Modelling the Developing Mind: From Structure to Change

This paper presents a theory of cognitive change. The theory assumes that the fundamental causes of cognitive change reside in the architecture of mind. Thus, the architecture of mind as specified by the theory is described first. It is assumed that the mind is a three-level universe involving (1) a processing system that constrains processing potentials, (2) a set of specialized capacity systems that guide understanding of different reality and knowledge domains, and (3) a hypecognitive system that monitors and controls the functioning of all other systems. The paper then specifies the types of change that may occur in cognitive development (changes within the levels of mind, changes in the relations between structures across levels, changes in the efficiency of a structure) and a series of general (e.g., metarepresentation) and more specific mechanisms (e.g., bridging, interweaving, and fusion) that bring the changes about. It is argued that different types of change require different mechanisms. Finally, a general model of the nature of cognitive development is offered. The relations between the theory proposed in the paper and other theories and research in cognitive development and cognitive neuroscience is discussed throughout the paper

LOGICAL AND PSYCHOLOGICAL PARTITIONING OF MIND: DEPICTING THE SAME MAP?

The aim of this paper is to demonstrate that empirically delimited structures of mind are also differentiable by means of systematic logical analysis. In the sake of this aim, the paper first summarizes Demetriou's theory of cognitive organization and growth. This theory assumes that the mind is a multistructural entity that develops across three fronts: the processing system that constrains processing potentials, a set of specialized structural systems (SSSs) that guide processing within different reality and knowledge domains, and a hypecognitive system that monitors and controls the functioning of all other systems. In the second part the paper focuses on the SSSs, which are the target of our logical analysis, and it summarizes a series of empirical studies demonstrating their autonomous operation. The third part develops the logical proof showing that each SSS involves a kernel element that cannot be reduced to standard logic or to any other SSS. The implications of this analysis for the general theory of knowledge and cognitive development are discussed in the concluding part of the paper

A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

We propose a unified deep learning framework for generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. In order to model and generate scenarios of trajectories with different length, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of self-driving cars. Second, we develop an architecture based on Recurrent Autoencoder with GANs in order to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, in order to obtain further insights on the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection

A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and generate scenarios of trajectories with different lengths, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us the flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of autonomous driving. Second, we develop an architecture based on Recurrent Autoencoder with GANs to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, to obtain further insights into the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection

Understanding and facilitating the development of intellect

Information flows continuously in the environment. As we attempt to do something, our senses receive large volumes of information. In any conversation, messages are exchanged rapidly. To understand meaning, we have to focus, record, choose and process relevant information at every moment, before it is displaced by other information. Often, information is incomplete or masked by other information or the problems to be solved are new to us. Thus, we must compare different aspects of information or other messages, and use deduction to fill in the gaps in the information, connect it with what we already know or invent solutions to new problems. Children at school learn new concepts every day. Reading, arithmetic or science are very demanding for them. To learn, children must hold information in their heads, use previously acquired concepts to interpret new information and then change their understanding as required. These tasks are possible because we can focus on information and process it before it disappears, alternate between stimuli or concepts according goals, and make decisions based on an understanding and evaluation of information through reasoning. At the same time, we adjust our strategies according to what we already know or depending on our strengths and weaknesses. To understand human intelligence, psychological and cognitive sciences try to specify what cognitive processes are involved in dealing with the above-mentioned tasks, how these processes change during learning, why individuals have different capacities, and how biology and culture may influence them. Any systematic attempt to improve intelligence through education would have to build on the knowledge assembled by research since the end of the nineteenth century. In this booklet we outline how the sciences of the mind view intelligence and suggest a programme for instruction that may uild upon its various processes

Forced retirement transition : a narrative case study of an elite Australian Rules football player

Retirement from elite sport is a complex and often-problematic process. The current study explores a negative case study of an athlete recently retired from a team sport (Australian Rules Football) in order to generate knowledge on how to improve the retirement process. Three semi-structured interviews were undertaken less than 5 years post retirement, and archival career records were gathered from online sources. Data were coded to construct a narrative account of the participant’s career and retirement. Narrative analysis also revealed that the retirement transition from elite sports for this athlete was problematic and caused considerable personal distress. We suggest that the means of improving retirement transition and reducing harm to players include fostering alternative life narratives and increasing self-complexity, utilising norm appropriate communication strategies, and recognising retirement as a potential grieving period for loss of community. © 2018, © 2018 International Society of Sport Psychology

Molecular beacon-based real-time PCR detection of primary isolates of Salmonella Typhimurium and Salmonella Enteritidis in environmental and clinical samples

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract Background A fast and simple two-step multiplex real-time PCR assay has been developed to replace the traditional, laborious Salmonella serotyping procedure. Molecular beacons were incorporated into the assay as probes for target DNA. Target sequences were regions of the invA, prot6E and fliC genes specific for Salmonella spp. Salmonella Enteritidis and Salmonella Typhimurium, respectively, the two most clinically relevant serotypes. An internal amplification positive control was included in the experiment to ensure the optimal functioning of the PCR and detect possible PCR inhibition. Three sets of primers were used for the amplification of the target sequences. The results were compared to those of the Kauffmann-White antigenic classification scheme. Results The assay was 100% sensitive and specific, correctly identifying all 44 Salmonella strains, all 21 samples of S. Enteritidis and all 17 samples of S. Typhimurium tested in this work. Therefore, the entire experiment had specificity and sensitivity of 100%. The detection limit was down to 10 copies of DNA target per 25 μl reaction. Conclusion The assay can amplify and analyse a large number of samples in approximately 8 hours, compared to the 4 to 5 days conventional identification takes, and is thus considered a very promising method for detecting the two major serotypes of Salmonella quickly and accurately from clinical and environmental samples.Published versio