The effect of partial and continuous reinforcement on the generalization of conditioned fear in humans

Zhao, Shaochen; Chen, Wei; Jie, Jing; Fan, Min; Li, Junjiao; Rong, Muxin; Yang, Zhi; Zheng, Xifu

doi:10.1016/j.lmot.2022.101812

Cited by 9 publications

(21 citation statements)

References 76 publications

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“…Surprisingly, no effect of the reinforcement rate was evident in SCR during acquisition and generalization despite some evidence that SCR is modulated by US prediction (de Berker et al, 2016;Ojala & Bach, 2020). However, Zhao et al (2022) used a 50% reinforcement schedule for all groups in generalization which matched the reinforcement schedule of one group (i.e., the 50% group) during acquisition. This means that each group experienced a different reduction (while none for the 50%) of CS-US contingency, making the generalization test difficult to compare across groups.…”

Section: No Influence Of Threat Uncertainty On Fear Generalizationmentioning

confidence: 98%

“…Despite the inherent uncertainty associated with partial reinforcement rates, studies on fear generalization use various reinforcement schedules during acquisition, ranging from 33% (Morey et al, 2015) to 75% or even 100% (Lemmens et al, 2021;Lissek et al, 2010) making it difficult to compare. To our knowledge the only study that directly investigated the effect of partial and continuous reinforcement schedules on fear generalization is the one by Zhao et al (2022). The authors compared three groups with reinforcement schedules of 50%, 75% and 100% in acquisition and found overall increased generalization magnitudes for threat expectancy ratings for the groups with partial (50% and 75%) reinforcement while the continuous reinforcement group showed a less steep generalization gradient.…”

Section: No Influence Of Threat Uncertainty On Fear Generalizationmentioning

confidence: 99%

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No Influence of Threat Uncertainty on Fear Generalization

Aslanidou

Andreatta

Wong

et al. 2023

Preprint

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Fear overgeneralization and perceived uncertainty about future outcomes have been suggested as risk factors for clinical anxiety. However, little is known regarding how they influence each other. In this study, we investigated whether different levels of threat uncertainty influence fear generalization. Three groups of healthy participants underwent a differential fear conditioning protocol followed by a generalization test. All groups learned to associate one female face (conditioned stimulus, CS+) with a female scream (unconditioned stimulus, US) while the other face (CS-) was not associated with the scream. In order to manipulate threat uncertainty, one group (low uncertainty, n = 26) received 80%, the second group (moderate uncertainty, n = 32) received 60%, and the third group (high uncertainty, n = 30) 40% CS-US contingency. In the generalization test, all groups saw CS+ and CS- again as well as four morphs that varied in similarity with the CS+ in steps of 20%. Subjective (expectancy, valence, and arousal ratings), psychophysiological (skin conductance response, SCR), and visuocortical (steady-state visual evoked potentials, ssVEPs) indices of fear were registered. Participants expected the US in accordance with their reinforcement schedules but displayed stronger skin conductance with more uncertainty. However, acquisition of conditioned fear was not evident in ssVEPs. During the generalization test, we found no effect of threat uncertainty in any of the measured variables, but the strength of generalization for threat expectancy ratings was positively correlated with dispositional intolerance of uncertainty. This study suggests that mere threat uncertainty does not modulate fear generalization.

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Section: No Influence Of Threat Uncertainty On Fear Generalizationmentioning

confidence: 98%

Section: No Influence Of Threat Uncertainty On Fear Generalizationmentioning

confidence: 99%

No Influence of Threat Uncertainty on Fear Generalization

Aslanidou

Andreatta

Wong

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…When the values of episode and t are very large, the constant term in the T(episode,t) function and the coefficients of T and N have negligible effects on episode and t. At the same time, we should note that the main influencing factor of the T (episode,t) function is T � N, then the time complexity of the algorithm used in this study is shown in Equation (25).…”

Section: Algorithm Time Complexity Calculationmentioning

confidence: 98%

“…As mentioned above, the generalisation ability of reinforcement learning affects the deployment of reinforcement learning models in the real world. Therefore, how to improve the generalisation ability of the reinforcement model is an important issue for the application of reinforcement learning in air combat decision‐making [25].…”

Section: Introductionmentioning

confidence: 99%

Autonomous air combat decision‐making of UAV based on parallel self‐play reinforcement learning

Huang

Bai

et al. 2022

CAAI Trans on Intel Tech

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Aiming at addressing the problem of manoeuvring decision-making in UAV air combat, this study establishes a one-to-one air combat model, defines missile attack areas, and uses the non-deterministic policy Soft-Actor-Critic (SAC) algorithm in deep reinforcement learning to construct a decision model to realize the manoeuvring process. At the same time, the complexity of the proposed algorithm is calculated, and the stability of the closed-loop system of air combat decision-making controlled by neural network is analysed by the Lyapunov function. This study defines the UAV air combat process as a gaming process and proposes a Parallel Self-Play training SAC algorithm (PSP-SAC) to improve the generalisation performance of UAV control decisions. Simulation results have shown that the proposed algorithm can realize sample sharing and policy sharing in multiple combat environments and can significantly improve the generalisation ability of the model compared to independent training.

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“…It is important to note, however, that all of the aforementioned studies concurrently varied the threat reinforcement rate and the amount of learning experience in their experimental designs. That is, higher threat reinforcement rates corresponded to proportionally higher numbers of CS-US pairings, with Dunsmoor et al (2007) using reinforcement rates (and # of CS-US trials) of 50% (20) and 100% (40), Grady et al (2016) using 50% (20), 50%-100% (30), 100%-50% (30), and 100% (40), Zhao et al (2022) using 50% (4), 75% (6) and 100% (8) and Chin et al (2016) using 50% (4) and 75% (6). This is important because more learning experience could lead to enhanced threat discrimination by reducing estimation uncertainty (Payzan-LeNestour et al, 2013), thus this feature of the experimental design challenges direct attributions of enhanced threat discrimination learning to reinforcement rate alone.…”

Section: Introductionmentioning

confidence: 99%

Evidence for different roles of inhibitory and prospective intolerance of uncertainty during threat discrimination learning

Johnson¹,

Ho²,

Uddin³

et al. 2022

Preprint

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Uncertainty is a core component of threat and associated learning processes. One methodological factor impacting uncertainty in threat learning paradigms is the threat reinforcement rate, which refers to the proportion of times a cue is reinforced with an aversive stimulus. This study tested the effect of partial vs continuous threat reinforcement on threat / safety discrimination learning, as indexed by skin conductance response (SCR). Using a within-participants design, fifty-nine participants completed a task in which three colored shapes were paired with electric shock at reinforcement schedules of 100%, 50% (CS+) and 0% (CS-). In addition, the study examined the relationship between the Intolerance of Uncertainty scale (IU) and two subfactors – inhibitory and prospective IU – with threat discrimination learning. The data show heightened SCR in the continuous vs partial reinforcement condition to all stimuli, but no enhanced discrimination learning. Furthermore, no association was observed between total IU score and threat-safety discrimination. However, using a two-factor model of IU, findings showed higher inhibitory IU and higher prospective IU were associated with diminished and heightened threat discrimination, respectively. These results contribute to a fast-growing literature exploring how the uncertainty inherent to predictors of threat, individual differences in sensitivity to uncertainty, and interactions between these two factors, can shape the acquisition of threat memory.

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The effect of partial and continuous reinforcement on the generalization of conditioned fear in humans

Cited by 9 publications

References 76 publications

No Influence of Threat Uncertainty on Fear Generalization

No Influence of Threat Uncertainty on Fear Generalization

Autonomous air combat decision‐making of UAV based on parallel self‐play reinforcement learning

Evidence for different roles of inhibitory and prospective intolerance of uncertainty during threat discrimination learning

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