Connecting Context-specific Adaptation in Humans to Meta-learning
  Cognitive control, the ability of a system to adapt to the demands of a task,
is an integral part of cognition. A widely accepted fact about cognitive
control is that it is context-sensitive: Adults and children alike infer
information about a task's demands from contextual cues and use these
inferences to learn from ambiguous cues. However, the precise way in which
people use contextual cues to guide adaptation to a new task remains poorly
understood. This work connects the context-sensitive nature of cognitive
control to a method for meta-learning with context-conditioned adaptation. We
begin by identifying an essential difference between human learning and current
approaches to meta-learning: In contrast to humans, existing meta-learning
algorithms do not make use of task-specific contextual cues but instead rely
exclusively on online feedback in the form of task-specific labels or rewards.
To remedy this, we introduce a framework for using contextual information about
a task to guide the initialization of task-specific models before adaptation to
online feedback. We show how context-conditioned meta-learning can capture
human behavior in a cognitive task and how it can be scaled to improve the
speed of learning in various settings, including few-shot classification and
low-sample reinforcement learning. Our work demonstrates that guiding
meta-learning with task information can capture complex, human-like behavior,
thereby deepening our understanding of cognitive control.
