Fig 1.
Trial procedure and scene examples for the experiment.
(a) Trial sequence of the visual search paradigm. (b) An example of a search scene where the target (red polygon) is closely flanked by two distractors (green polygons) on both sides. (c) Another example of a search scene where the distractor (left) is relatively far from the target compared with another distractor (right). The scene photographs were taken manually by the author (XZ).
Fig 2.
Examples of labeled objects and groups in a search scene.
(a) labeled individual buildings that were counted in the set size. (b) labeled building groups formed by the individual buildings in (a).
Fig 3.
The fitting results of linear functions in two datasets.
(a) The fitting solutions for RTs in dataset 1. (b) The fitting solutions for RTs in dataset 2. Dots represent the mean RTs averaged over all observers or RTs of five different observers. Green dots represent the RTs or mean RTs of targets with no separation (Ds = 0). Purple dots represent the RTs or mean RTs of targets with separation (Ds > 0). Curve represents the fitting curve of the linear function.
Fig 4.
The influence of set size and grouping on search efficiency.
(a) Log-transformed mean RT of targets as a function of set size. Blue point is the log-transformed mean RT of a target with no separation (Ds = 0). Green point is the log-transformed mean RT of a target with separation (Ds > 0). (b) Mean RT of targets with different grouping conditions. Error bars represent ± 1 SDs.
Table 1.
Average characteristics of targets and scenes in two datasets.
Size and Ds are shown in pixels, eccentricity is shown in degree (°), SDs are shown in parentheses.
Fig 5.
RT as a function of size and Ds in two datasets when Ds is greater than 0.
(a) The distribution pattern of RTs in dataset 1. (b) The distribution pattern of RTs in dataset 2. Purple dots represent the mean RTs average over all observers. Green dots represent the RTs of five different observers. Blue or green line represents the RT of a dot.