Behavioral Anatomy of a Hunt: Using Dynamic Real-World Paradigm and Computer Vision to Compare Human User-Generated Strategies with Prey Movement Varying in Predictability

Shaktee Sandhu, Tauseef Gulrez, Warren Mansell

Research output: Contribution to journalArticlepeer-review

Abstract

It is commonly thought that the mind constructs predictive models of the environment to plan an appropriate behavioural response. Therefore a more predictable environment should entail better performance, and prey should move in an unpredictable (random) manner to evade capture, known as protean motion. To test this, we created a novel experimental design and analysis in which human participants took the role of predator or prey. The predator was set the task of capturing the prey, whilst the prey was set the task of escaping. Participants performed this task standing on separate sides of a board and controlling a marker representing them. In three conditions, the prey followed a pattern of movement with varying predictability (predictable, semi-random and random) and in one condition moved autonomously (user-generated). The user-generated condition illustrated a naturalistic, dynamic environment involving a purposeful agent whose degree of predictability was not known in advance. The average distance between participants was measured through a video analysis custom-built in MATLAB. The predictable condition had the smallest average distance as expected from the ‘predictive mind’ model, but the user-generated condition had the largest average distance. This indicated that, rather than moving randomly (protean motion), humans may naturally employ a cybernetic escape strategy that dynamically maximises perceived distance, regardless of the predictability of this strategy.
Original languageEnglish
JournalAttention, Perception, and Psychophysics
DOIs
Publication statusPublished - 13 May 2020

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