Kruijne, W. | bioRxiv Science

bioRxiv2020

Implicit Learning of Temporal Behavior in Complex Dynamic Environments

Humans can automatically detect and learn to exploit repeated aspects (regularities) of the environment. Timing research suggests that such learning is not only used to anticipate what will happen, but also when. However, in timing experiments, the intervals to be timed are presented in isolation from other stimuli and explicitly cued, contrasting with naturalistic environments in which intervals are embedded in a constant stream of events and individuals are hardly aware of them. It is unclear whether laboratory findings from timing research translate to a more ecologically valid, implicit environment. Here we show in a game-like experiment, specifically designed to measure naturalistic behavior, that participants implicitly use regular intervals to anticipate future events, even when these intervals are constantly interrupted by irregular yet behaviorally relevant events. This finding extends previous research by showing that individuals not only detect such regularities but can also use this knowledge to decide when to act in a complex environment. Furthermore, this finding demonstrates that this type of learning can occur independently from the ordinal sequence of motor actions, which contrasts this work with earlier motor learning studies. Taken together, our results demonstrate that regularities in the time between events are implicitly monitored and used to predict and act on what happens when, thereby showing that laboratory findings from timing research can generalize to naturalistic environments. Additionally, with the development of our game-like experiment we demonstrate an approach to test cognitive theories in less controlled, ecologically more valid environments.

neuroscience↗

bioRxiv2019

Flexible working memory through selective gating and attentional tagging

Working memory is essential for intelligent behavior as it serves to guide behavior of humans and nonhuman primates when task-relevant stimuli are no longer present to the senses. Moreover, complex tasks often require that multiple working memory representations can be flexibly and independently maintained, prioritized, and updated according to changing task demands. Thus far, neural network models of working memory have been unable to offer an integrative account of how such control mechanisms are implemented in the brain and how they can be acquired in a biologically plausible manner. Here, we present WorkMATe, a neural network architecture that models cognitive control over working memory content and learns the appropriate control operations needed to solve complex working memory tasks. Key components of the model include a gated memory circuit that is controlled by internal actions, encoding sensory information through untrained connections, and a neural circuit that matches sensory inputs to memory content. The network is trained by means of a biologically plausible reinforcement learning rule that relies on attentional feedback and reward prediction errors to guide synaptic updates. We demonstrate that the model successfully acquires policies to solve classical working memory tasks, such as delayed match-to-sample and delayed pro-saccade/antisaccade tasks. In addition, the model solves much more complex tasks including the hierarchical 12-AX task or the ABAB ordered recognition task, which both demand an agent to independently store and updated multiple items separately in memory. Furthermore, the control strategies that the model acquires for these tasks subsequently generalize to new task contexts with novel stimuli. As such, WorkMATe provides a new solution for the neural implementation of flexible memory control. Author SummaryWorking Memory, the ability to briefly store sensory information and use it to guide behavior, is a cornerstone of intelligent behavior. Existing neural network models of Working Memory typically focus on how information is stored and maintained in the brain, but do not address how memory content is controlled: how the brain can selectively store only stimuli that are relevant for a task, or how different stimuli can be maintained in parallel, and subsequently replaced or updated independently according to task demands. The models that do implement control mechanisms are typically not trained in a biologically plausible manner, and do not explain how the brain learns such control. Here, we present WorkMATe, a neural network architecture that implements flexible cognitive control and learns to apply these control mechanisms using a biologically plausible reinforcement learning method. We demonstrate that the model acquires control policies to solve a range of both simple and more complex tasks. Moreover, the acquired control policies generalize to new situations, as with human cognition. This way, WorkMATe provides new insights into the neural organization of Working Memory beyond mere storage and retrieval.

neuroscience↗