Light and Kennison proposed that bias effects in the forced-choice perceptual identification of words result from a strategy engaged in by subjects to retrieve explicit information about the words. This article enumerates several problems with this proposal and presents new experimental data against it. It is concluded that subjects do not ordinarily employ an explicit retrieval strategy. The data are discussed in the context of the general problem of separating implicit from explicit influences on performance.
Individuals with high anxiety show bias for threatening information, but it is unclear whether this bias affects memory. Recognition memory studies have shown biases for recognising and rejecting threatening items in anxiety, prompting the need to identify moderating factors of this effect. This study focuses on the role of semantic similarity: the use of many semantically related threatening words could increase familiarity for those items and obscure anxiety-related differences in memory. To test this, two recognition memory experiments varied the proportion (...) of threatening words in lists to manipulate the semantic-similarity effects. When similarity effects were reduced, participants with high trait anxiety were biased to respond “new” to threatening words, whereas when similarity effects were strong there was no effect of anxiety on memory bias. Analysis of the data with the drift diffusion model showed that the bias was due to differences in processing of the threatening stimuli rather than a simple response bias. These data suggest that the semantic similarity of the threatening words significantly affects the presence or absence of anxiety-related threat bias in recognition memory. The results indicate that trait anxiety is associated with a bias to decide that threatening stimuli were not previously studied, but only when semantic-similarity effects are controlled. Implications for theories of anxiety and future studies in this domain are discussed. (shrink)
Bastin et al. propose a dual-process model to understand memory deficits. However, results from state-trace analysis have suggested a single underlying variable in behavioral and neural data. We advocate the usage of unidimensional models that are supported by data and have been successful in understanding memory deficits and in linking to neural data.