Technology led to more abstract causal reasoning

Many animal species use tools, but human technical engagement is more complex. We argue that there is coevolution between technical engagement (the manufacturing and use of tools) and advanced forms of causal cognition in the human (Homo) 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: (1) We compare the casual thinking about forces of hominins with that of other primates. (2) 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. (3) We present neurophysiological results that indicate the facilitation of advanced causal thinking.https://link.springer.com/article/10.1007/s10539-020-09757-zCITATION: Gardenfors, P. & Lombard, M. 2020. Technology led to more abstract causal reasoning. Biology & Philosophy, 35:40, doi:10.1007/s10539-020-09757-z.The original publication is available at https://link.springer.comPublisher's versio

Robot task planning and explanation in open and uncertain worlds

A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization

Converting Endangered Species Categories to Probabilities of Extinction for Phylogenetic Conservation Prioritization

Categories of imperilment like the global IUCN Red List have been transformed to probabilities of extinction and used to rank species by the amount of imperiled evolutionary history they represent (e.g. by the Edge of Existence programme). We investigate the stability of such lists when ranks are converted to probabilities of extinction under different scenarios.Using a simple example and computer simulation, we show that preserving the categories when converting such list designations to probabilities of extinction does not guarantee the stability of the resulting lists.Care must be taken when choosing a suitable transformation, especially if conservation dollars are allocated to species in a ranked fashion. We advocate routine sensitivity analyses

An Epigenetic Approach to Semantic Categories

It is argued that early language learning in children emerges from five primary knowledge structures: Space, objects, actions, number and events. These structures constitute the basis for the semantic domains that are used to form categories that represent the meanings of early words. The domains are naturally modeled in conceptual spaces that are based on geometric notions rather than on symbolic representations. It is shown how these semantics domains can be used to generate an epigenetic model of language acquisition. The four first primary knowledge systems are used for prepositions, nouns, verbs and quantifiers respectively, while events form the meanings of declarative sentences. This semantic model leads to direct recommendations for how the model can be implemented in robots and other artificial systems

Demonstration and pantomime in the evolution of teaching

CITATION: Gardenfors, P. 2017. Demonstration and pantomime in the evolution of teaching. Frontiers in Psychology, 8:415, doi:10.3389/fpsyg.2017.00415.The original publication is available at https://www.frontiersin.orgDonald proposes that early Homo evolved mimesis as a new form of cognition. This article investigates the mimesis hypothesis in relation to the evolution of teaching. The fundamental capacities that distinguish hominin teaching from that of other animals are demonstration and pantomime. A conceptual analysis of the instructional and communicative functions of demonstration and pantomime is presented. Archaeological evidence that demonstration was used for transmitting the Oldowan technology is summarized. It is argued that pantomime develops out of demonstration so that the primary objective of pantomime is that the onlooker learns the motoric patterns shown in the pantomime. The communicative use of pantomime is judged to be secondary. This use of pantomime is also contrasted with other forms of gestures. A key feature of the analysis is that the meaning of a pantomime is characterized by the force patterns of the movements. These force patterns form the core of a model of the cognitive mechanism behind pantomime. Finally, the role of pantomime in the evolution of language is also discussed.https://www.frontiersin.org/articles/10.3389/fpsyg.2017.00415/fullPublisher's versio