It is widely thought that causal cognition underpins technical reasoning. Here we suggest that understanding causal cognition as a thinking system that includes theory of mind (i.e., social cognition) can be a productive theoretical tool for the field of evolutionary cognitive archaeology. With this contribution, we expand on an earlier model that distinguishes seven grades of causal cognition, explicitly presenting it together with a new analysis of the theory of mind involved in the different grades. We then suggest how such (...) thinking may manifest in the archaeological or stone tool record and techno-behaviors of the last three million years or so. Our thesis is threefold: (a) theory of mind is an integral element of causal cognition; (b) generally speaking, the more advanced causal cognition is, the more it is dependent on theory of mind; and (c) the evolution of causal cognition depends more and more on mental representations of hidden variables. Ultimately, the final or seventh grade of causal cognition allows us to reason from a network of hidden variables that, amongst other things, enables the learning, manufacture, and use of complex technological systems. It also facilitates the seamless mapping of knowledge between personal (egocentric), physical, and social networks that allows for newly devised and innovative technical and social outcomes. (shrink)

It is important for the theory of knowledge to understand the factors involved in the generation of the capacities of knowledge. In the history of modern philosophy, knowledge is generally held to originate in either one or two sources, and the debates about these sources between philosophers have concerned their existence, or legitimacy. Furthermore, some philosophers have advocated scepticism about the human capacity to understand the origins of knowledge altogether. However, the developmental aspects of knowledge have received relatively little attention (...) both by past philosophers and in current philosophical discussions. This dissertation provides a historical approach to this developmental problem of knowledge by interpreting the developmental theories of knowledge of Maine de Biran (1766–1824) and Henri Bergson (18591941) from the perspective of a theory of the ‘generative factors of knowledge.’ It first studies the philosophies of Maine de Biran and Bergson separately and then brings together and compares the metaphilosophical aims drawn from these philosophers. The dissertation’s novel analysis, provided by its theory and structure, has far-reaching consequences. From a wide point of view, it fills in considerable scholarly gaps and provides great opportunities for future research in the study of the history of philosophy. From more specific points of view, it provides its most decisive contributions in such metaphysical and epistemological topics as the nature of causality, self-generated activity, the role of effort in knowing and learning, the complementary relationship between philosophy and science, and the non-conceptual basis of knowledge. (shrink)

The aim of the article is to present a model of causal relations that is based on what is known about human causal reasoning and that forms guidelines for implementations in robots. I argue for two theses concerning human cognition. The first is that human causal cognition, in contrast to that of other animals, is based on the understanding of the forces that are involved. The second thesis is that humans think about causality in terms of events. I present a (...) two-vector model of events, developed by Gärdenfors and Warglien, which states that an event is represented in terms of two main components – the force of an action that drives the event, and the result of its application. Apart from the causal mapping, the event model contains representations of a patient, an agent, and possibly some other roles. Agents and patients are objects (animate or inanimate) that have different properties. Following my theory of conceptual spaces, they can be described as vectors of property values. At least two spaces are needed to describe an event, an action space and a result space. The result of an event is modeled as a vector representing the change of properties of the patient before and after the event. In robotics the focus has been on describing results. The proposed model also includes the causal part of events, typically described as an action. A central part of an event category is the mapping from actions to results. This mapping contains the central information about causal relations. In applications of the two-vector model, the central problem is how the event mapping can be learned in a way that is amenable to implementations in robots. Three processes are central for event cognition: causal thinking, control of action and learning by generalization. Although it is not yet clear which is the best way to model how the mappings can be learned, they should be constrained by three corresponding mathematical properties: monotonicity (related to qualitative causal thinking); continuity (plays a key role in activities of action control); and convexity (facilitates generalization and the categorization of events). I argue that Bayesian models are not suitable for these purposes, but some more geometrically oriented approach to event mappings should be used. (shrink)

The increasing availability of large‐scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that—while capable of generating predictive knowledge at unprecedented pace and scale—if (...) and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self‐conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine. (shrink)

Osiurak and Reynaud do not explain the evolutionary emergence and development of the elephant in the room, that is, technical cognition. We first argue that there is a tight correlation between the evolution of cumulative technological culture and the evolution of reasoning about abstract forces. Second, intentional teaching plays a greater role in CTC evolution than acknowledged in the target article.

It is widely thought that causal cognition underpins technical reasoning. Here we suggest that understanding causal cognition as a thinking system that includes theory of mind (i.e., social cognition) can be a productive theoretical tool for the field of evolutionary cognitive archaeology. With this contribution, we expand on an earlier model that distinguishes seven grades of causal cognition, explicitly presenting it together with a new analysis of the theory of mind involved in the different grades. We then suggest how such (...) thinking may manifest in the archaeological or stone tool record and techno-behaviors of the last three million years or so. Our thesis is threefold: (a) theory of mind is an integral element of causal cognition; (b) generally speaking, the more advanced causal cognition is, the more it is dependent on theory of mind; and (c) the evolution of causal cognition depends more and more on mental representations of hidden variables. Ultimately, the final or seventh grade of causal cognition allows us to reason from a network of hidden variables that, amongst other things, enables the learning, manufacture, and use of complex technological systems. It also facilitates the seamless mapping of knowledge between personal (egocentric), physical, and social networks that allows for newly devised and innovative technical and social outcomes. (shrink)

Many animal species use tools, but human technical engagement is more complex. We argue that there is coevolution between technical engagement and advanced forms of causal cognition in the human lineage. As an analytic tool, we present a classification of different forms of causal thinking. Human causal thinking has become detached from space and time, so that instead of just reacting to perceptual input, our minds can simulate actions and forces and their causal consequences. Our main thesis is that, unlike (...) the situation for other primate species, an increasing emphasis on technical engagement made some hominins capable of reasoning about the forces involved in causal processes. This thesis is supported in three ways: We compare the casual thinking about forces of hominins with that of other primates. We analyze the causal thinking required for Stone Age hunting technologies such as throwing spears, bow hunting and the use of poisoned arrows, arguing that they may serve as examples of the expansion of casual cognition about forces. We present neurophysiological results that indicate the facilitation of advanced causal thinking. (shrink)