From the foods we eat and the houses we construct, to our religious practices and political organization, to who we can marry and the types of games we teach our children, the diversity of cultural practices in the world is astounding. Yet, our ability to visualize and understand this diversity is limited by the ways it has been documented and shared: on a culture-by-culture basis, in locally-told stories or difficult-to-access repositories. In this paper we introduce D-PLACE, the Database of Places, Language, Culture, and Environment. This expandable and open-access database (accessible at https://d-place.org) brings together a dispersed corpus of information on the geography, language, culture, and environment of over 1400 human societies. We aim to enable researchers to investigate the extent to which patterns in cultural diversity are shaped by different forces, including shared history, demographics, migration/diffusion, cultural innovations, and environmental and ecological conditions. We detail how D-PLACE helps to overcome four common barriers to understanding these forces: i) location of relevant cultural data, (ii) linking data from distinct sources using diverse ethnonyms, (iii) variable time and place foci for data, and (iv) spatial and historical dependencies among cultural groups that present challenges for analysis. D-PLACE facilitates the visualisation of relationships among cultural groups and between people and their environments, with results downloadable as tables, on a map, or on a linguistic tree. We also describe how D-PLACE can be used for exploratory, predictive, and evolutionary analyses of cultural diversity by a range of users, from members of the worldwide public interested in contrasting their own cultural practices with those of other societies, to researchers using large-scale computational phylogenetic analyses to study cultural evolution. In summary, we hope that D-PLACE will enable new lines of investigation into the major drivers of cultural change and global patterns of cultural diversity.

Scholars have debated naturalistic theories of religion for thousands of years, but only recently have scientists begun to test predictions empirically. Existing databases contain few variables on religion, and are subject to Galton’s Problem because they do not sufficiently account for the non-independence of cultures or systematically differentiate the traditional states of cultures from their contemporary states. Here we present Pulotu: the first quantitative cross-cultural database purpose-built to test evolutionary hypotheses of supernatural beliefs and practices. The Pulotu database documents the remarkable diversity of the Austronesian family of cultures, which originated in Taiwan, spread west to Madagascar and east to Easter Island–a region covering over half the world’s longitude. The focus of Austronesian beliefs range from localised ancestral spirits to powerful creator gods. A wide range of practices also exist, such as headhunting, elaborate tattooing, and the construction of impressive monuments. Pulotu is freely available, currently contains 116 cultures, and has 80 variables describing supernatural beliefs and practices, as well as social and physical environments. One major advantage of Pulotu is that it has separate sections on the traditional states of cultures, the post-contact history of cultures, and the contemporary states of cultures. A second major advantage is that cultures are linked to a language-based family tree, enabling the use phylogenetic methods, which can be used to address Galton’s Problem by accounting for common ancestry, to infer deep prehistory, and to model patterns of trait evolution over time. We illustrate the power of phylogenetic methods by performing an ancestral state reconstruction on the Pulotu variable “headhunting", finding evidence that headhunting was practiced in proto-Austronesian culture. Quantitative cross-cultural databases explicitly linking cultures to a phylogeny have the potential to revolutionise the field of comparative religious studies in the same way that genetic databases have revolutionised the field of evolutionary biology.

Previous research suggests that organisms allocate more attention to stimuli associated with higher reinforcer rates. This finding has been replicated several times when stimuli are trained together as compounds, but not in other procedures. Thus, the generality of the relation between relative reinforcer rates and divided attention is not well established. Therefore, we investigated whether relative reinforcer rates determine attention allocation when stimuli are trained separately and then encountered together. Pigeons learned to associate two colors and two frequencies of key light on/off alternation with a left or right comparison key in a symbolic 0-s delayed matching-to-sample task. Across conditions, we varied the probability of reinforcement associated with each stimulus dimension during training. After training, we introduced test trials in which a color and flash-frequency stimulus were presented simultaneously. During sample-stimulus presentation in test trials, all pigeons preferred the stimulus associated with the higher reinforcer rate, suggesting that more attention was allocated to that stimulus. Interestingly, such attention allocation did not result in preference for the comparison that matched that stimulus. Instead, all pigeons preferred the comparison that was physically closer to the stimulus associated with the higher reinforcer rate, suggesting that comparison choice was controlled by the location of that stimulus. Nevertheless, overall, our results provide the first evidence that relative reinforcer rates determine divided attention between separately trained stimuli and thus demonstrate the generality of the relation between relative reinforcement and attention allocation. We suggest several avenues for future research to establish further the generality of this relation.

In concurrent schedules, reinforcers are often followed by a brief period of heightened preference for the just-productive alternative. Such 'preference pulses' may reflect local effects of reinforcers on choice. However, similar pulses may occur after nonreinforced responses, suggesting that pulses after reinforcers are partly unrelated to reinforcer effects. McLean, Grace, Pitts, and Hughes (2014) recommended subtracting preference pulses after responses from preference pulses after reinforcers, to construct residual pulses that represent only reinforcer effects. Thus, a reanalysis of existing choice data is necessary to determine whether changes in choice after reinforcers in previous experiments were actually related to reinforcers. In the present paper, we reanalyzed data from choice experiments in which reinforcers served different functions. We compared local choice, mean visit length, and visit-length distributions after reinforcers and after nonreinforced responses. Our reanalysis demonstrated the utility of McLean et al.'s preference-pulse correction for determining the effects of reinforcers on choice. However, visit analyses revealed that residual pulses may not accurately represent reinforcer effects, and reinforcer effects were clearer in visit analyses than in local-choice analyses. The best way to determine the effects of reinforcers on choice may be to conduct visit analyses in addition to local-choice analyses.

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