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Related Experiment Video

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A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

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Association and response accuracy in the wild.

Sudeep Bhatia¹, Lukasz Walasek²

¹Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA. bhatiasu@sas.upenn.edu.

Memory & Cognition

|October 17, 2018

Summary

Contestant accuracy on "Jeopardy!" was predicted by semantic memory associations. Stronger clue-response links led to correct answers, while weaker links resulted in errors, especially on easier questions.

Keywords:

Associative judgment Big data Distributional semantics Field data Response accuracy

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Area of Science:

Cognitive Psychology
Computational Linguistics
Neuroscience

Background:

Distributional models of semantic memory represent word meanings based on co-occurrence patterns.
These models capture semantic similarity and have been linked to human memory and judgment.
Understanding semantic associations is crucial for modeling cognitive processes.

Purpose of the Study:

To investigate the role of semantic memory strength in predicting contestant accuracy and error on the quiz show "Jeopardy!".
To evaluate the predictive power of vector-based knowledge representations in a naturalistic, high-stakes cognitive task.

Main Methods:

Utilized vector-based knowledge representations from distributional semantic models.
Computed association strengths between clues and responses across over 5,000 "Jeopardy!" games.
Analyzed the relationship between association strength and response accuracy, considering question difficulty and response urgency.

Main Results:

Contestants were more accurate when clue-response associations were strong.
Incorrect responses were more likely when correct responses had weak or negative associations with clues.
This effect was more pronounced for easier questions and in faster-paced gameplay.

Conclusions:

Distributional models of semantic memory effectively predict human behavior in complex judgment tasks.
Semantic association strength is a significant factor influencing performance in knowledge-based competitions.
Findings highlight the utility of computational models for understanding real-world cognitive processes and decision-making.