Schulz, E; Speekenbrink, M; Meder, B; (2016) Simple trees in complex forests: Growing Take The Best by Approximate Bayesian Computation. In: Papafragou, A and Grodner, D and Mirman, D and Trueswell, JC, (eds.) Proceedings of the 38th Annual Meeting of the Cognitive Science Society 2016. (pp. pp. 2531-2536). Cognitive Science Society: Austin, TX.

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Abstract

How can heuristic strategies emerge from smaller building blocks? We propose Approximate Bayesian Computation (ABC) as a computational solution to this problem. As a first proof of concept, we demonstrate how a heuristic decision strategy such as Take The Best (TTB) can be learned from smaller, probabilistically updated building blocks. Based on a self-reinforcing sampling scheme, different building blocks are combined and, over time, tree-like non-compensatory heuristicsemerge. This new algorithm, coined Approximately Bayesian Computed Take The Best (ABC-TTB), is able to recover data that was generated by TTB, leads to sensible inferences about cue importance and cue directions, can outperform traditional TTB, and allows to trade-off performance and computational effort explicitly.

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