scholarly journals Stress-sensitive inference of task controllability

2020 ◽  
Author(s):  
Romain Ligneul ◽  
Zachary Mainen ◽  
Verena Ly ◽  
Roshan Cools
Keyword(s):  
Statistical Learning ◽  
Neural Activity ◽  
Prediction Errors ◽  
Stress Exposure ◽  
Actor Model ◽  
Trial Analysis ◽  
Bold Responses ◽  
The Spectator ◽  
Decision Times ◽  
Spectator Model

ABSTRACTEstimating environmental controllability enables agents to better predict upcoming events and decide when to engage controlled action selection. How does the human brain estimate environmental controllability? Trial-by-trial analysis of choices, decision times and neural activity in an explore-and-predict task demonstrate that humans solve this problem by comparing the predictions of an “actor” model with those of a reduced “spectator” model of their environment. Neural BOLD responses within striatal and medial prefrontal areas tracked the instantaneous difference in the prediction errors generated by these two statistical learning models. BOLD activity in the posterior cingulate, parietal and prefrontal cortices covaried with changes in estimated controllability. Exposure to inescapable stressors biased controllability estimates downward and increased reliance on the spectator model in an anxiety-dependent fashion. Taken together, these findings provide a mechanistic account of controllability inference and its distortion by stress exposure.

scholarly journals Language statistical learning responds to reinforcement learning principles rooted in the striatum

PLoS Biology ◽  
2021 ◽  
Vol 19 (9) ◽  
pp. e3001119
Author(s):  
Joan Orpella ◽  
Ernest Mas-Herrero ◽  
Pablo Ripollés ◽  
Josep Marco-Pallarés ◽  
Ruth de Diego-Balaguer
Keyword(s):  
Reinforcement Learning ◽  
Language Learning ◽  
Statistical Learning ◽  
Dorsal Striatum ◽  
Rule Learning ◽  
Prediction Errors ◽  
Neural Basis ◽  
Structural Rules ◽  
Learning Principles ◽  
Striatal Function

Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate—on 2 different cohorts—that a temporal difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena.

scholarly journals Autoionization of the Ca 2p53d core resonances: Breakdown of the spectator model

1989 ◽  
Vol 39 (8) ◽  
pp. 4319-4322 ◽  
Author(s):  
M. Meyer ◽  
E. v. Raven ◽  
M. Richter ◽  
B. Sonntag ◽  
R. D. Cowan ◽  
...  
Keyword(s):  
The Spectator ◽  
Spectator Model

scholarly journals Linking the neural basis of hierarchical prediction with statistical learning: The paradox of attention

2021 ◽  
Author(s):  
Julie M. Schneider ◽  
Yi-Lun Weng ◽  
Anqi Hu ◽  
Zhenghan Qi
Keyword(s):  
Statistical Learning ◽  
Learning Task ◽  
Prediction Errors ◽  
Neural Basis ◽  
Attentional Shift ◽  
Cortical Responses ◽  
Distributional Information ◽  
Segmentation Task ◽  
Dependent Learning

Statistical learning, the process of tracking distributional information and discovering embedded patterns, is traditionally regarded as a form of implicit learning. However, recent studies proposed that both implicit (attention-independent) and explicit (attention-dependent) learning systems are involved in statistical learning. To understand the role of attention in statistical learning, the current study investigates the cortical processing of prediction errors in speech based on either local or global distributional information. We then ask how these cortical responses relate to statistical learning behavior in a word segmentation task. We found ERP evidence of pre-attentive processing of both the local (mismatching negativity) and global distributional information (late discriminative negativity). However, as speech elements became less frequent and more surprising, some participants showed an involuntary attentional shift, reflected in a P3a response. Individuals who displayed attentive neural tracking of distributional information showed faster learning in a speech statistical learning task. These results provide important neural evidence elucidating the facilitatory role of attention in statistical learning.

Neutron–neutron angular correlation in the reaction 2H(n, p)2n at 14.1 MeV

1968 ◽  
Vol 46 (9) ◽  
pp. 1132-1135
Author(s):  
H. Jeremie ◽  
B. Miremadi
Keyword(s):  
Angular Correlation ◽  
Experimental Results ◽  
The Other ◽  
Neutron Counter ◽  
Fixed Position ◽  
The Spectator ◽  
Spectator Model

The angular correlation between the two outgoing neutrons in the reaction n + d → 2n + p has been measured. One neutron counter was kept in a fixed position at 30° while the other was varied between 22.5° and 67.5°. A maximum in the number of counts was found for 42 ± 7°. A calculation according to the spectator model of Kuckes, Wilson, and Cooper (1961) does not reproduce the experimental results.

scholarly journals Quantitative relations between BOLD responses, cortical energetics, and impulse firing

2018 ◽  
Vol 119 (3) ◽  
pp. 979-989 ◽  
Author(s):  
M. R. Bennett ◽  
L. Farnell ◽  
W. G. Gibson
Keyword(s):  
Neural Activity ◽  
Blood Oxygen Level Dependent ◽  
Bold Signal ◽  
Neural Firing ◽  
Functional Magnetic Resonance ◽  
Blood Oxygen Level ◽  
Baseline Activity ◽  
Bold Responses ◽  
Negative Bold ◽  
Quantitative Account

The blood oxygen level-dependent (BOLD) functional magnetic resonance imaging signal arises as a consequence of changes in blood flow and oxygen usage that in turn are modulated by changes in neural activity. Much attention has been given to both theoretical and experimental aspects of the energetics but not to the neural activity. Here we identify the best energetic theory for the steady-state BOLD signal on the basis of correct predictions of experimental observations. This theory is then used, together with the recently determined relationship between energetics and neural activity, to predict how the BOLD signal changes with activity. Unlike existing treatments, this new theory incorporates a nonzero baseline activity in a completely consistent way and is thus able to account for both sustained positive and negative BOLD signals. We also show that the increase in BOLD signal for a given increase in activity is significantly smaller the larger the baseline activity, as is experimentally observed. Furthermore, the decline of the positive BOLD signal arising from deeper cortical laminae in response to an increase in neural firing is shown to arise as a consequence of the larger baseline activity in deeper laminae. Finally, we provide quantitative relations integrating BOLD responses, energetics, and impulse firing, which among other predictions give the same results as existing theories when the baseline activity is zero. NEW & NOTEWORTHY We use a recently established relation between energetics and neural activity to give a quantitative account of BOLD dependence on neural activity. The incorporation of a nonzero baseline neural activity accounts for positive and negative BOLD signals, shows that changes in neural activity give BOLD changes that are smaller the larger the baseline, and provides a basis for the observed inverse relation between BOLD responses and the depth of cortical laminae giving rise to them.

Improvements to the spectator model of B meson decays

1990 ◽  
Vol 235 (1-2) ◽  
pp. 187-192 ◽  
Author(s):  
V. Barger ◽  
C.S. Kim ◽  
R.J.N. Phillips
Keyword(s):  
B Meson ◽  
B Meson Decays ◽  
Meson Decays ◽  
The Spectator ◽  
Spectator Model
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