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Welcome to the mkturk wiki! temp

Original idea & implementation:

Build the simplest possible code to run psychophysics remotely on a tablet. The result was a couple client side files in html/javascript that run a delayed match to sample (DMS in mkturk.html) task and an automator for automatically updating the task parameters (mkturkautomator.js). The server side was left to dropbox (mkturkdropox.js) which not only hosted the webpage but also stored the images and data for the task.

The implemented code is very linear and long and by no means perfect. Now that this proof-of-concept system works, the intent is for anyone to be able to use it as a starting point for quickly implementing their own web-based, touchscreen systems. For additional examples, inquire about MMturk by shayo in DiCarlo lab or the implementation by W. Bendris in Conway lab. The goal of this repository is to give you resources to get up and running. Of course, we would love feedback on things you do differently or improve upon to relay those contributions back to the community. For our part, our future plans are below.

Desirables for mkturk_v2 in order

• ~~Bluetooth capability~~ (done): integrate bluetooth capability using chrome API. Requires android marshmallow.
• Update dropbox functionality (done): dropbox has released a cleaned up version of its API. mkturk will need to transition to this version. The advantage of using dropbox is in having a stable place to store data that requires little administrative overhead. More on dropbox API v2: http://www.hnwatcher.com/r/2788001/Dropbox-API-v2-Tutorial-Benefits-of-version-2-and-how-to-integrate-it-Android
• Async/Await (done): syntax sugar on top of generators to make promise code more readable [google] (https://developers.google.com/web/fundamentals/getting-started/primers/async-functions), microsoft
• Sensors: Create plugins for RFID and weight sensors
• Image hashing: refer to images by hashes, create single images folder (image repository) with image files with hashes as filenames and a master file with the correspondence between hashes and metaparameters.
• Automator improvements: create block modes to select subsets of images based on metaparameters or designated train/test splits.
• Database functionality: write a script to transfer data from storage location into a database. This step happens offline and may depend on server side hardware that requires within lab administrative effort.
• Image-to-Task mapping: Task specifies image groupings which are stored in a multi-image file called a pack. An arbitrary number of image to task mappings can be made and are stored separately in task_images folder. Image packs thus express the task structure in a way that is more transparent much like image folders in mkturk_v1 but with the flexibility of a hashed image repository.
• Inclusive Task Structuring: Task is a set of screens with rules between screens. Each screen is a container of precisely specified images (x,y,t). This structure ideally will accommodate DMS, Go-No Go, SR, and N-back tasks.
• Parameter GUI (postponed; instead added in situ parameter text editing at beginning of task): gui that allows user to view and set parameters for a given subject that day and for setting up training schedules for the automator. GUI could also display image previews or recent performance stats of subject.