TY - JOUR
T1 - Predictions as a window into learning
T2 - Anticipatory fixation offsets carry more information about environmental statistics than reactive stimulus-responses
AU - Notaro, Giuseppe
AU - van Zoest, Wieske
AU - Altman, Magda
AU - Melcher, David
AU - Hasson, Uri
N1 - Publisher Copyright:
© 2019 The Authors.
PY - 2019
Y1 - 2019
N2 - A core question underlying neurobiological and computational models of behavior is how individuals learn environmental statistics and use them to make predictions. Most investigations of this issue have relied on reactive paradigms, in which inferences about predictive processes are derived by modeling responses to stimuli that vary in likelihood. Here we deployed a novel anticipatory oculomotor metric to determine how input statistics impact anticipatory behavior that is decoupled from target-driven-response. We implemented transition constraints between target locations, so that the probability of a target being presented on the same side as the previous trial was 70% in one condition (pret70) and 30% in the other (pret30). Rather than focus on responses to targets, we studied subtle endogenous anticipatory fixation offsets (AFOs) measured while participants fixated the screen center, awaiting a target. These AFOs were small (< 0.48 from center on average), but strongly tracked globallevel statistics. Speaking to learning dynamics, trial-bytrial fluctuations in AFO were well-described by a learning model, which identified a lower learning rate in pret70 than pret30, corroborating prior suggestions that pret70 is subjectively treated as more regular. Most importantly, direct comparisons with saccade latencies revealed that AFOs: (a) reflected similar temporal integration windows, (b) carried more information about the statistical context than did saccade latencies, and (c) accounted for most of the information that saccade latencies also contained about inputs statistics. Our work demonstrates how strictly predictive processes reflect learning dynamics, and presents a new direction for studying learning and prediction.
AB - A core question underlying neurobiological and computational models of behavior is how individuals learn environmental statistics and use them to make predictions. Most investigations of this issue have relied on reactive paradigms, in which inferences about predictive processes are derived by modeling responses to stimuli that vary in likelihood. Here we deployed a novel anticipatory oculomotor metric to determine how input statistics impact anticipatory behavior that is decoupled from target-driven-response. We implemented transition constraints between target locations, so that the probability of a target being presented on the same side as the previous trial was 70% in one condition (pret70) and 30% in the other (pret30). Rather than focus on responses to targets, we studied subtle endogenous anticipatory fixation offsets (AFOs) measured while participants fixated the screen center, awaiting a target. These AFOs were small (< 0.48 from center on average), but strongly tracked globallevel statistics. Speaking to learning dynamics, trial-bytrial fluctuations in AFO were well-described by a learning model, which identified a lower learning rate in pret70 than pret30, corroborating prior suggestions that pret70 is subjectively treated as more regular. Most importantly, direct comparisons with saccade latencies revealed that AFOs: (a) reflected similar temporal integration windows, (b) carried more information about the statistical context than did saccade latencies, and (c) accounted for most of the information that saccade latencies also contained about inputs statistics. Our work demonstrates how strictly predictive processes reflect learning dynamics, and presents a new direction for studying learning and prediction.
KW - Anticipatory fixation offsets
KW - Information
KW - Prediction
KW - Statistical learning
UR - http://www.scopus.com/inward/record.url?scp=85061873180&partnerID=8YFLogxK
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U2 - 10.1167/19.2.8
DO - 10.1167/19.2.8
M3 - Article
C2 - 30779844
AN - SCOPUS:85061873180
SN - 1534-7362
VL - 19
SP - 1
EP - 22
JO - Journal of vision
JF - Journal of vision
IS - 2
ER -