Humans display interindividual differences in the latent mechanisms underlying fear generalization behaviour

Abstract

Human generalization research aims to understand the processes underlying the transfer of prior experiences to new contexts. Generalization research predominantly relies on descriptive statistics, assumes a single generalization mechanism, interprets generalization from mono-source data, and disregards individual differences. Unfortunately, such an approach fails to disentangle various mechanisms underlying generalization behaviour and can readily result in biased conclusions regarding generalization tendencies. Therefore, we combined a computational model with multi-source data to mechanistically investigate human generalization behaviour. By simultaneously modelling learning, perceptual and generalization data at the individual level, we revealed meaningful variations in how different mechanisms contribute to generalization behaviour. The current research suggests the need for revising the theoretical and analytic foundations in the field to shift the attention away from forecasting group-level generalization behaviour and toward understanding how such phenomena emerge at the individual level. This raises the question for future research whether a mechanism-specific differential diagnosis may be beneficial for generalization-related psychiatric disorders.

The assessment of gender differences in perceptual fear generalization and related processes

“…Circles of varying sizes, as white outlines against a black background, served as the stimuli for learning and testing. The dimension of circle size has been applied extensively in generalization studies with conditioned fear involving both healthy and clinical populations (Lange et al, 2017;Lissek, 2012;Lissek et al, 2008Lissek et al, , 2014Yu et al, 2023;. The stimulus set comprised ten circles, denoted as S1 through S10, with diameters ranging from 50.80 to 119.42 mm, spaced at intervals of 7.624 mm.…”

“…Recent advances in research on fear generalization suggest that a multitude of mechanisms can contribute to generalization behavior; an exclusive focus on fear responses renders researchers unable to scrutinize these mechanisms (Struyf et al, 2015;Yu et al, 2023;Zaman, Chalkia, et al, 2020;Zaman et al, 2023). Congruently assessing (variations in) stimulus perception, fear and safety learning, and generalized fear responses enables to account for inter-and intra-individual differences regarding the latter (Struyf et al, 2017;Zaman et al, 2022;Zaman, Struyf, et al, 2019.…”

“…Congruently assessing (variations in) stimulus perception, fear and safety learning, and generalized fear responses enables to account for inter-and intra-individual differences regarding the latter (Struyf et al, 2017;Zaman et al, 2022;Zaman, Struyf, et al, 2019. It also yields generalization indexes that are comparable across individuals as they control for differences regarding learning, perception, and memory (Yu et al, 2023;Zaman et al, 2023). In a recent paper, Yu et al (2023) investigated latent mechanisms in fear generalization behavior via a computational generalization model that parameterizes several key processes that contribute to generalized responding, including the speed of learning (Rescorla & Wagner, 1972) and the strength of a similarity-based generalization process (Shepard, 1987) while accounting for differences in stimulus perception.…”

“…It also yields generalization indexes that are comparable across individuals as they control for differences regarding learning, perception, and memory (Yu et al, 2023;Zaman et al, 2023). In a recent paper, Yu et al (2023) investigated latent mechanisms in fear generalization behavior via a computational generalization model that parameterizes several key processes that contribute to generalized responding, including the speed of learning (Rescorla & Wagner, 1972) and the strength of a similarity-based generalization process (Shepard, 1987) while accounting for differences in stimulus perception. In addition, one of the model's interesting features is that it allocates participants (based on multivariate data, i.e., perception, learning, and generalization) to distinct mechanistically and clinically relevant subgroups (Yu et al, 2023).…”

“…In a recent paper, Yu et al (2023) investigated latent mechanisms in fear generalization behavior via a computational generalization model that parameterizes several key processes that contribute to generalized responding, including the speed of learning (Rescorla & Wagner, 1972) and the strength of a similarity-based generalization process (Shepard, 1987) while accounting for differences in stimulus perception. In addition, one of the model's interesting features is that it allocates participants (based on multivariate data, i.e., perception, learning, and generalization) to distinct mechanistically and clinically relevant subgroups (Yu et al, 2023). Given existing evidence for sex or gender differences regarding many of the processes involved, including perceptual acuity (Shaqiri et al, 2016(Shaqiri et al, , 2018 and differential fear learning (Day et al, 2016(Day et al, , 2020Lonsdorf et al, 2017), the study's third aim is twofold:…”