The utility of a latent-cause framework for understanding addiction phenomena.

Computational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomena such as craving and relapse. Moreover, they cannot account for goal-directed aspects of addiction that necessitate contrasting, model-based formulations.

The challenges of lifelong learning in biological and artificial systems.

How do biological systems learn continuously throughout their lifespans, adapting to change while retaining old knowledge, and how can these principles be applied to artificial learning systems? In this Forum article we outline challenges and strategies of 'lifelong learning' in biological and artificial systems, and argue that a collaborative study of each system's failure modes can benefit both.

Lapses in perceptual decisions reflect exploration.

Perceptual decision-makers often display a constant rate of errors independent of evidence strength. These 'lapses' are treated as a nuisance arising from noise tangential to the decision, e.g.

Visual Evidence Accumulation Guides Decision-Making in Unrestrained Mice.

The ability to manipulate neural activity with precision is an asset in uncovering neural circuits for decision-making. Diverse tools for manipulating neurons are available for mice, but their feasibility remains unclear, especially when decisions require accumulating visual evidence. For example, whether mice' decisions reflect leaky accumulation is unknown, as are the relevant/irrelevant factors that influence decisions.

Decision Activity in Parietal Cortex - Leader or Follower?

Katz and colleagues demonstrate that inactivating the primate lateral intraparietal area (LIP) spares visual motion decisions, even though these same decisions strongly modulate LIP neurons. This work is the latest addition to an intense effort spanning sensory modalities, animals, and techniques to understand which structures comprise the circuits responsible for interpreting sensory signals to make decisions.