Hidden covariation detection produces faster, not slower, social judgments

In Lewicki’s (1986a) demonstration of Hidden Co-variation Detection (HCD), responses were slower to faces that corresponded with a co-variation encountered previously than to faces with novel co-variations. This slowing contrasts with the typical finding that priming leads to faster responding, and might suggest that HCD is a unique type of implicit process. We extended Lewicki’s (1986a) methodology and showed that participants exposed to nonsalient co-variations between hair length and personality were subsequently faster to respond to faces with those co-variations than to faces without, despite lack of awareness of the critical co-variations. This result confirms that people can detect subtle relationships between features of stimuli and that, as with other types of implicit cognition, this detection facilitates responding.</p

Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition

Two approaches are proposed for cross-pose face recognition, one is based on the 3D reconstruction of facial components and the other is based on the deep Convolutional Neural Network (CNN). Unlike most 3D approaches that consider holistic faces, the proposed approach considers 3D facial components. It segments a 2D gallery face into components, reconstructs the 3D surface for each component, and recognizes a probe face by component features. The segmentation is based on the landmarks located by a hierarchical algorithm that combines the Faster R-CNN for face detection and the Reduced Tree Structured Model for landmark localization. The core part of the CNN-based approach is a revised VGG network. We study the performances with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined. We investigate the performances of the network when it is employed as a classifier or designed as a feature extractor. The two recognition approaches and the fast landmark localization are evaluated in extensive experiments, and compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table

Increased motor cortex excitability for concealed visual information

Deceptive behaviour involves complex neural processes involving the primary motor cortex. The dynamics of this motor cortex excitability prior to lying are still not well understood. We sought to examine whether corticospinal excitability can be used to suggest the presence of deliberately concealed information in a modified version of the Guilty Knowledge Test (GKT). Participants pressed keys to either truthfully or deceitfully indicate their familiarity with a series of faces. Motor-evoked-potentials (MEPs) were recorded during response preparation to measure muscle-specific neural excitability. We hypothesised that MEPs would increase during the deceptive condition not only in the lie-telling finger but also in the suppressed truth-telling finger. We report a group-level increase in overall corticospinal excitability 300 ms following stimulus onset during the deceptive condition, without specific activation of the neural representation of the truth-telling finger. We discuss cognitive processes, particularly response conflict and/or automated responses to familiar stimuli, which may drive the observed non-specific increase of motor excitability in deception

Chronic-Pain Protective Behavior Detection with Deep Learning

In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities. As rehabilitation moves outside the clinic, technology should automatically detect such behavior to provide similar support. Previous works have shown the feasibility of automatic protective behavior detection (PBD) within a specific activity. In this paper, we investigate the use of deep learning for PBD across activity types, using wearable motion capture and surface electromyography data collected from healthy participants and people with chronic pain. We approach the problem by continuously detecting protective behavior within an activity rather than estimating its overall presence. The best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross validation. When protective behavior is modelled per activity type, performance is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This performance reaches excellent level of agreement with the average experts' rating performance suggesting potential for personalized chronic pain management at home. We analyze various parameters characterizing our approach to understand how the results could generalize to other PBD datasets and different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on Computing for Healthcar

Automatic Detection of Pain from Spontaneous Facial Expressions

This paper presents a new approach for detecting pain in sequences of spontaneous facial expressions. The motivation for this work is to accompany mobile-based self-management of chronic pain as a virtual sensor for tracking patients' expressions in real-world settings. Operating under such constraints requires a resource efficient approach for processing non-posed facial expressions from unprocessed temporal data. In this work, the facial action units of pain are modeled as sets of distances among related facial landmarks. Using standardized measurements of pain versus no-pain that are specific to each user, changes in the extracted features in relation to pain are detected. The activated features in each frame are combined using an adapted form of the Prkachin and Solomon Pain Intensity scale (PSPI) to detect the presence of pain per frame. Painful features must be activated in N consequent frames (time window) to indicate the presence of pain in a session. The discussed method was tested on 171 video sessions for 19 subjects from the McMaster painful dataset for spontaneous facial expressions. The results show higher precision than coverage in detecting sequences of pain. Our algorithm achieves 94% precision (F-score=0.82) against human observed labels, 74% precision (F-score=0.62) against automatically generated pain intensities and 100% precision (F-score=0.67) against self-reported pain intensities