Here, we compared three pathways by which avoidance of the US may be acquired both directly (i.e., through trial-and-error instrumental learning) and indirectly (i.e., via verbal instructions and social observation).
A popular computational framework posits that two update rules operate in parallel to learn action preferences during trial-and-error instrumental learning, known as "model-based" and "model-free" reinforcement learning (Daw, Niv, & Dayan, 2005; Keramati, Dezfouli, & Piray, 2011; Pezzulo, Rigoli, & Chersi, 2013).
This may partly reflect caudate involvement in signaling prediction error during instrumental learning tasks (O'Doherty 2004; Clarke et al. 2011).
We accounted for this potential error through instrumental variable estimation using the 2-Stage Least Square approach as recommended in the literature [ 38- 40].
There were significant increases in BOLD signal on reversal trials (with unexpected outcomes) in the placebo condition (t14 = 2.17, P = 0.048), consistent with a role in signaling prediction error in instrumental tasks (O'Doherty et al. 2004).
These results have been interpreted as the representation of reward prediction error in instrumental tasks (Morris et al., 2006; Roesch et al., 2007; Totah et al., 2013) (specifically, the Q-associated temporal difference prediction error, where Q-value is the value of selecting a particular action at a given state).
The final errors of comet polarization parameters include the errors of instrumental polarization σq,inst and σu,inst.
His protests regarding the British Nautical Almanac, then notorious for its errors, were instrumental in bringing about its reform.
This type of mismapping results from errors in instrumental pointing associated with the attitude of the spacecraft and the alignment of the instruments.
In this paper, we describe a recently developed suite of wavelet-based algorithms suitable for the analysis of multi-instrument multi-satellite and ground-based measurements with the aim to detect ULF wave events in tri-axial satellite magnetic field data and distinguish them from processing errors or instrumental noise in an automated way.
In humans, drugs enhancing dopaminergic function (e.g., dihydroxy-L-phenylalanine; L-DOPA) have been shown to augment a striatal signal that expresses reward prediction errors during instrumental learning, thereby increasing the likelihood of choosing stimuli associated with greater monetary gains [5].
