scholarly journals Mini-review: Prediction errors, attention and associative learning

2016 ◽  
Vol 131 ◽  
pp. 207-215 ◽  
Author(s):  
Peter C. Holland ◽  
Felipe L. Schiffino
Keyword(s):  
Associative Learning ◽  
Prediction Errors

The Associative Learning Prediction Error Feedback Loop as Reversible Driving Belt of the Bioenergetics of Longevity

2021 ◽  
Author(s):  
Matthew S Price
Keyword(s):  
Associative Learning ◽  
Prediction Error ◽  
Feedback Loop ◽  
Telomerase Activity ◽  
Behavioral Strategy ◽  
Prediction Errors ◽  
Error Feedback ◽  
Treatment Expectations ◽  
Telomere Shortening ◽  
Inhibitory Learning

Leukocyte telomere shortening is a useful biomarker of biological and cellular age that occurs at an accelerated rate in anxiety disorders and posttraumatic stress disorder (PTSD). Intriguingly, inhibitory learning — the systematic exposure to noxious stimuli that serves as a basis for many treatments for anxiety, phobia, and PTSD —reduces relative telomeres attrition rates and increases protective telomerase activity in a manner predictive of treatment response. How does inhibitory learning, a behavioral strategy, modulate organismal chromosomal activity? Inhibitory learning may induce repeated mismatch between treatment expectations, intrasession states, and eventual outcome. Nevertheless, inhibitory learning can incentivize repetition of the behavior. Thus, this paper aims to conceptualize inhibitory learning as involving a ‘prediction error feedback loop’, i.e., a series of self-perpetuating prediction errors — mismatches between expectations and outcomes — that enhances neural inhibitory regulation to effectuate extinction. Inhibitory learning is necessarily predicated upon an opposing process – excitatory learning – that may be conceptualized as a prediction error feedback loop that operates in reverse to inhibitory learning and enhances neural excitability as arousal. Together, excitatory and inhibitory learning may be elements of an associative learning prediction error feedback loop responsible for modulating neural bioenergetic rates, leading to changes in downstream cellular signaling that could explain reduced or increased rates of leukocyte telomere shortening and telomerase activity from each behavioral strategy, respectively.

scholarly journals Associative learning and animal cognition

2012 ◽  
Vol 367 (1603) ◽  
pp. 2733-2742 ◽  
Author(s):  
Anthony Dickinson
Keyword(s):  
Associative Learning ◽  
Practical Reasoning ◽  
Animal Cognition ◽  
Prediction Errors ◽  
Associative Processes ◽  
Directed Action ◽  
Processing Architectures ◽  
The Relationship ◽  
Theoretical Component

Associative learning plays a variety of roles in the study of animal cognition from a core theoretical component to a null hypothesis against which the contribution of cognitive processes is assessed. Two developments in contemporary associative learning have enhanced its relevance to animal cognition. The first concerns the role of associatively activated representations, whereas the second is the development of hybrid theories in which learning is determined by prediction errors, both directly and indirectly through associability processes. However, it remains unclear whether these developments allow associative theory to capture the psychological rationality of cognition. I argue that embodying associative processes within specific processing architectures provides mechanisms that can mediate psychological rationality and illustrate such embodiment by discussing the relationship between practical reasoning and the associative-cybernetic model of goal-directed action.

scholarly journals Dopamine transients delivered in learning contexts do not act as model-free prediction errors

2019 ◽  
Author(s):  
Melissa J. Sharpe ◽  
Hannah M. Batchelor ◽  
Lauren E. Mueller ◽  
Chun Yun Chang ◽  
Etienne J.P. Maes ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Associative Learning ◽  
Prediction Error ◽  
Error Term ◽  
Neural Correlates ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Model Free ◽  
Reward Prediction ◽  
Excess Value

AbstractDopamine neurons fire transiently in response to unexpected rewards. These neural correlates are proposed to signal the reward prediction error described in model-free reinforcement learning algorithms. This error term represents the unpredicted or ‘excess’ value of the rewarding event. In model-free reinforcement learning, this value is then stored as part of the learned value of any antecedent cues, contexts or events, making them intrinsically valuable, independent of the specific rewarding event that caused the prediction error. In support of equivalence between dopamine transients and this model-free error term, proponents cite causal optogenetic studies showing that artificially induced dopamine transients cause lasting changes in behavior. Yet none of these studies directly demonstrate the presence of cached value under conditions appropriate for associative learning. To address this gap in our knowledge, we conducted three studies where we optogenetically activated dopamine neurons while rats were learning associative relationships, both with and without reward. In each experiment, the antecedent cues failed to acquired value and instead entered into value-independent associative relationships with the other cues or rewards. These results show that dopamine transients, constrained within appropriate learning situations, support valueless associative learning.

scholarly journals The Kamin Blocking Effect in Sign and Goal Trackers

2020 ◽  
Author(s):  
Mayank Aggarwal ◽  
Jeffery R. Wickens
Keyword(s):  
Classical Conditioning ◽  
Associative Learning ◽  
Blocking Effect ◽  
Learning Processes ◽  
Temporal Contiguity ◽  
Prediction Errors ◽  
Parallel Learning ◽  
Auditory Cue ◽  
Goal Tracking

AbstractThe discovery of the Kamin blocking effect suggested that surprise or prediction errors are necessary for associative learning. This suggestion led to the development of a new theoretical framework for associative learning relying on prediction error rather than just temporal contiguity between events. However, many recent studies have failed to replicate the blocking effect, questioning the central role of blocking in associative learning theory. Here, we test the expression of Kamin blocking in rats that either approach and interact with the conditioned cue (sign trackers) or approach and interact with the reward location (goal trackers) during appetitive classical conditioning. The behavioral task involved three phases: classical conditioning of a lever cue, conditioning of a compound of the lever cue plus an auditory cue, and testing response to presentation of the auditory cue in extinction. The results show that only sign trackers express the blocking effect. Thus, groups that include goal trackers are less likely to be able to replicate the blocking effect. Our findings support the idea that sign and goal tracking responses arise as a result of distinct parallel learning processes. Psychological theories of learning that incorporate these parallel learning processes and their interactions will provide a better framework for understanding the blocking effect and related associative learning phenomena.

scholarly journals Dopamine neuron ensembles signal the content of sensory prediction errors

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Thomas A Stalnaker ◽  
James D Howard ◽  
Yuji K Takahashi ◽  
Samuel J Gershman ◽  
Thorsten Kahnt ◽  
...  
Keyword(s):  
Associative Learning ◽  
Prediction Error ◽  
Sensory Information ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Bold Response ◽  
Dopamine System ◽  
Sensory Event ◽  
Specific Teaching ◽  
Sensory Prediction

Dopamine neurons respond to errors in predicting value-neutral sensory information. These data, combined with causal evidence that dopamine transients support sensory-based associative learning, suggest that the dopamine system signals a multidimensional prediction error. Yet such complexity is not evident in the activity of individual neurons or population averages. How then do downstream areas know what to learn in response to these signals? One possibility is that information about content is contained in the pattern of firing across many dopamine neurons. Consistent with this, here we show that the pattern of firing across a small group of dopamine neurons recorded in rats signals the identity of a mis-predicted sensory event. Further, this same information is reflected in the BOLD response elicited by sensory prediction errors in human midbrain. These data provide evidence that ensembles of dopamine neurons provide highly specific teaching signals, opening new possibilities for how this system might contribute to learning.

scholarly journals Dopamine transients do not act as model-free prediction errors during associative learning

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Melissa J. Sharpe ◽  
Hannah M. Batchelor ◽  
Lauren E. Mueller ◽  
Chun Yun Chang ◽  
Etienne J. P. Maes ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Associative Learning ◽  
Prediction Error ◽  
Intrinsic Value ◽  
Learning Algorithms ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Model Free ◽  
Reward Prediction ◽  
Excess Value

AbstractDopamine neurons are proposed to signal the reward prediction error in model-free reinforcement learning algorithms. This term represents the unpredicted or ‘excess’ value of the rewarding event, value that is then added to the intrinsic value of any antecedent cues, contexts or events. To support this proposal, proponents cite evidence that artificially-induced dopamine transients cause lasting changes in behavior. Yet these studies do not generally assess learning under conditions where an endogenous prediction error would occur. Here, to address this, we conducted three experiments where we optogenetically activated dopamine neurons while rats were learning associative relationships, both with and without reward. In each experiment, the antecedent cues failed to acquire value and instead entered into associations with the later events, whether valueless cues or valued rewards. These results show that in learning situations appropriate for the appearance of a prediction error, dopamine transients support associative, rather than model-free, learning.

scholarly journals Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

2014 ◽  
Vol 37 (2) ◽  
pp. 205-206 ◽  
Author(s):  
Christian Keysers ◽  
David I. Perrett ◽  
Valeria Gazzola
Keyword(s):  
Associative Learning ◽  
Mirror Neurons ◽  
Hebbian Learning ◽  
Dynamic Networks ◽  
Mirror Neuron ◽  
Prediction Errors ◽  
Social Force ◽  
The Social

AbstractHebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization.

EXPRESS: Evidence for two attentional mechanisms during learning

2021 ◽  
pp. 174702182110193
Author(s):  
David Torrents-Rodas ◽  
Stephan Koenig ◽  
Metin Uengoer ◽  
Harald Lachnit
Keyword(s):  
Eye Movements ◽  
Associative Learning ◽  
Hybrid Model ◽  
Prediction Error ◽  
The Other ◽  
Prediction Errors ◽  
Second Stage ◽  
Initial Stage ◽  
Attentional Mechanism ◽  
Attentional Mechanisms

We sought to provide evidence for a combined effect of two attentional mechanisms during associative learning. Participants’ eye movements were recorded as they predicted the outcomes following different pairs of cues. Across the trials of an initial stage, a relevant cue in each pair was consistently followed by one of two outcomes, while an irrelevant cue was equally followed by either of them. Thus, the relevant cue should have been associated with small relative prediction errors, compared to the irrelevant cue. In a later stage, each pair came to be followed by one outcome on a random half of the trials and by the other outcome on the remaining half, and thus there should have been a rise in the overall prediction error. Consistent with an attentional mechanism based on relative prediction error, an attentional advantage for the relevant cue was evident in the first stage. On the other hand, in accordance with a mechanism linked to overall prediction error, the attention paid to both types of cues increased at the beginning of the second stage. These results showed up in both dwell times and within-trial patterns of fixations, and they were predicted by a hybrid model of attention.

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.

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