Biology subjects

Hoy, C. W.

Publications and source records attributed to Hoy, C. W..

4 recordsLinked to original sources

bioRxiv2025

Neural dynamics of proactive and reactive cognitive control in medial and lateral prefrontal cortex

Goal-directed behavior requires adjusting cognitive control to both react to and prepare for conflict. Previous work indicates theta oscillations and population activity in dorsomedial prefrontal cortex (dmPFC) and dorsolateral prefrontal cortex (dlPFC) are critical for reactive control. However, the neural mechanisms supporting proactive control are less clear. Here, we investigated the neural basis of behavioral adaptations when control is prepared in anticipation of conflict using intracranial EEG (iEEG) in dmPFC and dlPFC during a Stroop task where conflict frequency was manipulated across blocks. We observed canonical conflict-driven increases in dmPFC theta and in dmPFC and dlPFC local population activity, as indexed by high frequency activity (HFA). Conflict also suppressed theta power in both regions after the response, accentuated a pre-response beta desynchronization selectively in dlPFC, and increased a post-response beta rebound in both regions. Importantly, we identified a pre-trial marker of proactive control where dmPFC theta power increased before trials when conflict was expected, and theta, beta, and HFA conflict signals in both regions were enhanced when conflict was rare and diminished when conflict was common. These findings reveal shared HFA but dissociable oscillatory dynamics in dmPFC and dlPFC during reactive conflict processing, highlight pre-trial dmPFC theta as a potential substrate for proactive control, and refine the roles of dmPFC and dlPFC in control adaptations.

neuroscience↗

bioRxiv2023

Beta and theta oscillations track effort and previous reward in human basal ganglia and prefrontal cortex during decision making

Choosing whether to exert effort to obtain rewards is fundamental to human motivated behavior. However, the neural dynamics underlying the evaluation of reward and effort in humans is poorly understood. Here, we investigate this with chronic intracranial recordings from prefrontal cortex (PFC) and basal ganglia (BG; subthalamic nuclei and globus pallidus) in people with Parkinsons disease performing a decision-making task with offers that varied in levels of reward and physical effort required. This revealed dissociable neural signatures of reward and effort, with BG beta (12-20 Hz) oscillations tracking subjective effort on a single trial basis and PFC theta (4-7 Hz) signaling previous trial reward. Stimulation of PFC increased overall acceptance of offers in addition to increasing the impact of reward on choices. This work uncovers oscillatory mechanisms that guide fundamental decisions to exert effort for reward across BG and PFC, as well as supporting a causal role of PFC for such choices.

neuroscience↗

bioRxiv2022

Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex

The signed value and unsigned salience of reward prediction errors (RPEs) are critical to understanding reinforcement learning (RL) and cognitive control. Dorsomedial prefrontal cortex (dMPFC) and insula (INS) are key regions for integrating reward and surprise information, but conflicting evidence for both signed and unsigned activity has led to competing proposals for the nature of RPE representations in these brain areas. Recently, the distributional RL theory (dRL) has been used to explain RPE coding diversity in the rodent midbrain by proposing that dopaminergic neurons have differential sensitivity to positive and negative RPEs. Here, we use intracranially recorded high frequency activity (HFA) to show that this asymmetric scaling strategy captures RPE coding diversity in human dMPFC and INS. We found neural populations responding to valence-specific positive and negative RPEs, as well as unsigned RPE salience, which are spatially interleaved within each region. Furthermore, directional connectivity estimates suggest a leading role of INS in communicating positive and unsigned RPEs to dMPFC. These findings support asymmetric scaling across distinct but intermingled neural populations as a core principle in RPE coding, expand the scope of dRL, and reconcile longstanding theoretical debates on the role of dMPFC and INS in RL and cognitive control.

neuroscience↗

bioRxiv2020

Multiple sequential prediction errors during reward processing in the human brain

Recent developments in reinforcement learning, cognitive control, and systems neuroscience highlight the complimentary roles in learning of valenced reward prediction errors (RPEs) and non-valenced salience prediction errors (PEs) driven by the magnitude of surprise. A core debate in reward learning focuses on whether valenced and non-valenced PEs can be isolated in the human electroencephalogram (EEG). Here, we combine behavioral modeling and single-trial EEG regression revealing a sequence of valenced and non-valenced PEs in an interval timing task dissociating outcome valence, magnitude, and probability. Multiple regression across temporal, spatial, and frequency dimensions revealed a spatio-tempo-spectral cascade from valenced RPE value represented by the feedback related negativity event-related potential (ERP) followed by non-valenced RPE magnitude and outcome probability effects indexed by subsequent P300 and late frontal positivity ERPs. The results show that learning is supported by a sequence of multiple PEs evident in the human EEG.

neuroscience↗