@article{van_horn_editorial_2024,

title = {Editorial: New directions in forensic psychology: applying neuropsychology, biomarkers and technology in assessment & intervention},

author = {Joan E. Van Horn and Märta Wallinius and Yvonne H. A. Bouman and P. Renaud and Josanne D. M. Van Dongen},

url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1479498/full},

doi = {10.3389/fpsyg.2024.1479498},

issn = {1664-1078},

year  = {2024},

date = {2024-09-01},

urldate = {2024-09-01},

journal = {Frontiers in Psychology},

volume = {15},

pages = {1479498},

note = {Publisher: Frontiers Media SA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{agbodoh-falschau_when_2024,

title = {When Misleading Information Hits: How Canadian Companies React?},

author = {R. K. Agbodoh-Falschau and O. Lamzihri and S. Gagnon},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207355906&doi=10.1057%2fs41299-024-00203-4&partnerID=40&md5=d5f72a1918cee04b8585bad2153ca426},

doi = {10.1057/s41299-024-00203-4},

issn = {13633589 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Corporate Reputation Review},

abstract = {The concept of misleading information categorized as misinformation, disinformation, and mal-information among others is receiving much attention, as it is seen to have detrimental consequences on the organizations’ reputation and going concerns. Corporations face more challenges in honing responses to combat these cyber threats as there are still significant differences in the proposed governance solutions and a lack of empirical research investigating the most prevalent ones to fight these growing risks. Grounded on protection motivation theory, empirical evidence, and the data collected from the Statistics Canada Open Sources Database, this paper investigates the appropriateness of governance solutions that corporations contemplate in managing these cybersecurity risks. Our results show that governance solutions related to increasing online presence and customer outreach explained 52% of overall solutions contemplated by corporations to fight these cyber threats. The results also revealed that organizations ranked governance solutions associated with employee training, new increase cybersecurity measures, marketing campaigns, legal action, political advocacy, and reporting to external parties as the most adopted solutions to combat false or misleading information. Overall, our paper contributes to a growing disinformation, misinformation, and mal-information literature in business environments by providing empirical evidence on key governance solutions favored by corporations to combat misleading information. © The Author(s), under exclusive licence to Springer Nature Limited 2024.},

note = {Publisher: Springer Nature},

keywords = {C81, Cybersecurity risk, Disinformation, G34, Governance, K42, M15, Misinformation, Misleading information, Principal component analysis},

pubstate = {published},

tppubtype = {article}

}

@article{joudeh_predicting_2024,

title = {Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features †},

author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198382238&doi=10.3390%2fs24134398&partnerID=40&md5=cefa8b2e2c044d02f99662af350007db},

doi = {10.3390/s24134398},

issn = {14248220 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {13},

abstract = {The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features},

pubstate = {published},

tppubtype = {article}

}

@article{berube_parental_2024,

title = {Parental depression moderates the relationship between childhood maltreatment and the recognition of children expressions of emotions},

author = {A. Bérubé and R. Pétrin and C. Blais},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196266525&doi=10.3389%2ffpsyt.2024.1374872&partnerID=40&md5=ce03a1c39e709fc0f2c773d4f82f3a10},

doi = {10.3389/fpsyt.2024.1374872},

issn = {16640640 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Frontiers in Psychiatry},

volume = {15},

abstract = {Background: Sensitivity plays a crucial role in parenting as it involves the ability to perceive and respond appropriately to children’s signals. Childhood maltreatment and depression can negatively impact adults’ ability to recognize emotions, but it is unclear which of these factors has a greater impact or how they interact. This knowledge is central to developing efficient, targeted interventions. This paper examines the interaction between parents’ depressive symptoms and childhood maltreatment and its influence on their ability to recognize the five basic emotions (happiness, anger, sadness, fear, and disgust) in children’s faces. Method: The sample consisted of 52 parents. Depressive symptoms were measured by the depression subscale of the Brief Symptom Inventory-18 (BSI-18), and maltreatment history was assessed by the Childhood Trauma Questionnaire (CTQ). Children’s emotional stimuli were morphed images created using The Child Affective Facial Expression (CAFE) database. Results: Our findings indicate that depressive symptoms moderate the relationship between parents’ history of childhood maltreatment and emotion recognition skills. Parents with higher depressive symptoms had lower emotion recognition accuracy when they had not experienced maltreatment. When childhood maltreatment was severe, emotion recognition skills were more consistent across all levels of depression. The relationship between depression and emotion recognition was primarily linked to recognizing sadness in children’s faces. Conclusion: These findings highlight how different experiences can affect parental abilities in emotion recognition and emphasize the need for interventions tailored to individual profiles to improve their effectiveness. Copyright © 2024 Bérubé, Pétrin and Blais.},

note = {Publisher: Frontiers Media SA},

keywords = {adult, anger, article, Beck Depression Inventory, Child, Child Abuse, child parent relation, childhood maltreatment, Childhood Trauma Questionnaire, Depression, disease severity, disgust, educational status, emotion, Emotion Recognition, Facial Expression, female, happiness, human, income, major clinical study, male, parent-child relationship, parental sensitivity, preschool child, questionnaire, recognition, sadness},

pubstate = {published},

tppubtype = {article}

}

@article{sheehy_development_2024,

title = {Development and Initial Testing of an Artificial Intelligence-Based Virtual Reality Companion for People Living with Dementia in Long-Term Care},

author = {L. Sheehy and S. Bouchard and A. Kakkar and R. El Hakim and J. Lhoest and A. Frank},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205071099&doi=10.3390%2fjcm13185574&partnerID=40&md5=844732ff858a0d5feb0a95a54093ad4d},

doi = {10.3390/jcm13185574},

issn = {20770383 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Clinical Medicine},

volume = {13},

number = {18},

abstract = {Background/Objectives: Feelings of loneliness are common in people living with dementia (PLWD) in long-term care (LTC). The goals of this study were to describe the development of a novel virtual companion for PLWD living in LTC and assess its feasibility and acceptability. Methods: The computer-generated virtual companion, presented using a head-mounted virtual reality display, was developed in two stages. In Stage 1, the virtual companion asked questions designed to encourage conversation and reminiscence. In Stage 2, more powerful artificial intelligence tools allowed the virtual companion to engage users in nuanced discussions on any topic. PLWD in LTC tested the application at each stage to assess feasibility and acceptability. Results: Ten PLWD living in LTC participated in Stage 1 (4 men and 6 women; average 82 years old) and Stage 2 (2 men and 8 women; average 87 years old). Session lengths ranged from 0:00 to 5:30 min in Stage 1 and 0:00 to 53:50 min in Stage 2. Speech recognition issues and a limited repertoire of questions limited acceptance in Stage 1. Enhanced conversational ability in Stage 2 led to intimate and meaningful conversations with many participants. Many users found the head-mounted display heavy. There were no complaints of simulator sickness. The virtual companion was best suited to PLWD who could engage in reciprocal conversation. After Stage 2, response latency was identified as an opportunity for improvement in future versions. Conclusions: Virtual reality and artificial intelligence can be used to create a virtual companion that is acceptable and enjoyable to some PLWD living in LTC. Ongoing innovations in hardware and software will allow future iterations to provide more natural conversational interaction and an enhanced social experience. © 2024 by the authors.},

note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},

keywords = {aged, article, Artificial intelligence, cognitive decline, cognitive impairment, compassion, conversation, dementia, Elderly, female, human, large language models, long term care, long-term care, major clinical study, male, program acceptability, program feasibility, reaction time, reminiscence, speech discrimination, very elderly, virtual reality},

pubstate = {published},

tppubtype = {article}

}

@article{blais_diversity_2024,

title = {Diversity in Visual Perception: How Cultural Variability in Face Processing Can Inform Policymakers},

author = {C. Blais and D. Fiset and F. Gingras and M. -P. Plouffe-Demers and I. Charbonneau},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201022017&doi=10.1177%2f23727322241269039&partnerID=40&md5=a39ef8d37e4997448cc134104d69269d},

doi = {10.1177/23727322241269039},

issn = {23727322 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Policy Insights from the Behavioral and Brain Sciences},

volume = {11},

number = {2},

pages = {141–148},

abstract = {Psychology and behavioral sciences lack diversity in their participant samples. In visual perception, more specifically, common practice assumes that the processes studied are fundamental and universal. In contrast, cultural psychology has accumulated evidence of cultural variability in visual perception. In face processing, for instance, this cultural variability may sabotage intercultural relations. Policies aim to increase diversity in research, supporting cultural psychology, and to increase awareness among professional workforces, as well as the general population, concerning how cultural variability may influence their interpretation of another's behavior. © The Author(s) 2024.},

note = {Publisher: SAGE Publications Ltd},

keywords = {diversity, Face processing, facial expressions, other-race effect, psychology, societal applications, Visual Perception},

pubstate = {published},

tppubtype = {article}

}

@article{linnaranta_views_2024,

title = {Views on a Culturally Safe Psychotherapeutic Treatment by Inuit in Quebec: Co-Design of Cognitive Behavioral Therapy Manual and Virtual Exposure Environments},

author = {O. Linnaranta and L. G. Cardona and Q. Seon and A. Tukkiapik and J. Outerbridge and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199212719&doi=10.1016%2fj.cbpra.2024.04.006&partnerID=40&md5=196f0e70c5d372d4384226d9452bc8f8},

doi = {10.1016/j.cbpra.2024.04.006},

issn = {10777229 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Cognitive and Behavioral Practice},

abstract = {Cognitive-behavioral psychotherapy (CBT) can be combined with virtual reality (VR) to provide culturally safe and remotely delivered emotion regulation interventions. We conducted a co-design process of a CBT treatment manual and complementary VR environments for the Inuit populations from Nunavik. Here, we describe the knowledge gained during the adaptation process on the approach to mental well-being and psychotherapy. We followed qualitative, participatory, and research co-design methods. After an initial concept of VR-CBT, an advisory group made up of 7 adults identifying as or working with Inuit participated in 4 focus group meetings. A thematic analysis of the discussions was carried out. A non-symptom-focused approach with the therapist guiding the individual in empowerment and emotion management was accepted in the advisory group, replacing a symptom-focus. Several CBT in- and between-session techniques were seen critically or rejected, and time for working on a certain theme was increased. Some elements in the proposed landscape were rejected as unsafe, other elements added as culture-specific to increase safety. Future work should confirm broader acceptance and utility. Culturally specific factors play an essential role in acceptance of concepts and approaches used in psychotherapy. Accordingly, they can have an impact on acceptance and attendance in therapy. © 2024},

note = {Publisher: Elsevier Inc.},

keywords = {cognitive behavioral therapy, cultural adaptation, cultural safety, emotion regulation, Inuit, participatory co-design, virtual reality},

pubstate = {published},

tppubtype = {article}

}