Searching for two categories of target in dynamic visual displays impairs monitoring ability

Abstract

Target onsets in dynamically changing displays can be predicted when contingencies exist between different stimulus states over time. In the present study, we examined predictive monitoring when participants searched dynamically changing displays of numbers and colored squares for a color target, a number target, or both. Stimuli were presented in both contiguous and discrete spatial configurations. Response time (RT) and accuracy were recorded, and evidence of predictive monitoring was assessed via first fixations and refixations of target-predictive stimuli. RTs to target onsets and evidence of predictive monitoring were reduced in dual-target, relative to single-target, conditions. Further, predictive monitoring did not speed RTs but was influenced by display configuration. In particular, discrete displays impaired monitoring for number targets in the dual-target condition. Implications exist for real-world visual tasks involving multiple target categories and for visual display design.

Visual exploration mediates the influence of personal traits on responses to artworks in an art gallery setting.

“…Research practice is mixed on this issue, even in the lab setting where counterbalancing procedures is generally not problematic. Some studies measured individual differences before other visuo-cognitive measures were taken (e.g., McCaffery et al, 2018, in face matching task; Muhl-Richardson et al, 2018, in visual search task; Sarsam et al, 2021, in scene perception task; Colver & El-Alayli, 2016, Trawiński, Mestry, et al, 2021, in art appreciation task), others took visuo-cognitive before individual difference measures (Peltier & Becker, 2017, in visual search task; Ganczarek et al, 2015; Risko et al, 2012; Sherman et al, 2015), while some counterbalanced tasks (e.g., Megreya & Bindemann, 2013, in face identification task). Moreover, we could not find clear evidence that task order influences visual exploration, with the exception of one study for which subsequent analyses did not find evidence of such an effect (Trawiński, 2020).…”

Right place, right time: Spatiotemporal predictions guide attention in dynamic visual search.

“…Cravo and colleagues (2017) demonstrated that observers are able to utilize temporal intervals associated with specific contexts to guide attention—although in a task that utilized static displays with sudden target onsets. Muhl-Richardson, Cornes, et al (2018) and Muhl-Richardson, Godwin, et al (2018) showed that observers prioritized distractors in a dynamically changing display when these distractors had a high probability of soon becoming targets. Other studies showed how repeating sequences can inform attentional guidance (Boettcher et al, 2020; Heideman et al, 2018; Li & Theeuwes, 2020; Nobre & O’Reilly, 2004; O’Reilly et al, 2008; Zhao et al, 2013).…”

“…For example, researchers have shown that a regularly occurring sequence of events can attract attention automatically without the need of prior experiences (Zhao et al, 2013). Moreover, recent work has also demonstrated that people can use a continuous change of features (e.g., gradual change of color or change of number) within a dynamic task to anticipate when a stimulus becomes response relevant (Muhl-Richardson, Cornes, et al, 2018; Muhl-Richardson, Godwin, et al 2018). Li and Theeuwes (2020) demonstrated that observers anticipate targets based on regular patterns of target sequences (e.g., they can learn that the selection of a target at one location the visual field is likely to be followed by a target at another location).…”

Experience with searching in displays containing depth improves search performance by training participants to search more exhaustively

“…Training Participants to Search more Exhaustively Human observers are often engaged in visual search tasks that are a far cry from the visual environments that our visual systems evolved to process. The complex visualisations made available by modern technologies require searchers to learn how to interpret images that do not necessarily follow the same 'rules' that the real world follows (Muhl-Richardson et al, 2018;Richardson et al, 2018). For example, in airport baggage screening, screening personnel are required to examine X-ray images which contain a wide array of transparent overlapping objects (for a review, see Donnelly, Muhl-richardson, Godwin, & Cave, 2019).…”