scholarly journals Pupil dilation indicates the coding of past prediction errors: Evidence for attentional learning theory

2017 ◽  
Vol 55 (4) ◽  
pp. e13020 ◽  
Author(s):  
Stephan Koenig ◽  
Metin Uengoer ◽  
Harald Lachnit
Keyword(s):  
Learning Theory ◽  
Pupil Dilation ◽  
Prediction Errors ◽  
Attentional Learning

Reward Prediction Errors Drive Declarative Learning Irrespective of Agency

2020 ◽  
Author(s):  
Kate Ergo ◽  
Luna De Vilder ◽  
Esther De Loof ◽  
Tom Verguts
Keyword(s):  
Learning Theory ◽  
Learning Effect ◽  
Steady Increase ◽  
Prediction Errors ◽  
Experimental Paradigm ◽  
Reward Prediction ◽  
Declarative Learning

Recent years have witnessed a steady increase in the number of studies investigating the role of reward prediction errors (RPEs) in declarative learning. Specifically, in several experimental paradigms RPEs drive declarative learning; with larger and more positive RPEs enhancing declarative learning. However, it is unknown whether this RPE must derive from the participant’s own response, or whether instead any RPE is sufficient to obtain the learning effect. To test this, we generated RPEs in the same experimental paradigm where we combined an agency and a non-agency condition. We observed no interaction between RPE and agency, suggesting that any RPE (irrespective of its source) can drive declarative learning. This result holds implications for declarative learning theory.

scholarly journals Prayer as Inner Sense Cultivation: An Attentional Learning Theory of Spiritual Experience

Ethos ◽  
2012 ◽  
Vol 40 (4) ◽  
pp. 359-389 ◽  
Author(s):  
T. M. Luhrmann ◽  
Rachel Morgain
Keyword(s):  
Learning Theory ◽  
Spiritual Experience ◽  
Inner Sense ◽  
Attentional Learning

scholarly journals Neurocomputational mechanisms of prosocial learning and links to empathy

2016 ◽  
Vol 113 (35) ◽  
pp. 9763-9768 ◽  
Author(s):  
Patricia L. Lockwood ◽  
Matthew A. J. Apps ◽  
Vincent Valton ◽  
Essi Viding ◽  
Jonathan P. Roiser
Keyword(s):  
Reinforcement Learning ◽  
Prosocial Behavior ◽  
Learning Theory ◽  
Anterior Cingulate ◽  
Prediction Errors ◽  
Posterior Portion ◽  
Subgenual Anterior Cingulate Cortex ◽  
Actual Outcome ◽  
The Difference ◽  
Do So

Reinforcement learning theory powerfully characterizes how we learn to benefit ourselves. In this theory, prediction errors—the difference between a predicted and actual outcome of a choice—drive learning. However, we do not operate in a social vacuum. To behave prosocially we must learn the consequences of our actions for other people. Empathy, the ability to vicariously experience and understand the affect of others, is hypothesized to be a critical facilitator of prosocial behaviors, but the link between empathy and prosocial behavior is still unclear. During functional magnetic resonance imaging (fMRI) participants chose between different stimuli that were probabilistically associated with rewards for themselves (self), another person (prosocial), or no one (control). Using computational modeling, we show that people can learn to obtain rewards for others but do so more slowly than when learning to obtain rewards for themselves. fMRI revealed that activity in a posterior portion of the subgenual anterior cingulate cortex/basal forebrain (sgACC) drives learning only when we are acting in a prosocial context and signals a prosocial prediction error conforming to classical principles of reinforcement learning theory. However, there is also substantial variability in the neural and behavioral efficiency of prosocial learning, which is predicted by trait empathy. More empathic people learn more quickly when benefitting others, and their sgACC response is the most selective for prosocial learning. We thus reveal a computational mechanism driving prosocial learning in humans. This framework could provide insights into atypical prosocial behavior in those with disorders of social cognition.

scholarly journals Signals of anticipation of reward and of mean reward rates in the human brain

2019 ◽  
Author(s):  
Roberto Viviani ◽  
Lisa Dommes ◽  
Julia Bosch ◽  
Michael Steffens ◽  
Anna Paul ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Substantia Nigra ◽  
Human Brain ◽  
Learning Theory ◽  
Functional Neuroimaging ◽  
Theoretical Models ◽  
Prediction Errors ◽  
Deep Layers ◽  
Signal Changes ◽  
Reinforcement Learning Model

AbstractTheoretical models of dopamine function stemming from reinforcement learning theory have emphasized the importance of prediction errors, which signal changes in the expectation of impending rewards. Much less is known about the effects of mean reward rates, which may be of motivational significance due to their role in computing the optimal effort put into exploiting reward opportunities. Here, we used a reinforcement learning model to design three functional neuroimaging studies and disentangle the effects of changes in reward expectations and mean reward rates, showing recruitment of specific regions in the brainstem regardless of prediction errors. While changes in reward expectations activated ventral striatal areas as in previous studies, mean reward rates preferentially modulated the substantia nigra/ventral tegmental area, deep layers of the superior colliculi, and a posterior pontomesencephalic region. These brainstem structures may work together to set motivation and attentional efforts levels according to perceived reward opportunities.

scholarly journals Sequential Decisions from Sampling: Inductive Generation of Stopping Decisions Using Instance-Based Learning Theory

2021 ◽  
Author(s):  
Cleotilde Gonzalez ◽  
Palvi Aggarwal
Keyword(s):  
Learning Theory ◽  
Binary Choice ◽  
Prediction Errors ◽  
Sequential Decisions ◽  
Data Set ◽  
Exploration Process ◽  
Decisions From Experience ◽  
Instance Based Learning ◽  
Sampling Process ◽  
Inductive Generation

Sequential decisions from sampling are common in daily life: we often explore alternatives sequentially, decide when to stop such exploration process, and use the experience acquired during sampling to make a choice for what is expected to be the best option. In decisions from experience, theories of sampling and experiential choice are unable to explain the decision of when to stop the sequential exploration of alternatives. In this chapter, we propose a mechanism to inductively generate stopping decisions, and we demonstrate its plausibility in a large and diverse human data set of the binary choice sampling paradigm. Our proposed stopping mechanism relies on the choice process of a theory of experiential choice, Instance-Based Learning Theory (IBLT). The new stopping mechanism tracks the relative prediction errors of the two options during sampling, and stops when such difference is close to zero. Our results from simulation are able to accurately predict human stopping decisions distributions in the dataset. This model provides an integrated theoretical account of decisions from experience, where the stopping decisions are generated inductively from the sampling process.

Above and beyond the content: Feelings influence mental simulations

2020 ◽  
Vol 43 ◽  
Author(s):  
Kellen Mrkva ◽  
Luca Cian ◽  
Leaf Van Boven
Keyword(s):  
Mental Representation ◽  
Mental Simulation ◽  
Past Experience ◽  
Prediction Errors

Abstract Gilead et al. present a rich account of abstraction. Though the account describes several elements which influence mental representation, it is worth also delineating how feelings, such as fluency and emotion, influence mental simulation. Additionally, though past experience can sometimes make simulations more accurate and worthwhile (as Gilead et al. suggest), many systematic prediction errors persist despite substantial experience.

Utilizing learning theory to promote effectiveness of instruction in preclinical operative dentistry

1976 ◽  
Vol 40 (3) ◽  
pp. 154-157
Author(s):  
KW Hinkelman ◽  
NK Long
Keyword(s):  
Learning Theory ◽  
Operative Dentistry

Variational Bayesian Learning Theory

2019 ◽  
Author(s):  
Shinichi Nakajima ◽  
Kazuho Watanabe ◽  
Masashi Sugiyama
Keyword(s):  
Learning Theory ◽  
Bayesian Learning ◽  
Variational Bayesian

Socially Shared Regulation of Learning: A Review

2015 ◽  
Vol 20 (3) ◽  
pp. 190-203 ◽  
Author(s):  
Ernesto Panadero ◽  
Sanna Järvelä
Keyword(s):  
Empirical Evidence ◽  
Learning Theory ◽  
Qualitative Data ◽  
Social Regulation ◽  
Observation Data ◽  
Optimal Form ◽  
Self Regulated Learning ◽  
The Past ◽  
Recorded Observation ◽  
Socially Shared Regulation

Abstract. Socially shared regulation of learning (SSRL) has been recognized as a new and growing field in the framework of self-regulated learning theory in the past decade. In the present review, we examine the empirical evidence to support such a phenomenon. A total of 17 articles addressing SSRL were identified, 13 of which presented empirical evidence. Through a narrative review it could be concluded that there is enough data to maintain the existence of SSRL in comparison to other social regulation (e.g., co-regulation). It was found that most of the SSRL research has focused on characterizing phenomena through the use of mixed methods through qualitative data, mostly video-recorded observation data. Also, SSRL seems to contribute to students’ performance. Finally, the article discusses the need for the field to move forward, exploring the best conditions to promote SSRL, clarifying whether SSRL is always the optimal form of collaboration, and identifying more aspects of groups’ characteristics.

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