Anselmo Pontes - Academia.edu (original) (raw)

Papers by Anselmo Pontes

The American Naturalist, 2020

Learning is a widespread ability among animals and, like physical traits, is subject to evolution... more Learning is a widespread ability among animals and, like physical traits, is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers to these questions. Here, we take a novel approach by studying digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a nonlearning sessile ancestor, we evolve multiple populations in four different environments, each consisting of nutrient trails with various layouts. Trail nutrients cue organisms on which direction to follow, provided they evolve to acquire and use those cues. Thus, each organism is tested on how well it navigates a randomly selected trail before reproducing. We find that behavior evolves modularly and in a predictable sequence, where simpler behaviors are necessary precursors for more complex ones. Associative learning is only one of many successful behaviors to evolve, and its origin depends on the environment possessing certain information patterns that organisms can exploit. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations but remain consistent for periods within an organism's lifetime foster the evolution of learning behavior. Both types of environmental patterns are necessary, since the prior evolution of simple reflexive behaviors provides the building blocks for learning to arise. Finally, we observe that an intrinsic value system evolves alongside behavior and supports associative learning by providing reinforcement for behavior conditioning.

This zip file contains the Avida raw data output for Experiment 1. The data is divided in four fo... more This zip file contains the Avida raw data output for Experiment 1. The data is divided in four folders according to the experimental environment (treatment): Nutrient Cued, One Fixed Turn, Random Start, and Two Fixed Turns. Each of these folders contains a sub-folder "Experiment_configuration_files" with the files necessary to configure Avida to recreate the particular experiment. Finally, each environment folder contains another 50 sub-folders, with the data from each replicate

Video with the animated path of organisms from a single lineage from Experiment 2, showing differ... more Video with the animated path of organisms from a single lineage from Experiment 2, showing different stages in the evolution of behavior

This zip files contain the annotated assembly code of three organisms from a single lineage from ... more This zip files contain the annotated assembly code of three organisms from a single lineage from Experiment 2. The three organisms are Error Recoverer 2, First Learner, and Final Organism mentioned in the Supplementary Information. The zip file also contains figure S19

This zip file contains the R scripts used to plot figures 3, 4, 5, S9 and S19, and to perform the... more This zip file contains the R scripts used to plot figures 3, 4, 5, S9 and S19, and to perform the statistical calculations presented in the paper

This zip file contains a MS Word document with instructions on how to configure Avida to replicat... more This zip file contains a MS Word document with instructions on how to configure Avida to replicate the experiments from the paper. It also contains a folder "Analyze_file_for_lineage_extraction" with a sample Avida 'analyze file' used to extract the lineage of the final predominant organism from a given population

This zip file contains the software Avida Draw, written in Python and used to plot the organisms&... more This zip file contains the software Avida Draw, written in Python and used to plot the organisms' path in the arena. It saves .EPS images of both the arena and the organism's final path

This zip file contains the Avida raw data output for the Follow Up Experiment. The data is divide... more This zip file contains the Avida raw data output for the Follow Up Experiment. The data is divided in 200 folders, one for each replicate. In addition, the zip file contains a folder "Experiment_configuration_files" with the files necessary to configure Avida to recreate this experiment

This zip file contains the Avida raw data output for the Preliminary Experiment. The data is divi... more This zip file contains the Avida raw data output for the Preliminary Experiment. The data is divided in four folders according to the experimental environment (treatment): Nutrient Cued, One Fixed Turn, Random Start, and Two Fixed Turns. Under a given environment folder the data from each replicate is stored in individual sub-folders. In addition, each environment folder contains a sub-folder "Experiment_configuration_files" with the files necessary to configure Avida to recreate the particular experiment

This zip file is 1 of 4 files containing the Avida raw data output for Experiment 2. The data fro... more This zip file is 1 of 4 files containing the Avida raw data output for Experiment 2. The data from 225 out of 900 replicates is stored in individual folders. In addition, the zip file contains a folder "Test_Environments" with two additional environment configuration files used for evaluating an individual organism's performance

This zip file is 1 of 4 files containing the Avida raw data output for Experiment 2. The data fro... more This zip file is 1 of 4 files containing the Avida raw data output for Experiment 2. The data from 225 out of 900 replicates is stored in individual folders. In addition, the zip file contains a folder "Experiment_configuration_files" with the files necessary to configure Avida to recreate this experiment

Learning is a widespread ability among animals and, like physical traits, is subject to evolution... more Learning is a widespread ability among animals and, like physical traits, is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers to these questions. Here, we take a novel approach by studying digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a non-learning, sessile ancestor, we evolve multiple populations in four different environments, each consisting of nutrient trails with various layouts. Trail nutrients cue organisms on which direction to follow, provided they evolve to acquire and use those cues. Thus, each organism is tested on how well it navigates a randomly selected trail before reproducing. We find that behavior evolves modularly and in a predictable sequence, where simpler behaviors are necessary precursors for more complex ones. Associative learning is only one of many successful behaviors to evolve, and its origin depends on the environment possessing certain information patterns that organisms can exploit. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations, but remain consistent for periods within an organism’s lifetime, foster the evolution of learning behavior. Both types of environmental patterns are necessary, since the prior evolution of simple reflexive behaviors provides the building blocks for learning to arise. Finally, we observe that an intrinsic value system evolves alongside behavior and supports associative learning by providing reinforcement for behavior conditioning

Online enhancements: supplemental PDF. Dryad data: https://doi.org/10.5061/dryad.f45gh6s.

The American Naturalist, 2020

This zip file is 1 of 4 files containing the Avida raw data output for Experiment 2. The data fro... more This zip file is 1 of 4 files containing the Avida raw data output for Experiment 2. The data from 225 out of 900 replicates is stored in individual folders. In addition, the zip file contains a folder "Experiment_configuration_files" with the files necessary to configure Avida to recreate this experiment

Learning is a widespread ability among animals and, like physical traits, is subject to evolution... more Learning is a widespread ability among animals and, like physical traits, is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers to these questions. Here, we take a novel approach by studying digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a non-learning, sessile ancestor, we evolve multiple populations in four different environments, each consisting of nutrient trails with various layouts. Trail nutrients cue organisms on which direction to follow, provided they evolve to acquire and use those cues. Thus, each organism is tested on how well it navigates a randomly selected trail before reproducing. We find that behavior evolves modularly and in a predictable sequence, where simpler behaviors are necessary precursors for more complex ones. Associative learning is only one of many successful behaviors to evolve, and its origin depends on the environment possessing certain information patterns that organisms can exploit. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations, but remain consistent for periods within an organism’s lifetime, foster the evolution of learning behavior. Both types of environmental patterns are necessary, since the prior evolution of simple reflexive behaviors provides the building blocks for learning to arise. Finally, we observe that an intrinsic value system evolves alongside behavior and supports associative learning by providing reinforcement for behavior conditioning

Online enhancements: supplemental PDF. Dryad data: https://doi.org/10.5061/dryad.f45gh6s.