A temporal context repetition effect in rats during a novel object recognition memory task

Predictive Representations: Building Blocks of Intelligence

Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute them. This review integrates these theoretical ideas with work on cognition and neuroscience. We pay special attention to the successor representation and its generalizations, which have been widely applied as both engineering tools and models of brain function. This convergence suggests that particular kinds of predictive representations may function as versatile building blocks of intelligence.

The Successor Representation: Its Computational Logic and Neural Substrates

Reinforcement learning is the process by which an agent learns to predict long-term future reward. We now understand a great deal about the brain's reinforcement learning algorithms, but we know considerably less about the representations of states and actions over which these algorithms operate. A useful starting point is asking what kinds of representations we would want the brain to have, given the constraints on its computational architecture. Following this logic leads to the idea of the successor representation, which encodes states of the environment in terms of their predictive relationships with other states. Recent behavioral and neural studies have provided evidence for the successor representation, and computational studies have explored ways to extend the original idea. This paper reviews progress on these fronts, organizing them within a broader framework for understanding how the brain negotiates tradeoffs between efficiency and flexibility for reinforcement learning.

Predicting the Past, Remembering the Future

Rational analyses of memory suggest that retrievability of past experience depends on its usefulness for predicting the future: memory is adapted to the temporal structure of the environment. Recent research has enriched this view by applying it to semantic memory and reinforcement learning. This paper describes how multiple forms of memory can be linked via common predictive principles, possibly subserved by a shared neural substrate in the hippocampus. Predictive principles offer an explanation for a wide range of behavioral and neural phenomena, including semantic fluency, temporal contiguity effects in episodic memory, and the topological properties of hippocampal place cells.