Jonathan Birch

What makes fast, cumulative cultural evolution work? Where did it come from? Why is it the sole preserve of humans? We set out a self-assembly hypothesis: cultural evolution evolved culturally. We present an evolutionary account that shows this hypothesis to be coherent, plausible, and worthy of further investigation. It has the following steps: (0) in common with other animals, early hominins had significant capacity for social learning; (1) knowledge and skills learned by offspring from their parents began to spread because bearers had more offspring, a process we call CS1 (or Cultural Selection 1); (2) CS1 shaped attentional learning biases; (3) these attentional biases were augmented by explicit learning biases (judgements about what should be copied from whom). Explicit learning biases enabled (4) the high-fidelity, exclusive copying required for fast cultural accumulation of knowledge and skills by a process we call CS2 (or Cultural Selection 2) and (5) the emergence of cognitive processes such as imitation, mindreading and metacognition-'cognitive gadgets' specialized for cultural learning. This self-assembly hypothesis is consistent with archaeological evidence that the stone tools used by early hominins were not dependent on fast, cumulative cultural evolution, and suggests new priorities for research on 'animal culture'. This article is part of the theme issue 'Foundations of cultural evolution'.

When two agents engage in a joint action, such as rowing together, they exercise joint know-how. But what is the relationship between the joint know-how of the two agents and the know-how each agent possesses individually? I construct an “active mutual enablement” (AME) account of this relationship, according to which joint know-how arises when each agent knows how to predict, monitor, and make failure-averting adjustments in response to the behaviour of the other agent, while actively enabling the other to make such adjustments. I defend the AME account from three objections, and I then use this account as the platform for an examination of the reducibility (or otherwise) of joint know-how to joint propositional knowledge. A summative account of joint propositional knowledge is incompatible with the reduction of joint know-how to joint propositional knowledge, whereas a distributive account is not (although serious difficulties for any such reduction remain). I close by highlighting some open questions the AME account brings into view concerning the evolutionary origin and scaling up of joint know-how.

Can octopuses feel pain and pleasure? What about crabs, shrimps, insects, or spiders? How do we tell whether a person unresponsive after severe brain injury might be suffering? When does a fetus in the womb start to have conscious experiences? Could there even be rudimentary feelings in miniature models of the human brain, grown from human stem cells? And what about AI? These are questions about the edge of sentience, and they are subject to enormous, disorienting uncertainty. The stakes are immense, and neglecting the risks can have terrible costs. We need to err on the side of caution, yet it's often far from clear what 'erring on the side of caution' should mean in practice. When are we going too far? When are we not doing enough? The Edge of Sentience presents a comprehensive precautionary framework designed to help us reach ethically sound, evidence-based decisions despite our uncertainty.

We consider the relationship between neural and behavioural evidence for animal consciousness. We critically examine two recent studies: one neural and one behavioural. The first, on crows, finds different neural activity depending on whether a stimulus is reported as seen or unseen. However, to implicate this neural activity in consciousness, we must assume that a specific conditioned behaviour is a report of conscious experience. The second study, on macaques, records behaviours strikingly similar to patterns of conscious and unconscious perception in humans. However, confounds are only ruled out in human subjects, presupposing substantial neural similarity between humans and macaques. Taken together, the two studies reveal a sense in which neural and behavioural research rely on each other. Looking ahead, these two types of evidence could prove to be either mutually reinforcing or mutually undermining. The science of animal consciousness needs both neural and behavioural evidence, ideally obtained as part of a single coordinated programme.

Consciousness has an important role in ethics: when a being consciously experiences the frustration or satisfaction of its interests, those interests deserve higher moral priority than those of a behaviourally similar but non-conscious being. I consider the relationship between this ethical role and an a posteriori (or ‘type-B’) materialist solution to the mind-body problem. It is hard to avoid the conclusion that, if type-B materialism is correct, then the reference of the concept Phenomenal Consciousness is radically indeterminate between a neuronal-level property that is distinctive to mammals and a high-level functional property that is much more widely shared. This would leave many non-mammalian animals (such as birds, fish, insects and octopuses) with indeterminate moral status. There are ways to manage this radical moral indeterminacy, but all of these ways lead to profoundly troubling consequences.

‘Sentience’ sometimes refers to the capacity for any type of subjective experience, and sometimes to the capacity to have subjective experiences with a positive or negative valence, such as pain or pleasure. We review recent controversies regarding sentience in fish and invertebrates and consider the deep methodological challenge posed by these cases. We then present two ways of responding to the challenge. In a policy-making context, precautionary thinking can help us treat animals appropriately despite continuing uncertainty about their sentience. In a scientific context, we can draw inspiration from the science of human consciousness to disentangle conscious and unconscious perception (especially vision) in animals. Developing better ways to disentangle conscious and unconscious affect is a key priority for future research.

Over the past two decades, Ginsburg and Jablonka have developed a novel approach to studying the evolutionary origins of consciousness: the Unlimited Associative Learning (UAL) framework. The central idea is that there is a distinctive type of learning that can serve as a transition marker for the evolutionary transition from non-conscious to conscious life. The goal of this paper is to stimulate discussion of the framework by providing a primer on its key claims (Part I) and a clear statement of its main empirical predictions (Part II).

From mitochondria to meerkats, the natural world is full of spectacular examples of social behaviour. In the early 1960s Bill Hamilton changed the way we think about how such behaviour evolves. He introduced three key innovations - now known as Hamilton's rule, kin selection, and inclusive fitness - which have been enormously influential, but which remain the subject of fierce controversy. Hamilton's pioneering work kick-started a research program now known as social evolution theory. This is a book about the philosophical foundations and future prospects of that program. Part I, "Foundations", is a careful exposition and defence of Hamilton's ideas, with a few modifications along the way. In Part II, "Extensions", Jonathan Birch shows how these ideas can be applied to phenomena including cooperation in micro-organisms, cooperation among the cells of a multicellular organism, and culturally evolved cooperation in the earliest human societies. Birch argues that real progress can be made in understanding microbial evolution, evolutionary transitions, and human evolution by viewing them through the lens of social evolution theory, provided the theory is interpreted with care and adapted where necessary. The Philosophy of Social Evolution places social evolution theory on a firm philosophical footing and sets out exciting new directions for further work.

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

Motivational trade-off behaviours, where an organism behaves as if flexibly weighing up an opportunity for reward against a risk of injury, are often regarded as evidence that the organism has valenced experiences like pain. This type of evidence has been influential in shifting opinion regarding crabs and insects. Critics note that (i) the precise links between trade-offs and consciousness are not fully known; (ii) simple trade-offs are evinced by the nematode worm Caenorhabditis elegans, mediated by a mechanism plausibly too simple to support conscious experience; (iii) pain can sometimes interfere with rather than support making trade-offs rationally. However, rather than undermining trade-off evidence in general, such cases show that the nature of the trade-off, and its underlying neural substrate, matter. We investigate precisely how.

Could emotions be a uniquely human phenomenon? One prominent theory in emotion science, Lisa Feldman Barrett’s Theory of Constructed Emotion (TCE), suggests they might be. The source of the sceptical challenge is that TCE links emotions to abstract concepts tracking socio-normative expectations, and other animals are unlikely to have such concepts. Barrett’s own response to the sceptical challenge is to relativize emotion to the perspective of an interpreter, but this is unpromising. A more promising response may be to amend the theory, dropping the commitment to the abstract nature of emotion concepts and allowing that, like olfactory concepts, they have disjunctive sensory groundings. Even if other animals were emotionless, this would not imply they lack morally significant interests. Unconceptualized valenced experiences are a sufficient basis for morally significant interests, and such experiences may occur even in the absence of discrete, constructed emotions.

Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is 'cognitive-motor dissociation' (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient's family, because this information may confuse, alarm and mislead. Instead, we need a procedure for generating case-specific probabilistic assessments that can be communicated clearly. This article constructs a possible procedure with three key elements: (1) A shift from categorical 'responding or not' assessments to degrees of evidence; (2) The use of patient-centred priors to convert degrees of evidence to probabilistic assessments; and (3) The use of standardised probability yardsticks to convey those assessments as clearly as possible.

Definitions of animal welfare often invoke consciousness or sentience. Marian Stamp Dawkins has argued that to define animal welfare this way is a mistake. In Dawkins’s alternative view, an animal with good welfare is one that is healthy and “has what it wants.” The dispute highlights a source of strain on the concept of animal welfare: consciousness-involving definitions are better able to capture the normative significance of welfare, whereas consciousness-free definitions facilitate the validation of welfare indicators. I reflect on how the field should respond to this strain, ultimately recommending against splitting the concept and in favor of consciousness-involving definitions.

Skyrms, building on the work of Dretske, has recently developed a novel information-theoretic account of propositional content in simple signalling systems. Information-theoretic accounts of content traditionally struggle to accommodate the possibility of misrepresentation, and I show that Skyrms’s account is no exception. I proceed to argue, however, that a modified version of Skyrms’s account can overcome this problem. On my proposed account, the propositional content of a signal is determined not by the information that it actually carries, but by the information that it would carry at the nearest separating equilibrium of the underlying evolutionary dynamics. I show that this amended account yields reasonable ascriptions of false propositional content in a well-known formal model of the evolution of communication (the ‘Philip Sidney’ game), and close with a discussion of the serious but perhaps not insuperable difficulties we face in applying the account to examples of signalling in the real world.

The entomology literature has historically suggested insects cannot feel pain, leading to their exclusion from ethical debates and animal welfare legislation. However, there may be more neural and cognitive/behavioural evidence for pain in insects than previously considered. We use Birch et al. 's (2021) eight criteria for sentience to critically evaluate the evidence for pain in insects. We assess six orders (Blattodea, Coleoptera, Diptera, Hymenoptera, Lepidoptera, and Orthoptera) in at least two life stages (adult and first instar juveniles, as well as other instars where relevant data are found). Other insect orders have not received enough research effort to be evaluated. According to the Birch et al. framework, adult Diptera (flies and mosquitoes) and Blattodea (cockroaches and termites) satisfy six criteria, constituting strong evidence for pain. Adults of the remaining orders (except Coleoptera, beetles) and some juveniles (Blattodea and Diptera, as well as last instar Lepidoptera [butterflies and moths]) satisfy 3–4 criteria, or “substantial evidence for pain”. We found no good evidence that any insects failed a criterion. However, there were significant evidence gaps, particularly for juveniles, highlighting the importance of more research on insect pain. We conclude by considering the ethical implications of our findings where insects are managed in wild, farmed, and research contexts.

The entomology literature has historically suggested insects cannot feel pain, leading to their exclusion from ethical debates and animal welfare legislation. However, there may be more neural and cognitive/behavioural evidence for pain in insects than previously considered. We use Birch et al. 's (2021) eight criteria for sentience to critically evaluate the evidence for pain in insects. We assess six orders (Blattodea, Coleoptera, Diptera, Hymenoptera, Lepidoptera, and Orthoptera) in at least two life stages (adult and first instar juveniles, as well as other instars where relevant data are found). Other insect orders have not received enough research effort to be evaluated. According to the Birch et al. framework, adult Diptera (flies and mosquitoes) and Blattodea (cockroaches and termites) satisfy six criteria, constituting strong evidence for pain. Adults of the remaining orders (except Coleoptera, beetles) and some juveniles (Blattodea and Diptera, as well as last instar Lepidoptera [butterflies and moths]) satisfy 3–4 criteria, or “substantial evidence for pain”. We found no good evidence that any insects failed a criterion. However, there were significant evidence gaps, particularly for juveniles, highlighting the importance of more research on insect pain. We conclude by considering the ethical implications of our findings where insects are managed in wild, farmed, and research contexts.

How can we investigate non-human experiences scientifically? Given substantial differences in sensory abilities and the private nature of consciousness, this remains an open question. In this review, we propose a way to gain empirical traction on one key feature of experience: its temporal structure, or 'timescape'. Perceptual contents follow systematic temporal principles-synchronisation, revision, and persistence-and are sampled across attentional windows and vary in stability. These principles can be explored through temporal illusions and experimental paradigms. We conceptualise an animal's timescape in terms of five key windows, all of which are experimentally testable, and analyse evidence across animal species to highlight varying timescapes. Together, these ideas lay the foundations for a research programme comparing the temporal Umwelts of non-human animals.

The question of whether Artificial Intelligence can become conscious is not just a philosophical question but a political one. Given that an increasing number of people are forming social relationships with AI systems, the calls for treating them as persons with legal protections might not be far off. In this interview based on his book The Edge of Sentience: Risk and Precaution in Humans, Other Animals, and AI, Jonathan Birch argues that we shouldn’t be too quick to dismiss the possibility that AI could become conscious, but warns that we are not ready, conceptually or societally, for such an eventuality.