Mechanical Problem Solving in Goffin’s Cockatoos—Towards Modeling Complex Behavior

Goffin's cockatoos (<i>Cacatua goffiniana</i>) can solve a diverse set of mechanical problems, such as tool use, tool manufacture, and mechanical puzzles. However, the proximate mechanisms underlying this adaptive behavior are largely unknown. Similarly, engineering artificial agents that can as flexibly solve such mechanical puzzles is still a substantial challenge in areas such as robotics. This article is an interdisciplinary approach to study mechanical problem solving which we hope is relevant to both fields. The behavior we are studying results from the interaction between a complex environment (the lockbox) and different processes that govern the animals' behavior. We therefore jointly analyze the parrots' (1) engagement, (2) sensorimotor skill learning, and (3) action selection. We find that none of these aspects could solely explain the animals' behavioral adaptation and that a plausible model of proximate mechanisms must jointly address these aspects. We accompany this analysis with a discussion of methods to identify such mechanisms. At the same time, we argue, it is implausible to identify a detailed model from the limited behavioral data of just a few studies. Instead, we advocate for an incremental approach to model building in which one first establishes constraints on proximate mechanisms before specific, detailed models are formulated. To illustrate this idea, we apply it to the data presented here. We argue that as the field attempts to find mechanistic explanations for increasingly complex behaviors, such alternative modeling approaches will be necessary.

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Publication Details

Journal
Adaptive Behavior
Published
2024-08-15
DOI
https://doi.org/10.1177/10597123241270764
Citations
1
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.35

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article

Mechanical Problem Solving in Goffin’s Cockatoos—Towards Modeling Complex Behavior

Oliver Brock, Manuel Baum, Alice M. I. Auersperg, Antonio J. Osuna‐Mascaró et al.
1 citations
Adaptive Behavior
Evolutionary Algorithms and Applications
0.35
article

Mechanical Problem Solving in Goffin’s Cockatoos—Towards Modeling Complex Behavior

Oliver Brock, Manuel Baum, Alice M. I. Auersperg, Antonio J. Osuna‐Mascaró, Theresa Rößler
article en
1 citations

Abstract

Goffin's cockatoos (<i>Cacatua goffiniana</i>) can solve a diverse set of mechanical problems, such as tool use, tool manufacture, and mechanical puzzles. However, the proximate mechanisms underlying this adaptive behavior are largely unknown. Similarly, engineering artificial agents that can as flexibly solve such mechanical puzzles is still a substantial challenge in areas such as robotics. This article is an interdisciplinary approach to study mechanical problem solving which we hope is relevant to both fields. The behavior we are studying results from the interaction between a complex environment (the lockbox) and different processes that govern the animals' behavior. We therefore jointly analyze the parrots' (1) engagement, (2) sensorimotor skill learning, and (3) action selection. We find that none of these aspects could solely explain the animals' behavioral adaptation and that a plausible model of proximate mechanisms must jointly address these aspects. We accompany this analysis with a discussion of methods to identify such mechanisms. At the same time, we argue, it is implausible to identify a detailed model from the limited behavioral data of just a few studies. Instead, we advocate for an incremental approach to model building in which one first establishes constraints on proximate mechanisms before specific, detailed models are formulated. To illustrate this idea, we apply it to the data presented here. We argue that as the field attempts to find mechanistic explanations for increasingly complex behaviors, such alternative modeling approaches will be necessary.

Adaptive BehaviorVol. 32(6)
University of Vienna (AT), University of Veterinary Medicine Vienna (AT), Technische Universität Berlin (DE), Medical University of Vienna (AT)
Deutsche Forschungsgemeinschaft, Austrian Science Fund
Openalex Percentile: Top 35%
Evolutionary Algorithms and Applications
0.35
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