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2017 ; 37
(35
): 8412-8427
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Learning Predictive Statistics: Strategies and Brain Mechanisms
#MMPMID28760866
Wang R
; Shen Y
; Tino P
; Welchman AE
; Kourtzi Z
J Neurosci
2017[Aug]; 37
(35
): 8412-8427
PMID28760866
show ga
When immersed in a new environment, we are challenged to decipher initially
incomprehensible streams of sensory information. However, quite rapidly, the
brain finds structure and meaning in these incoming signals, helping us to
predict and prepare ourselves for future actions. This skill relies on extracting
the statistics of event streams in the environment that contain regularities of
variable complexity from simple repetitive patterns to complex probabilistic
combinations. Here, we test the brain mechanisms that mediate our ability to
adapt to the environment's statistics and predict upcoming events. By combining
behavioral training and multisession fMRI in human participants (male and
female), we track the corticostriatal mechanisms that mediate learning of
temporal sequences as they change in structure complexity. We show that learning
of predictive structures relates to individual decision strategy; that is,
selecting the most probable outcome in a given context (maximizing) versus
matching the exact sequence statistics. These strategies engage distinct human
brain regions: maximizing engages dorsolateral prefrontal, cingulate,
sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas
matching engages occipitotemporal regions (including the hippocampus) and basal
ganglia (ventral caudate). Our findings provide evidence for distinct
corticostriatal mechanisms that facilitate our ability to extract behaviorally
relevant statistics to make predictions.SIGNIFICANCE STATEMENT Making predictions
about future events relies on interpreting streams of information that may
initially appear incomprehensible. Past work has studied how humans identify
repetitive patterns and associative pairings. However, the natural environment
contains regularities that vary in complexity from simple repetition to complex
probabilistic combinations. Here, we combine behavior and multisession fMRI to
track the brain mechanisms that mediate our ability to adapt to changes in the
environment's statistics. We provide evidence for an alternate route for learning
complex temporal statistics: extracting the most probable outcome in a given
context is implemented by interactions between executive and motor
corticostriatal mechanisms compared with visual corticostriatal circuits
(including hippocampal cortex) that support learning of the exact temporal
statistics.
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