Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning.

Electroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity disorder (ADHD). In a series of ERGs collected in ASD ( = 77), ADHD ( = 43), ASD + ADHD ( = 21), and control ( = 137) groups, this analysis explores the use of machine learning and feature selection techniques to improve the classification between these clinically defined groups. Standard time domain and signal analysis features were evaluated in different machine learning models.

Remodeling the light-adapted electroretinogram using a bayesian statistical approach.

Objective: To present a remodeling of the electroretinogram waveform using a covariance matrix to identify regions of interest and distinction between a control and attention deficit/hyperactivity disorder (ADHD) group. Electroretinograms were recorded in n = 25 ADHD (16 male; age 11.9 ± 2.

Some Contributions from Embodied Cognition to Psychonarratology.

Psychonarratology is a discipline that combines classic frameworks in narratology and psycholinguistics. The goal of this review article is to highlight the contributions that recent evidence from causality processing studies, conceptual metaphor theory and embodied cognition can make to Psychonarratology, in order to promote a more comprehensive study of narrative. We argue that, in order to increase its descriptive and explanatory power, Psychonarratology would benefit from a more strongly interdisciplinary approach.

Advancing mechanistic explanations through natural and artificial embodied cognitive systems.

Mougenot and Matheson propose that mechanistic models can explain behavior by describing the complex interactions among components of the brain, body, and environment as an integrated system, which aligns with embodied cognition. However, we suggest incorporating cognitive ontology theory and addressing degeneracy and neuronal reuse. We also recommend studying natural embodied cognition through artificial systems to develop a comprehensive mechanistic framework.

Where the 'bad' and the 'good' go: A multi-lab direct replication report of Casasanto (2009, Experiment 1).

Casasanto (Journal of Experimental Psychology: General, 138, 351-367, 2009) conceptualised the body-specificity hypothesis by empirically finding that right-handed people tend to associate a positive valence with the right side and a negative valence with the left side, whilst left-handed people tend to associate a positive valence with the left side and negative valence with the right side. Thus, this was the first paper that showed a body-specific space-valence mapping. These highly influential findings led to a substantial body of research and follow-up studies, which could confirm the original findings on a conceptual level.

Social psychology: Spotting social faux pas with AI.

New research demonstrates AI, in the form of natural language models, can identify social norm violations in text and correctly distinguish the specific violated norm. This shows AI’s potential to recognize tricky social faux pas and support cross-cultural interactions.

EDA-Graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection.

The continuous detection of emotional states has many applications in mental health, marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA), a signal modulated by sympathetic nervous system activity, provides continuous insight into emotional states. However, EDA possesses intricate nonstationary and nonlinear characteristics, making the extraction of emotion-relevant information challenging.

Group comparisons of the individual electroretinogram time trajectories for the ascending limb of the b-wave using a raw and registered time series.

Objectives: The electroretinogram is a clinical test commonly used in the diagnosis of retinal disorders with the peak time and amplitude of the a- and b-waves used as the main indicators of retinal function. However, subtle changes that affect the shape of the electroretinogram waveform may occur in the early stages of disease or in conditions that have a neurodevelopmental or neurodegenerative origin. In such cases, we introduce a statistical approach to mathematically model the shape of the electroretinogram waveform that may aid clinicians and researchers using the electroretinogram or other biological signal recordings to identify morphological features in the waveforms that may not be captured by the time or time-frequency domains of the waveforms.

The role of perceptual and action effector strength of graphs and bases of mathematical metaphors in the metaphorical processing of mathematical concepts.

Metaphors that describe an abstract concept in terms of a motion concept are widely used to enhance our understanding of abstract concepts. These metaphors are used not only in our daily language but also in learning mathematics. As an example, in the process of understanding the abstract representation of a mathematical concept, a graphical representation may play the role of a mediatory domain.

Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics.

The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables.

Distributed embodiment of metaphorical in hand, head, and eyebrow gestures.

This study aimed to examine the embodied conceptualization of hope through metaphors. We asked a group of participants to discuss their hopes in a semi-structured interview. We examined the types of hand, head, and eyebrow gestures produced when they were talking about their future hopes.

How emotions are metaphorically embodied: measuring hand and head action strengths of typical emotional states.

This study measured hand and head action strengths of eight typical emotional states using an authentic but implicit emotion elicitation task. Participants listened to and then retold five stories in which eight typical emotional states were experienced by the narrators. The number of hand and head gestures that occur naturally while experiencing an emotional state was used as an index to determine the hand and head action strength of that emotional state.

Visual search and childhood vision impairment: A GAMLSS-oriented multiverse analysis approach.

The aim of this report was to analyze reaction times and accuracy in children with a vision impairment performing a feature-based visual search task using a multiverse statistical approach. The search task consisted of set sizes 4, 16, and 24, consisting of distractors (circle) and a target (ellipse) that were presented randomly to school-aged individuals with or without a vision impairment. Interactions and main effects of key variables relating to reaction times and accuracy were analyzed via a novel statistical method blending GAMLSS (generalized additive models for location, scale, and shape) and distributional regression trees.

A multi-analyses approach of inductive/deductive asymmetry in the affective priming paradigm.

Rapidly evaluating our environment's beneficial and detrimental features is critical for our successful functioning. A classic paradigm used to investigate such fast and automatic evaluations is the affective priming (AP) paradigm, where participants classify valenced target stimuli (e.g.

Generalised exponential-Gaussian distribution: a method for neural reaction time analysis.

Reaction times (RTs) are an essential metric used for understanding the link between brain and behaviour. As research is reaffirming the tight coupling between neuronal and behavioural RTs, thorough statistical modelling of RT data is thus essential to enrich current theories and motivate novel findings. A statistical distribution is proposed herein that is able to model the complete RT's distribution, including location, scale and shape: the generalised-exponential-Gaussian (GEG) distribution.

The Roles of Gestural and Symbolic Schematizations in Inhibition as a Component of Executive Functions.

The role of gestural schematization in enhancing thinking processes has been the subject of a large body of works. In this process, contextually unimportant or irrelevant information related to a concept (or a system of concepts) is deleted or ignored, while relevant spatial information is maintained. This process is a special type of inhibition, which is one of the key components of executive functions.

The roles of motion, gesture, and embodied action in the processing of mathematical concepts.

This article discusses perspective and frame of reference in the metaphorical description of mathematical concepts in terms of motions, gestures, and embodied actions. When a mathematical concept is described metaphorically in terms of gestures, embodied actions, or fictive motions, the motor system comes into play to ground and understand that concept. Every motion, gesture, or embodied action involves a perspective and a frame of reference.

A multi-lab test of the facial feedback hypothesis by the Many Smiles Collaboration.

Following theories of emotional embodiment, the facial feedback hypothesis suggests that individuals' subjective experiences of emotion are influenced by their facial expressions. However, evidence for this hypothesis has been mixed. We thus formed a global adversarial collaboration and carried out a preregistered, multicentre study designed to specify and test the conditions that should most reliably produce facial feedback effects.

Ingredients for Responsible Machine Learning: A Commented Review of .

In , Biecek, Kozak, and Zawada (here BKZ) provide an illustrated and engaging step-by-step guide on how to perform a machine learning (ML) analysis such that the algorithms, the software, and the entire process is interpretable and transparent for both the data scientist and the end user. This review summarises BKZ's book and elaborates on three elements key to ML analyses: inductive inference, causality, and interpretability.

Detecting Autism Spectrum Disorder Using Spectral Analysis of Electroretinogram and Machine Learning: Preliminary results.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that impacts language, communication and social interactions. The current diagnostic process for ASD is based upon a detailed multidisciplinary assessment. Currently no clinical biomarker exists to help in the diagnosis and monitoring of this condition that has a prevalence of approximately 1%.

Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder.

Background: To evaluate the electroretinogram waveform in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) using a discrete wavelet transform (DWT) approach.

Methods: A total of 55 ASD, 15 ADHD and 156 control individuals took part in this study. Full field light-adapted electroretinograms (ERGs) were recorded using a Troland protocol, accounting for pupil size, with five flash strengths ranging from -0.

The electroretinogram b-wave amplitude: a differential physiological measure for Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder.

Background: Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent childhood neurodevelopmental disorder. It shares some genetic risk with Autism Spectrum Disorder (ASD), and the conditions often occur together. Both are potentially associated with abnormal glutamate and GABA neurotransmission, which can be modelled by measuring the synaptic activity in the retina with an electroretinogram (ERG).

Gestures Enhance Executive Functions for the Understating of Mathematical Concepts.

This article discusses the role of gestures in enhancing inhibition, working memory, and cognitive flexibility as the three components of executive functions during the processing of mathematical concepts that are metaphorically described in terms of motion events. Gestures can contribute to the process of inhibition by highlighting the relevant information and keeping the irrelevant information out of focus of attention. Gestures contribute to working memory in two ways during mathematical processing.

Semantic Memory and Lexical Availability in Parkinson's Disease: A Statistical Learning Study.

Parkinson's disease (PD) is a neurodegenerative disorder that causes a progressive impairment in motor and cognitive functions. Although semantic fluency deficits have been described in PD, more specific semantic memory (SM) and lexical availability (LA) domains have not been previously addressed. Here, we aimed to characterize the cognitive performance of PD patients in a set of SM and LA measures and determine the smallest set of neuropsychological (lexical, semantic, or executive) variables that most accurately classify groups.