Children use inverse planning to detect social transmission in design of artifacts

Do children use objects to infer the people and actions that created them? We ask how children judge whether designs were socially transmitted (copied), asking if children use a simple perceptual heuristic (more similar = more likely copied), or make a rational, flexible inference (Bayesian inverse planning). We found evidence that children use inverse planning to reason about artifacts’ designs: When children saw two identical designs, they did not always infer copying occurred. Instead, similarity was weaker evidence of copying when an alternative explanation ‘explained away’ the similarity. Thus, children inferred copying had occurred less often when designs were efficient (Exp1, age 7-9; N=52), and when there was a constraint that limited the number of possible designs (Exp2, age 4-5; N=160). When thinking about artifacts, young children go beyond perceptual features and use a process like inverse planning to reason about the generative processes involved in design.

Entorhinal and ventromedial prefrontal cortices abstract and generalise the structure of reinforcement learning problems

Knowledge of the structure of a problem, such as relationships between stimuli, enables rapid learning and flexible inference. Humans and other animals can abstract this structural knowledge and generalise it to solve new problems. For example, in spatial reasoning, shortest-path inferences are immediate in new environments. Spatial structural transfer is mediated by grid cells in entorhinal and (in humans) medial prefrontal cortices, which maintain their structure across different environments. Here, using fMRI, we show that entorhinal and ventromedial prefrontal cortex (vmPFC) representations perform a much broader role in generalising the structure of problems. We introduce a task-remapping paradigm, where subjects solve multiple reinforcement learning (RL) problems differing in structural or sensory properties. We show that, as with space, entorhinal representations are preserved across different RL problems only if task structure is preserved. In vmPFC, representations of standard RL signals such as prediction error also vary as a function of task structure.

Trajectory-based differential expression analysis for single-cell sequencing data

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression levels during biological processes such as the cell cycle, cell type differentiation, and cellular activation. Downstream of trajectory inference, it is vital to discover genes that are associated with the lineages in the trajectory to illuminate the underlying biological processes. Furthermore, genes that are differentially expressed between developmental/activational lineages might be highly relevant to further unravel the system under study. Current data analysis procedures, however, typically cluster cells and assess differential expression between the clusters, which fails to exploit the continuous resolution provided by trajectory inference to its full potential. The few available non-cluster-based methods only assess broad differences in gene expression between lineages, hence failing to pinpoint the exact types of divergence. We introduce a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of (i) within-lineage differential expression by detecting associations between gene expression and pseudotime over an entire lineage or by comparing gene expression between points/regions within the lineage and (ii) between-lineage differential expression by comparing gene expression between lineages over the entire lineages or at specific points/regions. By incorporating observation-level weights, the model additionally allows to account for zero inflation, commonly observed in single-cell RNA-seq data from full-length protocols. We evaluate the method on simulated and real datasets from droplet-based and full-length protocols, and show that the flexible inference framework is capable of yielding biological insights through a clear interpretation of the data.