Vincent P. Ferrera
· Professor of Neuroscience (in Psychiatry)VerifiedColumbia University · Pathology & Cell Biology
Active 1985–2026
About
Vincent P. Ferrera, PhD, is a Professor of Neuroscience (in Psychiatry) at Columbia University Vagelos College of Physicians and Surgeons. His research focuses on two main areas: flexible decision-making in the prefrontal cortex and basal ganglia, and the neural mechanisms of attention and reward in the visual cortex. His work investigates how neural circuits underpin cognitive flexibility, including how the brain weighs evidence, adjusts decision criteria, and evaluates reward outcomes using neurophysiological and functional imaging approaches. Additionally, Ferrera explores how reward influences attention, particularly in the context of socially rewarding stimuli such as faces. His contributions include advancing understanding of neural mechanisms involved in decision-making, attention, and reward processing, with a particular emphasis on the frontal eye field and related brain regions. His research has provided insights into how the brain adapts behavior to changing circumstances and how reward signals modulate attention, contributing to the broader field of systems and circuits in cognitive and systems neuroscience.
Research topics
- Computer Science
- Neuroscience
- Psychology
- Radiology
- Internal medicine
- Medicine
Selected publications
NeuroImage · 2026-02-12
articleOpen accessSenior authoras a sensitive, non-contrast biomarker for both local and global BBB permeability changes induced by focused ultrasound, supporting its potential for longitudinal monitoring in preclinical and clinical neurotherapeutic applications.
Learning Decouples Accuracy and Reaction Time for Rapid Decisions in a Transitive Inference Task
Journal of Cognitive Neuroscience · 2025-12-30
articleOpen accessSenior authorTransitive inference (TI) is a cognitive process in which decisions are guided by internal representations of abstract relationships. Although the mechanisms underlying transitive learning have been well studied, the dynamics of the decision-making process during learning and inference remain less clearly understood. In this study, we investigated whether a modeling framework traditionally applied to perceptual decision-making-the drift diffusion model (DDM)-can account for performance in a TI transfer task involving rapid decisions that deviate from standard accuracy and response time (RT) patterns. We trained six macaque monkeys on a TI transfer task, in which they learned the implied order of a novel list of seven images in each behavioral session, indicating their decisions with saccadic eye movements or reaching movements. Consistent learning of the list structure was achieved within 200-300 trials per session. Behavioral performance exhibited a symbolic distance effect, with accuracy increasing as the ordinal distance between items grew. Notably, RTs remained relatively stable across learning, despite improvements in accuracy. We applied a generalized DDM implementation (PyDDM) [Shinn, M., Lam, N. H., & Murray, J. D. A flexible framework for simulating and fitting generalized drift-diffusion models. eLife, 9, e56938, 2020] to jointly fit accuracy and RT data. Model fits were achieved by incorporating both an increasing evidence accumulation rate and a collapsing decision bound, successfully capturing the RT distribution shapes observed during learning. Learning and transfer were fit by varying drift rate with little change in other parameters. Eye and reaching movements showed similar dynamics, with the difference in RT accounted for mainly by nondecision time. Our results highlight a distinct dynamical regime of the DDM framework, extending its applicability to cognitive domains involving symbolic reasoning and serial relational learning.
bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-03 · 2 citations
preprintOpen accessABSTRACT Gene therapy for neurodegenerative diseases faces significant challenges due to the blood-brain barrier (BBB), which limits drug delivery to the central nervous system (CNS). While clinical trials for Parkinson’s disease (PD) have progressed, administration of vectors expressing enzymatic or neurotrophic factor transgenes have required extensive optimization of the delivery method to achieve potentially therapeutic levels of transgene expression. Focused ultrasound (FUS) combined with microbubbles has emerged as a promising non-invasive strategy to transiently open the BBB for targeted gene delivery via viral nanocarriers including recombinant adeno-associated viruses (AAVs). However, key factors influencing FUS-mediated AAV delivery, including dose distribution and therapeutic efficacy, remain underexplored in non-human primates (NHPs). Here, we evaluated the feasibility of AAV9-CAG-GFP delivery using two portable therapeutic ultrasound modalities: ultrasound-guided, spherically-focused FUS (USgFUS) and a novel low-frequency linear array configuration for imaging and therapy called theranostic ultrasound (ThUS). In mice, FUS-sonicated regions exhibited a 25-fold increase in AAV9 biodistribution compared to systemic injection alone. Extending this approach to NHPs, we observed up to a 200-fold increase in AAV9 DNA in treated brain regions, including PD-relevant structures. In assessing the translational therapeutic potential of this technique, ThUS-mediated AAV9-hSyn-hNTRN (human neurturin) delivery in a toxin mouse model of PD facilitated the rescue of up to 80% and 75% of degenerated dopaminergic neurons in the substantia nigra and striatum, respectively. These findings demonstrate that portable ultrasound technologies can non-invasively enhance AAV9 delivery to targeted brain regions in both mice and NHPs relative to what can be achieved with intravenous (IV) delivery of the same capsid alone. With further development, these approaches may offer a clinically viable, non-invasive alternative for gene therapy in neurodegenerative diseases. One sentence summary BBB opening with portable therapeutic ultrasound non-invasively increased viral gene delivery to the brain after systemic AAV vector administration in mice and rhesus macaques.
Research Square · 2025-08-12
preprintOpen accessSenior authorLearning decouples accuracy and reaction time for rapid decisions in a transitive inference task
bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-12
preprintOpen accessSenior authorCorrespondingTransitive inference (TI) is a cognitive process in which decisions are guided by internal representations of abstract relationships. While the mechanisms underlying transitive learning have been well studied, the dynamics of the decision-making process during learning and inference remain less clearly understood. In this study, we investigated whether a modeling framework traditionally applied to perceptual decision-making-the drift diffusion model (DDM)-can account for performance in a TI transfer task involving rapid decisions that deviate from standard accuracy and response time (RT) patterns. We trained three macaque monkeys on a TI transfer task, in which they learned the implied order of a novel list of seven images in each behavioral session. Monkeys indicated their decisions with saccadic eye movements. Consistent learning of the list structure was achieved within 200-300 trials per session, with asymptotic accuracies reaching approximately 80-90%. Behavioral performance exhibited a symbolic distance effect, with accuracy increasing as the ordinal distance between items grew. Notably, RTs remained relatively stable across learning, despite improvements in accuracy. We applied a generalized DDM implementation (PyDDM; Shinn et al., 2020) to jointly fit accuracy and RT data. Model fits were achieved by incorporating both an increasing evidence accumulation rate and a collapsing decision bound, successfully capturing the RT distribution shapes observed during learning. These findings suggest that decision-making during serial learning and transfer in a TI task can be characterized by a "variable collapsing bound" DDM. Our results highlight a distinct dynamical regime of the DDM framework, extending its applicability to cognitive domains involving symbolic reasoning and serial relational learning.
2025-01-01 · 1 citations
book-chapterOpen accessSenior author2025-01-01
book-chapterOpen accessSenior authorWhy Do Computers Need Attention?
2025-01-01
book-chapterOpen accessResearch Square · 2025-08-29
preprintOpen accessSenior authorResearch Square · 2025-07-30
preprintOpen accessSenior author
Recent grants
Focused ultrasound for noninvasive brain stimulation
NIH · $2.1M · 2017–2023
NIH · $5.6M · 2018–2028
The neurophysiological basis of serial learning
NIH · $3.2M · 2008–2019
CATEGORICAL DECISION-MAKING IN FRONTO-STRIATAL CIRCUITS
NIH · $4.7M · 1998–2016
NIH · $443k · 2009
Frequent coauthors
- 121 shared
Greg Jensen
Columbia University Irving Medical Center
- 65 shared
H. S. Terrace
Columbia University
- 41 shared
Fabián Muñoz
Universidad de La Frontera
- 38 shared
Yelda Alkan
University of California, Los Angeles
- 28 shared
Tobias Teichert
University of Pittsburgh
- 24 shared
Elisa E. Konofagou
Columbia University
- 24 shared
L. F. Abbott
Columbia University
- 22 shared
Béchir Jarraya
Cognitive Neuroimaging Lab
Education
- 1996
Ph.D., Molecular Pharmacology
Columbia University
- 1992
M.D.
University of Pennsylvania School of Medicine
- 1988
B.S., Biology
University of Pennsylvania
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