Fast periodic visual stimulation (FPVS) has emerged as a promising tool for assessing cognitive function in individuals with dementia. This technique leverages electroencephalography (EEG) to measure brain responses to rapidly presented visual stimuli, offering a non-invasive and objective method for evaluating a range of cognitive functions. Unlike traditional cognitive assessments, FPVS does not rely on behavioural responses, making it particularly suitable for individuals with cognitive impairment. In this talk I will highlight a series of studies that have demonstrated its ability to detect subtle deficits in recognition memory, visual processing and attention in dementia patients using EEG in the lab, at home and in clinic. The method is quick, cost-effective, and scalable, utilizing widely available EEG technology. FPVS holds significant potential as a functional biomarker for early diagnosis and monitoring of dementia, paving the way for timely interventions and improved patient outcomes.

Fastball is a novel, fast, passive biomarker of cognitive function, that uses cheap, scalable electroencephalography (EEG) technology. It is sensitive to early dementia; language, education, effort and anxiety independent and can be used in any setting including patients’ homes. It can capture a range of cognitive functions including semantic memory, recognition memory, attention and visual function. We have shown that Fastball is sensitive to cognitive dysfunction in Alzheimer’s disease and Mild Cognitive Impairment, with data collected in patients’ homes using low-cost portable EEG. We are now preparing for significant scale-up and the validation of Fastball in primary and secondary care.

The successful recognition of familiar persons is critical for social interactions. Despite extensive research on the neural representations of familiar faces, we know little about how such representations unfold as someone becomes familiar. In three EEG experiments, we elucidated how representations of face familiarity and identity emerge from different qualities of familiarization: brief perceptual exposure (Experiment 1), extensive media familiarization (Experiment 2) and real-life personal familiarization (Experiment 3). Time-resolved representational similarity analysis revealed that familiarization quality has a profound impact on representations of face familiarity: they were strongly visible after personal familiarization, weaker after media familiarization, and absent after perceptual familiarization. Across all experiments, we found no enhancement of face identity representation, suggesting that familiarity and identity representations emerge independently during face familiarization. Our results emphasize the importance of extensive, real-life familiarization for the emergence of robust face familiarity representations, constraining models of face perception and recognition memory.

State-of-the-art machine vision models can predict human recognition memory for complex scenes with astonishing accuracy. In this talk I present work that investigated how memorable scenes are actually remembered and experienced by human observers. We found that memorable scenes were recognized largely based on recollection of specific episodic details but also based on familiarity for an entire scene. I thus highlight current limitations in machine vision models emulating human recognition memory, with promising opportunities for future research. Moreover, we were interested in what observers specifically remember about complex scenes. We thus considered the functional role of eye-movements as a window into the content of memories, particularly when observers recollected specific information about a scene. We found that when observers formed a memory representation that they later recollected (compared to scenes that only felt familiar), the overall extent of exploration was broader, with a specific subset of fixations clustered around later to-be-recollected scene content, irrespective of the memorability of a scene. I discuss the critical role that our viewing behavior plays in visual memory formation and retrieval and point to potential implications for machine vision models predicting the content of human memories.