Operationalizing Goal Directedness: An Empirical Route to Advancing a Philosophical Discussion

Goal directedness is one of the most commonly observed behavior patterns in biology, exemplified by systems ranging in complexity from cellular migration to human motivations. Philosophers have long tried to understand goal directedness in terms of necessary and sufficient conditions, but no consensus has been reached. Here we take an entirely novel approach to goal directedness, postponing the search for necessary and sufficient conditions, and instead trying to advance understanding by an empirical route. In particular, we introduce quantitative measures of goal directedness, applicable to systems that are generally agreed to be goal directed. The measures allow one to assess two signature properties of goal-directed systems, persistence and plasticity. Persistence is the tendency for an entity that is on a trajectory toward a goal to return to that trajectory following perturbations. Plasticity we understand as the tendency for an entity to find a trajectory toward a goal from a variety of different starting distances. We demonstrate the metrics by applying them to goal-directed behavior in two biological systems, bacteria moving up a chemoattractant gradient and a human following a heat gradient. Our approach reveals goal directedness to be an empirically tractable notion, one that makes possible a variety of comparative studies in biology, including comparing degree of goal directedness in different species, or in one species under different conditions, as well as studying evolutionary trends. More generally, the metrics make it possible to investigate the correlates and causes of goal-directed behavior. Finally, our approach challenges the conventional view of goal directedness as a discrete and unitary property, by showing that it can be treated as continuous, as a matter of degree, and that it can be broken down into at least two, and possibly more, partly independent components

Origin and Evolution of Large Brains in Toothed Whales

Toothed whales (order Cetacea: suborder Odontoceti) are highly encephalized, possessing brains that are significantly larger than expected for their body sizes. In particular, the odontocete superfamily Delphinoidea (dolphins, porpoises, belugas, and narwhals) comprises numerous species with encephalization levels second only to modern humans and greater than all other mammals. Odontocetes have also demonstrated behavioral faculties previously only ascribed to humans and, to some extent, other great apes. How did the large brains of odontocetes evolve? To begin to investigate this question, we quantified and averaged estimates of brain and body size for 36 fossil cetacean species using computed tomography and analyzed these data along with those for modern odontocetes. We provide the first description and statistical tests of the pattern of change in brain size relative to body size in cetaceans over 47 million years. We show that brain size increased significantly in two critical phases in the evolution of odontocetes. The first increase occurred with the origin of odontocetes from the ancestral group Archaeoceti near the Eocene-Oligocene boundary and was accompanied by a decrease in body size. The second occurred in the origin of Delphinoidea only by 15 million years ago

Goal directedness and the field concept

A long-standing problem in understanding goal-directed systems has been the insufficiency of mechanistic explanations to make sense of them. This paper offers a solution to this problem. It begins by observing the limitations of mechanistic decompositions when it comes to understanding physical fields. We argue that introducing the field concept, as it has been developed in field theory, alongside mechanisms is able to provide an account of goal directedness in the sciences

White-tailed deer (Odocoileus virginianus) positively affect the growth of mature northern red oak (Quercus rubra) trees

Understanding and predicting the effects of deer (Cervidae) on forest ecosystems present significant challenges in ecosystem ecology. Deer herbivory can cause large changes in the biomass and species composition of forest understory plant communities, including early life-cycle trees (i.e., seedlings and saplings). Such changes can impact juvenile to adult transitions and the future age structure and species composition of mature forests. Changes to understory vegetation also impact flow of energy and nutrients in forest ecosystems. Studies examining the influence of deer on mature trees, however, are rare and rely on extrapolating effects from early life cycle stages of trees. We tested the hypothesis that the absence of deer would result in an increase in the growth rate of mature trees by examining the impact of white-tailed deer (Odocoileus virginianus) on mature canopy trees. We examined incremental growth in individuals of Quercus rubra, an important component of temperate deciduous forests in North America, inside and outside 16-year deer exclosures in eastern U.S. deciduous forests. We found that adult trees inside exclosures grew less than those directly exposed to deer. Our findings highlight the indirect effects of white-tailed deer on the growth of adult individuals of Q. rubra in a way opposite of what would be expected from previous studies based on immature or understory tree populations. We suggest the increased growth of adult trees in the presence of deer may be explained by increased nutrient inputs through deer fecal and urine deposits and the alteration of the competitive environment belowground through the reduction of understory vegetation by browsing. Underscoring the ecological and demographic importance of adult trees in forest ecosystems, results from this study suggest the direct and indirect effects of deer on mature trees should not be overlooked

‘O sibling, where art thou?’ – a review of avian sibling recognition with respect to the mammalian literature

Avian literature on sibling recognition is rare compared to that developed by mammalian researchers. We compare avian and mammalian research on sibling recognition to identify why avian work is rare, how approaches differ and what avian and mammalian researchers can learn from each other. Three factors: (1) biological differences between birds and mammals, (2) conceptual biases and (3) practical constraints, appear to influence our current understanding. Avian research focuses on colonial species because sibling recognition is considered adaptive where ‘mixing potential’ of dependent young is high; research on a wider range of species, breeding systems and ecological conditions is now needed. Studies of acoustic recognition cues dominate avian literature; other types of cues (e.g. visual, olfactory) deserve further attention. The effect of gender on avian sibling recognition has yet to be investigated; mammalian work shows that gender can have important influences. Most importantly, many researchers assume that birds recognise siblings through ‘direct familiarisation’ (commonly known as associative learning or familiarity); future experiments should also incorporate tests for ‘indirect familiarisation’ (commonly known as phenotype matching). If direct familiarisation proves crucial, avian research should investigate how periods of separation influence sibling discrimination. Mammalian researchers typically interpret sibling recognition in broad functional terms (nepotism, optimal outbreeding); some avian researchers more successfully identify specific and testable adaptive explanations, with greater relevance to natural contexts. We end by reporting exciting discoveries from recent studies of avian sibling recognition that inspire further interest in this topic

The Minimal Complexity of Adapting Agents Increases with Fitness

What is the relationship between the complexity and the fitness of evolved organisms, whether natural or artificial? It has been asserted, primarily based on empirical data, that the complexity of plants and animals increases as their fitness within a particular environment increases via evolution by natural selection. We simulate the evolution of the brains of simple organisms living in a planar maze that they have to traverse as rapidly as possible. Their connectome evolves over 10,000s of generations. We evaluate their circuit complexity, using four information-theoretical measures, including one that emphasizes the extent to which any network is an irreducible entity. We find that their minimal complexity increases with their fitness

An evaluation of platforms for processing camera-trap data using artificial intelligence

Camera traps have quickly transformed the way in which many ecologists study the distribution of wildlife species, their activity patterns and interactions among members of the same ecological community. Although they provide a cost-effective method for monitoring multiple species over large spatial and temporal scales, the time required to process the data can limit the efficiency of camera-trap surveys. Thus, there has been considerable attention given to the use of artificial intelligence (AI), specifically deep learning, to help process camera-trap data. Using deep learning for these applications involves training algorithms, such as convolutional neural networks (CNNs), to use particular features in the camera-trap images to automatically detect objects (e.g. animals, humans, vehicles) and to classify species. To help overcome the technical challenges associated with training CNNs, several research communities have recently developed platforms that incorporate deep learning in easy-to-use interfaces. We review key characteristics of four AI platforms—Conservation AI, MegaDetector, MLWIC2: Machine Learning for Wildlife Image Classification and Wildlife Insights—and two auxiliary platforms—Camelot and Timelapse—that incorporate AI output for processing camera-trap data. We compare their software and programming requirements, AI features, data management tools and output format. We also provide R code and data from our own work to demonstrate how users can evaluate model performance. We found that species classifications from Conservation AI, MLWIC2 and Wildlife Insights generally had low to moderate recall. Yet, the precision for some species and higher taxonomic groups was high, and MegaDetector and MLWIC2 had high precision and recall when classifying images as either ‘blank’ or ‘animal’. These results suggest that most users will need to review AI predictions, but that AI platforms can improve efficiency of camera-trap-data processing by allowing users to filter their dataset into subsets (e.g. of certain taxonomic groups or blanks) that can be verified using bulk actions. By reviewing features of popular AI-powered platforms and sharing an open-source GitBook that illustrates how to manage AI output to evaluate model performance, we hope to facilitate ecologists' use of AI to process camera-trap data

The Geozoic Supereon

Geological time units are the lingua franca of earth sciences: they are a terminological convenience, a vernacular of any geological conversation, and a prerequisite of geo-scientific writing found throughout in earth science dictionaries and textbooks. Time units include terms formalized by stratigraphic committees as well as informal constructs erected ad hoc to communicate more efficiently. With these time terms we partition Earth’s history into utilitarian and intuitively understandable time segments that vary in length over seven orders of magnitude: from the 225-year-long Anthropocene (Crutzen and Stoermer, 2000) to the ,4-billion-year-long Precambrian (e.g., Hicks, 1885; Ball, 1906; formalized by De Villiers, 1969)

Integrated information increases with fitness in the evolution of animats

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.Comment: 27 pages, 8 figures, one supplementary figure. Three supplementary video files available on request. Version commensurate with published text in PLoS Comput. Bio