When has the world changed enough to warrant a new approach? The answer depends on current needs, behavioral flexibility and prior knowledge about the environment. Formal approaches solve the problem by integrating the recent history of rewards, errors, uncertainty and context via Bayesian inference to detect changes in the world and alter behavioral policy. Neuronal activity in posterior cingulate cortex - a key node in the default network - is known to vary with learning, memory, reward and task engagement. We propose that these modulations reflect the underlying process of change detection and motivate subsequent shifts in behavior.
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