.g., the belief that snakes are dangerous causes fear of snakes) and mechanisms that support evaluative consistency between related contents of evaluative reactions (e.g., people tend to align their belief that snakes are useful with their belief that snakes help maintain ecological balance). In the CAN model, the structure of attitude networks conforms to a small-world structure: evaluative reactions that are similar to each other form tight clusters, which are connected by a sparser set of "shortcuts" between them. We argue that the CAN model provides a realistic formalized measurement model of attitudes and therefore fills a crucial gap in the attitude literature. Furthermore, the CAN model provides testable predictions for the structure of attitudes and how they develop, remain stable, and change over time. Attitude strength is conceptualized in terms of the connectivity of attitude networks and we show that this provides a parsimonious account of the differences between strong and weak attitudes. We discuss the CAN model in relation to possible extensions, implication for the assessment of attitudes, and possibilities for further study.
The mammalian cortex is a laminar structure containing many areas and cell types that are densely interconnected in complex ways, and for which generalizable principles of organization remain mostly ...unknown. Here we describe a major expansion of the Allen Mouse Brain Connectivity Atlas resource
, involving around a thousand new tracer experiments in the cortex and its main satellite structure, the thalamus. We used Cre driver lines (mice expressing Cre recombinase) to comprehensively and selectively label brain-wide connections by layer and class of projection neuron. Through observations of axon termination patterns, we have derived a set of generalized anatomical rules to describe corticocortical, thalamocortical and corticothalamic projections. We have built a model to assign connection patterns between areas as either feedforward or feedback, and generated testable predictions of hierarchical positions for individual cortical and thalamic areas and for cortical network modules. Our results show that cell-class-specific connections are organized in a shallow hierarchy within the mouse corticothalamic network.
Recent research suggests that engagement with natural beauty (EWNB) is key to the well-being benefits of nature connectedness. The Wildlife Trust's
campaign provides a large-scale intervention for ...improving public engagement with nature and its beauty. The effect of
participation on levels of EWNB and the relationship between EWNB, nature connectedness and happiness was evaluated during the 2017 campaign. Of the 49,000 people who signed up to the campaign, 308 people fully completed measures of EWNB, nature connection, health, happiness, and conservation behaviors at baseline, post-30 days and post-2 months. There were sustained and significant increases for scores in nature connection, health, happiness, and conservation behaviors. In addition,
was the first intervention found to increase EWNB. Further, the significant increase in EWNB mediated the relationship between the increases in nature connectedness and happiness. In a supplementary study to understand the well-being benefits further (
= 153), emotional regulation was found to mediate the relationship between nature connectedness and happiness, but EWNB and emotional regulation were not related. The links between nature's beauty, nature connectedness and well-being are discussed within an account of affect-regulation.
3D Convolutional Neural Networks for Human Action Recognition Ji, Shuiwang; Yang, Ming; Yu, Kai
IEEE transactions on pattern analysis and machine intelligence,
2013-Jan., 2013, 2013-Jan, 2013-01-00, 20130101, Volume:
35, Issue:
1
Journal Article
Peer reviewed
We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. Convolutional ...neural networks (CNNs) are a type of deep model that can act directly on the raw inputs. However, such models are currently limited to handling 2D inputs. In this paper, we develop a novel 3D CNN model for action recognition. This model extracts features from both the spatial and the temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames. The developed model generates multiple channels of information from the input frames, and the final feature representation combines information from all channels. To further boost the performance, we propose regularizing the outputs with high-level features and combining the predictions of a variety of different models. We apply the developed models to recognize human actions in the real-world environment of airport surveillance videos, and they achieve superior performance in comparison to baseline methods.
Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper ...presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach.
Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature. The purpose of this paper is to provide a ...comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models. Since time delay is inevitable in practice, stability results of recurrent neural networks with different classes of time delays are reviewed in detail. For the case of delay-dependent stability, the results on how to deal with the constant/variable delay in recurrent neural networks are summarized. The relationship among stability results in different forms, such as algebraic inequality forms, M-matrix forms, linear matrix inequality forms, and Lyapunov diagonal stability forms, is discussed and compared. Some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed. Concluding remarks and future directions of stability analysis of recurrent neural networks are given.
Autoencoder for words Liou, Cheng-Yuan; Cheng, Wei-Chen; Liou, Jiun-Wei ...
Neurocomputing (Amsterdam),
09/2014, Volume:
139
Journal Article, Conference Proceeding
Peer reviewed
This paper presents a training method that encodes each word into a different vector in semantic space and its relation to low entropy coding. Elman network is employed in the method to process word ...sequences from literary works. The trained codes possess reduced entropy and are used in ranking, indexing, and categorizing literary works. A modification of the method to train the multi-vector for each polysemous word is also presented where each vector represents a different meaning of its word. These multiple vectors can accommodate several different meanings of their word. This method is applied to the stylish analyses of two Chinese novels, Dream of the Red Chamber and Romance of the Three Kingdoms.
Predictive processing (PP) approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek ...to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered under the predictive label. Here, we first present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications—with a firm commitment to modular, internalistic mental representation—to more moderate views emphasizing the importance of 'bodyrepresentations', and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. Any nascent predictive processing theory (e.g., of attention or consciousness) must take into account this continuum of views, and associated theoretical commitments. As a final point, we illustrate how the Free Energy Principle (FEP) attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal 'representations' arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind.
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