Welcome to the Iordanova LabOur lab combines sophisticated behavioral models with the latest neuroscience techniques to understand how learning takes place.
Mihaela Iordanova, Ph.D
Associate Professor, Concordia University
The ability to make accurate predictions about the world is fundamental to adaptive functioning and can be argued to be at the heart of many pathological conditions. This is exemplified in exaggerated expectations of aversive events in anxiety disorders (e.g., OCD, phobias, PTSD), in the ability of environmental cues to invigorate adaptive and maladaptive behaviours (e.g., drug- taking, eating disorders, gambling), as well as in classic memory dysfunction (e.g. forgetting where one put their glasses, forgetting past events).
Our research studies fundamental learning and memory concepts at the neurobiological and behavioural level of analysis with a focus on understanding how memories about upcoming threats or rewards are initially formed, how memories are updated with new potentially conflicting information, and how we use memory to make novel inferences about the world.
Our experimental approach integrates theoretically- informed behavioural designs, with causal methods such as pharmacology, optogenetics, and pharmacogenetics as well as correlational recording techniques including high density electrophysiology.
Our Latest Publication
Causal evidence supporting the proposal that dopamine transients function as temporal difference prediction errors.
Reward-evoked dopamine transients are well established as prediction errors. However, the central tenet of temporal difference accounts—that similar transients evoked by reward-predictive cues also function as errors—remains untested. In the present communication we addressed this by showing that optogenetically shunting dopamine activity at the start of a reward-predicting cue prevents second-order conditioning without affecting blocking. These results indicate that cue-evoked transients function as temporal-difference prediction errors rather than reward predictions. [PDF] [DOI]