My research is carried out within the group of Vision and Control of Action VISCA and the Institute for Brain, Cognition and Behaviour (IR3C). We study how people perform and make decisions in complex environments and the flexibility to adapt to changing conditions of the environment. We explore a broad range of behaviour including decisional judgements and goal-directed motor actions by using different methodologies: psychophysics, computational modeling, virtual really, eye and motion tracking and neuroimage.

Perceptual, decisional and motor processes in complex environments

The graph shows some basic (and incomplete) elements and their relations within the Perception-Action cycle. In different projects we focus on different components of this cycle. In this page you will find a general description of the components. You can then have a look at the different projects to know specific relations within this cycle in more detail. Finally you will find some process simulations implemented under the R code menu.

In daily-life behaviour humans have to decide between competing actions within complex and rich environments.

  • Optimal decision making depends on people ascertaining different states of the environment accurately and reliably. This is not trivial because the encoding of sensory information is ambiguous. The decoding combined with our priors guarantee a percept that reliably reflects the 3D layout of the environment.
  • Our actions will unfold to achieve certain goals. The inverse models in the motor system will consider the decoded 3D layout of the environment and the goals to program the corresponding motor commands.

  • The brain predicts the consequences (forward models) of the actions and also evaluates these consequences against the goals to make the necessary adjustments. The consequence can be gains or errors and humans will then adjust actions instantaneously or in future attempts.
  • Finally, the same consequences can involve different levels of reward for different organisms (e.g. different gain functions). The overall interaction among the different components (boxes in the figure) would lead to optimal actions or decisions which means maximizing the expected gain.

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Web page made from the rmarkdown template using R and Rstudio. Content created by Joan López-Moliner