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Abstract Two types of discontinuous morphemes are thought to be the basic building blocks of words in Semitic languages: roots and templates. However, the role of these morphemes in lexical access and representation is debated. Priming experiments, where reaction times to target words are predicted to be faster when preceded by morphologically-related primes compared to unrelated control primes, provide conflicting evidence bearing on this debate. We used meta-analysis to synthesise the findings from 229 priming experiments on 4710 unique Semitic speakers. With Bayesian modelling of the aggregate effect sizes, we found credible root and template priming in both nouns and verbs in Arabic and Hebrew. Our results show that root priming effects can be distinguished from the effects of overlap in form and meaning. However, more experiments are needed to determine if template priming effects can be distinguished from overlap in form and morphosyntactic function. 

From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning (RL), a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where RL holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable and discuss technical and social issues that arise when applying RL to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises and perils of experience-based decision-making. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’. 

Data that influence policy and major investment decisions risk entrenching social and political inequities