Dopaminergic specializations for flexible behavioral control: linking levels of analysis and functional architectures

Explaining dopamine through prediction errors and beyond

The most influential account of phasic dopamine holds that it reports reward prediction errors (RPEs). The RPE-based interpretation of dopamine signaling is, in its original form, probably too simple and fails to explain all the properties of phasic dopamine observed in behaving animals. This Perspective helps to resolve some of the conflicting interpretations of dopamine that currently exist in the literature. We focus on the following three empirical challenges to the RPE theory of dopamine: why does dopamine (1) ramp up as animals approach rewards, (2) respond to sensory and motor features and (3) influence action selection? We argue that the prediction error concept, once it has been suitably modified and generalized based on an analysis of each computational problem, answers each challenge. Nonetheless, there are a number of additional empirical findings that appear to demand fundamentally different theoretical explanations beyond encoding RPE. Therefore, looking forward, we discuss the prospects for a unifying theory that respects the diversity of dopamine signaling and function as well as the complex circuitry that both underlies and responds to dopaminergic transmission.

The neuroscience of active learning and direct instruction

Throughout the educational system, students experiencing active learning pedagogy perform better and fail less than those taught through direct instruction. Can this be ascribed to differences in learning from a neuroscientific perspective? This review examines mechanistic, neuroscientific evidence that might explain differences in cognitive engagement contributing to learning outcomes between these instructional approaches. In classrooms, direct instruction comprehensively describes academic content, while active learning provides structured opportunities for learners to explore, apply, and manipulate content. Synaptic plasticity and its modulation by arousal or novelty are central to all learning and both approaches. As a form of social learning, direct instruction relies upon working memory. The reinforcement learning circuit, associated agency, curiosity, and peer-to-peer social interactions combine to enhance motivation, improve retention, and build higher-order-thinking skills in active learning environments. When working memory becomes overwhelmed, additionally engaging the reinforcement learning circuit improves retention, providing an explanation for the benefits of active learning. This analysis provides a mechanistic examination of how emerging neuroscience principles might inform pedagogical choices at all educational levels.

Pupil dilation reflects effortful action invigoration in overcoming aversive Pavlovian biases

"Pavlovian" or "motivational" biases describe the phenomenon that the valence of prospective outcomes modulates action invigoration: Reward prospect invigorates action, whereas punishment prospect suppresses it. The adaptive role of these biases in decision-making is still unclear. One idea is that they constitute a fast-and-frugal decision strategy in situations characterized by high arousal, e.g., in presence of a predator, which demand a quick response. In this pre-registered study (N = 35), we tested whether such a situation-induced via subliminally presented angry versus neutral faces-leads to increased reliance on Pavlovian biases. We measured trial-by-trial arousal by tracking pupil diameter while participants performed an orthogonalized Motivational Go/NoGo Task. Pavlovian biases were present in responses, reaction times, and even gaze, with lower gaze dispersion under aversive cues reflecting "freezing of gaze." The subliminally presented faces did not affect responses, reaction times, or pupil diameter, suggesting that the arousal manipulation was ineffective. However, pupil dilations reflected facets of bias suppression, specifically the physical (but not cognitive) effort needed to overcome aversive inhibition: Particularly strong and sustained dilations occurred when participants managed to perform Go responses to aversive cues. Conversely, no such dilations occurred when they managed to inhibit responses to Win cues. These results suggest that pupil diameter does not reflect response conflict per se nor the inhibition of prepotent responses, but specifically effortful action invigoration as needed to overcome aversive inhibition. We discuss our results in the context of the "value of work" theory of striatal dopamine.

Etiopathogenic Models of Psychosis Spectrum Illnesses Must Resolve Four Key Features

Etiopathogenic models for psychosis spectrum illnesses are converging on a number of key processes, such as the influence of specific genes on the synthesis of proteins important in synaptic functioning, alterations in how neurons respond to synaptic inputs and engage in synaptic pruning, and microcircuit dysfunction that leads to more global cortical information processing vulnerabilities. Disruptions in prefrontal operations then accumulate and propagate over time, interacting with environmental factors, developmental processes, and homeostatic mechanisms, eventually resulting in symptoms of psychosis and disability. However, there are 4 key features of psychosis spectrum illnesses that are of primary clinical relevance but have been difficult to assimilate into a single model and have thus far received little direct attention: 1) the bidirectionality of the causal influences for the emergence of psychosis, 2) the catastrophic clinical threshold seen in first episodes of psychosis and why it is irreversible in some individuals, 3) observed biotypes that are neurophysiologically distinct but clinically both convergent and divergent, and 4) a reconciliation of the role of striatal dopaminergic dysfunction with models of prefrontal cortical state instability. In this selective review, we briefly describe these 4 hallmark features and we argue that theoretically driven computational perspectives making use of both algorithmic and neurophysiologic models are needed to reduce this complexity and variability of psychosis spectrum illnesses in a principled manner.