The capacity to generate recursive sequences is argued to be a behavioral marker of rich, algorithmic cognition, like that found perhaps distinctively in humans. However, the precise mechanisms underlying recursive sequence generation remain mysterious. We investigate three potential building blocks of recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We develop a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to a center-embedded sequence generation task. We further test whether recursive rule discovery depends upon relational information (either perceptual or conceptual) present in the task stimuli. The presence of relational information facilitates hierarchical reasoning that drives the generation of recursive sequences across various depths of center-embedding. Without relational information, the use of ordinal reasoning predominates. Our results suggest that hierarchical reasoning is an important building block of recursive pattern processing and can be deployed across embedding depths and relational domains.

There are two hypotheses about complex pattern processing: one proposes that people are predisposed towards simple, local patterns; the other proposes that prefer more complex hierarchical patterns. Children and adults can both learn to preferentially generate hierarchical sequences. One unanswered question is whether children and adults generate complex patterns spontaneously. We investigated the predispositions or inductive biases of human children and adults in a novel open-ended sequence generation task. We found that children and adults display strong biases to generate simple patterns. Both groups overcome this inductive bias by generating more complex patterns in subsequent learning tasks. However, children find it more difficult to override their biases. Our results suggest that people possess an inductive bias towards simple patterns that are well-organized but minimally hierarchical. With some experience, humans learn complex hierarchical patterns. These findings reveal substantial encoding flexibility for patterns in humans—a flexibility beginning in childhood.

Hierarchical cognitive mechanisms underlie sophisticated behaviors, including language, music, mathematics, tool-use, and theory of mind. The origins of hierarchical logical reasoning have long been, and continue to be, an important puzzle for cognitive science. Prior approaches to hierarchical logical reasoning have often failed to distinguish between observable hierarchical behavior and unobservable hierarchical cognitive mechanisms. Furthermore, past research has been largely methodologically restricted to passive recognition tasks as compared to active generation tasks that are stronger tests of hierarchical rules. We argue that it is necessary to implement learning studies in humans, non-human species, and machines that are analyzed with formal models comparing the contribution of different cognitive mechanisms implicated in the generation of hierarchical behavior. These studies are critical to advance theories in the domains of recursion, rule-learning, symbolic reasoning, and the potentially uniquely human cognitive origins of hierarchical logical reasoning.

Dendrophilia — a widespread proclivity toward hierarchical behavior — has long been argued to be central to human cognitive uniqueness. Alternative views emphasize the developmental and evolutionary continuity of complex hierarchical psychological processes with simpler sequencing mechanisms. We investigated the predispositions of human adults and 3-to-6-year-old children to spontaneously generate hierarchical patterns in an open-ended sequence generation task. We also compared the human ability to learn hierarchical patterns with that of rhesus macaques and carrion crows. Our Bayesian mixture model quantified the extent to which distinct mechanisms — associative chaining, linear iteration, queues, and stacks — were implicated in hierarchical behavior. Our results suggest that hierarchical behavior is possible across species. It emerges early in cognitive development and may be scaffolded by simpler cognitive processes that eventually increase in representational and computational complexity. Thus, our findings contradict the dendrophilia hypothesis and point to shared psychological processes underpinning hierarchical behavior.

Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the wisdom of crowds public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820-22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor-the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1-2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.