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![Effects of Q and R parameters
a, b, Effects of maintenance costs (Bi) on the corresponding tissue mass or skill level. Each Bi tends to decrease the value xi∗(τa)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{i}^{\ast }({\tau }_{{\rm{a}}})$$\end{document} for the corresponding i, but not necessarily for the other i (see c, d). c, d, Effect of Bi on adult brain mass, body mass and encephalization quotient. With power competence (c), when Bb = 310 and 340 MJ kg⁻¹ per year (y), the predicted adult brain mass is xb∗(τa)=1.0298\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{b}}}^{\ast }({\tau }_{{\rm{a}}})=1.0298$$\end{document} and 0.9133 kg, respectively. With exponential competence (d), when Bb = 310, 340 and 370 MJ kg⁻¹ y⁻¹, the predicted adult brain mass is xb∗(τa)=1.542\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{b}}}^{\ast }({\tau }_{{\rm{a}}})=1.542$$\end{document}, 1.3973 and 1.2767 kg, respectively. e, f, Effects of Br when Br is small. When Br varies between 70 and 2,700 MJ kg⁻¹ y⁻¹, Br has no detectable effect on adult brain mass and encephalization quotient. g, h, Ontogenetic fit with H. sapiens around the used values for each of the R parameters (except δ). The ontogenetic fit is approximately maximized around the benchmark values chosen previously²⁴, which are also used here (except for φ0 given our improved implementation of φ). i, Effect of Br on the predicted life history with exponential competence. In the left column, from top to bottom, as Br decreases, the allocation to the growth of reproductive tissue during adolescence increases (ur∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{{\rm{r}}}^{\ast }$$\end{document} between tm and ta) and adolescence shortens. In the central column, the increased allocation to the growth of reproductive tissue increases the mass of reproductive tissue, but brain mass does not change with Br for Br ≥ 70 MJ kg⁻¹ y⁻¹. In the right column, as the mass of reproductive tissue increases, body mass increases slightly, which is more noticeable for Br ≤ 100 MJ kg⁻¹ y⁻¹. An exceedingly small Br (<70 MJ kg⁻¹ y⁻¹) disrupts the predicted life history, which with Br = 60 MJ kg⁻¹ y⁻¹ is severely different from that of H. sapiens (for example, there is brain growth late in life and reproductive growth from birth). Similar results arise for even smaller Br. In a–i there are only ecological challenges and we use the previous²⁴ definition of φ.](publication/325321788/figure/fig8/AS:731590015590405@1551436060062/Effects-of-Q-and-R-parameters-a-b-Effects-of-maintenance-costs-Bi-on-the.jpg)
Effects of Q and R parameters a, b, Effects of maintenance costs (Bi) on the corresponding tissue mass or skill level. Each Bi tends to decrease the value xi∗(τa)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{i}^{\ast }({\tau }_{{\rm{a}}})$$\end{document} for the corresponding i, but not necessarily for the other i (see c, d). c, d, Effect of Bi on adult brain mass, body mass and encephalization quotient. With power competence (c), when Bb = 310 and 340 MJ kg⁻¹ per year (y), the predicted adult brain mass is xb∗(τa)=1.0298\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{b}}}^{\ast }({\tau }_{{\rm{a}}})=1.0298$$\end{document} and 0.9133 kg, respectively. With exponential competence (d), when Bb = 310, 340 and 370 MJ kg⁻¹ y⁻¹, the predicted adult brain mass is xb∗(τa)=1.542\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{{\rm{b}}}^{\ast }({\tau }_{{\rm{a}}})=1.542$$\end{document}, 1.3973 and 1.2767 kg, respectively. e, f, Effects of Br when Br is small. When Br varies between 70 and 2,700 MJ kg⁻¹ y⁻¹, Br has no detectable effect on adult brain mass and encephalization quotient. g, h, Ontogenetic fit with H. sapiens around the used values for each of the R parameters (except δ). The ontogenetic fit is approximately maximized around the benchmark values chosen previously²⁴, which are also used here (except for φ0 given our improved implementation of φ). i, Effect of Br on the predicted life history with exponential competence. In the left column, from top to bottom, as Br decreases, the allocation to the growth of reproductive tissue during adolescence increases (ur∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{{\rm{r}}}^{\ast }$$\end{document} between tm and ta) and adolescence shortens. In the central column, the increased allocation to the growth of reproductive tissue increases the mass of reproductive tissue, but brain mass does not change with Br for Br ≥ 70 MJ kg⁻¹ y⁻¹. In the right column, as the mass of reproductive tissue increases, body mass increases slightly, which is more noticeable for Br ≤ 100 MJ kg⁻¹ y⁻¹. An exceedingly small Br (<70 MJ kg⁻¹ y⁻¹) disrupts the predicted life history, which with Br = 60 MJ kg⁻¹ y⁻¹ is severely different from that of H. sapiens (for example, there is brain growth late in life and reproductive growth from birth). Similar results arise for even smaller Br. In a–i there are only ecological challenges and we use the previous²⁴ definition of φ.
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The human brain is unusually large. It has tripled in size from Australopithecines to modern humans¹ and has become almost six times larger than expected for a placental mammal of human size². Brains incur high metabolic costs³ and accordingly a long-standing question is why the large human brain has evolved⁴. The leading hypotheses propose benefit...
Citations
... Moreover, despite being a foundational assumption of the SIH [10], the link between social bonding and cognition remains unclear. Indeed, in principle, interacting repeatedly with the same partner(s) could reduce uncertainty and allow partners to pool their skills, thus reducing cognitive demands [53][54][55]. Conversely, information processing abilities that enable individuals to detect and respond to a partner's state could facilitate the maintenance of successful cooperative relationships [26,56]. To evaluate these possibilities, an important step is to examine whether individual socio-cognitive performance is positively associated with the maintenance of strong social bonds. ...
The need to maintain strong social bonds is widely thought to be a key driver of cognitive evolution. Cognitive abilities to track and respond to information about social partners may be favoured by selection if they vary within populations and confer fitness benefits. Here we evaluate four key assumptions of this argument in wild jackdaws (Corvus monedula), corvids whose long-term pair bonds exemplify one of the putative social drivers of cognitive evolution in birds. Combining observational and experimental behavioural data with long-term breeding records, we found support for three assumptions: (i) pair-bond strength varies across the population, (ii) is consistent within pairs over time and (iii) is positively associated with partner responsiveness, a measure of socio-cognitive performance. However, (iv) we did not find clear evidence that stronger pair bonds lead to better fitness outcomes. Strongly bonded pairs were better able to adjust hatching synchrony to environmental conditions but they did not fledge more or higher quality offspring. Together, these findings suggest that maintaining strong pair bonds is linked to socio-cognitive performance and may facilitate effective coordination between partners. However, they also imply that these benefits are insufficient to explain how selection acts on social cognition. We argue that evaluating how animals navigate trade-offs between investing in long-term relationships versus optimizing interactions in their wider social networks will be a crucial avenue for future research.
... Estudios recientes relacionan las fluctuaciones de la temperatura media anual con el tamaño corporal de los neandertales y Homo sapiens. Basándose en la hipótesis del estrés ambiental, 4 el que los homínidos más corpulentos viviesen en las regiones más frías estaba en línea con la regla biogeográfica de Bergmann 5 y estudios previos sobre homínidos y otros animales (Ruff, 1994;González-Forero y Gardner, 2018). Según esta hipótesis, el estrés térmico derivado de las temperaturas frías fue mitigado por la adaptación fenotípica: el cuerpo cada vez era más grande a causa de la selección natural, la plasticidad o ambas. ...
El análisis de nuestra filogenia, basado esencialmente en los registros fósiles y arqueológicos, nos ofrece información crucial sobre la trayectoria evolutiva del género Homo. Sin embargo, el problema radica es la escasez de muestras para un análisis concluyente, ya que la diversidad de especies en muchos casos se infiere a partir de unos pocos individuos o fragmentos óseos distribuidos en amplias zonas geográficas. No obstante, el notable éxito evolutivo de nuestro género no se atribuye únicamente al incremento en la capacidad cerebral -desde Australopithecus, con un volumen cerebral aproximado de 500 cm3 a Homo sapiens entre 1500-1700 cm3- o a las variaciones genómicas. También deben considerarse otros factores, como los culturales, sociales y ecológicos. La interacción entre las distintas especies y su entorno en un ecosistema es fundamental en cualquier proceso evolutivo. Todo ajuste, regulación o interacción en el mismo, como indica Margalef, determinará el equilibrio necesario hacia la supervivencia o el declive del sistema. Para establecer un diálogo interdisciplinar entre la ecología integral y la antropología evolutiva, hemos considerado incluir las teorías cognitivas modernas como la Teoría del Compromiso Material (MET), la ciencia cognitiva (CS) y la biología cognitiva (BC). Estas teorías resaltan la importancia de considerar la cognición no solo como un proceso interno, sino como algo que está profundamente arraigado en el contexto material y social. Este artículo examina cómo el ambiente pudo influir en la evolución del género Homo y viceversa, proponiendo un análisis interdisciplinario que integra ecología ambiental, económica, social y cultural, o lo que el papa Francisco denominó ecología integral, con la antropología evolutiva.
... This study also relates more broadly to the scientific literature on human brain size evolution; see Heldstab et al. (2022) for a survey. A recent study by Gonzalez-Forero and Gardner (2018) provides a quantitative analysis on the evolution of human brain and finds that ecological challenges for "finding, caching or processing food" are the main reason for human brain evolution. Robson and Kaplan (2003) provide an economic analysis on the development of human brain as health capital that is accumulated by bodily investment to reduce mortality. ...
... 4 See van Valen (1974) and Lynn (1990) for estimates of the cognitive advantage of a larger human brain size. See Gonzalez-Forero and Gardner (2018) for estimates of the metabolic costs of the human brain. 5 See Hansson and Stuart (1990) and Rogers (1994) for early economic models of natural selection of agents with different time preferences but not in a Malthusian environment. ...
Why did the human brain evolve? This study develops a Malthusian growth model with heterogeneous agents and natural selection to explore the evolution of human brain size. We find that if the cognitive advantage of a larger brain dominates its higher metabolic costs, then the average brain size increases over time, which is consistent with the rising trend in human brain size that started over 2 million years ago. Furthermore, an improvement in hunting-gathering productivity (e.g., the discovery of using stone tools and fire in hunting animals and cooking food) helps to trigger this human brain size evolution. As the average brain size increases, the average level of hunting-gathering productivity also rises over time. Quantitatively, our model is able to replicate the trend in hominin brain evolution over the last 10 million years.
... Evolutionary anthropologists, linguists and cognitive scientists have suggested that changes in socio-spatial behaviour accompanied the evolution of key attributes of Homo sapiens, including our large brains, advanced cognitive capabilities and language [1][2][3]. These evolutionary models motivate research into functional relationships between spatial behaviour, social structure, cognition and communication, aligned with the socio-spatial interface framework advocated by Webber et al. [4]. ...
Human evolutionary ecology stands to benefit by integrating theory and methods developed in movement ecology, and in turn, to make contributions to the broader field of movement ecology by leveraging our species’ distinct attributes. In this paper, we review data and evolutionary models suggesting that major changes in socio-spatial behaviour accompanied the evolution of language. To illustrate and explore these issues, we present a comparison of GPS measures of the socio-spatial behaviour of Hadza hunter–gatherers of northern Tanzania to those of olive baboons (Papio anubis), a comparatively small-brained primate that is also savanna-adapted. While standard spatial metrics show modest differences, measures of spatial diversity, landscape exploration and spatiotemporal displacement between individuals differ markedly. Groups of Hadza foragers rapidly accumulate a vast, diverse knowledge pool about places and things over the horizon, contrasting with the baboon’s narrower and more homogeneous pool of ecological information. The larger and more complex socio-spatial world illustrated by the Hadza is one where heightened cognitive abilities for spatial and episodic memory, navigation, perspective taking and communication about things beyond the here and now all have clear value.
This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’.
... Recent work has sought to apply this strategy to infer why the human brain size evolved. The steps so far have involved: (1) choosing the trajectories to explain as being the evolutionary trajectories of brain and body sizes over hominin evolution and the developmental trajectories of brain and body sizes for various hominin species from birth to adulthood; (2) formulating a mechanistic mathematical model, hereafter the brain model [31][32][33], that yields quantitative predictions for the development and evolution of hominin brain and body sizes; and (3) testing the predicted evolutionary and developmental trajectories of hominin brain and body sizes. ...
... An individual's genotype thus modulates the growth rate of her tissues, whereas an individual's phenotype is her brain size, body size, follicle count, and skill level at each age. This brain model yields a wide range of quantitative predictions, many of which correspond to observed patterns of development and evolution of human brain and body sizes, including the timing of human childhood, adolescence, and adulthood [31][32][33]. Yet, as step 4 has not been undertaken, this model only suggests why the human brain size could have evolved, and more models or model variations need to be studied before the strategy can converge on best causal explanations of why human brain expansion actually happened. ...
... We begin our qualitative testing of the model by comparing adult brain sizes predicted by the brain model [33] with adult brain sizes (proxied by endocranial volumes) observed in the hominin fossil record. The model has been shown to accurately recover the evolution of adult brain and body sizes for all major species of the genus Homo and less accurately for Australopithecus afarensis at the final points of the predicted evolutionary trajectories [32,33]. We here analyse the correspondence of the model predictions along the complete evolutionary trajectories rather than only at their end. ...
Why the human brain size evolved has been a major evolutionary puzzle since Darwin but addressing it has been challenging. A key reason is the lack of research tools to infer the causes of a unique event for which experiments are not possible. We describe how the analogous problem of why there is day and night has been successfully addressed in physics and learning from that experience, we outline a strategy to address why the human brain size evolved: hypotheses are expressed in mechanistic models that yield quantitative predictions for evolutionary and developmental trajectories of brain and body sizes, the predicted trajectories are compared to data, and models are chosen by their ability to explain the data. By pursuing this strategy, we present results from one model that predicts evolutionary and developmental trajectories for six hominin species. We compare these predictions to data, finding that the model recovers multiple but not all aspects of hominin evolution and development. Counterintuitively, the human brain size evolves in this model as a spandrel, that is, as a byproduct of selection for something else, specifically, preovulatory ovarian follicles. Our analysis describes an alternative way forward to infer why the human brain size evolved.
... Strategies that emerge under competition are less metabolically costly than the optimal strategy of a lone forager. This is in contrast to previous findings that competition between individuals has little impact on human brain size, and, presumably, on the ability to process sensory information [González-Forero et al., 2017;González-Forero and Gardner, 2018]. ...
Foraging strategies are shaped by interactions with the environment, and evolve under metabolic constraints. Optimal strategies for isolated and competing organisms have been studied extensively in the absence of evolution. Much less is understood about how metabolic constraints shape the evolution of an organism's ability to detect food and move through its environment to find it. To address this question, we introduce a minimal agent-based model of the coevolution of two phenotypic attributes critical for successful foraging in crowded environments: movement speed and perceptual acuity. Under competition higher speed and acuity lead to better foraging success, but at higher metabolic cost. We derive the optimal foraging strategy for a single agent, and show that this strategy is no longer optimal for foragers in a group. We show that mutation and selection can lead to the coexistence of two strategies: A metabolically costly strategy with high acuity and velocity, and a metabolically cheap strategy. Generally, in evolving populations speed and acuity co-vary. Therefore, even under metabolic constraints, trade-offs between metabolically expensive traits are not guaranteed.
... On the other hand, the "ecological intelligence hypothesis" suggests that environmental conditions, like foraging ecology, are the best correlates of brain morphology and cognitive abilities (Clutton-Brock and Harvey 1977;Iwaniuk and Nelson 2001;Hutcheon et al. 2002;DeCasien et al. 2017;Rosati 2017). There is an ongoing debate on the relative importance of these hypotheses (Powell et al. 2017;González-Forero and Gardner 2018), primarily due to the varying and conflicting research outcomes when testing various clades and taxa with varying biology and ecology (DeCasien and Higham 2019; Kappeler 2019). Therefore, studying species of closely related species of the same clade would eliminate some of these inherent biological and ecological variables. ...
Some cognitive abilities are suggested to be the result of a complex social life, allowing individuals to achieve higher fitness through advanced strategies. However, most evidence is correlative. Here, we provide an experimental investigation of how group size and composition affect brain and cognitive development in the guppy (Poecilia reticulata). For six months, we reared sexually mature females in one of three social treatments: a small conspecific group of three guppies, a large heterospecific group of three guppies and three splash tetras (Copella arnoldi)-a species that co-occurs with the guppy in the wild, and a large conspecific group of six guppies. We then tested the guppies' performance in self-control (inhibitory control), operant conditioning (associative learning) and cognitive flexibility (reversal learning) tasks. Using X-ray imaging, we measured their brain size and major brain regions. Larger groups of six individuals, both conspecific and heterospecific groups, showed better cognitive flexibility than smaller groups, but no difference in self-control and operant conditioning tests. Interestingly, while social manipulation had no significant effect on brain morphology, relatively larger telencephalons were associated with better cognitive flexibility. This suggests alternative mechanisms beyond brain region size enabled greater cognitive flexibility in individuals from larger groups. Although there is no clear evidence for the impact on brain morphology, our research shows that living in larger social groups can enhance cognitive flexibility. This indicates that the social environment plays a role in the cognitive development of guppies.
... Schöningen is pivotal in understanding early hunting strategies, hominin range expansion, technical and social skills, and human cognition. Human brain size has increased over the past 2 Ma and combinations of ecological, social, and cultural factors have been proposed to account for it (46)(47)(48)(49)(50)(51). The first phase of brain size increase between 2 and 1.5 Ma parallels the appearance of Homo erectus and the Acheulean technocomplex bringing forth more complex tool manufacturing concepts materialized in bifacial tools like handaxes. ...
... According to a predictive model, hominin brain size evolution is best explained when individuals face a combination of 60% ecolog ical, 30% cooperative, and 10% between-group competitive chal lenges (47). Ecological challenges are often met with technological improvement as part of a risk buffering strategy (59)(60)(61). ...
Ethnographic records show wooden tools played a pivotal role in the daily lives of hunter-gatherers including food procurement tools used in hunting (e.g. spears, throwing sticks) and gathering (e.g. digging sticks, bark peelers), as well as, domestic tools (e.g. handles, vessels). However, wood rarely survives in the archaeological record, especially in Pleistocene contexts and knowledge of prehistoric hunter-gatherer lifeways is strongly biased by the survivorship of more resilient materials such as lithics and bones. Consequently, very few Palaeolithic sites have produced wooden artefacts and among them, the site of Schöningen stands out due to its number and variety of wooden tools. The recovery of complete wooden spears and throwing sticks at this 300,000-year-old site (MIS 9) led to a paradigm shift in the hunter vs scavenger debate. For the first time and almost 30 years after their discovery, this study introduces the complete wooden assemblage from Schöningen 13 II-4 known as the Spear Horizon. In total, 187 wooden artefacts could be identified from the Spear Horizon demonstrating a broad spectrum of wood working techniques, including the splitting technique. A minimum of 20 hunting weapons is now recognised and two newly identified artefact types comprise 35 tools made on split woods, which were likely used in domestic activities. Schöningen 13 II-4 represents the largest Pleistocene wooden artefact assemblage worldwide and demonstrates the key role woodworking had in human evolution. Finally, our results considerably change the interpretation of the Pleistocene lakeshore site of Schöningen.
... Meanwhile, the cultural intelligence hypothesis maintains that hominid cognitive evolution stems from the influence of cultural artifacts on cultural members, including cognitive processes engaged in observational learning and skills teaching (Moll & Tomasello, 2007;van Schaik & Burkart, 2011). Recent studies suggest that the development of adult Homo sapiens-sized brains resulted from 60% ecological challenges, 30% cooperative challenges, and 10% intergroup competitive challenges (González-Forero & Gardner, 2018). The authors propose that cultural factors could have played a substantial role in facilitating hominid brain expansion, consequently leading to cognitive development. ...
Background: Chat generative retrained transformer (ChatGPT) represents a groundbreaking advancement in Artificial Intelligence (AI-chatbot) technology, utilizing transformer algorithms to enhance natural language processing and facilitating their use for addressing specific tasks. These AI chatbots can respond to questions by generating verbal instructions similar to those a person would provide during the problem-solving process. Aim: ChatGPT has become the fastest growing software in terms of user adoption in history, leading to an anticipated widespread use of this technology in the general population. Current literature is predominantly focused on the functional aspects of these technologies, but the field has not yet explored hypotheses on how these AI chatbots could impact the evolutionary aspects of human cognitive development. Thesis: The “neuronal recycling hypothesis” posits that the brain undergoes structural transformation by incorporating new cultural tools into “neural niches,” consequently altering individual cognition. In the case of technological tools, it has been established that they reduce the cognitive demand needed to solve tasks through a process called “cognitive offloading.” In this theoretical article, three hypotheses were proposed via forward inference about how algorithms such as ChatGPT and similar models may influence the cognitive processes and structures of upcoming generations. Conclusions: By forecasting the neurocognitive effects of these technologies, educational and political communities can anticipate future scenarios and formulate strategic plans to either mitigate or enhance the cognitive influence that these factors may have on the general population.
... Moreover, despite being a foundational assumption of the SIH (9), the link between social bonding and cognition remains unclear. Indeed, in principle, interacting repeatedly with the same partner(s) could reduce uncertainty and allow partners to pool their skills, thus reducing cognitive demands (46,47). Conversely, information-processing abilities that enable individuals to detect and respond to a partner's state could facilitate the maintenance of successful cooperative relationships (25,48). ...
The need to maintain strong social bonds is widely held to be a key driver of cognitive evolution. This assumes that the maintenance of strong bonds is a stable trait that is cognitively demanding but generates fitness benefits, and so can come under selection. However, these fundamental micro-evolutionary tenets have yet to be tested together within a single study system. Combining observational and experimental behavioural data with long-term breeding records, we tested four key assumptions in wild jackdaws (Corvus monedula), corvids whose long-term pair-bonds exemplify the putative social drivers of cognitive evolution in birds. We found support for three assumptions: (1) pair-bond strength varies across the population, (2) is consistent within pairs over time and (3) is positively associated with a measure of socio-cognitive performance. However, we did not find evidence that stronger pair-bonds lead to better fitness outcomes (prediction 4). While strongly bonded pairs were better able to adjust hatching synchrony to environmental conditions, they did not fledge more or higher quality offspring. Together, these findings provide important evidence that the maintenance of strong pair bonds is linked to socio-cognitive performance and facilitates effective coordination between partners. However, they also imply that these benefits may not be sufficient to explain how selection acts on social cognition. We argue that evaluating how animals navigate trade-offs between investing in long-term relationships versus optimising interactions in their wider social networks will be a crucial avenue for future research.
