File size: 4,590 Bytes
eb72cfa
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
# PolyCAT Datasheet

Following the "Datasheets for Datasets" framework (Gebru et al., 2021).

## Motivation

**For what purpose was the dataset created?**
PolyCAT was created to study how geometric viewing constraints (polygon-shaped apertures) affect visual attention allocation on natural images. It enables saliency prediction research under non-rectangular viewing conditions.

**Who created the dataset and on behalf of which entity?**
Created by researchers at [institution name]. Funded by [funding source].

**Who funded the creation of the dataset?**
[To be filled]

## Composition

**What do the instances that comprise the dataset represent?**
Each instance is one eye-tracking trial: a participant's gaze behavior while viewing a CAT2000 image through a polygon aperture for 4 seconds.

**How many instances are there in total?**
~21,000 trials across 30 participants (702 trials per fully included participant, 2 parts x 351 trials). All 30 participants are fully included with both parts.

**Does the dataset contain all possible instances or is it a sample?**
It is a complete recording of all trials for all included participants. 600 of the ~2000 CAT2000 images were used.

**What data does each instance consist of?**
- Sample-level gaze coordinates at 500 Hz (binocular)
- Fixation events with position, duration, and timing
- Trial metadata (stimulus, polygon, cue position)
- Session metadata (calibration, validation)

**Is there a label or target associated with each instance?**
No explicit labels. The gaze data itself can be used to derive saliency maps.

**Is any information missing from individual instances?**
Some trials have tracking loss (blinks, look-away). Quality metrics are provided per-trial.

**Are there any errors, sources of noise, or redundancies?**
- EyeLink tracking noise (~0.25-0.5 deg typical accuracy)
- Blinks cause data gaps
- Some edge fixations may be at aperture boundaries

## Collection Process

**How was the data associated with each instance acquired?**
Eye movements were recorded using an EyeLink 1000+ eye tracker while participants viewed stimuli on a 27" 4K monitor.

**What mechanisms or procedures were used to collect the data?**
Custom PsychoPy experiment with EyeLink integration. 13-point calibration before each block, drift check before each trial.

**Who was involved in the data collection process?**
Trained experimenters supervised each session. Participants were university students.

**Over what timeframe was the data collected?**
January 15 - February 25, 2026.

**Were any ethical review processes conducted?**
Yes. The study was approved by the institutional ethics review board. All participants provided written informed consent.

## Preprocessing

**Was any preprocessing/cleaning/labeling of the data done?**
- Raw EDF files were converted to CSV/TSV format
- Fixations were extracted using EyeLink's built-in detection algorithm
- Split sessions were concatenated using trial UIDs
- Quality metrics were computed per trial and participant

**Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data?**
Yes. The original session logs and EDF files are preserved in `data/raw_output_data/`.

## Uses

**Has the dataset been used for any tasks already?**
Initial analyses are presented in the accompanying ETRA 2026 paper.

**What (other) tasks could the dataset be used for?**
- Saliency prediction with aperture constraints
- Scanpath modeling and prediction
- Center bias analysis under geometric constraints
- Polygon geometry effects on visual exploration

**Is there anything about the composition of the dataset or the way it was collected that might impact future uses?**
The polygon apertures create a unique viewing constraint not present in standard saliency datasets. Models should account for the masked regions.

## Distribution

**How will the dataset be distributed?**
Via the university lab's SharePoint repository. A permanent link will be provided in the published paper.

**When was the dataset first released?**
2026 (ETRA 2026 conference).

**What license is the dataset distributed under?**
CC BY 4.0. Note that the CAT2000 images are subject to their original license.

## Maintenance

**Who is supporting/hosting/maintaining the dataset?**
[Institution name and contact information]

**Will the dataset be updated?**
Version updates will be documented with changelogs.

**How can the owner/curator/manager of the dataset be contacted?**
[Contact email]