Two types of motifs enhance human recall and generalization of long sequences.

Whether it is listening to a piece of music, learning a new language, or solving a mathematical equation, people often acquire abstract notions in the sense of motifs and variables-manifested in musical themes, grammatical categories, or mathematical symbols. How do we create abstract representations of sequences? Are these abstract representations useful for memory recall? In addition to learning transition probabilities, chunking, and tracking ordinal positions, we propose that humans also use abstractions to arrive at efficient representations of sequences. We propose and study two abstraction categories: projectional motifs and variable motifs.

An inductive bias for slowly changing features in human reinforcement learning.

Identifying goal-relevant features in novel environments is a central challenge for efficient behaviour. We asked whether humans address this challenge by relying on prior knowledge about common properties of reward-predicting features. One such property is the rate of change of features, given that behaviourally relevant processes tend to change on a slower timescale than noise.

Allogeneic hematopoietic cell transplantation in elderly patients with myelodysplastic syndromes: Considerations and challenges.

Myelodysplastic syndromes/neoplasms (MDS) and related diseases are highly heterogeneous myeloid stem cell cancers that predominantly affect the elderly. The only curative treatment is allogeneic hematopoietic cell transplantation (HCT). Given the prevalence of age-related comorbidities, HCT in patients aged 65 years or older requires a highly personalized approach.

Unifying Principles of Generalization: Past, Present, and Future.

Generalization, defined as applying limited experiences to novel situations, represents a cornerstone of human intelligence. Our review traces the evolution and continuity of psychological theories of generalization, from its origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relationships) to domains such as reinforcement learning and latent structure learning. Historically, there have been fierce debates between approaches based on rule-based mechanisms, which rely on explicit hypotheses about environmental structure, and approaches based on similarity-based mechanisms, which leverage comparisons to prior instances.

Neonatal Survival and Outcomes following Periviable Rupture of Membranes.

Objective:  To clarify survival for infants affected by periviable prolonged preterm premature rupture of membranes (PPROM) in the military health system (MHS). To add to current literature on outcomes following expectant management, including long-term neurodevelopment outcomes.

Study Design:  Retrospective matched cohort review of six level 3 military neonatal intensive care units (NICUs; 2010-2020).