The representation of time depends heavily on spatial skills. Saj et al. (2014) demonstrated that left-hemispatial neglect patients, who lost the ability to detect objects in their left visual field, have a selective deficit in remembering items corresponding to the past, i.e., the left side of their mental timeline. The current study used the same memory task but tested neurotypical individuals (N = 76) to examine whether individual differences in spatial ability as well as learning order (chronological vs. random) predict how well participants remember items and associations between the item and time (past or future). Our results indicate that higher spatial ability and chronological learning both lead to better memory. This study is among the first to demonstrate how individual differences may impact time representation and memory that relies on a mental timeline.

A large body of evidence demonstrates that emotion impacts memory. Although visual information dominates emotional memories, previous studies have not examined the role of visual imagery as an individual difference variable in the representation of emotional memories. This study examines the role of visual imagery skills (namely, object and spatial imagery) on emotional memories. Participants (N = 115) recalled positive, negative, and neutral events in response to the cue words and then rated the phenomenological characteristics of each event. Event accounts were coded for episodic detail categories (event, place, perceptual, time, emotion-thought details). The results showed that visual imagery skills contributed to the remembrance of the episodic details of positive memories and the phenomenology of both positive and negative events. Overall, this study emphasizes the importance of considering the individual differences in memory research and highlights the differences between emotional and neutral events.

Social media promotes social connectedness, but social media users can still be lonely which is an important preceding condition to various mental health disorders such as anxiety and depression. Here we aim to describe online loneliness in individuals from the linguistic and social features of their platform use. We define a sample of Twitter users who explicitly report being lonely and compare their language to a matching random control sample. For each user, we create a text embedding - a numerical representation of the content of their online posts, excluding terms and expressions related to loneliness. We utilize principal component analysis on the resulting embeddings to condense the data into a smaller number of variables, while still retaining the majority of the variance. By doing so, we are able to position each user within a two-dimensional space, defined by the first two principal components, which capture the most significant amount of variation in the data. Lonely individuals are spatially separated from the control sample, indicating that lonely individuals exhibit distinct language patterns that is often self-referential, e.g. “I should” and “but I”. Indicators of online social relations, such as the number of online friends, favorites, mentions, show that lonely individuals have fewer social relations, while a sentiment analysis demonstrates that their posts have lower valence. Our results provide insights into the lexical, social, and affective markers that characterize loneliness online, providing a starting point for the development of diagnostics and prevention.