Towards longitudinal data analytics in Parkinson's Disease

The CloudUPDRS app has been developed as a Class I med- ical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis pro- cess based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal pro- cessing and longitudinal data analytics microservices developed to carry out these assessments and to forecast the long-term development of the disease. We also report on early findings from the application of these techniques in the wild with a cohort of early adopters

Semi-supervised Multi-modal Emotion Recognition with Cross-Modal Distribution Matching

Automatic emotion recognition is an active research topic with wide range of applications. Due to the high manual annotation cost and inevitable label ambiguity, the development of emotion recognition dataset is limited in both scale and quality. Therefore, one of the key challenges is how to build effective models with limited data resource. Previous works have explored different approaches to tackle this challenge including data enhancement, transfer learning, and semi-supervised learning etc. However, the weakness of these existing approaches includes such as training instability, large performance loss during transfer, or marginal improvement. In this work, we propose a novel semi-supervised multi-modal emotion recognition model based on cross-modality distribution matching, which leverages abundant unlabeled data to enhance the model training under the assumption that the inner emotional status is consistent at the utterance level across modalities. We conduct extensive experiments to evaluate the proposed model on two benchmark datasets, IEMOCAP and MELD. The experiment results prove that the proposed semi-supervised learning model can effectively utilize unlabeled data and combine multi-modalities to boost the emotion recognition performance, which outperforms other state-of-the-art approaches under the same condition. The proposed model also achieves competitive capacity compared with existing approaches which take advantage of additional auxiliary information such as speaker and interaction context.Comment: 10 pages, 5 figures, to be published on ACM Multimedia 202

The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain

The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates

Modelling distractor devaluation (DD) and its neurophysiological correlates

A series of recent studies have shown that selective attention can influence the emotional value of both selected as well as ignored items. Specifically, ignored items (distractors) were consistently rated less positively in emotional evaluations, following attentional selection, relative to (typically) simultaneously presented items (targets). Furthermore, a known electrophysiological index of attentional selectivity (N2pc) was shown to correlate with the magnitude of the observed ‘distractor devaluation’ (DD). A neural model is presented here to account for these findings by means of a plausible mechanism linking attentional processes to emotional evaluations. This mechanism relies on the transformation of attentional inhibition of the distractor into a reduction of the value of that distractor. The model is successful in reproducing the existent behavioural results as well as the observed link between the magnitude of the attentional N2pc and the magnitude of DD. Moreover, the model proposes a series of testable hypotheses as well as specific predictions that call for further experimental investigation

Feature-based inhibition underlies the affective consequences of attention

International audienc

Feature-based inhibition underlies the affective consequences of attention

Rapid selection of a target in the presence of similar distractors can cause subsequent affective evaluation of a distractor to be more negative than that for the selected object. This distractor devaluation effect has previously been attributed to an association of attentional inhibition with the distractor's representation. Here, we investigated whether the associated inhibition leading to distractor devaluation is object based or feature based. Using colour-tinted face and building stimuli in a two-item simple visual search, followed by evaluation of face stimuli on a trustworthiness scale, we report that emotional evaluation was modified by prior attention whenever the search stimuli and the to-be-evaluated face shared the distractor feature, regardless of whether face identity seen in the two successive tasks matched or not. These data support the notion that inhibition can be feature-based and show that such inhibition can have emotional consequences