scholarly journals Experience-driven recalibration of learning from surprising events

2021 ◽  
Author(s):  
Leah Bakst ◽  
Joseph McGuire
Keyword(s):  
Eye Movements ◽  
Adaptive Learning ◽  
Judgment Task ◽  
Spatial Uncertainty ◽  
Meaningful Change ◽  
Belief Updating ◽  
Learning Rates

Different contexts favor different patterns of adaptive learning. A surprising event that in one context would drive rapid belief updating might, in another context, be interpreted as a meaningless outlier. Here, across two experiments, we examined whether participants performing a target judgment task under spatial uncertainty (n=31, n=64) would spontaneously adapt their patterns of predictive gaze according to the informativeness or uninformativeness of surprising events in their current environment. Uninstructed predictive eye movements exhibited a form of metalearning in which event-by-event learning rates were modulated differently by surprise across contexts. Participants also appropriately readjusted their patterns of adaptive learning when the statistics of the environment underwent an unsignaled change. Although significant metalearning was observed in all conditions, performance was consistently superior in contexts in which surprising events reflected meaningful change, potentially reflecting a bias toward interpreting surprise as informative. Overall, our results demonstrate remarkable flexibility in contextually adaptive metalearning.

The time-course of morphological constraints

2008 ◽  
Vol 3 (2) ◽  
pp. 149-175 ◽  
Author(s):  
Ian Cunnings ◽  
Harald Clahsen
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Language Processing ◽  
Time Course ◽  
Judgment Task ◽  
General Constraint ◽  
Movement Data ◽  
Word Forms ◽  
Eye Movements During Reading ◽  
Acceptability Judgment

The avoidance of regular but not irregular plurals inside compounds (e.g., *rats eater vs. mice eater) has been one of the most widely studied morphological phenomena in the psycholinguistics literature. To examine whether the constraints that are responsible for this contrast have any general significance beyond compounding, we investigated derived word forms containing regular and irregular plurals in two experiments. Experiment 1 was an offline acceptability judgment task, and Experiment 2 measured eye movements during reading derived words containing regular and irregular plurals and uninflected base nouns. The results from both experiments show that the constraint against regular plurals inside compounds generalizes to derived words. We argue that this constraint cannot be reduced to phonological properties, but is instead morphological in nature. The eye-movement data provide detailed information on the time-course of processing derived word forms indicating that early stages of processing are affected by a general constraint that disallows inflected words from feeding derivational processes, and that the more specific constraint against regular plurals comes in at a subsequent later stage of processing. We argue that these results are consistent with stage-based models of language processing.

AN ULTRASONIC MOTOR SPEED CONTROL SCHEME BASED ON NEURAL NETWORKS AND ITERATIVE CONTROLLER

2004 ◽  
Vol 01 (03) ◽  
pp. 457-470
Author(s):  
X. H. SHI ◽  
Y. C. LIANG ◽  
X. XU
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Speed Control ◽  
Ultrasonic Motor ◽  
Random Disturbance ◽  
Motor Speed ◽  
Neural Network Controller ◽  
Learning Rates ◽  
Network Controller ◽  
Control Scheme

An ultrasonic motor speed control scheme is presented in this paper based on neural networks and iterative controller. Suitable ranges of the adaptive learning rates of neural network controller are presented through the theoretical analysis on the proposed model, which could guarantee its stability. The convergence of iterative controller is also discussed. Numerical results show that the control scheme is effective for various kinds of reference speeds of ultrasonic motors. Comparisons with the existing method show that the precision of control could be increased using the proposed method. Simulations also show that the proposed scheme is fairly robust against random disturbance to the control variables.

scholarly journals Monetary feedback modulates performance and electrophysiological indices of belief updating in reward learning

2017 ◽  
Author(s):  
Daniel Bennett ◽  
Karen Sasmita ◽  
Ryan T. Maloney ◽  
Carsten Murawski ◽  
Stefan Bode
Keyword(s):  
Bayesian Inference ◽  
Cognitive Strategies ◽  
Learning Task ◽  
Feedback Condition ◽  
Event Related Potential ◽  
Reward Learning ◽  
Instructive Feedback ◽  
Motivational State ◽  
Belief Updating ◽  
Learning Rates

AbstractBelief updating entails the incorporation of new information about the environment into internal models of the world. Bayesian inference is the statistically optimal strategy for performing belief updating in the presence of uncertainty. An important open question is whether the use of cognitive strategies that implement Bayesian inference is dependent upon motivational state and, if so, how this is reflected in electrophysiological signatures of belief updating in the brain. Here we recorded the electroencephalogram of participants performing a simple reward learning task with both monetary and non-monetary instructive feedback conditions. Our aim was to distinguish the influence of the rewarding properties of feedback on belief updating from the information content of the feedback itself. A Bayesian updating model allowed us to quantify different aspects of belief updating across trials, including the size of belief updates and the uncertainty of beliefs. Faster learning rates were observed in the monetary feedback condition compared to the instructive feedback condition, while belief updates were generally larger, and belief uncertainty smaller, with monetary compared to instructive feedback. Larger amplitudes in the monetary feedback condition were found for three event-related potential components: the P3a, the feedback-related negativity (FRN) and the late positive potential (LPP). These findings suggest that motivational state influences inference strategies in reward learning, and this is reflected in the electrophysiological correlates of belief updating.

scholarly journals Biased belief updating and suboptimal choice in foraging decisions

2019 ◽  
Author(s):  
Neil Garrett ◽  
Nathaniel D. Daw
Keyword(s):  
Prey Selection ◽  
Learning Rule ◽  
Belief Updating ◽  
Reward Rate ◽  
Mate Search ◽  
Learning Rates ◽  
Foraging Decisions ◽  
Foraging Models ◽  
Choice Setting

AbstractIn many choice scenarios, including prey, employment, and mate search, options are not encountered simultaneously and so cannot be directly compared. Deciding which ones optimally to engage, and which to forego, requires developing accurate beliefs about the overall distribution of prospects. However, the role of learning in this process – and how biases due to learning may affect choice – are poorly understood. In three experiments, we adapted a classic prey selection task from foraging theory to examine how individuals kept track of an environment’s reward rate and adjusted their choices in response to its fluctuations. In accord with qualitative predictions from optimal foraging models, participants adjusted their selectivity to the richness of the environment: becoming less selective in poorer environments and increasing acceptance of less profitable options. These preference shifts were observed not just in response to global (between block) manipulations of the offer distributions, but also to local, trial-by-trial offer variation within a block, suggesting an incremental learning rule. Further offering evidence into the learning process, these preference changes were more pronounced when the environment improved compared to when it deteriorated. All these observations were best explained by a trial-by-trial learning model in which participants estimate the overall reward rate, but with upward vs. downward changes controlled by separate learning rates. A failure to adjust expectations sufficiently when an environment becomes worse leads to suboptimal choices: options that are valuable given the environmental conditions are rejected in the false expectation that better options will materialize. These findings offer a previously unappreciated parallel in the serial choice setting of observations of asymmetric updating and resulting biased (often overoptimistic) estimates in other domains.

A Self-Constructing Wavelet Neural Network Controller to Mitigate the Subsynchronous Oscillations

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Mohsen Farahani ◽  
Soheil Ganjefar
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Adaptive Learning ◽  
Control Process ◽  
Wavelet Neural Network ◽  
Intelligent Control System ◽  
Intelligent Technique ◽  
Neural Network Controller ◽  
Learning Rates ◽  
On Line

This study proposes a new intelligent controller based on self-constructing wavelet neural network (SCWNN) to suppress the subsynchronous resonance (SSR) in power systems compensated by series capacitors. In power systems, the use of intelligent technique is inevitable, because of the uncertainties such as operating condition variations, different kinds of disturbances, etc. Accordingly, an intelligent control system that is an on-line trained SCWNN controller with adaptive learning rates is used to mitigate the SSR. The Lyapunov stability method is used to extract the adaptive learning rates. Hence, the convergence of the proposed controller can be guaranteed. At first, there is no wavelet in the structure of controller. They are automatically generated and begin to grow during the control process. In the whole design process, the identification of the controlled plant dynamic is not necessary according to the ability of the proposed controller. The effectiveness and robustness of the proposed controller are demonstrated by using the simulation results.

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