However, such a single mechanism perspective is not realistic. One is that it relies on the implicit presumption of a single mechanism (i.e., generalization) that underlies the behaviour and thus does not consider the possibility that different latent mechanisms can yield the same observable behaviour. The currently dominant approach of inferring a generalization mechanism (i.e., the propensity to generalize past learning to encountered unfamiliar stimuli) directly from the observed (averaged) response gradient has several limitations. Along with the summary statistics, numerous statistical models and theories were applied to quantitatively describe and predict generalization behaviour 10, 11, 12, 13, 14, 15, 16. Traditionally, researchers in the field have focused on the group gradient, which is the average of the individual response gradients 7. This relationship is presumed to reflect a latent similarity-based generalization process, with a flatter response gradient implying a greater proclivity for generalization. As the physical resemblance to the CS diminishes, the response strengths tend to decrease 7, 9, 10. Following that, generalized responses are obtained by measuring conditioned responding to multiple novel test stimuli (TS) that most often differ slightly in certain physical dimensions from the stimuli used during training (e.g., the size or color of a circle). In a generalization experiment, participants first learn about the relationship between one or more cues (conditioned stimulus, CS e.g., a circle) and their associated consequences (unconditioned stimulus, US e.g., an electrical shock). Typically, generalization is studied using a conditioning paradigm 7, 8, 9. Consequently, assessing generalization behaviour and quantifying the proclivity of a latent generalization process is an important goal of generalization research. Maladaptive generalization behaviour is associated with several psychopathologies, such as anxiety disorder 1, 2, 3, autism 4, and obsessive-compulsive disorder 5, 6. The fear of a particular dog, however, might become rigid and harmful if it is spread to a wide range of harmless dogs or dog-like animals. This capability of generalizing prior learning to unfamiliar situations is crucial because it enhances our adaptivity in a constantly changing environment. Having been bitten by a dog, we may naturally be wary of other dogs in the future. This raises the question for future research whether a mechanism-specific differential diagnosis may be beneficial for generalization-related psychiatric disorders.Īs humans, we frequently encounter instances of ‘once bitten, twice shy’ in our daily lives. 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. By simultaneously modelling learning, perceptual and generalization data at the individual level, we revealed meaningful variations in how different mechanisms contribute to generalization behaviour. Therefore, we combined a computational model with multi-source data to mechanistically investigate human generalization behaviour. Unfortunately, such an approach fails to disentangle various mechanisms underlying generalization behaviour and can readily result in biased conclusions regarding generalization tendencies. Generalization research predominantly relies on descriptive statistics, assumes a single generalization mechanism, interprets generalization from mono-source data, and disregards individual differences. Human generalization research aims to understand the processes underlying the transfer of prior experiences to new contexts.
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