Introduction to the special issue on Machine learning for multiple modalities in interactive systems and robots

This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech with its actions, taking into account (audio-)visual feedback during their execution. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. The articles in this special issue represent examples that contribute to filling this gap

Mental Training as a Tool in the Neuroscientific Study of Brain and Cognitive Plasticity

Although the adult brain was once seen as a rather static organ, it is now clear that the organization of brain circuitry is constantly changing as a function of experience or learning. Yet, research also shows that learning is often specific to the trained stimuli and task, and does not improve performance on novel tasks, even very similar ones. This perspective examines the idea that systematic mental training, as cultivated by meditation, can induce learning that is not stimulus or task specific, but process specific. Many meditation practices are explicitly designed to enhance specific, well-defined core cognitive processes. We will argue that this focus on enhancing core cognitive processes, as well as several general characteristics of meditation regimens, may specifically foster process-specific learning. To this end, we first define meditation and discuss key findings from recent neuroimaging studies of meditation. We then identify several characteristics of specific meditation training regimes that may determine process-specific learning. These characteristics include ongoing variability in stimulus input, the meta-cognitive nature of the processes trained, task difficulty, the focus on maintaining an optimal level of arousal, and the duration of training. Lastly, we discuss the methodological challenges that researchers face when attempting to control or characterize the multiple factors that may underlie meditation training effects

Nonpar MANOVA via Independence Testing

The $k$-sample testing problem tests whether or not $k$ groups of data points are sampled from the same distribution. Multivariate analysis of variance (MANOVA) is currently the gold standard for $k$-sample testing but makes strong, often inappropriate, parametric assumptions. Moreover, independence testing and $k$-sample testing are tightly related, and there are many nonparametric multivariate independence tests with strong theoretical and empirical properties, including distance correlation (Dcorr) and Hilbert-Schmidt-Independence-Criterion (Hsic). We prove that universally consistent independence tests achieve universally consistent $k$-sample testing and that $k$-sample statistics like Energy and Maximum Mean Discrepancy (MMD) are exactly equivalent to Dcorr. Empirically evaluating these tests for $k$-sample scenarios demonstrates that these nonparametric independence tests typically outperform MANOVA, even for Gaussian distributed settings. Finally, we extend these non-parametric $k$-sample testing procedures to perform multiway and multilevel tests. Thus, we illustrate the existence of many theoretically motivated and empirically performant $k$-sample tests. A Python package with all independence and k-sample tests called hyppo is available from https://hyppo.neurodata.io/.Comment: 15 pages main + 4 pages appendix, 9 figure

The Decoupled Mind : Mind-wandering Disrupts Cortical Phase-locking to Perceptual Events

The mind flows in a "stream of consciousness," which often neglects immediate sensory input in favor of focusing on intrinsic, self-generated thoughts or images. Although considerable research has documented the disruptive influences of task-unrelated thought for perceptual processing and task performance, the brain dynamics associated with these phenomena are not well understood. Here we investigate the possibility, suggested by several convergent lines of research, that task-unrelated thought is associated with a reduction in the trial-to-trial phase consistency of the oscillatory neural signal in response to perceptual input. Using an experience sampling paradigm coupled with continuous high-density electroencephalography, we observed that task-unrelated thought was associated with a reduction of the P1 ERP, replicating prior observations that mind-wandering is accompanied by a reduction of the brain-evoked response to sensory input. Time-frequency analysis of the oscillatory neural response revealed a decrease in theta-band cortical phase-locking, which peaked over parietal scalp regions. Furthermore, we observed that task-unrelated thought impacted the oscillatory mode of the brain during the initiation of a task-relevant action, such that more cortical processing was required to meet task demands. Together, these findings document that the attenuation of perceptual processing that occurs during task-unrelated thought is associated with a reduction in the temporal fidelity with which the brain responds to a stimulus and suggest that increased neural processing may be required to recouple attention to a task. More generally, these data provide novel confirmatory evidence for the mechanisms through which attentional states facilitate the neural processing of sensory input

A neurophenomenological approach to non-ordinary states of consciousness: hypnosis, meditation, and psychedelics

No contemporary unifying framework has been provided for the study of non-ordinary states of consciousness (NSCs) despite increased interest in hypnosis, meditation, and psychedelics. NSCs induce shifts in experiential contents (what appears to the experiencer) and/or structure (how it appears). This can allow the investigation of the plastic and dynamic nature of experience from a multiscale perspective that includes mind, brain, body, and context. We propose a neurophenomenological (NP) approach to the study of NSCs which highlights their role as catalysts of transformation in clinical practice by refining our understanding of the relationships between experiential (subjective) and neural dynamics. We outline the ethical implications of the NP approach for standard conceptions of health and pathology as well as the crucial role of experience-based know-how in NSC-related research and application. Keywords: agency; altered states of consciousness; hypnosis; meditation; psychedelics; sel

Temporal dynamics of emotional responding: amygdala recovery predicts emotional traits

An individual’s affective style is influenced by many things, including the manner in which an individual responds to an emotional challenge. Emotional response is composed of a number of factors, two of which are the initial reactivity to an emotional stimulus and the subsequent recovery once the stimulus terminates or ceases to be relevant. However, most neuroimaging studies examining emotional processing in humans focus on the magnitude of initial reactivity to a stimulus rather than the prolonged response. In this study, we use functional magnetic resonance imaging to study the time course of amygdala activity in healthy adults in response to presentation of negative images. We split the amygdala time course into an initial reactivity period and a recovery period beginning after the offset of the stimulus. We find that initial reactivity in the amygdala does not predict trait measures of affective style. Conversely, amygdala recovery shows predictive power such that slower amygdala recovery from negative images predicts greater trait neuroticism, in addition to lower levels of likability of a set of social stimuli (neutral faces). These data underscore the importance of taking into account temporal dynamics when studying affective processing using neuroimaging

Effects of Meditation Training and Non-Native Language Training on Cognition in Older Adults: A Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE: Nonpharmacological interventions are a potential strategy to maintain or promote cognitive functioning in older adults. OBJECTIVE: To investigate the effects of 18 months' meditation training and 18 months' non-native language training on cognition in older adults. DESIGN, SETTING, AND PARTICIPANTS: This study was a secondary analysis of the Age-Well trial, an 18-month, observer-masked, randomized clinical trial with 3 parallel arms. Eligible participants were community-dwelling adults aged 65 years and older residing in Caen, France. Participants were enrolled from November 24, 2016, to March 5, 2018, and randomly assigned (1:1:1) to meditation training, non-native language (English) training, or no intervention arms. Final follow-up was completed on February 6, 2020. Data were analyzed between December 2021 and November 2022. INTERVENTIONS: The 18-month meditation and non-native language training interventions were structurally equivalent and included 2-hour weekly group sessions, daily home practice of 20 minutes or longer, and 1 day of more intensive home practice. The no intervention group was instructed not to change their habits and to continue living as usual. MAIN OUTCOMES AND MEASURES: Cognition (a prespecified secondary outcome of the Age-Well trial) was assessed preintervention and postintervention via the Preclinical Alzheimer Cognitive Composite 5 (PACC5), and composites assessing episodic memory, executive function, and attention. RESULTS: Among 137 randomized participants, 2 were excluded for not meeting eligibility criteria, leaving 135 (mean [SD] age, 69.3 [3.8] years; 83 female [61%]) eligible for analysis. One participant among the remaining 135 did not complete the trial. In adjusted mixed effects models, no interaction effects were observed between visit and group for PACC5 (F2,131.39 = 2.58; P = .08), episodic memory (F2,131.60 = 2.34; P = .10), executive function (F2,131.26 = 0.89; P = .41), or attention (F2,131.20 = 0.34; P = .79). Results remained substantively unchanged across sensitivity and exploratory analyses. CONCLUSIONS AND RELEVANCE: In this secondary analysis of an 18-month randomized trial, meditation and non-native language training did not confer salutary cognitive effects. Although further analyses are needed to explore the effects of these interventions on other relevant outcomes related to aging and well-being, these findings did not support the use of these interventions for enhancing cognition in cognitively healthy older adults. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02977819

Exposure to negative socio-emotional events induces sustained alteration of resting-state brain networks in older adults.

peer reviewedBasic emotional functions seem well preserved in older adults. However, their reactivity to and recovery from socially negative events remain poorly characterized. To address this, we designed a 'task-rest' paradigm in which 182 participants from two independent experiments underwent functional magnetic resonance imaging while exposed to socio-emotional videos. Experiment 1 (N = 55) validated the task in young and older participants and unveiled age-dependent effects on brain activity and connectivity that predominated in resting periods after (rather than during) negative social scenes. Crucially, emotional elicitation potentiated subsequent resting-state connectivity between default mode network and amygdala exclusively in older adults. Experiment 2 replicated these results in a large older adult cohort (N = 127) and additionally showed that emotion-driven changes in posterior default mode network-amygdala connectivity were associated with anxiety, rumination and negative thoughts. These findings uncover the neural dynamics of empathy-related functions in older adults and help understand its relationship to poor social stress recovery

From generative models to generative passages: a computational approach to (Neuro) phenomenology

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience