Short-Term EEG Spectral Pattern as a Single Event in EEG Phenomenology

Spectral decomposition, to this day, still remains the main analytical paradigm for the analysis of EEG oscillations. However, conventional spectral analysis assesses the mean characteristics of the EEG power spectra averaged out over extended periods of time and/or broad frequency bands, thus resulting in a “static” picture which cannot reflect adequately the underlying neurodynamic. A relatively new promising area in the study of EEG is based on reducing the signal to elementary short-term spectra of various types in accordance with the number of types of EEG stationary segments instead of using averaged power spectrum for the whole EEG. It is suggested that the various perceptual and cognitive operations associated with a mental or behavioural condition constitute a single distinguishable neurophysiological state with a distinct and reliable spectral pattern. In this case, one type of short-term spectral pattern may be considered as a single event in EEG phenomenology. To support this assumption the following issues are considered in detail: (a) the relations between local EEG short-term spectral pattern of particular type and the actual state of the neurons in underlying network and a volume conduction; (b) relationship between morphology of EEG short-term spectral pattern and the state of the underlying neurodynamical system i.e. neuronal assembly; (c) relation of different spectral pattern components to a distinct physiological mechanism; (d) relation of different spectral pattern components to different functional significance; (e) developmental changes of spectral pattern components; (f) heredity of the variance in the individual spectral pattern and its components; (g) intra-individual stability of the sets of EEG short-term spectral patterns and their percent ratio; (h) discrete dynamics of EEG short-term spectral patterns. Functional relevance (consistency) of EEG short-term spectral patterns in accordance with the changes of brain functional state, cognitive task and with different neuropsychopathologies is demonstrated

Dimensions of Timescales in Neuromorphic Computing Systems

This article is a public deliverable of the EU project "Memory technologies with multi-scale time constants for neuromorphic architectures" (MeMScales, https://memscales.eu, Call ICT-06-2019 Unconventional Nanoelectronics, project number 871371). This arXiv version is a verbatim copy of the deliverable report, with administrative information stripped. It collects a wide and varied assortment of phenomena, models, research themes and algorithmic techniques that are connected with timescale phenomena in the fields of computational neuroscience, mathematics, machine learning and computer science, with a bias toward aspects that are relevant for neuromorphic engineering. It turns out that this theme is very rich indeed and spreads out in many directions which defy a unified treatment. We collected several dozens of sub-themes, each of which has been investigated in specialized settings (in the neurosciences, mathematics, computer science and machine learning) and has been documented in its own body of literature. The more we dived into this diversity, the more it became clear that our first effort to compose a survey must remain sketchy and partial. We conclude with a list of insights distilled from this survey which give general guidelines for the design of future neuromorphic systems

Attention in Psychology, Neuroscience, and Machine Learning

Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored

Listening effort allocation, stimulus-driven, goal-driven, or both?

The research in audiology to date about how people listen has been focused too narrowly on the impact of the task demand (e.g., speech complexity) on the effort exerted for listening. Very few studies conducted on how intention associated factors affect listening effort regulation, and little is known about how to characterize the individual quality of effort expenditure in terms of efficiency. This study primarily aimed to fill the gap by testing a compensatory control model for effort regulation, specifically, to investigate how reward would modulate the effect of task demand on listening effort. The secondary aim was to propose a modified computational approach for effort efficiency calculation. The nonclinical sample was comprised of 40 college volunteer participants with normal hearing. All participants completed the Need for Cognition scale, a speech comprehension task which required a cost-benefit decision making, and a self-report strategy use survey. The pupil dilation was measured throughout the speech comprehension task as the indicator of listening effort. Results supported the model in that effort regulation during an intended activity is determined not only by stimulus-driven factors such as task demand, but also by goal-driven factors such as reward. Significant interaction effects emerged. Furthermore, the effort efficiency derived by using goal-oriented performance variables demonstrated the superiority of distinguishing individuals compared to the use of mere performance accuracy. This study contributes to the limited literature available on proactive listening effort regulation. Examining further how hearing, cognitions, and personality interact neurophysiologically and functionally in normal hearing and hearing impaired populations can help clinicians and researchers better understand the underlying mechanism of listening effort control, and facilitate implementing strategies to aid effective listening through audiologic interventions

Resource and Bottleneck Mechanisms of Attention in Language Performance

The view that impairments of attention may constitute an important factor underlying impaired language performance in aphasia has gained support in recent years. Aphasiologists taking this view have generally proceeded from resource allocation models of attention, with little or no attention given to alternative models. One alternative model of dual-task performance is the central bottleneck model, which proposes a single-channel limit at response selection or other central processing stages. The first purpose of the present experiments was to further examine the effects on word production of lexical frequency in the context of the psychological refractory period (PRP) dual-task method. The second purpose was to examine whether the reaction time (RT) patterns obtained under conditions promoting equal task emphasis are more consistent with the central bottleneck or central resource models. Three dual-task experiments were conducted using speeded picture naming and tone identification tasks presented at varying timulus onset asynchronies (SOAs). In experiment 1, lexical frequency affected primary-task naming and secondary-task tone identification RTs approximately equally. In experiment 2, lexical frequency affected secondary-task naming RTs similarly at all levels of SOA, after potentially confounding variables were taken into account. It was concluded that frequency-sensitive lexical processing in picture-naming participates in the central processing stage of the dual-task models under study. In the third experiment, the two tasks were presented in variable order and subjects were instructed to give equal attention to both. On tone-primary trials, tone RTs increased with decreasing SOA, a result consistent with the central resource model and inconsistent with the central bottleneck model, unless augmented by the assumption that particpants grouped responses on short SOA trials. Also, additional analyses restricted to those participants demonstrating a lexical frequency effect on the secondary naming task found that lexical frequency and SOA interacted on primary-task tone RTs such that tone responses preceding low-frequency naming responses were slower than those preceding high-frequency names. This further suggests that these subjects allocated more central processing capacity to the naming task on low-frequency trials. Comparison of results across the three experiments suggested that participants in Experiment 3 demonstrated less dual-task interference than predicted by either model