Beyond an Anthropomorphic Template

In our endeavours to explore all possible forms that non-terrestrial communication may encompass, eventually we must throw off our anthropomorphic bias and investigate the implications of post-biological intelligence on SETI search strategies. In the event a candidate signal is detected, our initial categorization and assessment will focus on analyzing its comprising constructs, to ascertain whether information content is present; a fundamental signature of intelligence. To ensure our systems are capable of encompassing such intelligent communicators, we need to investigate both the contrasts and similarities of such non-biological communication and how this extends the known spectrum. In this paper, we begin to investigate the likely signatures and contrasting structures such non-biological communicators may present to us, across a range of known machine communication phenomena, and discuss how such contrasting forms of information exchange can aid, extend and refine our detection and decipherment capabilities

The I in Autism:severity and social functioning in Autism is related to self-processing

It is well established that children with autism spectrum disorder (ASD) show impaired understanding of others and deficits within social functioning. However, it is still unknown whether self-processing is related to these impairments and to what extent self impacts social functioning and communication. Using an ownership paradigm, we show that children with ASD and chronological- and verbal-age-matched typically developing (TD) children do show the self-referential effect in memory. In addition, the self-bias was dependent on symptom severity and socio-communicative ability. Children with milder ASD symptoms were more likely to have a high self-bias, consistent with a low attention to others relative to self. In contrast, severe ASD symptoms were associated with reduced self-bias, consistent with an ‘absent-self’ hypothesis. These findings indicate that deficits in self-processing may be related to impairments in social cognition for those on the lower end of the autism spectrum

How nouns and verbs differentially affect the behavior of artificial organisms

This paper presents an Artificial Life and Neural Network (ALNN) model for the evolution of syntax. The simulation methodology provides a unifying approach for the study of the evolution of language and its interaction with other behavioral and neural factors. The model uses an object manipulation task to simulate the evolution of language based on a simple verb-noun rule. The analyses of results focus on the interaction between language and other non-linguistic abilities, and on the neural control of linguistic abilities. The model shows that the beneficial effects of language on non-linguistic behavior are explained by the emergence of distinct internal representation patterns for the processing of verbs and nouns

Normal aging and Alzheimer's disease : hippocampal and episodic memory differences

Alzheimer’s Disease (AD) and normal aging (NA) are characterized by structural brain changes as well as cognitive changes that appear over the lifespan. The hippocampus is an area susceptible to early atrophy in both AD and NA; however the differential causes of atrophy are not entirely clear. Hippocampal volume loss in AD is attributed to neuronal death due to underlying pathology. AD often is diagnosed years after the onset of pathology and subsequent atrophy. NA is a continuation of cognitive decline that does not become dementia. Episodic memory (EM) is processed within the hippocampus and is one of the first systems to show deficits in conjunction with both patterns of aging. This review focuses on hippocampal volume loss and EM decline in NA and AD.Communication Sciences and Disorder

Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum

This paper presents a spectral attention-driven reinforcement learning based intelligent method for effective and efficient detection of important signals in a wideband spectrum. In the work presented in this paper, it is assumed that the modulation technique used is available as a priori knowledge of the targeted important signal. The proposed spectral attention-driven intelligent method is consists of two main components, a spectral correlation function (SCF) based spectral visualization scheme and a spectral attention-driven reinforcement learning mechanism that adaptively selects the spectrum range and implements the intelligent signal detection. Simulations illustrate that the proposed method can achieve high accuracy of signal detection while observation of spectrum is limited to few ranges via effectively selecting the spectrum ranges to be observed. Furthermore, the proposed spectral attention-driven machine learning method can lead to an efficient adaptive intelligent spectrum sensor designs in cognitive radio (CR) receivers.Comment: 6 pages, 11 figure