We found that combinations of the environmental factors „resource composition” and „predation” can select for a variety of cognitive styles. Depending on the value of these factors, our results are in line with the overall predictions of the proactive-reactive framework : under certain circumstances, proactive (reactive) individuals invest less (more) in learning abilities. However, under just slightly different environmental conditions, the patterns are reversed, thereby being consistent with findings which oppose the predictions of the proactive-reactive framework. Showing how sensitive the occurrence of cognitive styles toward environmental circumstances can be in theory provides context for interpreting the vast variation that has been empirically observed. This responsiveness is consistent with Niemelä and Dingemanse’s  view that non-linear relationships such as thresholds and interactions are common in animal personalities.
How can we explain the specific patterns observed in our simulations? For example, in dangerous environments, in which resources are easy to exploit and thus do not necessitate any learning, individuals can gain the highest fitness by adopting a risky strategy. Individuals which accept a higher predation risk can explore more and thereby collect more resource items if they manage to survive long enough. This style, which represents a more proactive behaviour type, comes to predominate in the population because shy (reactive) types collect few resources despite suffering less predation. However, if circumstances allow for effective anti-predation learning, increased learning skills combined with high exploration tendencies become the most adaptive cognitive style. Such a fast learning and highly active cognitive style is opposed to what is commonly expected by the proactive-reaction framework, but has been found in several species [e.g. 13, 15, 35].
When resources are present for which an investment in higher learning abilities is needed in order to exploit them, a different set of cognitive styles can be found. Under these circumstances, fast learning strategies become adaptive if lifespans are long enough to allow for handling the resources through learning. Whether individuals show high or low exploration tendencies depends both on how easily resources are found and on how severe predation pressure is.
Furthermore, we found under a large range of environmental conditions that different cognitive styles can co-exist within the same population. Due to specializing on a resource type and its interplay with optimal search pattern (exploration tendency), fast and slow styles can co-exist. Frequency-dependence of these styles may stabilize their co-existence as suggested by Boogert and colleagues [5, compare also 36]. For example, in one population some individuals can specialize on easy-to-find and easy-to-handle resources and thus exhibit a slow learning / fast exploration style, whereas other individuals can exploit resources which are hard to find and require learning abilities, thus exhibiting a fast learning / slow exploration style. Almost all other possible combinations of these two individual traits can co-exist under specific environmental circumstances in our simulations. These results can therefore help to explain why different studies find such a large variety of behaviour and cognitive styles in nature, even within the same study system and under similar environmental conditions. Furthermore, it is conceivable that in two studies either some uncontrolled variables of the environment can cause slightly different circumstances (e.g. small differences in predation pressure or in resource composition between two populations). Or, depending on the sampling regime, one of two or more co-existing cognitive styles may be captured more frequently in one study than another. When behavioural and cognitive tasks are conducted with these non-random subsets of individuals it will likely lead to different population-averages in performance.
In line with what has been suggested for individual specialisation in general , the co-existence of different cognitive styles may stabilize populations as microhabitats can more efficiently be occupied and within-species competition can be reduced as individuals with different styles, at least partly, exploit different resources [compare 38]. Inter-individual differences can also facilitate speciation [e.g. 39, 40], underlining its importance for ecology and evolution in general.
In our simulations, predation strongly influences the existence of cognitive styles, as has previously been shown for behavioural syndromes [reviewed in 30]. Predation can cause the evolution of alternative styles in an otherwise similar environment. In general, predation reduces exploration tendency. But under some circumstances, this effect is not found [see also 31, 41]. For example, lifespan can be so short that individuals need to have a high exploration tendency and face the risk of predation, because otherwise they may not collect any resources at all. Or, if learning of predator avoidance is efficient enough to render the predation risk negligible, high exploration becomes more adaptive.
Furthermore, predation can also break down the co-existence by making only one strategy adaptive under given circumstances. However, predation can also cause the co-existence of cognitive styles e.g. by reducing lifespans to such an extent that investment in learning becomes less profitable, thus rendering slow-learning strategies competitive. These effects were found in a limited parameter space only, which, however, is in line with findings of predators’ effects on co-existence of interspecific competitors [reviewed in 42].
In line with the suggestion of Sih and Del Giudice  we found that the influence of behaviour and cognitive traits on each other can go in both directions. The effect which these (sets of) traits have on each other’s evolution can be positive or negative (see Supplementary Figure S4). For example, increased exploration results in increased encounters with specific resources which allows for effective learning and thus drives the evolution of fast learning (not shown). On the other hand, increased exploration may also constrain learning because increased exploration reduces lifespan under severe predation pressure and thereby reduces opportunities to learn (compare Supplementary Figure S2 B).
It would be interesting to investigate how social learning may influence this pattern. For example, in group-living species, shy individuals may learn anti-predator behaviour by observing bolder or more explorative individuals coping with predator encounters. Thereby, slow explorer or shyer individuals could possibly reduce predation pressure without increasing their own predation risk by doing so. This could create an interesting interplay of the evolution of bold individual learners and shy social learners.
Of course, our simulations are based on many simplifications, which limits their transferability to natural systems. However, these simplifications allow to identify some general principles.
We assumed that the trait „L" allows for learning in two different situations: anti-predator behaviour and handling resources. One might argue that this is an unjustified simplification as these situations represent cognitive problems from two different domains. Indeed, this could be a valid point. However, we intuitively expect that even with two independently evolving learning traits our main findings would remain qualitatively similar, i.e. that different environmental conditions can select for all combinations of exploration- and learning-styles and that these styles could in principle co-exist in the same population. Yet, certainly the parameter space under which similar strategies would be found will shift to some degree. And of course, with more evolving traits, we would likely find more cognitive styles e.g. some fast explorers which are good at anti-predator learning but slow at reducing resource handling times and vice versa.
Anyways, the assumption that learning abilities such as associative learning can be domain-general or at least underlie the performance in different cognitive tasks may not be an unjustified simplification after all. In fact, studies have shown that, at least in some taxa, animals show „general intelligence”, meaning that species, or individuals, which score high in one cognitive task also score high in tasks of other cognitive domains [reviewed e.g. in 43]. It is conceivable that mechanisms such as simple associative abilities may allow to learn in different situations and that our simulation may be realistic in this regard.
We also want to point out that, although the models presented here are based on genetic adaptation, we would expect similar outcomes if adaptive phenotypes, in our case specialized cognitive styles, would develop via developmental plasticity. Whether plastic responses are to be expected depends mostly on the timescale on which local conditions change. When environmental conditions change intermediately fast, plastic development are favoured, while under very fast or very slow changing conditions, fixed development (adjusted by genetic adaptation) dominates [e.g. 44]. Anyhow, both fixed and plastic development should usually lead to phenotypes that are adapted to local conditions. We therefore expect, as mentioned in the introduction, that the general conclusions of the present study can be transferred to systems in which differences in cognitive styles are generated by plasticity.
In this study, we regard the interplay of five aspects: exploration, learning, environmental complexity (implemented as “resource composition”), predation pressure, and maximum lifespan. We chose these aspects because they are often investigated and discussed in regard to animal personality, coping or cognitive style. However, of course, many other aspects of the environment and the species living in it are likely to influence the evolution of cognitive styles. For example, instead of handling resources, other environmental aspects may need to be learned, such as navigation through space , or nest-building . Also, when interacting with conspecifics, cognitive styles may strongly be influenced by social learning skills. If learning is involved in interactions with other intelligent agents such as conspecifics or predators, interesting dynamics may occur in the evolution of cognitive styles. This may be a worthy field of further investigations which may help to understand the evolution of animal intelligence in general.
As a final remark we want to point out that there has been much work done, both theoretical and experimental, on the co-existence of competing species and some general conclusions may be transferable to a within-species context. Thereby, the scientific younger field of individual differences (i.e. behavioural types, coping styles, animal personality or cognitive styles) may benefit from decade-long research of interactions between species. On the other hand, no such generalisations may be possible when within-species processes such as sexual selection or kin competition are involved.