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9 results for “Cumulative culture”
Cumulative cultural evolution and mechanisms for cultural selection in wild bird songs
<p>Cumulative cultural evolution, the accumulation of sequential changes within a single socially learned behaviour that results in improved function, is prominent in humans and has been documented in experimental studies of captive animals and managed wild populations. Here, we provide evidence that cumulative cultural evolution has occurred in the learned songs of Savannah sparrows. In a first step, "click trains" replaced "high note clusters" over a period of three decades. We use mathematical modeling to show that this replacement is consistent with the action of selection, rather than drift or frequency-dependent learning biases. Generations later, young birds elaborated the "click train" song form by adding more clicks. We show that the new songs with more clicks elicit stronger behavioural responses from both males and females. Therefore, we suggest that a combination of social learning, innovation, and sexual selection favoring a specific discrete trait was followed by directional sexual selection that resulted in naturally occurring cumulative cultural evolution in the songs of this wild animal population.</p>
Data from: Social network architecture and the tempo of cumulative cultural evolution
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Cumulative cultural evolution and mechanisms for cultural selection in wild bird songs
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Cognitive flexibility supports the development of cumulative cultural learning in children
<p><span>The scale of cumulative cultural evolution (CCE), the improvement of cultural traits over generations via social transmission, is widely believed to be one of humans' most defining characteristics. Our capacity to build upon others' knowledge, skills, and technologies has produced the most diverse and complex technological repertoire on the planet. Despite growing interest in the field of CCE, the cognitive underpinnings supporting its development remain relatively understudied. In this study, we examined the role that cognitive flexibility plays in supporting cumulative cultural learning by studying U.S. children's (</span><span>N</span><span> = 167, 3-5-year-olds) propensity to relinquish an inefficient solution to a problem in favor of a more efficient alternative. We also examined whether children would resist revertin</span><span>g</span><span> back to earlier versions and omit redundant actions from previous behaviors. In contrast to previous work with chimpanzees, most children who first learned to solve a puzzlebox in a highly inefficient way switched to an observed, more efficient alternative. However, over multiple task interactions, 85% of children who did switch also reverted back to the original, inefficient method. Moreover, almost all children in a control condition (who first learned the efficient method before observing the inefficient method) switched to the inefficient method. This suggests that </span><span>children were keen to explore an alternative modeled solution but were overall conservative in reverting to their first-learned method across subsequent task interactions. We discuss these findings in the context of their implications for the cognitive ontogeny of CCE.</span></p>
Cumulative culture in artificial navigators
<p>This project is on how cumulative culture can spontaneously emerge in agents who are bound by just four simple rules:</p> <ul> <li><strong>Goal direction</strong>: Having a sense of (roughly) where the goal is.</li> <li><strong>Social proximity</strong>: Aiming to stay close to other agents by moving in the direction they are expected to be next.</li> <li><strong>Route memory</strong>: Agents remember landmarks along the route, and aim to follow along these landmarks. Their memory precision improves over several journeys.</li> <li><strong>Continuity</strong>: To avoid erratic/jerky movements, agents aim to move mostly in the direction that they are currently travelling in.</li> </ul> <p>Despite the lack of explicit social transmission or evaluation of outcomes, pairs of agents with generational turnover show gradual improvements in route efficiency (they converge on the direct line between start and goal). For more information, please read the manuscript on arXiv (linked below).</p> <h1>Current version</h1> <p>Due to uploading restrictions on Zenodo, included here are the reduced format data files. This is easier, as they can be readily used with the scripts from the linked GitHub repository.</p> <h2>Files</h2> <p>Each of the files below contains data for 50 repeats of the following parameter ranges:</p> <ul> <li>p_goal_range: [0.01, 0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35]</li> <li>p_social_range: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35]</li> <li>p_memory_range: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5]</li> <li>sd_goal_range: [1.0]</li> <li>sd_memory_max_range: [0.9]</li> <li>simulation_types: ["experimental", "solo", "pair"]</li> <li>n_repetitions: 50</li> <li>n_agents: 2</li> <li>n_generations: 5</li> <li>n_flights_per_generation: 12</li> <li>max_path_length: 200</li> </ul> <p>Note that the maximum path length for the reduced format is limited to 200 steps, whereas the simulation's maxmimum path length is a lot longer. This thus cuts a (very limited) number of true paths short. This only impacts the (x,y) coordinates in the x.dat and y.dat files, but not the efficiency data (which was computed using the full path).</p> <h3>efficiency.dat</h3> <p>This file contains the efficiency computed from full paths from each condition, repeat, and set of parameters. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, and n_flights_per_generation. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12).</p> <p>Values coded as "nan" exist, but they are rare. This occurs if the goal is never reached, and in generations after a generation in which the goal was never reached. (Because these never fly in the first place, as their predecessors never made it to the goal.)</p> <h3>x.dat</h3> <p>This file contains the horizontal (x) coordinates of agents' paths. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, n_flights_per_generation, and max_path_length. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12, 200).</p> <p>Values are coded as "nan" if there is not supposed to be a number for them. For example, there will be "nan" values for the second agent in the solo condition because it did not exist. There will also be NaN values after the path has reached the goal, because the path does not continue beyond this.</p> <h3>y.dat</h3> <p>This file contains the vertical (y) coordinates of agents' paths. The file can be loaded as a NumPy memory map. Its data type is `numpy.float64`, and its shape is determined by: p_goal_range, p_social_range, p_memory_range, sd_goal_range, sd_memory_max_range, simulation_types, n_repetitions, n_agents, n_generations, n_flights_per_generation, and max_path_length. For the current set, that is (9, 7, 10, 1, 1, 3, 50, 2, 5, 12, 200).</p> <p>Values are coded as "nan" if there is not supposed to be a number for them. For example, there will be "nan" values for the second agent in the solo condition because it did not exist. There will also be NaN values after the path has reached the goal, because the path does not continue beyond this.</p> <h1><strong>Past versions</strong></h1> <h2><strong>Version 1<br></strong></h2> <p>There are two data archives in this set: <strong>data_simulation_narrow.zip</strong> (41 GB; 2680 folders containing a total of 80399 subfolders for a total of 5225935 CSV files) and <strong>data_simulation_wide.zip</strong> (7 GB; 557 folders containing a total of 16710 subfolders for a total of 1086150 CSV files).</p> <p>Each archive contains subfolders, each of which represent a unique combination of simulation parameters. These are named with the following naming scheme: "Pgoal-{w}_SDgoal-{sd}_Pcontinuity-{p}_SDcontinuity-{sd}_Psocial-{p}_SDsocial-{sd}_Pmemory-{p}_SDmemoryMax-{sd}_SDmemoryMin-{sd}_SDmemorySteps-5", where {p} is 1000 times the weight parameter, {sd} is 1000 times the equivalent standard deviation for the kappa parameter, and both are rounded to the nearest integer. Example: "Pgoal-100_SDgoal-1000_Pcontinuity-100_SDcontinuity-350_Psocial-100_SDsocial-800_Pmemory-700_SDmemoryMax-2000_SDmemoryMin-400_SDmemorySteps-5"</p> <p>Within each simulation folder, a number of subfolders can be found. This should normally be 30. Each of these represents a single run of a simulation within a condition. The naming convention is "{condition}_run-{run_counter}", where {condition} is the name of the condition ("experimental", "pair", or "solo"), and {run_counter} is a counter that starts at 1 and goes up from there (should be 1-10 in the current set).</p> <p>Within each simulation run subfolder, there are CSV files. These hold the actual data from journeys by artificial navigators. There are two types of data file, one for efficiency, and one for the travelled path. Both types of CSV have a header row with the names of the columns, followed by data rows.</p> <p>Efficiency files are named "efficiency_gen-{gen_nr}.csv", with {gen_nr} indicating the generation number. This starts at 0, ends at 4 (inclusive), and increments by 1. Efficiency files have three columns: "flight" for the flight counter (int, starts at 1), "efficiency_agent1" for the efficiency for the first agent's efficiency (float, between 0 and 1), and "efficiency_agent2" (float, between 0 and 1).</p> <p>Flight path files are named "xy_gen-{gen_nr}_flight-{flight_nr}.csv", with {gen_nr} being the same as above, and {flight_nr} being the journey number. This starts at 0, ends at 11 (inclusive), and increments by 1. Path files have four columns: "x_agent1" for the first agent's horizontal coordinate (float), "y_agent1" for the first agent's vertical coordinate (float), "x_agent2" for the second agent's horizontal coordinate (float), and "y_agent2" for the second agent's vertical coordinate (float).</p> <p>From generation 2 in the experimental condition, the first is the experienced agent, and the second is the naive agent. Float values coded as "nan" reflect there is no data. This occurs for e.g. the second agent in the solo condition and the first-generation experimental condition.</p>
Cognitive flexibility supports the development of cumulative cultural learning in children
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Cumulative Cultural Evolution scheme
<p>Schematic summary of the characteristics of Cumulative Cultural Evolution (CCE) as described by Mesoudi A., Thornton A. 2018. “What is cumulative cultural evolution?” http://dx.doi.org/10.1098/rspb.2018.0712</p>
Data from: Variant at serotonin transporter gene predicts increased imitation in toddlers: relevance to the human capacity for cumulative culture
Cumulative culture ostensibly arises from a set of sociocognitive processes which includes high-fidelity production imitation, prosociality and group identification. The latter processes are facilitated by unconscious imitation or social mimicry. The proximate mechanisms of individual variation in imitation may thus shed light on the evolutionary history of the human capacity for cumulative culture. In humans, a genetic component to variation in the propensity for imitation is likely. A functional length polymorphism in the serotonin transporter gene, the short allele at 5HTTLPR, is associated with heightened responsiveness to the social environment as well as anatomical and activational differences in the brain's imitation circuity. Here, we evaluate whether this polymorphism contributes to variation in production imitation and social mimicry. Toddlers with the short allele at 5HTTLPR exhibit increased social mimicry and increased fidelity of demonstrated novel object manipulations. Thus, the short allele is associated with two forms of imitation that may underlie the human capacity for cumulative culture. The short allele spread relatively recently, possibly due to selection, and its frequency varies dramatically on a global scale. Diverse observations can be unified via conceptualization of 5HTTLPR as influencing the propensity to experience others' emotions, actions and sensations, potentially through the mirror mechanism.
Data from: Variant at serotonin transporter gene predicts increased imitation in toddlers: relevance to the human capacity for cumulative culture
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