**Abstract:**

Few cultural practices beyond language are as widespread as string figure games. Their global
distribution and potential to yield insights into cultural transmission and cognition have long been
noted. Yet, it remains unknown how or when this behavior originated and to what extent shared
motifs are signals of repeated innovations or deep cultural transmission. Here, we combined a
global cross-cultural inventory of string figures with a novel methodology based on knot theory,
which enables the unequivocal numerical coding of string figures. We performed a computational
analysis of a sample of 826 figures from 92 societies around the world. Across these societies, we
found 83 recurring string figure designs, some of which are regionally restricted while others
display a global distribution. The cognitively opaque nature of string figure designs and their clear
geographic distribution reveals processes of cultural transmission, innovation, and convergent
evolution. Most strikingly, the global distribution of some figures raises the possibility of shared
ancient origins.

**Speaker's social media:**

Twitter: @roopekaaronen, Bluesky: @roope.bsky.social

RECORDING - A global cross-cultural analysis of string figures reveals evidence of deep transmission and innovation

**Abstract:**

Cultural evolution requires appropriate transmission strategies. While previous studies have examined the cognitive demands of learning and teaching as well as social learning strategies, limited attention has been paid to the behavioural aspects involved in transmission of cultural traits emerging between 3,2 million and 10,000 years ago.  We have investigated the behavioural transmission dynamics of 103 Palaeolithic cultural traits  using the theoretical grid proposed by d’Errico and Banks (2015). We constructed a comprehensive database in which, for each cultural trait, a score (0 to 4) was assigned to each transmission behaviour in the spatial, temporal and social dimensions. Statistical analyses reveals a clear evolutionary pattern characterised by (a) an initial phase dominated by simple transmission behaviours, (b) a transitional ‘search phase’ in which simple and complex transmission behaviours coexist and interact, and (c) a final stage with complex transmission behaviours becoming widespread. Our findings also identify: 1) a gradual increase in overt communication; 2) a progressive temporal structuring of cultural transmission; 3) a slow development of division of labour. They also suggest that modern language emerged gradually as a key transmission behaviour between 600,000 to 200,000 years ago.

**Speaker's social media:**



RECORDING - An empirically-based scenario for the evolution of cultural transmission in the human lineage

Herbert Simon (1947) observed that human learning and decision making does not strive for optimal solutions but merely ones that are good enough (known as Satisficing). However, the absence of a theory of what precisely counts as good enough has limited the utility of Satisficing in the social and behavioral sciences. We draw insights from recent work in cognitive science that provides a precise criterion for good enough learning (termed the “Tolerance Principle” or TP) which describes how humans form generalizations based on limited or inconsistent information. To date, TP has only been defined for individual learning. Here, we show how TP provides a precise model of how social norms emerge in a paradigmatic coordination game frequently used in research on cultural evolution and collective intelligence - i.e., the Wittgensteinian name game as designed by Centola & Baronchelli (2015). We compare different agent-based models in their ability to predict norm emergence in experimental runs of the name game involving human participants. TP considerably outperforms models which assume that agents leverage optimal rather than good enough predictions for social behavior. We also show how TP provides a more accurate model of tipping point dynamics in the name game. Altogether, we show how social coordination follows the same simple formula for “good enough” solutions as individual learning, providing a novel synthesis of psychological and sociological constructs. 

LIVE - Social satisficing: How a simple cognitive constraint governs norm emergence

**Abstract:**

"Humans’ ability to create and use tools is one of our species’ defining characteristics and has irreversibly altered our social and ecological environments. All human
societies create and use tools, and no other species does so with the complexity and diversity of humans. Yet, paradoxically, it is well-established that western children are remarkably poor at simple tool innovation challenges, such as reshaping pipecleaners to retrieve-out-of reach rewards (widely known as the hook task). Studies have further shown that children in non-western populations perform worse than western ones with some small-scale societies performing at floor. We suggest that the practice of taking western designed and validated cognitive measures and transporting them to non-western populations is flawed and can lead to erroneous and damaging conclusions.

In this ESRC funded project, we will develop, for the first time, culturally fair measures of tool innovation for 4-12-year-old children in four geographically and culturally diverse populations; urban Western children (UK), two small-scale subsistence societies (Congo) and a rural Indigenous population (Malaysia). We will do so by taking a bottom-up approach to developing culturally grounded tool innovation measures, using ethnographic data alongside cycles of feedback from community members and protocol refinement to ensure maximal validity. 

**Speaker's social media:**

@bruce_rawlings

Developing a culturally fair framework for measuring the development of tool innovation across cultures​

**Abstract:**

Current global emergencies like economic crises, pandemics, environmental perils, and armed conflicts have brought uncertainty to the forefront as a key concept for understanding human behavior. However, uncertainty is an inherent property of most environmental and social challenges the human species has faced in its evolution. Nevertheless, research conceptualizations of uncertainty vary greatly, and its complexity poses theoretical and methodological challenges. Several cultural-evolutionary approaches argue that the capacity of beliefs and behavioral patterns to saturate the needs arising from cognitive and emotional processing of uncertainty positively affects the social dissemination of these beliefs and behavioral patterns (e.g. religion, conspiracy theories, social conservatism). To address the inner complexity of such hypotheses, we argue that uncertainty must be theoretically and methodologically approached at various levels (as an epistemic situation, as a psychological state, and as a socio-cultural construct). Uncertainty must also be operationalized in interaction but independently from concepts of threat and costs. We will combine ethnographic research, surveys, interviews, and experiments across multiple sites to pursue these scientific goals. Finally, by mapping and analyzing coping strategies in different contexts, we seek to identify more vs. less successful coping mechanisms that may have practical applications.

**Speaker's social media:**

@Peter_Mano_
@BahnaVladimir
@Danija_Jerot

Dealing with uncertainty: strategies and contexts

**Abstract:**

We proceed on anthropological theories of cultural and behavioural transmission where culture can be transmitted vertically (between local generations), horizontally between current populations and incoming groups, and by reciprocal oblique transmission through multiple modes. We compare the two transmission theories in a cognitive explanation of the general perception of cultural traits in urban area. The theory of natural selection of culture is based on the evolutionary paradigm of biased selection of the most appropriate and promising solutions for oneself and the reference community. For this theory we chose a research sample of the local population in Kaunas city. Cultural attractors are theoretical propositions that capture the way in which certain ideational variants are more likely to be the outcome of transformations than others. This way of reasoning is characteristic when a person becomes acquainted with an unfamiliar and new place. For this theory we chose a research sample of visitors, tourists in Kaunas. Both of these theories are based on different perceptions of place and different priorities from a long-term perspective. Applying such findings will improve our ability to recognize how to keep a particular place vital and sustainable in the long term.

**Speaker's social media:**

https://www.heritageresearch-hub.eu/project/herindep/
https://herindep.ff.cuni.cz/

The cognitive frames modeling as an approach to analyze a place transformation from attractors and selectionist theory: Case Kaunas

**Abstract:**

Enquist et al. (2023) suggested that human cumulative cultural evolution was enabled by enhanced abilities to process and remember sequential information, leading to cultural and cognitive achievements unprecedented in other animals. This hypothesis mainly rests on results of sequence discrimination learning tasks, showing that unlike for humans, learning to discriminate between sequences is difficult for non-human animals, requiring them hundreds, often thousands of trials. It is unclear whether animals’ difficulty in these tasks reflect difficulties with remembering sequences per se or is mainly due to the arbitrary ordering of the sequence items, thus underestimating their sequencing abilities. To disentangle these effects, we investigate sequence discrimination learning with a physical task in which we contrast an “arbitrary” condition with one in which we use causal relations between stimuli to indicate which sequence yields a reward, aiming to facilitate encoding of sequential information. We present results from zoo-housed primates (23 squirrel and 24 capuchin monkeys, 13 chimpanzees), 77 human adults and 239 children (3-11 years) in an “AB vs BA, BB, AA” task, providing the first sequence discrimination study with chimpanzees and children, demonstrating that a lack of meaningful relations between sequence stimuli impedes learning in humans and potentially also great apes. 

**Speaker's social media:**



Sequence discrimination learning is facilitated by causal-logical framing in humans and chimpanzees 


**Abstract:**

Culture is a complex adaptive system characterized by the interactions of many individuals exchanging numerous cultural traits. The self-organization of cultural traits into systems has recently attracted some attention (Friedkin et al. 2016, Buskell et al. 2019, Yeh et al. 2019), but these models are not capturing all aspects of the complex dynamics of these systems, such as the alternating periods of stability and change exemplified by the change from a geocentric to a heliocentric view of the solar system, or the transition to a digital society. In this work, we leverage recent advances in the theory of complex systems (Thurner et al. 2018) to provide new models of the self-organization of cultural systems, stressing the importance of interactions between traits, such as recombination and filtering processes. We study an agent-based model in which individuals carry cultural systems and exchange traits, we show that self-organizing processes active at the individual level influence the culture state at the population level and displays punctuated equilibria, i.e., periods of stability separated by periods of rapid change. In particular, the trait-individual duality that results from this modelling approach criticizes the standard frequency-based selection of cultural traits.

**Speaker's social media:**



LIVE - Self-organization in cultural systems

**Abstract:**

This abstract is for a master’s thesis. Data collection will be completed before September. 

The rice theory of culture hypotheses that wet-rice farming fosters the development of collectivistic cultures. This study aims to determine whether the rice theory of culture can predict between-prefecture variation in the degree of collectivism/individualism in Japan. Utilizing agricultural data in conjunction with self-report data from 4,254 respondents to the Japanese Social Survey (JGSS), I tested whether the proportion of agricultural land set aside for wet-rice farming in each prefecture predicted cross-prefecture variation in collectivism. In a second analysis, this study assessed whether the historical duration for which peoples have been wet-rice farming predicts cross-regional variation in collectivism. In order to answer this question, this study introduced a novel methodology, incorporating archaeological data for the introduction of rice farming to various regions of Japan. As far as the author is aware, this is the first time a study has analyzed whether temporal “dosing” of exposure to rice farming practices predicts cross-regional variation in psychology. Through its use of archaeological and JGSS data, this work tests the rice theory of culture in previously unexplored ways and investigates the origins of regional psychological variation across Japan.


**Speaker's social media:**

Twitter: @LiamMcClainAnthro

LIVE - The Cultural Coevolution of Rice Farming and Collectivism in Japan

The introduction of conversational systems have made synthesized speech technologies common tools for daily activities. However, not all synthetic speech systems are designed with the needs of people with disabilities in mind. This paper describes a study in which 198 people -- 80 participants with self-reported disabilities and 118 participants without -- were recruited to listen to navigation instructions from a spoken dialogue system with different prosodic features. Results showed that slowing down speech rate aids in participants' number recall, but not in noun recall. From our results, we provide suggestions for developers for building accessible synthetic speech systems.

Examining Prosody in Spoken Navigation Instructions for People with Disabilities

In many Navy-relevant applications, dedicated datasets for machine learning are noisy in nature, small in quantity, and challenging to obtain. This work uses applications of transfer learning to exploit small noisy datasets for machine learning tasks. Our focus is on electroencephalogram (EEG) neural datasets for human in the loop optimization of warfighter performance. Specifically, we apply transfer learning to classification tasks on small noisy datasets which leverage larger, publicly available laboratory setting EEG datasets on similar but not identical task domains. We find that transfer learning from a large publicly available motor imagery dataset improves performance and reduces the amount of data required for cognitive state classification of participants learning to walk with an exoskeleton dataset (data source: Army Research Lab). Further, we are exploring the impact of transfer learning on real-time EEG state classification for user interface (UI) development. Following a similar approach as above, we will first build a transfer-learning model by harnessing laboratory-collected EEG data during cognitive workload tasks, and then apply it beta-testers as they interact with UI prototypes. Together, these examples highlight the potential impact that transfer learning can have on Navy-relevant applications by taking advantage of data sources collected in privileged non-Naval environments.

Transfer learning for human in the loop optimization of warfighter performance

Introduction: Sleep deprivation is a serious problem for many military professionals, such as pilots and sonar operators. The vigilance these tasks demand is degraded by prolonged wakefulness, which can jeopardize operations. Detecting a prolonged wakefulness state is not simple, as many of these professions have staggered shifts and high workload that may necessitate irregular sleep-wake hours. Previously, we presented a deep learning analysis of a dataset consisting of three sleep deprivation studies that resulted in a model with a 90% accuracy score on the validation subset of data. We noted that the generalizability of this model was limited due to the data from participants being available in both the training and validation datasets, despite individual data points being clearly separated between datasets. We have corrected this limitation by updating the analysis to split the train and validation datasets by participant (i.e., all data from a given participant is included on only one side of the split). We hypothesize that the accuracy score of this new model on the validation dataset will be less than the 90% previously observed, but will still be at least 80%. If so, this update to our current model will improve confidence in the generalizability of our predictions of sleep deprivation. 
Methods: The studies used in this analysis were approved by Institutional Review Boards from Naval Submarine Medical Research Laboratory (NSMRL.2020.0002), Naval Health Research Center (NHRC.2019.0006), and University of Connecticut (H14-328 & H18-062) in compliance with all applicable Federal regulations governing the protection of human subjects. Using three datasets, containing eye tracking, heart-rate variability, electrodermal activity, and cognitive performance data (including a simulated mine hunting task, simulated sonar task, and operational task battery) that we collected during acute sessions of sleep deprivation, we applied a deep learning modeling (using TensorFlow in Python). By this means, we created a generalized sleep deprivation prediction model with the goal of achieving an 80+% classification-accuracy across all included datasets. 70% of the participants’ data was used to train the model, and 30% was used as the validation dataset. Separation based on participant ensured that none of the participant data in the validation set is present in the training set. Additionally, sub-analyses looking at different combinations of the available physiological domains were performed to determine the relative predictive contributions of the physiological domains. 
Results: A deep learning model was trained using 70% of the participants across studies and all three available physiological domains. The final overall validation accuracy score of this model was 80% using all measures. In the sub-analysis models using subsets of measures, the combination of eye tracking and heartrate variability data yielded the best final validation accuracy of 76%, while the combination of eye tracking and eletrodermal activity yielded a final validation accuracy of 70%, and the combination of heartrate variability and electrodermal activity yielded a final validation accuracy of 65%. 
Conclusion: As expected, the separation of data by subjects led to a notable drop in the final accuracy score of the model. With a final accuracy score of 80%, our updated model is at the lower end of supporting our hypothesis but is more valid and generalizable now. The models from the sub-analyses suggest the relative contributions of each physiological domain to the final model, with eye tracking being the most valuable followed by heartrate variability, and finally electrodermal activity. The output of this model is potentially task and individual independent, regarding the cognitive tasks used in each study. 


Underline digital video library

RECORDING - A global cross-cultural analysis of string figures reveals evidence of deep transmission and innovation

RECORDING - An empirically-based scenario for the evolution of cultural transmission in the human lineage

LIVE - Social satisficing: How a simple cognitive constraint governs norm emergence

Developing a culturally fair framework for measuring the development of tool innovation across cultures

Dealing with uncertainty: strategies and contexts

The cognitive frames modeling as an approach to analyze a place transformation from attractors and selectionist theory: Case Kaunas

Sequence discrimination learning is facilitated by causal-logical framing in humans and chimpanzees

LIVE - Self-organization in cultural systems

LIVE - The Cultural Coevolution of Rice Farming and Collectivism in Japan

Examining Prosody in Spoken Navigation Instructions for People with Disabilities

Transfer learning for human in the loop optimization of warfighter performance

Update on a Deep Learning based model for Predicting Sleep Deprivation using Wearable Technologies

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Underline digital video library

RECORDING - A global cross-cultural analysis of string figures reveals evidence of deep transmission and innovation

RECORDING - An empirically-based scenario for the evolution of cultural transmission in the human lineage

LIVE - Social satisficing: How a simple cognitive constraint governs norm emergence

Developing a culturally fair framework for measuring the development of tool innovation across cultures​

Dealing with uncertainty: strategies and contexts

The cognitive frames modeling as an approach to analyze a place transformation from attractors and selectionist theory: Case Kaunas

Sequence discrimination learning is facilitated by causal-logical framing in humans and chimpanzees

LIVE - Self-organization in cultural systems

LIVE - The Cultural Coevolution of Rice Farming and Collectivism in Japan

Examining Prosody in Spoken Navigation Instructions for People with Disabilities

Transfer learning for human in the loop optimization of warfighter performance

Update on a Deep Learning based model for Predicting Sleep Deprivation using Wearable Technologies

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Developing a culturally fair framework for measuring the development of tool innovation across cultures