Epibranchial organs (EBOs), found in at least five of the eight otomorphan families, are used to aggregate small prey inside the buccopharyngeal cavity and range in morphological complexity from a singular, small slit on the pharyngeal roof to several, elongated soft tissue tubes. Despite broad phylogenetic representation, little is known about the origin, development, or evolution of EBOs. We hypothesize that both heterochronic and heterotopic changes throughout the evolution of EBOs are at the root of their morphological diversity. Heterochrony is a foundational explanation in developmental studies, however, heterotopy, a developmental change in
Understanding the structure and evolution of natural cognition is a topic of broad scientific interest, as is the development of an engineering toolkit to construct artificial cognitive systems. One open question is determining which components and techniques to use in such a toolkit. To investigate this question, we employ agent-based AI, using simple computational substrates (i.e., digital brains) undergoing rapid evolution. Such systems are an ideal choice as they are fast to process, easy to manipulate, and transparent for analysis. Even in this limited domain, however, hundreds of different computational substrates are used. While benchmarks exist to compare the quality of different substrates, little work has been done to build broader theory on how substrate features interact. We propose a technique called the Comparative Hybrid Approach and develop a proof-of-concept by systematically analyzing components from three evolvable substrates: recurrent artificial neural networks, Markov brains, and Cartesian genetic programming. We study the role and interaction of individual elements of these substrates by recombining them in a piecewise manner to form new hybrid substrates that can be empirically tested. Here, we focus on network sparsity, memory discretization, and logic operators of each substrate. We test the original substrates and the hybrids across a suite of distinct environments with different logic and memory requirements. While we observe many trends, we see that discreteness of memory and the Markov brain logic gates correlate with high performance across our test conditions. Our results demonstrate that the Comparative Hybrid Approach can identify structural subcomponents that predict task performance across multiple computational substrates.
Understanding the structure and evolution of cognition is a topic of broad scientific interest. Computational substrates are ideal for conducting investigations into this topic because they can be incorporated in rapidly evolving Artificial Life systems and are easy to manipulate. However, design differences between currently existing digital systems make it difficult to identify which manipulations are responsible for broad patterns in evolved behavior. This is further confounded if we are trying to disentangle how multiple features interact. Here we systematically analyze components from two evolvable digital neural substrates (Recurrent Artificial Neural Networks (RNNs) and Markov brains) to develop a proofof-concept for a comparative hybrid approach. We identified elements of the logic and memory storage architectures in each substrate, then altered and recombined properties of the original substrates to create hybrid substrates. In particular, we chose to investigate the differences between RNNs and Markov Brains relating to network sparsity, whether memory is discrete or continuous, and the basic logic operator in each substrate. We then tested the original substrates and the hybrids across a suite of distinct environments with different logic and memory requirements. While we observed trends across all three of the axes that we investigated, we identified discreteness of memory as an especially important determinant of performance across our test conditions. However, the specific effect of discretization varied by environment and whether the associated task relied on information integration. Our results demonstrate that the comparative hybrid approach can identify structural components that enable cognition and facilitate task performance across multiple computational structures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.