Human-Memory-and-Cognition
OpenML dataset with id 43596
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102421/Human-Memory-and-Cognition.arff
Upload date: 24 March 2022
Dataset Characteristics
Number of features: 23 (numeric: 11, symbolic: 0 and in total binary: 0 )
Number of instances: 6,854
Number of instances with missing values: 6,854
Number of missing values: 16,924
Context Models of human cognition hold that information processing occurs in a series of stages. Cognitive psychology, in particular, is concerned with the internal mental processes that begin with the appearance of an external stimulus and result in a behavioural response. Content Explore human cognitive processes around the generation of narrativeswith a focus on the language employed in stories about events that have been experienced versus imagined. Investigate and characterize cognitive processes involved in storytelling, contrasting imagination and recollection of events with the help of Data Science. Build a machine learning model that would help you to categorize cognitive processes involved in storytelling - Imagined, Recalledor Retold. These are the columns in the data:
AssignmentId: Unique ID of this story WorkTimeInSeconds: Time in seconds that it took the worker to do the entire HIT (reading instructions, story writing, questions) WorkerId: Unique ID of the worker (random string, not MTurk worker ID) annotatorAge: Lower limit of the age bucket of the worker. Buckets are: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55+ annotatorGender: Gender of the worker annotatorRace: Race/ethnicity of the worker distracted: How distracted were you while writing your story? (5-point Likert) draining: How taxing/draining was writing for you emotionally? (5-point Likert) frequency: How often do you think about or talk about this event? (5-point Likert) importance: How impactful, important, or personal is this story/event to you? (5-point Likert) logTimeSinceEvent: Log of time (days) since the recalled event happened mainEvent: Short phrase describing the main event described memType: Type of story (recalled, imagined, retold) - The target variable mostSurprising: Short phrase describing what the most surprising aspect of the story was openness: Continuous variable representing the openness to experience of the worker recAgnPairId: ID of the recalled story that corresponds to this retold story (null for imagined stories). Group on this variable to get the recalled-retold pairs. recImgPairId: ID of the recalled story that corresponds to this imagined story (null for retold stories). Group on this variable to get the recalled-imagined pairs. similarity: How similar to your life does this event/story feel to you? (5-point Likert) similarityReason: Free text annotation of similarity story: Story about the imagined or recalled event (15-25 sentences) stressful: How stressful was this writing task? (5-point Likert) summary: Summary of the events in the story (1-3 sentences) timeSinceEvent: Time (number of days) since the recalled event happened
Likert scaling is a bipolar scaling method, measuring either positive or negative response to a statement.
Acknowledgements
Maarten Sap, Eric Horvitz, Yejin Choi, Noah A. Smith, and James Pennebaker (2020) Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models. ACL.
Inspiration
Explore the human cognitive process using machine learning.
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