Gotlieb, N. | Search bioRxiv

bioRxiv2019

Attentional Networks during the Menstrual Cycle

The menstrual cycle is characterized partially by fluctuations of the ovarian hormones estradiol (E2) and progesterone (P4), which are implicated in the regulation of cognition. Research on attention in the different stages of the menstrual cycle is sparse, and the three attentional networks (alerting, orienting and executive) and their interaction were not explored during the menstrual cycle. In the current study, we used the ANT-I (attentional network test - interactions) to examine two groups of women: naturally cycling (NC) - those with a regular menstrual cycle, and oral contraceptives (OC) - those using OC and characterized with low and steady ovarian hormone levels. We tested their performance at two time points that fit, in natural cycles, the early follicular phase and the early luteal phase. We found no differences in performance between NC and OC in low ovarian hormone states (Both phases for the OC group and early follicular phase for the NC group). However, the NC group in the early luteal phase exhibited the same pattern of responses for alerting and no-alerting conditions, resulting in a better conflict resolution (executive) when attention is oriented to the target. Results-driven exploratory regression analysis of E2 and P4 suggested that change in P4 from early follicular to early luteal phases was a mediator for the alerting effect found. In conclusion, the alerting state found with or without alertness manipulation suggests that there is a progesterone mediated activation of the alerting system during the mid-luteal phase.

animal behavior and cognition↗

bioRxiv2019

Distentangling the systems contributing to changes in learning during adolescence

Multiple neurocognitive systems contribute simultaneously to learning. For example, dopamine and basal ganglia (BG) systems are thought to support reinforcement learning (RL) by incrementally updating the value of choices, while the prefrontal cortex (PFC) contributes different computations, such as actively maintaining precise information in working memory (WM). It is commonly thought that WM and PFC show more protracted development than RL and BG systems, yet their contributions are rarely assessed in tandem. Here, we used a simple learning task to test how RL and WM contribute to changes in learning across adolescence. We tested 187 subjects ages 8 to 17 and 53 adults (25-30). Participants learned stimulus-action associations from feedback; the learning load was varied to be within or exceed WM capacity. Participants age 8-12 learned slower than participants age 13-17, and were more sensitive to load. We used computational modeling to estimate subjects use of WM and RL processes. Surprisingly, we found more robust changes in RL than WM during development. RL learning rate increased significantly with age across adolescence and WM parameters showed more subtle changes, many of them early in adolescence. These results underscore the importance of changes in RL processes for the developmental science of learning.\n\nHighlights- Subjects combine reinforcement learning (RL) and working memory (WM) to learn\n- Computational modeling shows RL learning rates grew with age during adolescence\n- When load was beyond WM capacity, weaker RL compensated less in younger adolescents\n- WM parameters showed subtler and more puberty-related changes\n- WM reliance, maintenance, and capacity had separable developmental trajectories\n- Underscores importance of RL processes in developmental changes in learning

neuroscience↗