Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on early-stage dementia detection. We provide a new spontaneous speech corpus for this challenge. This corpus includes answers from three prompts designed by neurologists to better capture the cognition of speakers. Our baseline models achieved an F1-score of 55.0% on the classification task and an RMSE of 2.98 on the regression task.

Early signs of cognitive decline, such as dementia and mild cognitive impairment (MCI), often manifest in conversational speech. Early and accurate identification is essential for potential interventions prior to the onset of more severe stages of neurodegenerative diseases. We present CognoMemory, a system for detecting cognitive decline based on a person's speech, to collect 307 hrs of real-world conversational speech, corresponding to 1.92 million Whisper-transcribed words, from 1,639 participants. Speech recordings were collected as participants answered 14 memory-probing, clinically effective questions asked by a virtual agent, starting with a motivation prompt, followed by memory, cognitive functioning, fluency, picture description and reading task. Both acoustic and linguistic features, along with large language model (LLM) embeddings, were extracted from all 1,639 participants. A subset of 614 participants, either with an unconfirmed diagnosis or younger than 50 years, was used for pre-training. The remaining three groups (64 dementia, 169 MCI and 792 healthy participants) were used to fine-tune our proposed model. Our multimodal feature fusion and CNN/Bi-LSTM-based transfer learning approach outperforms LLM-based (BART, DistilBERT, RoBERTa and HuBERT) approaches while achieving the highest $F_{1}$-scores of 0.83 & 0.54 using just the initial 'motivation' question for 2-way & 3-way classification; exhibiting a 3% performance increase due to the application of transfer learning, while being also 38% faster. Finally, the classifiers trained on the CognoMemory data, the largest of its kind, were tested on the second-largest available DementiaBank dataset (Pitt corpus), and a CNN-based transfer learning architecture achieved an $F_{1}$-score of 0.89, demonstrating better stability and generalisation across datasets and of our novel feature fusion and architecture.

Arguably one of the most important characteristics of virtual reality (VR) is its ability to induce higher feelings of presence. Still, research has remained inconclusive on how presence is affected by human factors such as emotion and agency. Here we adopt a novel design to investigate their effects by testing virtual environments inducing either happiness or fear, with or without user agency. Results from 121 participants showed that the dominant emotion induced by a virtual environment is positively correlated with presence. In addition, agency had a significant positive effect on presence and, furthermore, moderated the effect of emotion on presence. We show for the first time that the effects of emotion and agency on presence are not straightforward but they can be modelled by separating design factors from subjective measures. We discuss how these findings can explain seemingly conflicting results of related work and their implications for VR design.