Learning temporal statistics for sensory predictions in mild cognitive impairment

Caroline Di Bernardi Luft, Rosalind Baker, Peter Bentham, Zoe Kourtzi

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
130 Downloads (Pure)


Training is known to improve performance in a variety of perceptual and cognitive skills. However, there is accumulating evidence that mere exposure (i.e. without supervised training) to regularities (i.e. patterns that co-occur in the environment) facilitates our ability to learn contingencies that allow us to interpret the current scene and make predictions about future events. Recent neuroimaging studies have implicated fronto-striatal and medial temporal lobe brain regions in the learning of spatial and temporal statistics. Here, we ask whether patients with mild cognitive impairment due to Alzheimer′s disease (MCI-AD) that are characterized by hippocampal dysfunction are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards orientated gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. However, our fMRI results demonstrate that MCI-AD patients recruit an alternate circuit to hippocampus to succeed in learning of predictive structures. In particular, we observed stronger learning-dependent activations for structured sequences in frontal, subcortical and cerebellar regions for patients compared to age-matched controls. Thus, our findings suggest a cortico-striatal–cerebellar network that may mediate the ability for predictive learning despite hippocampal dysfunction in MCI-AD.
Original languageEnglish
Pages (from-to)368-380
Early online date18 Jun 2015
Publication statusPublished - Aug 2015


  • Sequence learning
  • Sensory predictions
  • fMRI


Dive into the research topics of 'Learning temporal statistics for sensory predictions in mild cognitive impairment'. Together they form a unique fingerprint.

Cite this