Heterogeneity in strategy use during arbitration between experiential and observational learning.

To navigate our complex social world, it is crucial to deploy multiple learning strategies, such as learning from directly experiencing action outcomes or from observing other people's behavior. Despite the prevalence of experiential and observational learning in humans and other social animals, it remains unclear how people favor one strategy over the other depending on the environment, and how individuals vary in their strategy use. Here, we describe an arbitration mechanism in which the prediction errors associated with each learning strategy influence their weight over behavior. We designed an online behavioral task to test our computational model, and found that while a substantial proportion of participants relied on the proposed arbitration mechanism, there was some meaningful heterogeneity in how people solved this task. Four other groups were identified: those who used a fixed mixture between the two strategies, those who relied on a single strategy and non-learners with irrelevant strategies. Furthermore, groups were found to differ on key behavioral signatures, and on transdiagnostic symptom dimensions, in particular autism traits and anxiety. Together, these results demonstrate how large heterogeneous datasets and computational methods can be leveraged to better characterize individual differences.

Distribution Characteristics and Restoration Application of Vegetation in Chengcun Bay Surrounding Areas of Yangjiang City.

In recent years, global warming and sea level rise have further aggravated the risk of coastal erosion. Coastal vegetation plays an important role in resisting storm surges and alleviating coastal erosion. Therefore, screening plant species for the purpose of constructing ecological seawalls to protect or repair damaged coastal zones has become a hot issue. In this paper, a field survey was conducted to investigate the vegetation in Chengcun Bay surrounding areas of Yangjiang City by combining a line survey and sample plot survey. By understanding the vegetation types, distribution and community structure in the bay's surrounding areas and analyzing the restricting environmental factors of those plants, we put forward some countermeasures for coastal vegetation restoration in difficult site conditions from the aspects of plant species selection, vegetation configuration and restoration technology, so as to provide reference for ecological vegetation restoration in similar locations.

Space, Time, and Fear: Survival Computations along Defensive Circuits.

Naturalistic observations show that decisions to avoid or escape predators occur at different spatiotemporal scales and that they are supported by different computations and neural circuits. At their extremes, proximal threats are addressed by a limited repertoire of reflexive and myopic actions, reflecting reduced decision and state spaces and model-free (MF) architectures. Conversely, distal threats allow increased information processing supported by model-based (MB) operations, including affective prospection, replay, and planning. However, MF and MB computations are often intertwined, and under conditions of safety the foundations for future effective reactive execution can be laid through MB instruction of MF control. Together, these computations are associated with distinct population codes embedded within a distributed defensive circuitry whose goal is to determine and realize the best policy.