scholarly journals How predictive learning influences choice: Evidence for a GPCR‐based memory process necessary for Pavlovian‐instrumental transfer

2021 ◽  
Author(s):  
Vincent Laurent ◽  
Bernard W. Balleine
Keyword(s):  
Memory Process ◽  
Predictive Learning

A Novel GPCR-Based Memory Process is Necessary for the Influence of Predictive Learning on Choice

2019 ◽  
Author(s):  
Ashleigh Morse ◽  
Beatrice Leung ◽  
Emily Heath ◽  
Jesus Bertran-Gonzalez ◽  
Elise Pepin ◽  
...  
Keyword(s):  
Memory Process ◽  
Predictive Learning

The Effect of Outcome Probability on Generalization in Predictive Learning

2019 ◽  
Vol 66 (1) ◽  
pp. 23-39 ◽  
Author(s):  
Hadar Ram ◽  
Dieter Struyf ◽  
Bram Vervliet ◽  
Gal Menahem ◽  
Nira Liberman
Keyword(s):  
High Probability ◽  
Lower Probability ◽  
Perceptual Similarity ◽  
Learning Paradigm ◽  
Predictive Learning ◽  
Lightning Bolt ◽  
Outcome Probability

Abstract. People apply what they learn from experience not only to the experienced stimuli, but also to novel stimuli. But what determines how widely people generalize what they have learned? Using a predictive learning paradigm, we examined the hypothesis that a low (vs. high) probability of an outcome following a predicting stimulus would widen generalization. In three experiments, participants learned which stimulus predicted an outcome (S+) and which stimulus did not (S−) and then indicated how much they expected the outcome after each of eight novel stimuli ranging in perceptual similarity to S+ and S−. The stimuli were rings of different sizes and the outcome was a picture of a lightning bolt. As hypothesized, a lower probability of the outcome widened generalization. That is, novel stimuli that were similar to S+ (but not to S−) produced expectations for the outcome that were as high as those associated with S+.

scholarly journals Position Tracking Techniques Using Multiple Receivers for Anti-Drone Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 35
Author(s):  
Jae-Min Shin ◽  
Yu-Sin Kim ◽  
Tae-Won Ban ◽  
Suna Choi ◽  
Kyu-Min Kang ◽  
...  
Keyword(s):  
Traffic Control ◽  
Tracking System ◽  
Estimation Method ◽  
Signal Strength ◽  
Low Energy ◽  
Position Tracking ◽  
Memory Process ◽  
Multiple Receivers ◽  
Received Signal Strength Indication ◽  
Control Management

The need for drone traffic control management has emerged as the demand for drones increased. Particularly, in order to control unauthorized drones, the systems to detect and track drones have to be developed. In this paper, we propose the drone position tracking system using multiple Bluetooth low energy (BLE) receivers. The proposed system first estimates the target’s location, which consists of the distance and angle, while using the received signal strength indication (RSSI) signals at four BLE receivers and gradually tracks the target based on the estimated distance and angle. We propose two tracking algorithms, depending on the estimation method and also apply the memory process, improving the tracking performance by using stored previous movement information. We evaluate the proposed system’s performance in terms of the average number of movements that are required to track and the tracking success rate.

scholarly journals Emergent Goal‐Anticipatory Gaze in Infants via Event‐Predictive Learning and Inference

2021 ◽  
Vol 45 (8) ◽  
Author(s):  
Christian Gumbsch ◽  
Maurits Adam ◽  
Birgit Elsner ◽  
Martin V. Butz
Keyword(s):  
Predictive Learning
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