DOI: http://dx.doi.org/10.7554/eLife.07902.004 To ensure that behavioural performance (proportion correct) was matched across stimulus types, and that no learning was shown across task runs, we conducted a 3 (task: scenes, faces, size) × 2 (run number: run 1, run 2, run 3) analysis of variance (ANOVA).
In each block, participants performed one of three viewing tasks: scene memorization, preference judgment, or scene search.
All of this makes the task of scene change recognition that much harder.
Handcrafted image features designed for the task of scene classification are used as the baseline for comparison.
Results were mixed, varying by type of fusion, task, and scene content.
In the change discrimination task, the scenes were constructed such that scene A always contained four sound sources; thus, there was the possibility that participants could complete the AX task by counting sources rather than comparing scenes A and X.
Using the proposed algorithm, we compute eye-movement statistics for both eyes for samples of about 70 participants across a variety of common tasks and stimuli (two reading tasks, static scene viewing, and search) and provide descriptive statistics for them, which reveal that individual differences in fixation durations and saccade sizes are large.
In both tasks, the scene was displayed with the target object floating over a "floor"—a flat plane with a cross-hatch pattern textured on it.
The general-purpose representation likely developed for ecologically important tasks like scene perception, but also likely supports understanding of web pages, in part because design develops to make use of existing visual processing architecture in the human visual system.
The distractors filtered in the TPJs, however, may include distractors other than task-introduced scene distractors.
