Rises in the number of animal abuse cases are reported around the world. While chatbots have been effective in influencing their users' perceptions and behaviors, little if any research has hitherto explored the design of chatbots that embody animal identities for the purpose of eliciting empathy toward animals. We therefore conducted a mixed-methods experiment to investigate how specific design cues in such chatbots can shape their users' perceptions of both the chatbots' identities and the type of animal they represent. Our findings indicate that such chatbots can significantly increase empathy, improve attitudes, and promote prosocial behavioral intentions toward animals, particularly when they incorporate emotional verbal expressions and authentic details of such animals' lives. These results expand our understanding of chatbots with non-human identities and highlight their potential for use in conservation initiatives, suggesting a promising avenue whereby technology could foster a more informed and empathetic society.

In this paper, we study an under-explored area of language and vocabulary learning: keyword mnemonics, a technique for memorizing vocabulary through memorable associations with a target word via a verbal cue. Typically, creating verbal cues requires extensive human effort and is quite time-consuming, necessitating an automated method that is more scalable. We propose a novel overgenerate-and-rank method via prompting large language models (LLMs) to generate verbal cues and then ranking them according to psycholinguistic measures and takeaways from a pilot user study. To assess cue quality, we conduct both an automated evaluation of imageability and coherence, as well as a human evaluation involving English teachers and learners. Results show that LLM-generated mnemonics are comparable to human-generated ones in terms of imageability, coherence, and perceived usefulness, but there remains plenty of room for improvement due to the diversity in background and preference among language learners.

A familiar scent could help individuals with depression recall memories more easily than verbal cues, a recent study published in JAMA Network Open noted. For people with major depressive disorder (MDD), a familiar smell might help them recall autobiographical memories and potentially help with mental health treatment, according to a group of researchers and social workers from the University of Pittsburgh School of Medicine. Depression has been linked to issues with short-term memory, according to Healthline. IMPROVING MEMORY MAY BE AS EASY AS POPPING A MULTIVITAMIN, STUDY FINDS: 'PREVENTS VASCULAR DEMENTIA' "The main takeaway from the study is that individuals with depression do have specific memories and positive memories, but they just have trouble accessing them," study co-author Dr. Kymberly Young, PhD, an associate professor of psychiatry and neuroscience researcher at the University of Pittsburgh, told Fox News Digital. "By using odors, we can help them access these memories."

In second language vocabulary learning, existing works have primarily focused on either the learning interface or scheduling personalized retrieval practices to maximize memory retention. However, the learning content, i.e., the information presented on flashcards, has mostly remained constant. Keyword mnemonic is a notable learning strategy that relates new vocabulary to existing knowledge by building an acoustic and imagery link using a keyword that sounds alike. Beyond that, producing verbal and visual cues associated with the keyword to facilitate building these links requires a manual process and is not scalable. In this paper, we explore an opportunity to use large language models to automatically generate verbal and visual cues for keyword mnemonics. Our approach, an end-to-end pipeline for auto-generating verbal and visual cues, can automatically generate highly memorable cues. We investigate the effectiveness of our approach via a human participant experiment by comparing it with manually generated cues.

Strategy training is a multidisciplinary rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Strategy training has been shown in randomized, controlled clinical trials to be a more feasible and efficacious intervention for promoting independence than traditional rehabilitation approaches. A standardized fidelity assessment is used to measure adherence to treatment principles by examining guided and directed verbal cues in video recordings of rehabilitation sessions. Although the fidelity assessment for detecting guided and directed verbal cues is valid and feasible for single-site studies, it can become labor intensive, time consuming, and expensive in large, multi-site pragmatic trials. To address this challenge to widespread strategy training implementation, we leveraged natural language processing (NLP) techniques to automate the strategy training fidelity assessment, i.e., to automatically identify guided and directed verbal cues from video recordings of rehabilitation sessions. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task. The best performance was achieved by the BERT model with a 0.8075 F1-score. This BERT model was verified on an external validation dataset collected from a separate major regional health system and achieved an F1 score of 0.8259, which shows that the BERT model generalizes well. Introduction Stroke is a leading cause of disability in the United States. Meta-cognitive strategy training (henceforth referred to as strategy training) is a multidisciplinary rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke.

The attention on technology and risk in financial institutions has often been laser focused on transactions. That makes sense because of the large volume of trades and other transactions that are at risk from fraud, hacking and other issues. However, financial institutions also have other risks. Organizations have been slowly increasing the use of artificial intelligence (AI) in other areas, and there are signs that AI is supporting a convergence of risk management tools in financial institutions. Financial transactions, trading in particular, have long been a focus of technology.

Collaborative robotics requires effective communication between a robot and a human partner. This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature. These principles are evaluated through studies where English-speaking human subjects view animations of simulated robots instructing pick-and-place tasks. The evaluation distinguishes two classes of pointing actions that arise in pick-and- place tasks: referential pointing (identifying objects) and locating pointing (identifying locations). The study indicates that human subjects show greater flexibility in interpreting the intent of referential pointing compared to locating pointing, which needs to be more deliberate. The results also demonstrate the effects of variation in the environment and task context on the interpretation of pointing. Our corpus, experiments and design principles advance models of context, common sense reasoning and communication in embodied communication.

Billions of dollars and years of study have been poured into research trying to discover if someone is lying or not. Researchers from the University of Rochester are now using data science and an online crowdsourcing framework called ADDR (Automated Dyadic Data Recorder) to further understanding of deception based on facial and verbal cues. By playing an online game, the researchers have already collected 1.3 million frames of facial expressions from 151 pairs of individuals in just a few weeks. 'Basically, our system is like Skype on steroids,' said Tay Sen, a PhD student in the lab. The researchers have two people sign up on Amazon Mechanical Turk.

Google Assistant is becoming more conversational with better AI, but what happens when you want to ask a question that you'd rather not say out loud? Today, Google added the ability to type into Google Assistant, making it easier to converse with the AI helper -- even if you're in a loud (or extra-quiet) environment. The ability to query Assistant with text has been part of Allo for a while, but this is the first time it's been enabled directly in Google Assistant, which is also now on your iPhone.