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Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss (...) |
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The argument against innatism at the heart of Cognitive Gadgets is provocative but premature, and is vitiated by dichotomous thinking, interpretive double standards, and evidence cherry-picking. I illustrate my criticism by addressing the heritability of imitation and mindreading, the relevance of twin studies, and the meaning of cross-cultural differences in theory of mind development. Reaching an integrative understanding of genetic inheritance, plasticity, and learning is a formidable task that demands a more nuanced evolutionary approach. |
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Fodor argued that learning a concept by hypothesis testing would involve an impossible circularity. I show that Fodor's argument implicitly relies on the assumption that actually φ-ing entails an ability to φ. But this assumption is false in cases of φ-ing by luck, and just such luck is involved in testing hypotheses with the kinds of generative random sampling methods that many cognitive scientists take our minds to use. Concepts thus can be learned by hypothesis testing without circularity, and it (...) |
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Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with (...) |
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As modern deep networks become more complex, and get closer to human‐like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations (...) |
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In ‘Computing Machinery and Intelligence’, Turing, sceptical of the question ‘Can machines think?’, quickly replaces it with an experimentally verifiable test: the imitation game. I suggest that for such a move to be successful the test needs to be relevant, expansive, solvable by exemplars, unpredictable, and lead to actionable research. The Imitation Game is only partially successful in this regard and its reliance on language, whilst insightful for partially solving the problem, has put AI progress on the wrong foot, prescribing (...) |
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Cognitive science began as a multidisciplinary endeavor to understand how the mind works. Since the beginning, cognitive scientists have been asking questions about the right methodologies and levels of explanation to pursue this goal, and make cognitive science a coherent science of the mind. Key questions include: Is there a privileged level of explanation in cognitive science? How do different levels of explanation fit together, or relate to one another? How should explanations at one level inform or constrain explanations at (...) |
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The notion of intelligence is relevant to several fields of research, including cognitive and comparative psychology, neuroscience, artificial intelligence, and philosophy, among others. However, there is little agreement within and across these fields on how to characterise and explain intelligence. I put forward a behavioural, operational characterisation of intelligence that can play an integrative role in the sciences of intelligence, as well as preserve the distinctive explanatory value of the notion, setting it apart from the related concepts of cognition and (...) |
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Emerging cybertechnologies, such as social digibots, bend epistemological conventions of life and culture already complicated by human and animal relationships. Virtually-augmented niches of machines and organic life promise new free-energy-governed selection of intelligent digital life. These provocative eco-evolutionary contexts demand a theory of (natural and artificial) minds to characterize and validate the immersive social phenomena universally-shaping cultural affordances. |
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This paper traces the empiricist program from early debates between nativism and behaviorism within philosophy, through debates about early connectionist approaches within the cognitive sciences, and up to their recent iterations within the domain of deep learning. We demonstrate how current debates on the nature of cognition via deep network architecture echo some of the core issues from the Chomsky/Quine debate and investigate the strength of support offered by these various lines of research to the empiricist standpoint. Referencing literature from (...) |
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Butz, Achimova, Bilkey, and Knott provide a topic overview and discuss whether the special issue contributions may imply that event‐predictive abilities constitute a root for conceptual human thought, because they enable complex, mutually beneficial, but also intricately competitive, social interactions and language communication. |
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Butz, Achimova, Bilkey, and Knott provide a topic overview and discuss whether the special issue contributions may imply that event‐predictive abilities constitute a root for conceptual human thought, because they enable complex, mutually beneficial, but also intricately competitive, social interactions and language communication. |
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Cognitive scientists often describe the mind as constructing and using models of aspects of the environment, but it is not obvious what makes something a model as opposed to a mere representation.... |
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Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels (...) |
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In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...) |
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Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally accepted explanation (...) |
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Perception solves computationally demanding problems at lightning fast speed. It recovers sophisticated representations of the world from degraded inputs, often in a matter of milliseconds. Any theory of perception must be able to explain how this is possible; in other words, it must be able to explain perception’s computational tractability. One of the few attempts to move toward such an explanation is the information encapsulation hypothesis, which posits that perception can be fast because it keeps computational costs low by forgoing (...) |
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A theoretical pillars of vision science in the information-processing tradition is that perception involves unconscious inference. The classic support for this claim is that, since retinal inputs underdetermine their distal causes, visual perception must be the conclusion of a process that starts with premises representing both the sensory input and previous knowledge about the visible world. Focus on this “argument from underdetermination” gives the impression that, if it fails, there is little reason to think that visual processing involves unconscious inference. (...) |
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Constructing an intuitive theory from data confronts learners with a “chicken‐and‐egg” problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken‐and‐egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We (...) |
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Pointing to similarities between challenges encountered in today's neural coding and twentieth-century behaviorism, we draw attention to lessons learned from resolving the latter. In particular, Perceptual Control Theory posits behavior as a closed-loop control process with immediate and teleological causes. With two examples, we illustrate how these ideas may also address challenges facing current neural coding paradigms. |
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Many researchers and intellectuals warn about extreme risks from artificial intelligence. However, these warnings typically came without systematic arguments in support. This paper provides an argument that AI will lead to the permanent disempowerment of humanity, e.g. human extinction, by 2100. It rests on four substantive premises which it motivates and defends: first, the speed of advances in AI capability, as well as the capability level current systems have already reached, suggest that it is practically possible to build AI systems (...) |
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Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...) |
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Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...) |
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Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. (...) |
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Predictive processing has recently been advanced as a global cognitive architecture for the brain. I argue that its commitments concerning the nature and format of cognitive representation are inadequate to account for two basic characteristics of conceptual thought: first, its generality—the fact that we can think and flexibly reason about phenomena at any level of spatial and temporal scale and abstraction; second, its rich compositionality—the specific way in which concepts productively combine to yield our thoughts. I consider two strategies for (...) |
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A large body of research in cognitive psychology and neuroscience draws on Bayesian statistics to model information processing within the brain. Many theorists have noted that this research seems to be in tension with a large body of experimental results purportedly documenting systematic deviations from Bayesian updating in human belief formation. In response, proponents of the Bayesian brain hypothesis contend that Bayesian models can accommodate such results by making suitable assumptions about model parameters. To make progress in this debate, I (...) |
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Much of contemporary philosophy assumes a close connection between thought and language. It is widely assumed, for example, that the structural units, semantic properties, and forms of reasoning as... |
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Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNNs rapidly evolve, and today's limitations are often tomorrow's successes. In addition, providing explanations as well as prediction and image-computability are model desiderata; one should not be favoured at the expense of the other. |
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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 (...) |
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Cognitive Science, Volume 46, Issue 4, April 2022. |
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ABSTRACTWe propose a compatibilist theory of free will in the tradition of naturalized philosophy that attempts to: 1) provide a synthesis of a variety of well-known theories, capable of addressing problems of the latter; 2) account for the fact that free will comes in degrees; and 3) interface with neurobiology. We argue that free will comes in degrees, and that these degrees vary with the agent's capacity to make assumptions and use theories. Our model, then, highlights that free-willed actions are (...) |
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We review publications in automated scientific discovery using deep learning, with the aim of shedding light on problems with strong connections to philosophy of science, of physics in particular. We show that core issues of philosophy of science, related, notably, to the nature of scientific theories; the nature of unification; and of causation loom large in scientific deep learning. Therefore, advances in deep learning could, and ideally should, have impact on philosophy of science, and vice versa. We suggest lines of (...) |
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L’humilité est usuellement considérée comme une vertu morale, mais aussi épistémique. Une exception à cet égard dans la tradition philosophique est le Commentaire au Premier Alcibiade de Proclus. Celui-ci nous décrit le choix d’Alcibiade par Socrate, les premières étapes de leur relation pédagogique, mais aussi et surtout les principes théoriques qui les justifient, en faisant la part belle à ce qu’on pourrait appeler l’orgueil épistémique, pourvu de trois composantes : la fierté (φρόνημα), le mépris (καταφρονεῖν, etc.) et l’ambition (φιλοτιμία). Après (...) |
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Despite the success of artificial intelligence, we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, we follow Bayesian inference to combine intuitive models and (...) |
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Consumers can have difficulty expressing their buying intentions on an explicit level. The most common explanation for this intention-action gap is that consumers have many cognitive biases that interfere with decision making. The current resource-rational approach to understanding human cognition, however, suggests that brain environment interactions lead consumers to minimize the expenditure of cognitive energy. This means that the consumer seeks as simple of a solution as possible for a problem requiring decision making. In addition, this resource-rational approach to decision (...) |
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I argue, with examples, that most human cognitive skills are neither instincts nor gadgets but mechanisms shaped both by evolved dispositions and by cultural inputs. This shaping can work either through evolved skills fulfilling their function with the help of cultural skills that they contribute to shape, or through cultural skills recruiting evolved skills and adjusting to them. |
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Since the early days of artificial intelligence (AI), many logics have been explored as tools for knowledge representation and reasoning. In the spirit of the Crossley Festscrift and recognizing John Crossley’s diverse interests and his legacy in both mathematical logic and computer science, I discuss examples from my own research that sit in the overlap of logic and AI, with a focus on supporting human–AI interactions. |
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Topics in Cognitive Science, Volume 14, Issue 1, Page 31-44, January 2022. |
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Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for (...) |
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A basic deep neural network (DNN) is trained to exhibit a large set of input–output dispositions. While being a good model of the way humans perform some tasks automatically, without deliberative reasoning, more is needed to approach human‐like artificial intelligence. Analysing recent additions brings to light a distinction between two fundamentally different styles of computation: content‐specific and non‐content‐specific computation (as first defined here). For example, deep episodic RL networks draw on both. So does human conceptual reasoning. Combining the two takes (...) |
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