Lessons from the Stories of Women in Neuroscience.

Women have been contributing to the field of neuroscience since its inception, but their accomplishments are often overlooked. Lack of recognition, among other issues, has led to progressively fewer women at each academic stage; although half of neuroscience graduate students are women, women comprise less than one-third of neuroscience faculty, and even fewer full professors. Those who reach this level continue to struggle to get their work recognized. Women from historically excluded backgrounds are even more starkly underrepresented and face added challenges related to racial, ethnic, and other biases. To increase the visibility of women in neuroscience, promote their voices, and learn about their career journeys, we created Stories of Women in Neuroscience (Stories of WiN). Stories of WiN shares the scientific and personal stories of women neuroscientists with diverse backgrounds, identities, research interests, and at various career stages. From >70 women highlighted thus far, a major theme has emerged: there is not a single archetype of a woman neuroscientist, nor a single path to "success." Yet, through these diverse experiences run common threads, such as the importance of positive early research experiences, managing imposter syndrome, the necessity of work-life balance, and the challenges of fitting into-or resisting-the "scientist mold" within a patriarchal, racialized academic system. These commonalities reveal important considerations for supporting women neuroscientists. Through the lens of women highlighted by Stories of WiN, we explore the similarities among their journeys and detail specific actionable items to help encourage, support, and sustain women in neuroscience.

Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG.

Learning signals during reinforcement learning and cognitive control rely on valenced reward prediction errors (RPEs) and non-valenced salience prediction errors (PEs) driven by surprise magnitude. A core debate in reward learning focuses on whether valenced and non-valenced PEs can be isolated in the human electroencephalogram (EEG). We combine behavioral modeling and single-trial EEG regression to disentangle sequential PEs in an interval timing task dissociating outcome valence, magnitude, and probability. Multiple regression across temporal, spatial, and frequency dimensions characterized a spatio-tempo-spectral cascade from early valenced RPE value to non-valenced RPE magnitude, followed by outcome probability indexed by a late frontal positivity. Separating negative and positive outcomes revealed the valenced RPE value effect is an artifact of overlap between two non-valenced RPE magnitude responses: frontal theta feedback-related negativity on losses and posterior delta reward positivity on wins. These results reconcile longstanding debates on the sequence of components representing reward and salience PEs in the human EEG.