According to complementary learning systems theory, integrating new memories into the neocortex of the brain without interfering with what is already known depends on a gradual learning process, interleaving new items with previously learned items. However, empirical studies show that information consistent with prior knowledge can sometimes be integrated very quickly. We use artificial neural networks with properties like those we attribute to the neocortex to develop an understanding of the role of consistency with prior knowledge in putatively neocortex-like learning systems, providing new insights into when integration will be fast or slow and how integration might be made more efficient when the items to be learned are hierarchically structured. The work relies on deep linear networks that capture the qualitative aspects of the learning dynamics of the more complex nonlinear networks used in previous work. The time course of learning in these networks can be linked to the hierarchical structure in the training data, captured mathematically as a set of dimensions that correspond to the branches in the hierarchy. In this context, a new item to be learned can be characterized as having aspects that project onto previously known dimensions, and others that require adding a new branch/dimension. The projection onto the known dimensions can be learned rapidly without interleaving, but learning the new dimension requires gradual interleaved learning. When a new item only overlaps with items within one branch of a hierarchy, interleaving can focus on the previously known items within this branch, resulting in faster integration with less interleaving overall. The discussion considers how the brain might exploit these facts to make learning more efficient and highlights predictions about what aspects of new information might be hard or easy to learn. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

Replications are important to science, but who will do them? One proposal is that students can conduct replications as part of their training. As a proof-of-concept for this idea, here we report a series of 11 pre-registered replications of findings from the 2015 volume of Psychological Science, all conducted as part of a graduate-level course.Congruent with larger, more systematic efforts, replications typically yielded smaller effects than originals: The modal outcome was partial support for the original claim.This work documents the challenges facing motivated students as they attempt to replicate previously published results on a first attempt. We describe the workflow and pedagogical methods that were used in the class and discuss implications both for the adoption of this pedagogical model and for replication research more broadly.Keywords: Replication; Reproducibility; Pedagogy; Experimental Methods REPLICATION THROUGH PEDAGOGY 3 Improving the Replicability of Psychological Science Through PedagogyReplicability is a core value for empirical research and there is increasing concern throughout psychology that more independent replication is necessary (Open Science Collaboration, 2015; Wagenmakers, Wetzels, Borsboom, Maas, & Kievit, 2012). Yet under the current incentive structure for science, replication is not typically valued for publication in top journals (Makel, Plucker, & Hegarty, 2012) or in metrics of research productivity (Koole & Lakens, 2012). One potential solution to this problem is to make replication an explicit part of pedagogy: that is, to teach students about experimental methods by asking them to run replication studies (Frank & Saxe, 2012; Grahe et al., 2012). Despite enthusiasm for this idea (Everett & Earp, 2015; M. King et al., 2016;LeBel, 2015;Standing, 2016), there is limited data beyond anecdotal reports and individual projects (Lakens, 2013; e.g., Phillips et al., 2015) to support its efficacy in producing wide-scale pedagogical adoption.In the current article, we describe the pedagogical and methodological approach to replication research taken in our graduate-level experimental methods course and address the practical barriers faced by instructors planning to incorporate replications into their courses. In our course, students conducted replications of published articles from the 2015 volume of the journal Psychological Science with rigorous instructor review at each major stage. The results of these replications are a microcosm of larger replication efforts, providing insight into both the difficulties of pedagogical replications and their promise as a method for improving the robustness of psychological research.We assess the challenges facing a student in choosing an article of interest and -in a single attempt, within constraints of budget, expertise, and effort -reproducing the findings. We consider a number of criteria for evaluating replication success, including statistical significance, effect size, a Bayesian measure of evidence (Etz & REPLICATION THROUG...

According to complementary learning systems theory, integrating new memories into the neocortex of the brain without interfering with what is already known depends on a gradual learning process, interleaving new items with previously learned items. However, empirical studies show that information consistent with prior knowledge can be integrated very quickly. We use artificial neural networks with properties like those we attribute to the neocortex to develop a theoretical understanding of the role of consistency with prior knowledge in putatively neocortex-like learning systems, providing new insights into when integration will be fast or slow and how integration might be made more efficient when the items to be learned are hierarchically structured. The work relies on deep linear networks that capture the qualitative aspects of the learning dynamics of the more complex non-linear networks used in previous work. The time course of learning in these networks can be linked to the hierarchical structure in the training data, captured mathematically as a set of dimensions that correspond to the branches in the hierarchy. In this context, a new item to be learned can be characterized as having aspects that project onto previously known dimensions, and others that require adding a new branch/dimension. The projection onto the known dimensions can be learned rapidly without interleaving, but learning the new dimension requires gradual interleaved learning. When a new item only overlaps with items within one branch of a hierarchy, interleaving can focus on the previously-known items within this branch, resulting in faster integration with less inter-leaving overall. The discussion considers how the brain might exploit these facts to make learning more efficient and highlights predictions about what aspects of new information might be hard or easy to learn.

Continuous first-person 3D environments pose unique exploration challenges to reinforcement learning (RL) agents, because of their high-dimensional state and action spaces. These challenges can be ameliorated by using semantically meaningful state abstractions to define novelty for exploration. We propose that learned representations shaped by natural language provide exactly this form of abstraction. In particular, we show that vision-language representations, when pretrained on image captioning datasets sampled from the internet, can drive meaningful, task-relevant exploration and improve performance on 3D simulated environments. We also characterize why and how language provides useful abstractions for exploration by comparing the impacts of using representations from a pretrained model, a language oracle, and several ablations. We demonstrate the benefits of our approach in two very different task domains-one that stresses the identification and manipulation of everyday objects, and one that requires navigational exploration in an expansive world-as well as two popular deep RL algorithms: Impala and R2D2. Our results suggest that using language-shaped representations could improve exploration for various algorithms and agents in challenging environments. * Equal contribution Preprint. Under review.

The ability to use symbols is the pinnacle of human intelligence, but has yet to be fully replicated in machines. Here we argue that the path towards symbolically fluent artificial intelligence (AI) begins with a reinterpretation of what symbols are, how they come to exist, and how a system behaves when it uses them. We begin by offering an interpretation of symbols as entities whose meaning is established by convention. But crucially, something is a symbol only for those who demonstrably and actively participate in this convention. We then outline how this interpretation thematically unifies the behavioural traits humans exhibit when they use symbols. This motivates our proposal that the field place a greater emphasis on symbolic behaviour rather than particular computational mechanisms inspired by more restrictive interpretations of symbols. Finally, we suggest that AI research explore social and cultural engagement as a tool to develop the cognitive machinery necessary for symbolic behaviour to emerge. This approach will allow for AI to interpret something as symbolic on its own rather than simply manipulate things that are only symbols to human onlookers, and thus will ultimately lead to AI with more human-like symbolic fluency.