Marta Halina

The evolution of phenomenal consciousness remains a central challenge in the philosophy of mind and cognitive science. Although many argue that consciousness can be understood as a biological phenomenon, there is considerable disagreement about its origins. Competing theories range from early evolution views, which trace consciousness back to the Cambrian period around 540 million years ago, to latecomer theories, which associate it with complex cognition in mammals or humans. We argue that this divergence stems largely from unresolved debates about how to identify consciousness in extant animals. Which behavioural and neural properties we regard as evidence of consciousness directly impacts how we reconstruct its evolutionary history. These issues carry significant ethical implications for how we treat nonhuman animals. This paper maps the key positions in the debate, analyses their underlying disagreements and explores how evolutionary approaches might move the discussion beyond anthropocentric assumptions towards a more inclusive understanding of animal consciousness.

Artificial Intelligence is making rapid and remarkable progress in the development of more sophisticated and powerful systems. However, the acknowledgement of several problems with modern machine learning approaches has prompted a shift in AI benchmarking away from task-oriented testing towards ability-oriented testing, in which AI systems are tested on their capacity to solve certain kinds of novel problems. The Animal-AI Environment is one such benchmark which aims to apply the ability-oriented testing used in comparative psychology to AI systems. Here, we present the first direct human-AI comparison in the Animal-AI Environment, using children aged 6–10. We found that children of all ages were significantly better than a sample of 30 AIs across most of the tests we examined, as well as performing significantly better than the two top-scoring AIs, “ironbar” and “Trrrrr,” from the Animal-AI Olympics Competition 2019. While children and AIs performed similarly on basic navigational tasks, AIs performed significantly worse in more complex cognitive tests, including detour tasks, spatial elimination tasks, and object permanence tasks, indicating that AIs lack several cognitive abilities that children aged 6–10 possess. Both children and AIs performed poorly on tool-use tasks, suggesting that these tests are challenging for both biological and non-biological machines.

In The edge of sentience, Jonathan Birch proposes a democratically inclusive framework for protecting potentially sentient beings. While experts assess and communicate evidence of sentience, the public deliberates on proportionate policy responses to avoid causing gratuitous suffering. While we think there are many virtues to Birch's analysis and approach, in this commentary, we raise the concern that the proposed framework fails to sufficiently account for the presence of non‐epistemic values in science. We discuss strategies from the values in science literature for evaluating and managing these values in a democratic and inclusive way.

New types of artificial intelligence (AI), from cognitive assistants to social robots, are challenging meaningful comparison with other kinds of intelligence. How can such intelligent systems be catalogued, evaluated, and contrasted, with representations and projections that offer meaningful insights? To catalyse the research in AI and the future of cognition, we present the motivation, requirements and possibilities for an atlas of intelligence: an integrated framework and collaborative open repository for collecting and exhibiting information of all kinds of intelligence, including humans, non-human animals, AI systems, hybrids and collectives thereof. After presenting this initiative, we review related efforts and present the requirements of such a framework. We survey existing visualisations and representations, and discuss which criteria of inclusion should be used to configure an atlas of intelligence.

Despite there being little consensus on what intelligence is or how to measure it, the media and the public have become increasingly preoccupied with the concept owing to recent accomplishments in machine learning and research on artificial intelligence (AI). Governments and corporations are investing billions of dollars to fund researchers who are keen to produce an ever‐expanding range of artificial intelligent systems. More than 30 countries have announced such research initiatives over the past 3 years 1. For example, the EU Commission pledged to increase the investment in AI research to €1.5 billion by 2020 (from €500 million in 2017), while China has committed $2.1 billion towards an AI technology park in Beijing alone 1. This global investment in AI is astonishing and prompts several questions: What are the true possibilities and limitations of AI? What do AI researchers and developers mean by “intelligence”? How does this compare to the everyday concept of intelligence and how the term is other branches of cognitive science? And can machine learning produce anything that is truly “intelligent”?

Animal minds are complex and diverse, making them difficult to study. This Element focuses on a question that has received much attention in the field of comparative cognition: 'Do animals reason about unobservable variables like force and mental states?' The Element shows how researchers design studies and gather evidence to address this question. Despite the many virtues of current methods, hypotheses in comparative cognition are often underdetermined by the empirical evidence. Given this, philosophers and scientists have recently called for additional behavioral constraints on theorizing in the field. The Element endorses this proposal (known as 'signature testing'), while also arguing that studies on animal minds would benefit from drawing more heavily on neuroscience and biology.

A common strategy in comparative cognition is to require that one reject associative learning as an explanation for behavior before concluding that an organism is capable of causal reasoning. In this paper, I argue that standard causal-reasoning tasks can be explained by a powerful form of associative learning: unlimited associative learning (UAL). The lesson, however, is not that researchers should conduct more studies to reject UAL, but that they should instead focus on 1) enriching the cognitive hypothesis space and 2) testing a broader range of information processing patterns—errors, biases and limits, rather than successful problem solving alone.

Recent debates about the biological and evolutionary conditions for sentience have generated a renewed interest in fine-grained functionalism. According to one such account advanced by Peter Godfrey-Smith, sentience depends on the fine-grained activities characteristic of living organisms. Specifically, the scale, context and stochasticity of these fine-grained activities. One implication of this view is that contemporary artificial intelligence is a poor candidate for sentience. Insofar as current AI lacks the ability to engage in such living activities it will lack sentience, no matter what its coarse-grained functions. In this paper, we review the case for fine-grained functionalism and show that there are contemporary machines that fulfil the fine-grained functional criteria identified by Godfrey-Smith, and thus are candidates for sentience. Molecular machines such as Brownian computers are analogous to metabolic activity in their scale, context and stochasticity, and can serve as the basis of AI. Molecular computation is a promising candidate for artificial sentience according to contemporary philosophical accounts of sentience.

In March 2016, DeepMind's computer programme AlphaGo surprised the world by defeating the world‐champion Go player, Lee Sedol. AlphaGo exhibits a novel, surprising and valuable style of play and has been recognised as “creative” by the artificial intelligence (AI) and Go communities. This article examines whether AlphaGo engages in creative problem solving according to the standards of comparative psychology. I argue that AlphaGo displays one important aspect of creative problem solving (namely mental scenario building in the form of Monte Carlo tree search), while lacking another (domain generality). This analysis has consequences for how we think about creativity in humans and AI.

We evaluate a common reasoning strategy used in community ecology and comparative psychology for selecting between competing hypotheses. This strategy labels one hypothesis as a “null” on the grounds of its simplicity and epistemically privileges it as accepted until rejected. We argue that this strategy is unjustified. The asymmetrical treatment of statistical null hypotheses is justified through the experimental and mathematical contexts in which they are used, but these contexts are missing in the case of the “pseudo-null hypotheses” found in our case studies. Moreover, statistical nulls are often not epistemically privileged in practice over their alternatives because failing to reject the null is usually a negative result about the alternative, experimental hypothesis. Scientists should eschew the appeal to pseudo-nulls. It is a rhetorical strategy that glosses over a commitment to valuing simplicity over other epistemic virtues in the name of good scientific and statistical methodology.

There is currently a consensus among comparative psychologists that nonhuman animals are capable of some forms of mindreading. Several philosophers and psychologists have criticized this consensus, however, arguing that there is a “logical problem” with the experimental approach used to test for mindreading in nonhuman animals. I argue that the logical problem is no more than a version of the general skeptical problem known as the theoretician’s dilemma. As such, it is not a problem that comparative psychologists must solve before providing evidence for mindreading

The professionalization of science is a recent phenomenon. Before the mid-1800s, investigations of the natural world were largely performed by those hobbyists who had the leisure time to do so. Things are very different today. Open one of the over twenty thousand scientific journals currently in circulation, and you would be hard pressed to decipher the technical prose, much less the methodological and conceptual strategies being employed. This is changing, however. People are not only taking greater interest in how science works, but choosing to actively participate in the process, whether this involves discovering new protein configurations , identifying and categorizing cancer cells from tissue samples , or analyzing the vocalizations of canids .Why start a review on what might seem like an academic book in contemporary philosophy of biology with a discussion of citizen science? Craver and Darden’s book, In Search of Mechanisms, is positi ..