Dr. María Herrojo Ruiz


Goldsmiths University of London
Department of Psychology



Current academic positions

Reader in Psychology at Goldsmiths University of London (UK).

Co-Director of the MSc in Computational Cognitive Neuroscience at Goldsmiths (UK).

Leading Research Fellow at the Institute of Cognitive Neuroscience of the Higher School of Economics, Moscow (Russia).

Research interests

My research utilises methodological and computational approaches to explore central questions regarding the neural bases of sensorimotor learning and motor control in healthy and clinical populations.

Some of our recent work focuses on understanding the role of neuronal oscillations in the modulation of reward-based motor learning and decision making.

Another line of research investigates the effect of anxiety on these processes. We are using neuroscientific methods and computational modelling to understand how anxiety modulates the neural mechanisms underlying predictive processing and uncertainty, and the resultant effects on learning and decision making. Psychiatric conditions such as bipolar disorder or obsessive compulsive disorder are also the focus of my current research.

Extrapolating to the domain of skilled performance, we are examining the neural processes associated with learning about success and failure, fast motor decision making, and performance anxiety. To address these questions, we are conducting new research studies in expert video game players and musicians.

In an additional line of research, we are examining the contribution of interoceptive processes to motor skill learning and skilled performance. We are studying the role of  implicit cardiovascular interoceptive information in piano performance. In addition, we’re working on applying predictive coding models of interoception to the area of motor control.




In our research, we use electroencephalography, magnetoencephalography, intracraneal recordings, deep brain stimulation and non-invasive stimulation in combination with computational modelling and machine learning methods.

General research interests:

  • Sensorimotor learning and motor control
  • Music cognition and performance
  • Computational Modelling
  • Expectation and prediction
  • Anxiety and cognitive biases
  • Bayesian inference
  • Statistics
  • Neurological disorders
  • Deep brain stimulation
  • Dynamical systems
  • Stochastic processes