Release v0.1.2

Release Note: Enhanced A Rank B & New PAQ Query Type

We're excited to announce significant enhancements to our A Rank B query type and the introduction of a brand new PAQ (Perceptual Adjustment Query) system.
These updates bring improved data quality, enhanced user experience, and expanded research capabilities to the NEXT platform.

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🚀 A Rank B - Major Enhancements

🔹 Enhanced User Interface & Experience

• Single Submission Enforcement: Prevents participants from spam-submitting responses, ensuring data integrity and avoiding duplicates
• Progress Bar Integration: Visual indicator tracks completion status throughout the ranking task
• Improved Item Selection: Enhanced drag-and-drop functionality for smoother item arrangement within the green ranking box

🔹 Advanced Data Quality Features

• Trap Question System: Identifies inattentive or dishonest participants
  • Configurable frequency and tolerance thresholds
  • Automatic expulsion policies for participants failing checks
  • Example: "Choose the option with the word positive in it" → options: Positive, Negativity, War, Peace
• Quality Assurance: Ensures only high-quality, attentive responses make it into final datasets

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🆕 PAQ (Perceptual Adjustment Query) - New Query Type

🔹 Overview

A sophisticated query system that presents participants with a reference item and allows them to adjust a slider along a perceptual continuum to find the most similar or dissimilar target item.
Supports multiple media types and both static and dynamic sampling approaches.

🔹 Supported Media Types

• Color PAQ: Match reference colors through continuously changing color paths
• Image PAQ: Match reference images through morphed image transformations
• Text PAQ: Match reference text through textual variations

🔹 Core Components

• Reference Item: Target participants aim to match
• Perceptual Continuum: Slider-based interface for fine-tuned adjustments
• Start/End Items: Define perceptual boundaries
• Configurable Precision: Adjustable tick counts (recommended: 100 for color vision accuracy)

🔹 Sampling Algorithms

• ColorVision: Generates color paths in xyY color space with directional sampling
• ImageTransformation: Creates morphing transitions between start and end images
• DynamicPAQ: Active learning approach dynamically selecting items for each query
  • Currently supports ImageTransformation algorithm

🔹 Configuration Options

• Reference items, directional vectors (ColorVision)
• Start/end items, tick count, tick visibility (ImageTransformation)
• YAML templates for easy setup and customization

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⚙️ Technical Improvements

Enhanced User Experience

• Single submission enforcement prevents duplication
• Progress tracking improves engagement
• Smoother drag-and-drop interactions

Data Quality Assurance

• Trap question system ensures response integrity
• Configurable thresholds and expulsion policies
• Automatic detection of inattentive participants

Research Capabilities

• PAQ system expands experimental possibilities
• Support for multiple media types and perceptual tasks
• Dynamic sampling algorithms for active learning experiments

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✨ These enhancements significantly improve the robustness and versatility of the NEXT platform, providing researchers with better tools for data collection and quality assurance.

Release v0.1.1

Release Note: New Supported Query Types

We're excited to introduce new query types that enhance interactive ranking and sentiment classification capabilities based on the platform NEXT created by Neuromatch. Below is a summary of the newly supported query types and their features.

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A Rank B

Overview:
Participants are presented with a pool of A items and must select and rank B items (with B < A) based on a designated anchor item.

User Interface:

• Red Box: Displays the full pool of items.
• Green Box: Hosts the B items selected for ranking.
• Interactions:
  • Clicking an item in the red box moves it to the green box.
  • Clicking an item in the green box returns it to the red box.
  • Drag and drop within the green box allows for rearrangement of the selected items.
• Submission Rule:
  • Responses can only be submitted when exactly B items are present in the green box.

Dynamic Sampling Algorithms:

• Random Sampling:
  • Randomly selects A+1 items from the user-provided targets.
  • Configurable parameters include A, B, and the target set.
• Info-Tuple Sampling:
  • Dynamically updates an embedding based on real-time responses.
  • Selects the tuple of items expected to yield the highest information gain.
  • Offers additional parameters such as burn-in iterations, total iterations, and down-sampling rate, adjustable via the YAML template.

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Binary Sentiment Word Classification (BSWC)

Overview:
This query type focuses on sentiment classification of words. Participants are tasked with either:

• Rank One: Selecting the most positive word from a pool of n words.
• Rank N: Ranking words from the most positive to the most negative.

Query Interfaces:

• Rank One Interface:
  • A central box displays the full pool of words.
  • Clicking a word highlights it to indicate the participant's choice.
• Rank N Interface:
  • Similar to Rank One with a central display of words.
  • Clicking highlights the selected word.
  • Submission is enabled only when exactly one word is highlighted.

Static Sampling Algorithm:

• Read CSV:
  • Queries are loaded from a CSV file, with each row representing one query.
  • The first number_of_queries rows are truncated, and the remaining rows are presented sequentially as queries.

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These enhancements offer robust options for both dynamic and static query sampling, supporting a variety of research and data collection needs. For further configuration details, please consult the respective YAML template documentation.

Happy Ranking!