Running Head: MODELING MEMORY

Modeling Memory 2 The Natural Input Memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically-informed perceptual pre-processing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming pre-processed natural input to stored representations. By complementing the recognition memory process with a perceptual front-end, the NIM model is able to make predictions about memorability based directly on individual natural stimuli. We demonstrate that the NIM model is able to simulate experimentally-obtained similarity ratings and recognition memory for individual stimuli (i.e., face images)

The natural input memory model

A new recognition memory model is proposed which differs from the existing memory models in that it operates on natural input. Therefore it is called the natural input memory (NIM) model. A biologically-informed perceptual pre-processing method takes local samples from a natural image and translates these into a feature-vector representation. The feature-vector representations reside in a similarity space in which perceptual similarity corresponds to proximity. By using the similarity structure of natural input, the model by-passes assumptions about distributional statistics of real-world input. Our simulations on the list-strength effect, the list-length effect, and the false memory effect support the validity of the proposed model. In particular, we conducted a face recognition simulation with the NIM model and found that it is able to replicat

An online survey to study the relationship between patients' health literacy and coping style and their preferences for self-management-related information

Objective: To evaluate patients' preferences for message features and assess their relationships with health literacy, monitor-blunter coping style, and other patient-dependent characteristics. Methods: Patients with coronary heart disease completed an internet-based survey, which assessed health literacy and monitor-blunter coping style, as well as various other patient characteristics such as sociodemographics, disease history, and explicit information preferences. To assess preferences for message features, nine text sets differing in one of nine message features were composed, and participants were asked to state their preferences. Results: The survey was completed by 213 patients. For three of the nine text sets, a relationship was found between patient preference and health literacy or monitor-blunter coping style. Patients with low health literacy preferred the text based on patient experience. Patients with a monitoring coping style preferred information on short-term effects of their treatment and mentioning of explicit risks. Various other patient characteristics such as marital status, social support, disease history, and age also showed a strong association. Conclusion: Individual differences exist in patients' preferences for message features, and these preferences relate to patient characteristics such as health literacy and monitor-blunter coping styl