• Wheeler, N. E., Allidina, S., Long, E. U., Schneider, S., Haas, I. J., & Cunningham, W. A. (2020). Ideology and Predictive Processing: Coordination, bias, and polarization in socially constrained error minimization. Current Opinion in Behavioral Sciences, 34, 192-198.

Recent models of cognition suggest that the brain may implement predictive processing, in which top-down expectations constrain incoming sensory data. In this perspective, expectations are updated (error minimization) only if sensory data sufficiently deviate from these expectations (prediction error). Although originally applied to perception, predictive processing is thought to generally characterize cognitive architecture, including the social cognitive processes involved in ideological thinking. Scaling up these simple computational principles to the social sphere outlines a path by which group members may adopt shared ideologies and beliefs to predict behavior and cooperate with each other. Because ideological judgments are of specific interest to others in our political groups, we may increasingly regulate each other’s thinking, sharing the process of error minimization. In this paper, we outline how this process of shared error minimization may lead to shared ideologies and beliefs that allow group members to predict and cooperate with each other, and how, as a consequence, political polarization and extremism may result.

• Jackson, E.C., Daley, M. (2019). Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance. The Genetic and Evolutionary Computation Conference (GECCO).

Reinforcement learning (RL) problems often feature deceptive local optima, and learning methods that optimize purely for reward signal often fail to learn strategies for overcoming them. Deep neuroevolution and novelty search have been proposed as effective alternatives to gradient-based methods for learning RL policies directly from pixels. In this paper, we introduce and evaluate the use of novelty search over agent action sequences by string edit metric distance as a means for promoting innovation. We also introduce a method for stagnation detection and population resampling inspired by recent developments in the RL community that uses the same mechanisms as novelty search to promote and develop innovative policies. Our methods extend a state-of-the-art method for deep neuroevolution using a simple-yet-effective genetic algorithm (GA) designed to efficiently learn deep RL policy network weights. Experiments using four games from the Atari 2600 benchmark were conducted. Results provide further evidence that GAs are competitive with gradient-based algorithms for deep RL. Results also demonstrate that novelty search over action sequences is an effective source of selection pressure that can be integrated into existing evolutionary algorithms for deep RL.

• Hoyer, D. (Under review). Decline and Fall, Growth and Spread, or Resilience? Approaches to Studying How and Why Societies Change.

An avid reader of history will be quite familiar with the rich, emotive narratives detailing the tragic decline and ultimate fall of once mighty civilizations; Rome succumbing to barbarian hordes, Alexander of Macedon’s and Chinggis Khan’s spear-won empires splitting into warring factions, and the demise of the great Inca or Maya civilizations are just a few such examples. On the other side of the stacks, similarly grandiose narratives document some group’s incredible growth and spread taking over vast territories and populations. These tell typically of societies coming to dominate a region, often in the face of overwhelming odds and tribulation or through some precocious development of a key technology or strategy that later becomes widespread. Here, I take stock of previous approaches to studying function – from growth and development to crisis and collapse to resilience – and ask what is the most fruitful lens with which to view fluctuations in how societies function and change over time, as this review essay attempts to accomplish.

• Charlesworth, T. E. S., Yang, V., Mann, T. C., Kurdi, B., & Banaji, M. R. (2021) Gender stereotypes in natural language: Word embeddings show robust consistency across child and adult language corpora of 65+ million words. Psychological Science, 32, 218-240.

Stereotypes are associations between social groups and semantic attributes that are widely shared within societies. The spoken and written language of a society affords a unique way to measure the magnitude and prevalence of these widely shared collective representations. Here, we used word embeddings to systematically quantify gender stereotypes in language corpora that are unprecedented in size (65+ million words) and scope (child and adult conversations, books, movies, TV). Across corpora, gender stereotypes emerged consistently and robustly for both theoretically selected stereotypes (e.g., work–home) and comprehensive lists of more than 600 personality traits and more than 300 occupations. Despite underlying differences across language corpora (e.g., time periods, formats, age groups), results revealed the pervasiveness of gender stereotypes in every corpus. Using gender stereotypes as the focal issue, we unite 19th-century theories of collective representations and 21st-century evidence on implicit social cognition to understand the subtle yet persistent presence of collective representations in language.

• Allidina, S. & Cunningham, W. A. (2021). Avoidance begets avoidance: A computational account of negative stereotype persistence. Journal of Experimental Psychology: General, 150(10), 2078–2099.

Research on stereotype formation has proposed a variety of reasons for how inaccurate stereotypes arise, focusing largely on accounts of motivation and cognitive efficiency. Here, we instead consider how stereotypes arise from basic processes of approach and avoidance in social learning. Across five studies, we show that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes in two ways. First, when information gain is contingent on approaching the target, avoidance restricts the information available with which to update one’s beliefs. Second, computational models that consider the perceiver’s full reinforcement history demonstrate that avoidance directly reinforces itself, such that initial avoidance of group members increases the probability of later acts of avoidance toward that group. Finally, we find initial evidence for a potential dissociation between behavior and explicit beliefs, with avoidance reinforcing avoidant behaviors without necessarily affecting self-reported beliefs. Overall, these results suggest that avoidance behaviors toward members of social groups can perpetuate inaccurate negative beliefs and expectations about those groups, such that initial interactions with a group have a compounding effect on overall impressions.

• Hoyer, D., Bennett, J. S., Whitehouse, H., François, P., Feeney, K., Levine, J., Reddish, J., Davis, D., Holder, S., & Turchin, P. (In prep). Flattening the Curve: Learning the Lessons of World History to Mitigate Societal Crises.

The world is experiencing myriad crises, from global climate change to a major pandemic to runaway inequality, mass impoverishment, and rising sectarian violence. Such crises are not new, but have been recurrent features of past societies. Although these periods have typically led to massive loss of life, the failure of critical institutions, and even complete societal collapse, lessons can be learned from societies that managed to avoid the more devastating and destructive outcomes. Here, we present a preliminary analysis of outcomes from periods of crisis in 50 historical societies and examine closely four cases of averted crisis in world history, highlighting common features. A key observation is that the structural-demographic cycles that give rise to societal crises typically incorporate a ‘gilded age’ during which more future-minded governance could avert future crises. To accomplish more forward-thinking public policy, capable not just of ‘flattening the curve’, but of actually breaking the cycle that produces societal crises in the first place, we argue that systematic quantitative analysis of patterns in world history is a necessary first step.

Selected Other Publications

• Allidina, S. & Cunningham, W. A. (2021). Avoidance begets avoidance: A computational account of negative stereotype persistence. Journal of Experimental Psychology: General, 150(10), 2078–2099.
• Allidina, S., Arbuckle, N. L., & Cunningham, W. A. (2019). Considerations of Mutual Exchange in Prosocial Decision-Making. Frontiers in Psychology, 10.
• Allidina, S. & Cunningham, W. A. (2018). Moral cues from ordinary behaviour. Behavioural and Brain Sciences, 41, E96
• Arbuckle, N. & Cunningham, W. A. (2012). Understanding everyday psychopathy: Shared group identity leads to increased concern for others amongst undergraduates higher in psychopathy. Social Cognition, 5, 564–583.
• Chapman, H. A., & Cunningham, W.A. (2014). Social groups: Both our destruction and our salvation? In W Sinott-Armstrong (Ed.), Moral Psychology, Volume IV: Free Will and Moral Responsibility, 397–402. Cambridge, MA: MIT Press.
• Charlesworth, T. E. S., & Banaji, M. R. (in press) Word embeddings reveal social group attitudes and stereotypes in large language corpora. The Atlas of Language Analysis in Psychology (eds. Dehghani, M. & Boyd, R.). New York: Guildford Press. 
• Charlesworth, T. E. S., & Banaji, M. R. (in press) The development of social group cognition. The Oxford Handbook of Social Cognition 2nd ed, (eds. Carlston, D., Johnson, K., & Hugenberg, K.). New York: Oxford University Press.
• Charlesworth, T. E. S., & Banaji, M. R. (2021) Patterns of Implicit and Explicit Attitudes II. Long-term change and stability, regardless of group membership. American Psychologist, 76(6), 851–869.
• Charlesworth, T. E. S., & Banaji, M. R. (2021) Patterns of Implicit and Explicit Stereotypes III. Long-term change in Gender-Science and Gender-Career stereotypes. Social Psychological and Personality Science.
• Charlesworth, T.E.S., & Banaji, M.R. (2019) Patterns of Implicit and Explicit Attitudes I. Long-term change from 2007-2016. Psychological Science, 30(2), 174–192
• Charlesworth, T. E. S., *Yang, V., Mann, T. C., Kurdi, B., & Banaji, M. R. (2021) Gender stereotypes in natural language: Word embeddings show robust consistency across child and adult language corpora of 65+ million words. Psychological Science, 32, 218-240.
• Charlesworth, T. E. S., Kurdi, B. & Banaji, M. R. (2019) Children’s Implicit Attitude Acquisition: Evaluative Statements Succeed, Repeated Pairings Fail. Developmental Science.
• Collins, Christina, Oluwole Oyebamiji, Neil R. Edwards, Philip P. Holden, Alice Williams, Greine Jordan, Daniel Hoyer, et al. “Combining Historical and Archaeological Data with Crop Models to Estimate Agricultural Productivity in Past Societies.” SocArXiv, November 22, 2020.
• Cunningham, W. A., Van Bavel, J. J., Arbuckle, N. L., Packer, D. J., & Waggoner, A. S. (2012). Rapid social perception is flexible: Approach and avoidance motivational states shape P100 responses to other– race faces. Frontiers in Human Neuroscience, 6, 1–7.
• Davis, B.D., Jackson, E.C., Lizotte, D.J. “Decision-Directed Data Decomposition”. arXiv preprint (arXiv:1909.08159, 2019)
• Falben, J. K., Tsamadi, D., Golubickis, M., Olivier, J., Persson, L., Cunningham, W. A., & Macrae, C .N. (2019). Predictably Confirmatory: The Influence of Stereotypes During Decisional Processing. Quarterly Journal of Experimental Psychology, 72(10), 2437-2451.
• Gawronski, B., Cunningham, W. A., LeBel, E. P., & Deutsch, R. (2010). Attentional influences on affective priming: Does categorization (always) influence spontaneous evaluation? Cognition and Emotion, 24, 1008–1025.
• Gelpi, R., Allidina, S., Hoyer, D., & Cunningham, W. A. (in press). The labelled container: Conceptual development of social group representations. Behavioral and Brain Sciences, 44
• Gelpi, R., Cunningham, W. A., & Buchsbaum, D. (2020). Belief as a non-epistemic adaptive benefit. Behavioral and Brain Sciences, 43, e36.
• Gelpi, R., Prystawski, B., Lucas, C. G., & Buchsbaum, D. (2020). Incremental hypothesis revision in causal reasoning across development. In Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (pp. 974–980).
• Haas, I. J., & Cunningham, W. A. (2014). The uncertainty paradox: Perceived threat moderates the effect of uncertainty on tolerance. Political Psychology, 35, 291–302.
• Hoyer, D. (Under review). Decline and Fall, Growth and Spread, or Resilience? Approaches to Studying How and Why Societies Change.
• Hoyer, D. (2018). Money, Culture, and Well-Being in Rome’s Economic Development, 0-275 CE. BRILL.
• Hoyer, D. (2017). Regionalism in Rome’s Third Century Fiscal Crisis. A Statistical Approach to Ancient Economic History.” Topoi: Occident/Orient 21, no. 1 : 233–62.
• Hoyer, D., Bennett, J. S., Whitehouse, H., François, P., Feeney, K., Levine, J., Reddish, J., Davis, D., Holder, S., & Turchin, P. (In prep). Flattening the Curve: Learning the Lessons of World History to Mitigate Societal Crises.
• Hoyer, D., & Manning, J. G. (2018). Empirical Regularities across Time, Space, and Culture: A Critical Review of Comparative Methods in Ancient Historical Research. Historia: Zeitschrift Fur Alte Geschichte 67, no. 2 (2018): 160–90. 
• Hoyer, D. & Reddish, J. (2019). Seshat History of the Axial Age. Chaplin, CT: Beresta Books.
• Jackson, E. C. (2019). Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, and Novelty Search in Deep Reinforcement Learning. Electronic Thesis and Dissertation Repository. 6510. (PhD Thesis)
• Jackson, E. C. , Daley, M. (2019). Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance. The Genetic and Evolutionary Computation Conference (GECCO 2019).
• Kurdi, B., Mann, T. C., Charlesworth, T. E. S., & Banaji, M. R. (2019). The relationship between implicit intergroup attitudes and beliefs. Proceedings of the National Academy of Sciences, 116(13), 5862–5871.
• Luttrell, A., Stillman, P. E., Hasinski, A., & Cunningham, W. A. (2016). Neural dissociations in attitude strength: Distinct regions of cingulate cortex track ambivalence and certainty. Journal of Experimental Psychology: General, 145, 419-433.
• Manning, P., Francois, P., Hoyer, D., & Zadorozhny, V. (2017). Collaborative Historical Information Analysis.” In Comprehensive Geographic Information Systems, edited by Bo Huang. Amsterdam: Elsevier.
• Melnikoff, D. E., Mann, T. C., Stillman, P. E., Shen, X., & Ferguson, M. J. (2020). Tracking Prejudice: A Mouse-Tracking Measure of Evaluative Conflict Predicts Discriminatory Behavior. Social Psychological and Personality Science. 
• Mullins, D. A., Hoyer, D., Collins, C., Currie, T., Feeney, K., François, P., Savage, P. E., Whitehouse, H., & Turchin, P. (2018). A Systematic Assessment of ‘Axial Age’ Proposals Using Global Comparative Historical Evidence. American Sociological Review 83, no. 3: 596–626.
• Prystawski, B., Gelpi, R., Lucas, C. G., & Buchsbaum, D. (2021). Modelling recognition in human puzzle solving. In Proceedings of the 43rd Annual Meeting of the Cognitive Science Society (pp. 1907–1913).
• Stillman, P. E. & Ferguson, M. J. (2019). Decisional conflict predicts impatience. Journal of the Association for Consumer Research, 4, 47-56.
• Stillman, P. E., Krajbich, I., & Ferguson, M. J. (in press). Using dynamic monitoring of choices to predict and understand risk preferences. Proceedings of the National Academy of Sciences.
• Stillman, P. E., Lee, H., Deng, X., Unnava, H. R., Cunningham, W. A., & Fujita, K. (2017). Neurological evidence for the role of construal level in future-directed thought. Social Cognitive and Affective Neuroscience, 12(6), 937-947.
• Stillman, P. E., Shen, X., & Ferguson, M. J. (2018). How mouse-tracking can advance social cognitive theory. Trends in Cognitive Sciences, 22, 531-543.
• Teoh, Y. Y., Yao, Z., Cunningham, W. A., & Hutcherson, C. A. (2020). Attentional priorities drive effects of time pressure on altruistic choice. Nature Communications, 11(1), 1-13.
• Todd, R. M., Cunningham, W. A., Anderson, A. K., & Thompson, E. (2012). Affect-biased attention as emotion regulation. Trends in Cognitive Sciences, 16, 365–372.
• Turchin, P., Currie, T. E., Whitehouse, H., François, P., Feeney, K., Mullins, D., Hoyer, D., et al. (2018). Quantitative Historical Analysis Uncovers a Single Dimension of Complexity That Structures Global Variation in Human Social Organization.” Proceedings of the National Academy of Sciences 115, no. 2: E144–51.
• Turchin, P., Hoyer, D., Korotayev, A., Kradin, N., Nefedov, S., Feinman, G., Levine, J., et al. (2021). Rise of the War Machines: Charting the Evolution of Military Technologies from the Neolithic to the Industrial Revolution. PLOS ONE 16, no. 10: e0258161.
• Turchin, P., Witoszek, N., Thurner, S., Garcia, D., Griffin, R., Hoyer, D., Midttun, A., Bennett, J. S., Næss, K. N., & Gavrilets, S. (2018). A History of Possible Futures: Multipath Forecasting of Social Breakdown, Recovery, and Resilience. Cliodynamics 9, no. 2.
• Turchin, P., Currie, T., Collins, C., Levine, J., Oyebamiji, O., Edwards, N. R., Holden, P. B., et al. (2021). An Integrative Approach to Estimating Productivity in Past Societies Using Seshat: Global History Databank: The Holocene 31, no. 6: 1055–65.
• Turchin, P., Whitehouse, H., Gavrilets, S., Hoyer, D., François, P., Bennett, J. S., Feeney, K., Peregrine, P., Feinman, G., & Korotayev, A. (Under Review). Disentangling the Evolutionary Drivers of Social Complexity in Human History: A Comprehensive Test of Hypotheses.
• Turchin, P., Whitehouse, H., François, P., Hoyer, D., Nugent, S., Larson, Covey, A., et al. (Forthcoming). Explaining the Rise of Moralizing Religions: A Test of Competing Hypotheses Using the Seshat Databank.
• Turchin, P., Brennan, R., Currie, T., Feeney, K., Francois, P., Hoyer, D., Manning, J. et al. (2015). “Seshat: The Global History Databank.” Cliodynamics 6, no. 1.
• Wheeler, N. E., Allidina, S., Long, E. U., Schneider, S., Haas, I. J., & Cunningham, W. A. (2020). Ideology and Predictive Processing: Coordination, bias, and polarization in socially constrained error minimization. Current Opinion in Behavioral Sciences, 34, 192-198.
• Van Bavel, J. J., & Cunningham, W. A. (2012). A social identity approach to person memory: Group membership, collective identification, and social role shape attention and memory. Personality and Social Psychology Bulletin, 38(12), 1566–1578.
• Van Bavel, J. J., Swencionis, J. K., O’Connor, R. C., & Cunningham, W. A. (2012). Motivated social memory: Belonging needs moderate the own-group bias in face recognition. Journal of Experimental Social Psychology, 48, 707–713.
• Van Bavel, J. J., Packer, D. J., & Cunningham, W. A. (2011). Modulation of the Fusiform Face Area following minimal exposure to motivationally relevant faces: Evidence of in-group enhancement (not out-group disregard). Journal of Cognitive Neuroscience, 23, 3343–3354.
• Van Bavel, J. J. & Cunningham, W. A. (2010). A social neuroscience approach to self and social categorization: A New Look at an old issue. European Review of Social Psychology, 21, 237–284.
• Vrantsidis, T. H., & Cunningham, W. A. (2021). The Effect of Knowledge about a Group on Perceived Group Variability and Certainty about Stereotype-Based Inferences. Social Cognition, 39(4), 457-488.