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Raw data from memory experiments using HCU400 from Amazon Turk

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HCU400-Memory-Game-Data

Raw data from memory experiments using HCU400 from Amazon Turk

please see The Intrinsic Memorability of Everyday Sounds for details; if you use this data in research please cite:

Ramsay, David, Ishwarya Ananthabhotla, and Joseph Paradiso. "The Intrinsic Memorability of Everyday Sounds." Audio Engineering Society Conference: 2019 AES International Conference on Immersive and Interactive Audio. Audio Engineering Society, 2019.

Please see the included python notebook for loading data and a rough introduction. Included are survey results and raw memory game data, as well as processed memorability scores based on each sound. In general, the memory task includes dozens of sounds which we refer to as 'target'-- the sounds under test that are separated by 60 sounds each (denoted 't' frequently) and 'vigilance'-- the sounds that are spaced 2-3 apart to make sure the person is paying attention and engaged throughout the memory task.

Note that the raw game data has a full list of the order of the sound samples, as well as whether there was a keypress indicating that sound was selected as having been heard before, in ms from the start of the sound (a zero indicates no press).

Our study was conducted off of Turk, and thus we tracked people with tokens/user ids ourselves, and then cross-referenced those with their Amazon assigned IDs. Some people have multiple amazon turk accounts, and some close their browser in between sessions, so sync between these two IDs is highly variable. We attempted to best identify/match people with a cannonical 'USER_ID', which runs throughout this data.

We are happy for others to use/play with it, please cite us and/or contact us if you have any questions or would like access to some of our scripts; we're happy to collaborate!

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