Our recent work has been published in Feb 5 issue of Neuron, and chosen for a cover image. The cover illustration depicts the approach of establishing specific computational accounts for the functions of different brain regions involved in behavioral control by using a combination of computational modeling with behavioral and fMRI data. The graph-based layout with circles denotes state-space representations that are part of a computational framework for understanding how the brain encodes a model of a particular decision-problem or task, while the rectangles symbolize the degree of uncertainty about those state-space representations. In this issue, Lee et al. (pages 687–699) show that a region of inferior lateral prefrontal cortex (the bottom middle brain image) alongside an area of frontopolar cortex (the top middle brain image) contains neural computations that could be used to allocate the degree of control exerted over behavior by two learning systems: a deliberative model-based system that uses state-prediction errors (left middle) to learn a world model, which in turn is used to flexibly compute state-action values (left diagonal), and a model-free system that uses reward-prediction errors (right middle) to directly update the state-action value (right diagonal).
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Alma Gharib1, Daniela Mier1,2, Ralph Adolphs1, Shinsuke Shimojo1
1 Division of Biology Caltech, Division of Computation and Neural Systems, Caltech,US.
2 Department of Clinical Psychology, Central Institute of Mental Health, Mannheim,Germany
Preference and gaze interact in a positive feedback loop to produce a phenomenon known
as the ‘gaze cascade’ effect. In the few seconds before a decision is made, a gaze bias occurs
toward the stimulus that is eventually chosen. This gaze cascade is especially robust in tasks
that involve face preference decisions. Autism is a pervasive developmental disorder where
deficits in evaluating and making social judgments about faces occur. Persons with autism
typically have inattention to faces and direct gaze aversion. The present study was set up to
examine whether these known aberrations in visual face processing interfere with
preference choice decision making in ASD, reflected in a deviant gaze cascade pattern.
4 ASD subjects and 3 age and gender matched healthy controls (HC) performed a 2‐
alternative forced‐choice task, while their eye‐gaze was tracked. Their task was to select
the stimulus they prefer by pressing a button under a free viewing condition. Stimulus types
consisted of faces and natural scenes.
First, we were able to replicate the findings of a gaze cascade in the HCs, already with this
temporary group size. Interestingly, the known gaze aversion for faces in ASD did not
interfere with the gaze bias toward the to‐be‐chosen picture at decision time, independent
of stimulus type. Indeed, the probability of a gaze bias towards the chosen picture at 40 ms
before response was even significantly higher in the autism group than in the HCs (p< 0.001
for each of the conditions). On the other hand, the course of their viewing patterns clearly
deviated from that of the HCs and is not in agreement with the typical gaze cascade. These
findings implicate that while gaze is clearly involved in preference formation in autistic
subjects, the psychological process that leads to the decision may differ from that of HCs.