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To navigate changes within a highly dynamic and complex environment, it is crucial to compare current visual representations of a scene to previously formed representations stored in memory. This process of mental comparison requires integrating information from multiple sources to inform decisions about changes within the environment. In the present article, we combine a novel systems factorial technology change detection task (Blunden et al., 2022) with a set size manipulation. Participants were required to detect 0, 1, or 2 changes of low and high detectability between a memory and probe array of 1-4 spatially separated luminance discs. Analyses using systems factorial technology indicated that the processing architecture was consistent across set sizes but that capacity was always limited and decreased as the number of distractors increased. We developed a novel model of change detection based on the statistical principles of basic sampling theory (Palmer, 1990; Sewell et al., 2014). The sample size model, instantiated parametrically, predicts the architecture and capacity results a priori and quantitatively accounted for several key results observed in the data: (a) increasing set size acted to decrease sensitivity (d') in proportion to the square root of the number of items in the display; (b) the effect of redundancy benefited performance by a factor of the square root of the number of changes; and (c) the effect of change detectability was separable and independent of the sample size costs and redundancy benefits. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

In the study of perceptual categorization, a key distinction is made between separable and integral dimensions. Separable dimensions are easy to attend in isolation, while integral dimensions are not. Little et al. (2016) showed that when trial-by-trial responses are analyzed, a consistent pattern of sequential effects was found in a modified Garner paradigm using integral-dimension stimuli. The present experiments investigated whether these pronounced sequential effects are also found with separable-dimension stimuli. Four experiments using two different types of separable dimensions were conducted. The results indicated that similar patterns of sequential effects were present for separable-dimension stimuli, but, unlike for integral dimensions, the effect of a change in the irrelevant dimension in the filtering task was not found. Further, for separable dimensions, the overall pattern of sequential effects did not vary between the Garner tasks (i.e., control, correlated, and filtering). To explain these results, we fit a sequence-sensitive exemplar model and compared the fits of this model to a novel sequence-sensitive feature model, in which only the relevant feature influences the categorization decision. We found that the full exemplar model provided a more compelling account of both our separable dimension data and the integral dimension data of Little et al. (2016). These findings provide a more complete understanding of perceptual categorization and add to the growing body of literature on the prevalence and critical implications of strong sequential effects in cognitive tasks.

Episodic memory theories have postulated that in recognition, a probe is accepted or rejected on the basis of its global similarity to studied items. Mewhort and Johns (2000) directly tested global similarity predictions by manipulating the feature compositions of probes-novelty rejection was facilitated when probes contained novel features even when other features strongly matched, an advantage dubbed the extralist feature effect, which greatly challenged global matching models. In this work, we conducted similar experiments using continuously valued separable- and integral-dimension stimuli. Analogs of extralist lures were constructed where one stimulus dimension contained a value that was more novel than the other dimensions, whereas overall similarity was equated to another class of lures. Facilitated novelty rejection for lures with extralist features was only found for separable-dimension stimuli. While integral-dimension stimuli were well described by a global matching model, the model failed to account for extralist feature effects with separable-dimension stimuli. We applied global matching models-including variants of the exemplar-based linear ballistic accumulator-that employed different means of novelty rejection afforded by separable-dimension stimuli, including decisions based on the global similarity of the individual dimensions and selective attention being directed toward novel probe values (a diagnostic attention model). While these variants produced the extralist feature effect, only the diagnostic attention model succeeded in providing a sufficient account of all of the data. The model was also able to account for extralist feature effects in an experiment with discrete features similar to those from Mewhort and Johns (2000). (PsycInfo Database Record (c) 2023 APA, all rights reserved).