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To promote collaboration across canine science, address replicability issues, and advance open science practices within animal cognition, we have launched the ManyDogs consortium, modeled on similar ManyX projects in other fields. We aimed to create a collaborative network that (a) uses large, diverse samples to investigate and replicate findings, (b) promotes open science practices of pre-registering hypotheses, methods, and analysis plans, (c) investigates the influence of differences across populations and breeds, and (d) examines how different research methods and testing environments influence the robustness of results. Our first study combines a phenomenon that appears to be highly reliable—dogs’ ability to follow human pointing—with a question that remains controversial: do dogs interpret pointing as a social communicative gesture or as a simple associative cue? We collected data (N = 455) from 20 research sites on two conditions of a 2-alternative object choice task: (1) Ostensive (pointing to a baited cup after making eye-contact and saying the dog’s name); (2) Non-ostensive (pointing without eye-contact, after a throat-clearing auditory control cue). Comparing performance between conditions, while both were significantly above chance, there was no significant difference in dogs’ responses. This result was consistent across sites. Further, we found that dogs followed contralateral, momentary pointing at lower rates than has been reported in prior research, suggesting that there are limits to the robustness of point-following behavior: not all pointing styles are equally likely to elicit a response. Together, these findings underscore the important role of procedural details in study design and the broader need for replication studies in canine science. 

Impulsivity is an important behavioral trait in dogs that affects many aspects of their relationship with humans. But how well do owners know their dog’s levels of impulsivity? Two studies have investigated how owner perceptions of their dog’s impulsivity correlate with the distance traveled in a spatial impulsivity task requiring choices between smaller, closer vs. larger, more distant food treats (Brady et al., 2018; Mongillo et al., 2019). However, these studies have demonstrated mixed results. The current project aimed to replicate these studies by correlating owner responses to the Dog Impulsivity Assessment Survey (DIAS) and the dog’s maximum distance traveled in a spatial impulsivity task. We found that neither the DIAS overall score nor its three subcomponent scores correlated with dogs’ distance traveled. This result replicates Mongillo et al.’s lack of a relationship but does not replicate Brady et al.’s effect, questioning the generalizability of owner reports of dog impulsivity. The lack of replication could result from differences in methodology and sample populations, but it raises intriguing questions about possible differences in dog characteristics and owner knowledge of their dogs across cultures. 

Intertemporal choices involve assessing options with different reward amounts available at different time delays. The similarity approach to intertemporal choice focuses on judging how similar amounts and delays are. Yet we do not fully understand the cognitive process of how these judgments are made. Here, we use machine-learning algorithms to predict similarity judgments to (1) investigate which algorithms best predict these judgments, (2) assess which predictors are most useful in predicting participants’ judgments, and (3) determine the minimum number of judgments required to accurately predict future judgments. We applied eight algorithms to similarity judgments for reward amount and time delay made by participants in two data sets. We found that neural network, random forest, and support vector machine algorithms generated the highest out-of-sample accuracy. Though neural networks and support vector machines offer little clarity in terms of a possible process for making similarity judgments, random forest algorithms generate decision trees that can mimic the cognitive computations of human judgment making. We also found that the numerical difference between amount values or delay values was the most important predictor of these judgments, replicating previous work. Finally, the best performing algorithms such as random forest can make highly accurate predictions of judgments with relatively small sample sizes (~ 15), which will help minimize the numbers of judgments required to extrapolate to new value pairs. In summary, machine-learning algorithms provide both theoretical improvements to our understanding of the cognitive computations involved in similarity judgments and intertemporal choices as well as practical improvements in designing better ways of collecting data. 

Alternative-based approaches to decision making generate overall values for each option in a choice set by processing information within options before comparing options to arrive at a decision. By contrast, attribute-based approaches compare attributes (such as monetary cost and time delay to receipt of a reward) across options and use these attribute comparisons to make a decision. Because they compare attributes, they may not use all available information to make a choice, which categorizes many of them as heuristics. Attribute-based models can better predict choice compared to alternative-based models in some situations (e.g., when there are many options in the choice set, when calculating an overall value for an option is too cognitively taxing). Process data comparing alternative-based and attribute-based processing obtained from eye-tracking and mouse-tracking technology support these findings. Data on attribute-based models thus align with the notion of bounded rationality that people make use of heuristics to make good decisions when under time pressure, informational constraints, and computational constraints. Further study of attribute-based models and processing would enhance our understanding of how individuals process information and make decisions.