The central concept of this edited volume is "blended cognition", the natural skill of human beings for combining constantly different heuristics during their several task-solving activities. Something that was sometimes observed like a problem as “bad reasoning”, is now the central key for the understanding of the richness, adaptability and creativity of human cognition. The topic of this book connects in a significant way with the disciplines of psychology, neurology, anthropology, philosophy, logics, engineering, logics, and AI. In a nutshell: understanding better humans for designing better machines. It contains a Preface by the editors and 12 chapters

Müller, Vincent C.

Vallverdú, Jordi

English

PhilPapers

Springer Series in Cognitive and Neural Systems 12Blended CognitionJordi VallverdúVincent C. Müller EditorsThe Robotic ChallengeSpringer Series in Cognitive and Neural SystemsVolume 12Series EditorVassilis Cutsuridis, School of Computer Science, University of Lincoln,Lincoln, UKvmueller@act.eduMore information about this series at http://www.springer.com/series/8572vmueller@act.eduJordi Vallverdú • Vincent C. MüllerEditorsBlended CognitionThe Robotic Challenge123vmueller@act.eduEditorsJordi VallverdúUniversitat Autònoma de BarcelonaBellaterra (Cerdanyola del Vallès)Catalonia, SpainVincent C. MüllerAmerican College of ThessalonikiThessaloniki, GreeceISSN 2363-9105 ISSN 2363-9113 (electronic)Springer Series in Cognitive and Neural SystemsISBN 978-3-030-03103-9 ISBN 978-3-030-03104-6 (eBook)https://doi.org/10.1007/978-3-030-03104-6Library of Congress Control Number: 2019930998© Springer Nature Switzerland AG 2019Chapter 8: This is a U.S. government work and not under copyright protection in the U.S.; foreigncopyright protection may apply 2019This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part ofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,broadcasting, reproduction on microfilms or in any other physical way, and transmission or informationstorage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodologynow known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoes not imply, even in the absence of a specific statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.The publisher, the authors, and the editors are safe to assume that the advice and information in this bookare believed to be true and accurate at the date of publication. Neither the publisher nor the authors orthe editors give a warranty, express or implied, with respect to the material contained herein or for anyerrors or omissions that may have been made. The publisher remains neutral with regard to jurisdictionalclaims in published maps and institutional affiliations.This Springer imprint is published by the registered company Springer Nature Switzerland AG.The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerlandvmueller@act.eduPrefaceIntroduction: Blended CognitionTwo views on natural and artificial cognition seem to be locked in a contesttoday: the naturalistic, bottom-up and embodied/enactive/extended/morphologicalapproach to cognitive systems vs. formal, top-down, and highly symbolic/statisticalapproaches. Facing these traditional battles, we suggest an ambitious hypothesis:these two views are not only biased, but are also theoretically overrated—there is acoherent middle way.Embodiment contains, in its own structure, a syntax that expresses specificmethods of semantics; while pure symbolic systems are not able to generate meta-discourses. Eventually both views converge at the same problem: how to deal withthe multiplicity of strategies in cognition, and the choice between these strategies.The main idea of the present volume is to ask experts from different fields (e.g.,engineering, logic, anthropology, computer sciences, and psychology) questions onhow to design machines that are able to decide on combining multiple heuristics, orto explain how humans are able to make these decisions. This ability appears to befundamental for many cognitive skills in humans.This volume is a serious attempt to face both challenges: to study the pros andcontras of morphology and symbolic processing without considering how both aredeeply intertwined in decision-making. The structure (physical or formal) in whicha reasoning process is performed shapes the individual’s own reasoning, and this isnever completely perfect (e.g., because of lack of data, inaccurate data, wrong dataintegration, misleading reasoning formalization, and changing conditions), but canbe, at least, designed to satisfy minimal requirements. The natural way of integratingdifferent heuristics and reasoning strategies is a good example of how adaptabilityprovides some benefits, while it produces some ‘biases’. For example, at a certainpoint in decision-making procedures, we need to choose between the accuracy andthe speed of the answer. And even this process can vary depending on whetherwe are considering the performance of individual tasks or, rather, whether we arelooking at social-network joint collaborative work, which can be either not directlyvvmueller@act.eduvi Prefacesupervised, as happens with swarm intelligence, or supervised and coordinated, ashappens in human hierarchical collaborative actions. Humans do not act as rationalagents, and think in different ways according their individual conditions: such aswhether they are alone, sad, as members of a crowd, or excited. This vast rangeof ways of combining even imprecise or unconventional reasoning styles is ofthe greatest interest for the future of artificial intelligence (AI) and the design ofartificial general intelligence (AGI).This book introduces a new concept to cognitive sciences research: blendedcognition, a concept that will contribute to the design of more realistic andefficient robots. After the extensive introduction of recent ideas into the field, suchas extended, embodied, enactive, grounded, or morphological cognition (amongseveral others), it may seem unnecessary to introduce a new category. But the truthis the opposite: we need a new conceptual space from which we can be able tounderstand real complex cognitive systems, such as human ones. Looking at thedaily decision-making procedures undertaken by human beings, we can observe anunprincipled mixture of methods, as well as many intuition-driven actions. Whatcan be affirmed is that humans blend and combine several kinds of heuristics,consciously or not, at symbolic or sensorimotor levels. This blending can occurin parallel or procedurally (one step at a time, completely sequential, or usingturns/changes/dead ends).Let us introduce the idea with an example: a young researcher who buys a cupof coffee. She has a headache and decides to walk to the coffee machine, she looksinto her pockets (in fact, she feels with her fingers), grasping some coins while sheis looking at the number code of the milk coffee. Then she inserts the coins intothe machine, selects the desired option, and waits in front of the machine, thinkingabout what she has in the fridge for dinner. A characteristic smell makes her awareof the fact that the coffee is ready. She takes the cup, which is too hot to handleand must be changed from hand to hand every few seconds, and moves toward theoffice, but her phone rings; it is her best friend, who has a serious problem. Shelooks for a place to put down the hot coffee cup and meanwhile she downloadssome files, checks them, and then decides to forward some to her friend. Afterthat she carefully picks up the coffee and comes to the office. In this examplesome basic sensorimotor processes have been required to allow her movements;among them are object identification and selection (tactile as well as visual andauditory), action planning, object grasping, context evaluation, parallel actions, andmental calculations; some of the movements have followed automated processes(e.g., walking, looking at the pathway, pain arousal, and object identification andavoidance), while some of the movements have required several decisions (holdingthe cup alternatively in each hand until the required task was performed could lead toskin damage, and she decides to put the cup on a stable surface), as well as cognitiveheuristics (how to solve the friend’s problem while pending duties are also waitingfor her at her desk and the headache needs resolution). She has undertaken all theseprocesses, and more not described here, without falling to the ground, collapsing, orfeeling blocked (some possible situations also experienced by stressed humans). Ourvmueller@act.eduPreface viianalysis is that she has blended several heuristics and strategies in order to satisfythe demands of the actions she must solve.Humans combine several heuristics and even think in meta-heuristic proceduresin order to achieve their necessities. Induction, common sense, analogy, syllogisticprocedures, abduction, Bayesian statistics, classic logical reasoning, frequentiststatistics, imitation, likelihoodism, amodal thinking, non-monotonic logics, deduc-tion, algorithmic rules, moral codes, attribute substitution, fuzzy logics, swarmreactions, the availability heuristic, the representativeness heuristic, and the an-choring heuristic—among a very long list of possible strategies or heuristics—areused indistinctively, being combined into a flowchart of possibilities while an actionis required to be solved by the experiencer. The uses of these possible strategiesor heuristics will, seemingly, vary according to the external and internal contextof the human. From sensorimotor to symbolic, from intuitive to highly formalized,and from conscious to unconscious processes, performed sequentially or in parallel,humans select from among a long list of options in order to solve complex tasks.This process is what we call blended cognition.The design of modern robotics and AI cannot be placed under the umbrellaof a single and over-simplistic research field: good old-fashioned AI (GOFAI)is not worse or better at all possible scenarios than embodied or morphologicalapproaches. It is true that the symbolic framework requires a bodily revolution,exemplified by the new ideas provided by embodied, enactive, grounded, orextended cognition. And that, after the alternate dominion of one of both sides (themental and the corporal), it is time to be pragmatic and try to design new strategiesto implement multi-heuristic procedures.We will need to define a subsumption architecture and layer-managing systemthat makes possible this jump and combination between possible heuristics. At thesame time, a discretization of possible flows of actions will be required. This processforces us to think about a grid of procedures, mechanisms, heuristics, and codes thatare combined at different places according to the necessities and skills of the agent.Blended cognition is, thus, the study of how an intelligent system can use oreven partially combine several methods to decide among possible action outputsor data evaluation and storage. Here, there is a combination of possible dataand task demands: semantics-body-mind. By ‘semantics’ we mean the value ofinformation at a specific moment for the agent; by ‘body’ we mean the bodilyrequirements and possibilities (DOF (Degrees of Freedom), for example, flexibilityand impact absorption) that the agent exhibits; and by ‘mind’ we refer to theheuristics mechanisms designed to give answers to the data flows. The importanceof blended cognition is that there is no pre-established and rigid hierarchy of controlamong these possible main layers, or among their sub-layers. There are optimizedfunctional strategies of agreement and combination, but the key point here is theflexibility and adaptability of the system.Thanks to the collaboration of experts from a very broad range of academic fields,we have been able to create this book about blended informational processes. Afterthe introduction by the editors, Professor Jordi Vallverdú explains the conceptualnature of the concept of blended cognition, following a naturalistic approach tovmueller@act.eduviii Prefacemulti-heuristic reasoning that can be applied to AI. Chapter 10 is a fundamentalanthropological view of the naturalistic analysis of activity-based AI. Writtenby Corentin Chanet and David Eubelen, this chapter connects anthropologicalknowledge with possible ways of modeling AI. In Chap. 5, Gabriele Ferretti andEris Chinellato propose an embodied model for neurorobotics. After this embodiedapproach, Chap. 6 suggests a specific way of implementing a bioinspired modelusing neurosimulation and emotional mechanisms; from an engineering perspective,Max Talanov, Alexey Leukhin, Fail Gafarov, and Jordi Vallverdú suggest a complexcomputational model that is very close to physical implementation via memristors.From the psychological research, Kay-Yut Chen and Daniel S. Levine explore theheuristics of numerical choice, adding more evidence in Chap. 9 about multiplereasoning strategies used by humans. Chapter 2 explores, with Professor LorenzoMagnani, the possible connections between abduction and blended cognition, afield to be explored in more detail in the future. Chapter 7, by Qiang Zhang,Stef van der Struijk, and Toyoaki Nishida, analyzes the challenges of cognitiverobotics, taking into account this multi-tasking problem and the presence of severalheuristics. Robert Earl Patterson and Robert G. Eggleston, from the United StatesAir Force, make a sound contribution in Chap. 8, exploring the ways of blendingthe cognitions of humans and autonomous machines. In Chap. 3, Professor DouglasWalton constructs a deep analysis of the formal mechanisms present in practicalreasoning in the deliberations of intelligent autonomous systems. Chapter 11,written by Professor Pei Wang, constitutes a fundamental logical analysis of thelogics of everyday reasoning, something that is of the utmost interest for robots,which must operate in complex and diverse ‘trivial’ (for humans) contexts. Finally,Chap. 4 is a theoretical exploration, by Vassilis Galanos, of possible theoreticalaspects of increasing intelligence automation to consider in the near future.Bellaterra (Cerdanyola del Vallès), Catalonia, Spain Jordi VallverdúThessaloniki, Greece Vincent C. Müllervmueller@act.eduContentsPart I Philosophy1 Blended Cognition: The Robotic Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Jordi Vallverdú2 Manipulative Abduction, External Semiotic Anchors, andBlended Cognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Lorenzo Magnani3 Practical Reasoning in the Deliberations of an IntelligentAutonomous Agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Douglas Walton4 Blended Automation: The Language-Game of PsychoanalyticAutomatism and Cybernetic Automata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85Vassilis GalanosPart II Engineering5 Can Our Robots Rely on an Emotionally ChargedVision-for-Action? An Embodied Model for Neurorobotics . . . . . . . . . . . 99Gabriele Ferretti and Eris Chinellato6 Modeling Psycho-Emotional States via Neurosimulation ofMonoamine Neurotransmitters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Max Talanov, Alexey Leukhin, Hugo Lövheim, Jordi Vallverdú,Alexander Toschev, and Fail Gafarov7 Towards Conversation Envisioning for Cognitive Robots . . . . . . . . . . . . . . 157Maryam Sadat Mirzaei, Qiang Zhang, Stef van der Struijk,and Toyoaki Nishidaixvmueller@act.edux ContentsPart III Psychology8 The Blending of Human and Autonomous-Machine Cognition . . . . . . . 199Robert Earl Patterson and Robert G. Eggleston9 Heuristics of Numerical Choice in Economic Contexts . . . . . . . . . . . . . . . . 217Kay-Yut Chen and Daniel S. LevinePart IV Anthropology10 Towards Autonomous Artificial Agents? Proposal fora Naturalistic Activity-Based Model of (Artificial) Life . . . . . . . . . . . . . . . . 245Corentin Chanet and David EubelenPart V Logics11 Toward a Logic of Everyday Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275Pei WangIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303vmueller@act.edu

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