Q&A: What is human language, when did it evolve and why should we care?

The impact of alcohol on affiliative verbal behavior: A systematic review and meta-analysis

BACKGROUND

Language is a fundamental aspect of human social behavior that is linked to many rewarding social experiences, such as social bonding. Potential effects of alcohol on affiliative language may therefore be an essential feature of alcohol reward and may elucidate pathways through which alcohol is linked to social facilitation. Examinations of alcohol's impact on language content, however, are sparse. Accordingly, this investigation represents the first systematic review and meta-analysis of alcohol's effects on affiliative language. We test the hypothesis that alcohol increases affiliative verbal approach behaviors and discuss future research directions.

METHODS

PsycInfo and Web of Science were systematically searched in March 2023 according to our preregistered plan. Eligible studies included social alcohol administration experiments in which affiliative verbal language was assessed. We present a random-effects meta-analysis that examines the effect of alcohol compared to control on measures of affiliative verbal behavior.

RESULTS

Our search identified 16 distinct investigations (comprising 961 participants) that examined the effect of alcohol on affiliative verbal behavior. Studies varied greatly in methods and measures. Meta-analytic results demonstrated that alcohol is modestly associated with increases in affiliative verbal behavior (Hedges' g = 0.164, 95% CI [0.027, 0.301], p = 0.019). Study quality was rated using an adapted version of the Quality Assessment Tool for Quantitative Studies and did not significantly moderate alcohol's effects.

CONCLUSIONS

This study provides preliminary evidence that alcohol can increase affiliative verbal behaviors. This effect may be an important feature of alcohol reward. Given heterogeneity in study features, low study quality ratings, and limited reporting of effect size data, results simultaneously highlight the promise of this research area and the need for more work. Advances in language processing methodologies that could allow future work to systematically expand upon this finding are discussed.

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Purpose

Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies.

Methods

The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms.

Originality

This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014-2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse.

Findings

The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).

Mapping brain function in adults and young children during naturalistic viewing with high-density diffuse optical tomography

Human studies of early brain development have been limited by extant neuroimaging methods. MRI scanners present logistical challenges for imaging young children, while alternative modalities like functional near-infrared spectroscopy have traditionally been limited by image quality due to sparse sampling. In addition, conventional tasks for brain mapping elicit low task engagement, high head motion, and considerable participant attrition in pediatric populations. As a result, typical and atypical developmental trajectories of processes such as language acquisition remain understudied during sensitive periods over the first years of life. We evaluate high-density diffuse optical tomography (HD-DOT) imaging combined with movie stimuli for high resolution optical neuroimaging in awake children ranging from 1 to 7 years of age. We built an HD-DOT system with design features geared towards enhancing both image quality and child comfort. Furthermore, we characterized a library of animated movie clips as a stimulus set for brain mapping and we optimized associated data analysis pipelines. Together, these tools could map cortical responses to movies and contained features such as speech in both adults and awake young children. This study lays the groundwork for future research to investigate response variability in larger pediatric samples and atypical trajectories of early brain development in clinical populations.

The tri-flow adaptiveness of codes in major evolutionary transitions

Life codes increase in both number and variety with biological complexity. Although our knowledge of codes is constantly expanding, the evolutionary progression of organic, neural, and cultural codes in response to selection pressure remains poorly understood. Greater clarification of the selective mechanisms is achieved by investigating how major evolutionary transitions reduce spatiotemporal and energetic constraints on transmitting heritable code to offspring. Evolution toward less constrained flows is integral to enduring flow architecture everywhere, in both engineered and natural flow systems. Beginning approximately 4 billion years ago, the most basic level for transmitting genetic material to offspring was initiated by protocell division. Evidence from ribosomes suggests that protocells transmitted comma-free or circular codes, preceding the evolution of standard genetic code. This rudimentary information flow within protocells is likely to have first emerged within the geo-energetic and geospatial constraints of hydrothermal vents. A broad-gauged hypothesis is that major evolutionary transitions overcame such constraints with tri-flow adaptations. The interconnected triple flows incorporated energy-converting, spatiotemporal, and code-based informational dynamics. Such tri-flow adaptations stacked sequence splicing code on top of protein-DNA recognition code in eukaryotes, prefiguring the transition to sexual reproduction. Sex overcame the spatiotemporal-energetic constraints of binary fission with further code stacking. Examples are tubulin code and transcription initiation code in vertebrates. In a later evolutionary transition, language reduced metabolic-spatiotemporal constraints on inheritance by stacking phonetic, phonological, and orthographic codes. In organisms that reproduce sexually, each major evolutionary transition is shown to be a tri-flow adaptation that adds new levels of code-based informational exchange. Evolving biological complexity is also shown to increase the nongenetic transmissibility of code.

The origins of music in (musi)language

The view of music as a byproduct of other cognitive functions has been deemed incomplete or incorrect. Revisiting the six lines of evidence that support this conclusion, it is argued that it is unclear how the hypothesis that music has its origins in (musi)language is discarded. Two additional promising research lines that can support or discard the byproduct hypothesis are presented.

Within the framework of the dual-system model, voluntary action is central to cognition

A new version of the dual-system hypothesis is described. Consistent with earlier models, the improvisational subsystem of the instrumental system, which includes the occipital cortex, inferior temporal cortex, and medial temporal cortex, especially the hippocampus, directs the construction of visual representations of the world and constructs ad-hoc responses to novel targets. The habit system, which includes the occipital cortex; parietal cortex; premotor, supplementary motor, and ventrolateral areas of frontal cortex; and the basal ganglia, especially the caudate nucleus, encodes sequences of actions and generates previously successful actions to familiar targets. However, unlike in previous dual-system models, human cognitive activity involved in task performance is not exclusively associated with one system or the other. Rather, the two systems make it possible for people to learn a variety of skills that draw on the competencies of both systems. The collective effects of these skills define human cognition. So, in contrast with earlier versions of the dual-system hypothesis, which identified the habit system solely with procedural learning and implicit improvements in task performance, the model presented here attributes a direct role in declarative-memory tasks to the habit system. Furthermore, within the model, the computational competencies of the two systems are used to construct purposeful sequences of actions-that is, skills. Human cognition is the result of the performance of these skills. Thus, voluntary action is central to human cognition.