Psychological review最新文献

Implicit leadership theories (ILTs) are people's lay theories, definitions, or conceptualizations of leadership. In adults, they determine what actions we perceive as leadership, influence to whom we grant leadership status, and shape our own behaviors when we want to be seen as leader. Naturally, there has been an enduring interest in how these ILTs develop in children. Current theorizing on the development of leadership conceptualizations in children aligns with a stepwise progression mirroring Piaget's stage-based approach to cognitive development. However, contemporary approaches to cognitive development, such as Siegler's overlapping waves theory (OWT), acknowledge that children's development is linked to cognitive success and failure. This article integrates the findings from empirical studies into children's leadership conceptualizations and reinterprets them against OWT. This reinterpretation resolves findings that align poorly with a stepwise approach and demonstrates a strong fit with OWT. As such, children's leadership conceptualizations develop by generating and testing cognitive approaches-physical-spatiotemporal, functional, socioemotional, and humanitarian-and instead of progressing through these in order and according to age, they display variation and selection, that with experience and exposure, lay down selective combinations, which often engage multiple dimensions simultaneously. Consequently, the development of children's understanding of leaders is nonlinear, can be multidimensional, and is based on trial and error largely in response to their experiences. The article concludes with a discussion of the implications for future research and practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Rescorla-Wagner rule remains the most popular tool to describe human behavior in reinforcement learning tasks. Nevertheless, it cannot fit human learning in complex environments. Previous work proposed several hierarchical extensions of this learning rule. However, it remains unclear when a flat (nonhierarchical) versus a hierarchical strategy is adaptive, or when it is implemented by humans. To address this question, current work applies a nested modeling approach to evaluate multiple models in multiple reinforcement learning environments both computationally (which approach performs best) and empirically (which approach fits human data best). We consider 10 empirical data sets (N = 407) divided over three reinforcement learning environments. Our results demonstrate that different environments are best solved with different learning strategies; and that humans adaptively select the learning strategy that allows best performance. Specifically, while flat learning fitted best in less complex stable learning environments, humans employed more hierarchically complex models in more complex environments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Most of us have experienced moments when we could not recall some piece of information but felt that it was just out of reach. Research in metamemory has established that such judgments are often accurate; but what adaptive purpose do they serve? Here, we present an optimal model of how metacognitive monitoring (feeling of knowing) could dynamically inform metacognitive control of memory (the direction of retrieval efforts). In two experiments, we find that, consistent with the optimal model, people report having a stronger memory for targets they are likely to recall and direct their search efforts accordingly, cutting off the search when it is unlikely to succeed and prioritizing the search for stronger memories. Our results suggest that metamemory is indeed adaptive and motivate the development of process-level theories that account for the dynamic interplay between monitoring and control. (PsycInfo Database Record (c) 2024 APA, all rights reserved).