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. However, direct replications of the original study are scarce. Against this backdrop and given the replication crisis in psychology, we conducted a direct replication of Casasanto's original study with 2,222 participants from 12 countries to examine the aforementioned effects in general and also in a cross-cultural comparison. Our results support Casasanto's findings that right-handed people associate the right side with positivity and the left side with negativity and vice versa for left-handers.

The different effects of a sensorimotor grounding on AoA between bilingual concepts.

Introduction: Psycholinguistic studies have argued for the age of acquisition (AoA) of words as a marker of concept learning, showing that the semantic features of concepts themselves influence the age at which their labels are learned. However, empirical evidence suggests that semantic features such as imageability and linguistic phenomena such as frequency do not adequately predict AoA. The present study takes the developmental approach of embodied cognition and investigates the effects of sensorimotor experiences on the ease of acquisition of the concept acquired in bilinguals. Specifically, we investigated (1) whether the sensorimotor experience can explain AoA beyond frequency; (2) and whether these patterns are consistent across L1 Chinese and L2 English. Methods: We conducted sensorimotor rating measures in both Chinese and English on 207 items in which Chinese-English bilingual adults were requested to evaluate the extent to which they experienced concepts by employing six perceptual senses and five effectors for actions located in various regions of the body. Meanwhile, data on AoA and frequency were collected. Results: The present study showed the sensorimotor experience was closely linked with AoAs in both languages. However, the correlation analysis revealed a trend of higher correlations between AoAs for the same concepts and L1 Chinese, relative to L2 English for the present Chinese-English bilinguals. Importantly, the hierarchical regression analysis demonstrated that after controlling for frequency, sensorimotor experience explained additional variance in L1 AoA. However, L2 sensorimotor experience did not explain the variance in L2 AoA. Sensorimotor experience explained more share of variance in L1 AoA but frequency accounted for more variance in L2 AoA. Discussion: The findings suggest that concept acquisition should consider the grounding in appropriate sensorimotor experience beyond linguistic phenomena like frequency.

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. We propose a novel graph signal processing (GSP) approach to model EDA signals as graphical networks, termed EDA-graph. The GSP leverages graph theory concepts to capture complex relationships in time-series data. To test the usefulness of EDA-graphs to detect emotions, we processed EDA recordings from the CASE emotion dataset using GSP by quantizing and linking values based on the Euclidean distance between the nearest neighbors. From these EDA-graphs, we computed the features of graph analysis, including total load centrality (TLC), total harmonic centrality (THC), number of cliques (GNC), diameter, and graph radius, and compared those features with features obtained using traditional EDA processing techniques. EDA-graph features encompassing TLC, THC, GNC, diameter, and radius demonstrated significant differences (p < 0.05) between five emotional states (Neutral, Amused, Bored, Relaxed, and Scared). Using machine learning models for classifying emotional states evaluated using leave-one-subject-out cross-validation, we achieved a five-class F1 score of up to 0.68.

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. We present a statistical graphics-based analysis of the ascending limb of the b-wave (AL-b) of the electroretinogram in children with and without a diagnosis of autism spectrum disorder (ASD) with a narrative explanation of the statistical approach to illustrate how different features of the waveform based on location and scale derived from raw and registered time series can reveal subtle differences between the groups. RESULTS: Analysis of the raw time trajectories confirmed findings of previous studies with a reduced and delayed b-wave amplitude in ASD. However, when the individual time trajectories were registered then group differences were visible in the mean amplitude at registered time ~ 0.6 suggesting a novel method to differentiate groups using registration of the ERG waveform.

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. This graphical representation could have a high degree of perceptual and action effector strength. This is particularly the case when a gestures (as a motion) is used to depict the graphical representation. After looking at this example, we discuss perceptual and action effector strength of the base domains of several mathematical metaphors that describe mathematical concepts in terms of spatial and motion concepts. Then, based on the data in the Lancaster Sensorimotor Norms, it is suggested that high degrees of perceptual and action effector strength of the base domains of these metaphors play an important role in the grounding of abstract mathematical concepts in the physical environment.

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. This article overviews the power and flexibility of GAMLSS in relation to some ML techniques. Also, GAMLSS' capability to be tailored toward causality via causal regularization is briefly commented. This overview is illustrated via a data set from the field of LA. This article is categorized under:Application Areas > Education and LearningAlgorithmic Development > StatisticsTechnologies > Machine Learning.

Distributed embodiment of metaphorical hope 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. The obtained results showed that when participants talked about their future hopes, they mainly used forward hand gestures, rightward head gestures, and upward eyebrow gestures. Based on these results, it is suggested that various semantic components and emotional associations of hope are metaphorically embodied in different manners in various parts of the body. The future aspect of hope is conceptualized as a forward movement and is embodied as a forward hand gesture. The good or positive emotional aspect associated with future hopes is metaphorically conceptualized as a rightward head gesture or an upward eyebrow gesture. We call this process distributed embodiment of a metaphorical concept. Our proposal is supported by the findings of past studies that have found future is metaphorically embodied as something in front of us (or forward movement), and good is metaphorically embodied as upper space (or upward movement) or right side (or rightward movement).

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. Results showed a larger number of head gestures than hand gestures, suggesting that head action strengths of the eight emotional states are stronger than their hand action strengths. These findings are consistent with the data extracted from Lancaster Sensorimotor Norms (LSN), although the two sets of data were gathered in two completely different experimental conditions and in two different languages. Furthermore, our data showed a prototypical directionality effect for the typical emotional states, specifically, happiness, anger and pride were primarily accompanied by upward gestures but downward gestures for sadness and shame; surprise was primarily accompanied by forward gestures but backward gestures for fear and disgust.

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. Reaction times for the target-present and target-absent conditions were significantly slower in the vision impairment group with increasing set sizes (p < .001). Female participants were significantly slower than were males for set sizes 16 and 24 in the target-absent condition (p < .001), with male participants being significantly slower than females in the target-present condition (p < .001). Accuracy was only significantly worse (p = .03) for participants less than 14 years of age for the target-absent condition with set sizes 16 and 24. There was a positive association between binocular visual acuity and search time (p < .001). The application of GAMLSS with distributional regression trees to the analysis of visual search data may provide further insights into underlying factors affecting search performance in case-control studies where psychological or physical differences may influence visual search outcomes.

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., words) as good or bad while ignoring the valenced primes (e.g., words). We investigate the differential impact that verbs and adjectives used as primes and targets have on the AP paradigm. Based on earlier work on the Linguistic Category Model, we expect AP effect to be modulated by non-evaluative properties of the word stimuli, such as the linguistic category (e.g., if the prime is an adjective and the target is a verb versus the reverse). A reduction in the magnitude of the priming effect was predicted for adjective-verb prime-target pairs compared to verb-adjective prime-target pairs. Moreover, we implemented a modified crowdsourcing of statistical analyses implementing independently three different statistical approaches. Deriving our conclusions on the converging/diverging evidence provided by the different approaches, we show a clear deductive/inductive asymmetry in AP paradigm (exp. 1), that this asymmetry does not require a focus on the evaluative dimension to emerge (exp. 2) and that the semantic-based asymmetry weakly extends to valence (exp. 3).

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 GEG distribution enables shifting the attention from traditional means and standard deviations to the entire RT distribution. The mathematical properties of the GEG distribution are presented and investigated via simulations. Additionally, the GEG distribution is featured via four real-life data sets. Finally, we discuss how the proposed distribution can be used for regression analyses via generalised additive models for location, scale and shape (GAMLSS).

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. In this short paper, it is suggested that gestural schematization is a special type of symbolic schematization, a much more general process through which irrelevant information related to features of a concept (or a system of concepts) is suppressed, while relevant information (spatial and non-spatial) is maintained. Through symbolic schematization, abstract structural similarity between two concepts or between two systems of concepts can be discovered. In this way, an individual's knowledge about the first situation can be generalized to the second situation. Symbolic schematization is the basis of abstraction, knowledge generalization, and knowledge development. This is particularly the case with abstract mathematical thinking. This proposal offers a picture of cognitive mechanisms through which knowledge of abstract mathematical concepts is created and developed in the mind.

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. The flexibility in taking perspective and frame of reference allows people to embody a mathematical concept or idea in various ways. Based on the findings of past studies, it is suggested that the graphical representation of a mathematical concept may activate those areas of the motor system that are involved in the production of that graphical representation. This is supported by studies showing that when observers look at a painting or handwritten letters, they simulate the painter's or writer's hand movements during painting or writing. Likewise, the motor system can contribute to the grounding of abstract mathematical concepts, such as functions, numbers, and arithmetic operations.

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. Data from n = 3,878 participants spanning 19 countries indicated that a facial mimicry and voluntary facial action task could both amplify and initiate feelings of happiness. However, evidence of facial feedback effects was less conclusive when facial feedback was manipulated unobtrusively via a pen-in-mouth task.

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%. The electroretinogram (ERG), is a clinical test that records the electrical response of the retina to light. The ERG is a promising way to study different neurodevelopmental and neurodegenerative disorders, including ASD. In this study, we have proposed a machine learning based method to detect ASD from control subjects using the ERG waveform. We collected ERG signals from 47 control (CO) and 96 ASD individuals. We analyzed ERG signals both in the time and the spectral domain to gain insight into the statistically significant discriminating features between CO and ASD individuals. We evaluated the machine learning (ML) models using a subject independent cross validation-based approach. Time-domain features were able to detect ASD with a maximum 65% accuracy. The classification accuracy of our best ML model using time-domain and spectral features was 86%, with 98% sensitivity. Our preliminary results indicate that spectral analysis of ERG provides helpful information for the classification of ASD.

Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker's Guide to Responsible Machine Learning.

In The hitchhiker's guide to responsible machine learning, 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.

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 -0.12 to 1.20 log photopic cd.s.m-2. A DWT analysis was performed using the Haar wavelet on the waveforms to examine the energy within the time windows of the a- and b-waves and the oscillatory potentials (OPs) which yielded six DWT coefficients related to these parameters. The central frequency bands were from 20-160 Hz relating to the a-wave, b-wave and OPs represented by the coefficients: a20, a40, b20, b40, op80, and op160, respectively. In addition, the b-wave amplitude and percentage energy contribution of the OPs (%OPs) in the total ERG broadband energy was evaluated. Results: There were significant group differences (p < 0.001) in the coefficients corresponding to energies in the b-wave (b20, b40) and OPs (op80 and op160) as well as the b-wave amplitude. Notable differences between the ADHD and control groups were found in the b20 and b40 coefficients. In contrast, the greatest differences between the ASD and control group were found in the op80 and op160 coefficients. The b-wave amplitude showed both ASD and ADHD significant group differences from the control participants, for flash strengths greater than 0.4 log photopic cd.s.m-2 (p < 0.001). Conclusion: This methodological approach may provide insights about neuronal activity in studies investigating group differences where retinal signaling may be altered through neurodevelopment or neurodegenerative conditions. However, further work will be required to determine if retinal signal analysis can offer a classification model for neurodevelopmental conditions in which there is a co-occurrence such as ASD and ADHD.

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). Reduction of retinal responses in ASD has been reported, but little is known about retinal activity in ADHD. In this study, we compared the light-adapted ERGs of individuals with ADHD, ASD and controls to investigate whether retinal responses differ between these neurodevelopmental conditions. METHODS: Full field light-adapted ERGs were recorded from 15 ADHD, 57 ASD (without ADHD) and 59 control participants, aged from 5.4 to 27.3 years old. A Troland protocol was used with a random series of nine flash strengths from -0.367 to 1.204 log photopic cd.s.m-2. The time-to-peak and amplitude of the a- and b-waves and the parameters of the Photopic Negative Response (PhNR) were compared amongst the three groups of participants, using generalised estimating equations. RESULTS: Statistically significant elevations of the ERG b-wave amplitudes, PhNR responses and faster timings of the b-wave time-to-peak were found in those with ADHD compared with both the control and ASD groups. The greatest elevation in the b-wave amplitudes associated with ADHD were observed at 1.204 log phot cd.s.m-2 flash strength (p < .0001), at which the b-wave amplitude in ASD was significantly lower than that in the controls. Using this measure, ADHD could be distinguished from ASD with an area under the curve of 0.88. CONCLUSIONS: The ERG b-wave amplitude appears to be a distinctive differential feature for both ADHD and ASD, which produced a reversed pattern of b-wave responses. These findings imply imbalances between glutamate and GABA neurotransmission which primarily regulate the b-wave formation. Abnormalities in the b-wave amplitude could provisionally serve as a biomarker for both neurodevelopmental conditions.