Neural representations can be characterized as falling along a continuum, from distributed representations, in which neurons are responsive to many related features of the environment, to localist representations, where neurons orthogonalize activity patterns despite any input similarity. Distributed representations support powerful learning in neural network models and have been posited to exist throughout the brain, but it is unclear under what conditions humans acquire these representations and what computational advantages they may confer. In a series of behavioral experiments, we present evidence that interleaved exposure to new information facilitates the rapid formation of distributed representations in humans. As in neural network models with distributed representations, interleaved learning supports fast and automatic recognition of item relatedness, affords efficient generalization, and is especially critical for inference when learning requires statistical integration of noisy information over time. We use the data to adjudicate between several existing computational models of human memory and inference. The results demonstrate the power of interleaved learning and implicate the use of distributed representations in human inference.

How do we build up our knowledge of the world over time? Many theories of memory formation and consolidation have posited that the hippocampus stores new information, then “teaches” this information to the neocortex over time, especially during sleep. But it is unclear, mechanistically, how this actually works—How are these systems able to interact during periods with virtually no environmental input to accomplish useful learning and shifts in representation? We provide a framework for thinking about this question, with neural network model simulations serving as demonstrations. The model is composed of hippocampus and neocortical areas, which replay memories and interact with one another completely autonomously during simulated sleep. Oscillations are leveraged to support error-driven learning that leads to useful changes in memory representation and behavior. The model has a non–rapid eye movement (NREM) sleep stage, where dynamics between the hippocampus and neocortex are tightly coupled, with the hippocampus helping neocortex to reinstate high-fidelity versions of new attractors, and a REM sleep stage, where neocortex is able to more freely explore existing attractors. We find that alternating between NREM and REM sleep stages, which alternately focuses the model’s replay on recent and remote information, facilitates graceful continual learning. We thus provide an account of how the hippocampus and neocortex can interact without any external input during sleep to drive useful new cortical learning and to protect old knowledge as new information is integrated.

How do we build up our knowledge of the world over time? Many theories of memory formation and consolidation have posited that the hippocampus stores new information, then "teaches" this information to neocortex over time, especially during sleep. But it is unclear, mechanistically, how this actually works — how are these systems able to interact during periods with virtually no environmental input to accomplish useful learning and shifts in representation? We provide a framework for thinking about this question, with neural network model simulations serving as demonstrations. The model contains hippocampus and neocortical areas, which replay memories and interact with one another completely autonomously during simulated sleep. Oscillations are leveraged to support error-driven learning that leads to useful changes in memory representation and behavior. The model has a non-Rapid Eye Movement (NREM) sleep stage, where dynamics between hippocampus and neocortex are tightly coupled, with hippocampus helping neocortex to reinstate high-fidelity versions of new attractors, and a REM sleep stage, where neocortex is able to more freely explore existing attractors. We find that alternating between NREM and REM sleep stages, which alternately focuses the model’s replay on recent and remote information, facilitates graceful continual learning. We thus provide an account of how the hippocampus and neocortex can interact without any external input during sleep to drive useful new cortical learning and to protect old knowledge as new information is integrated.

Inferring relationships that go beyond our direct experience is essential for understanding our environment. This capacity requires either building representations that directly reflect structure across experiences as we encounter them or deriving the indirect relationships across experiences as the need arises. Building structure directly into overlapping representations allows for powerful learning and generalization in neural network models, but building these so-called distributed representations requires inputs to be encountered in interleaved order. We test whether interleaving similarly facilitates the formation of representations that directly integrate related experiences in humans and what advantages such integration may confer for behavior. In a series of behavioral experiments, we present evidence that interleaved learning indeed promotes the formation of representations that directly link across related experiences. As in neural network models, interleaved learning gives rise to fast and automatic recognition of item relatedness, affords efficient generalization, and is especially critical for inference when learning requires statistical integration of noisy information over time. We use the data to adjudicate between several existing computational models of human memory and inference. The results demonstrate the power of interleaved learning and implicate the formation of integrated, distributed representations that support generalization in humans. Public Significance StatementThe study provides evidence that presenting information in an intermixed order facilitates the storage of this information in a format that promotes the efficient discovery of hidden relationships.

Religion has been a widely present feature of human beings. This review explores developments in the evolutionary cognitive psychology of religion and provides critical evaluation of the different theoretical positions. Generally scholars have either believed religion is adaptive, a by-product of adaptive psychological features or maladaptive and varying amounts of empirical evidence supports each position. The adaptive position has generated the costly signalling theory of religious ritual and the group selection theory. The by-product position has identified psychologicalmachinery that has been co-opted by religion. The maladaptive position has generated the meme theory of religion. The review concludes that the by-product camp enjoys the most support in the scientific community and suggests ways forward for an evolutionarily significant study of religion.