Understanding how the brain updates representations of objects amid continuous changes in their features is crucial for constructing accurate internal models of the world. Such changes are typically temporally autocorrelated, referred to here as smooth, meaning the features at one moment are correlated with those at previous and subsequent moments. The attentional drag theory (Callahan-Flintoft et al., 2020) instantiated a computational model of representational updating that is supported by empirical evidence. In the model, attentional engagement that has been allocated to an object is prolonged by smooth feature change, which in turn increases the likelihood of reporting feature values presented after a probe cue. In contrast, abrupt feature changes, or visual transients, trigger quicker disengagement, allowing subjects to report earlier feature values, nearly coincident with a probe. The current study extended this theory, examining how transients in one feature affected sampling in another feature of the same object. Experiments revealed that orientation transients affected color selection latency (even when orientation was task irrelevant), but color transients did not affect orientation sampling. Moreover, only transients in the attended object or location affected sampling. These findings suggest a nuanced form of spatial or object-based attention where certain features, such as orientation, may play a more significant role in defining object continuity over time. This asymmetry suggests that temporal attentional episodes do not parse continuous visual input in its totality but rather may be local to the object or even feature level depending on how attentional disengagement occurs. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Flintoft et al. (2026) studied this question.
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