Roberto Cordeschi

Complex systems are to be seen as typically having multiple levels of organization. For instance, in the behavioural and cognitive sciences, there has been a long lasting trend, promoted by the seminal work of David Marr, putting focus on three distinct levels of analysis: the computational level, accounting for the What and Why issues, the algorithmic and the implementational levels specifying the How problem. However, the tremendous developments in neuroscience knowledge about processes at different scales of organization together with the complexity of today cognitive theories suggest that there will hardly be only three levels of explanation. Instead, there will be many different degrees of commitments corresponding to the different granularities—from high-level (behavioural) models to low-level (neural and molecular) models of the cognitive research program. For instance, Bayesian approaches, that are usually advocated for formalizing Marr’s computational level and rational behaviour, have even been adopted to model synaptic plasticity and axon guidance by molecular gradients. As a result, we can consider the behavioural scientist as dealing with models at a multiplicity of levels. The purpose of this Research Topic in Frontiers in Theoretical and Philosophical Psychology is to promote an approach to the role of the levels and explanation and models which is of interest for cognitive scientists, neuroscientists, psychologists, behavioural scientists, and philosophers of science.

Is any unified theory of brain function possible? Following a line of thought dat- ing back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gain- ing the “Grand Unified Theory of the Mind”—or “painting the big picture,” as Edelman (2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with experimental findings and explana- tory capabilities (Edelman, 2012). The point we are making in this note is that a brain with predictive capa- bilities is certainly necessary to endow the agent situated in the environment with forethought or foresight, a crucial issue to outline the unified account advocated by Clark. But the capacity for fore- thought is deeply entangled with the capacity for emotions and when emotions are brought into the game, cogni- tive functions become part of a large-scale functional brain network. However, for such complex networks a consistent view of hierarchical organization in large-scale functional networks has yet to emerge (Bressler and Menon, 2010), whilst heterarchical organization is likely to play a strategic role (Berntson et al., 2012). This raises the necessity of a multilevel approach that embraces causal relations across levels of explanation in either direc- tion (bottom–up or top–down), endorsing mutual calibration of constructs across levels (Berntson et al., 2012). Which, in turn, calls for a revised perspective on Marr’s levels of analysis framework (Marr, 1982). In the following we highlight some drawbacks of Clark’s proposal in address- ing the above issues.

The rise and some more recent developments of the machine-simulation methodology of living-organism behavior are discussed in this paper. In putting forward these issue, my aim is that of isolating recurring themes which help understanding the development of such a machine-simulation methodology, from its, so to speak, discovery during the first half of the twentieth century up to the present time. The machine designed by the engineer S. Bent Russell in 1913 seems to share the core of at least some points of such a methodology. This machine was designed with the aim of embodying certain hypotheses on the plasticity of nervous connections, pointed out at the time by psychologists in order to explain the physical bases of learning. I would like to suggest that this machine might be viewed as a case-study of the discovery of the above mentioned simulative methodology, further on developed by cyberneticians beginning from the 1940s. Certain present-day steps toward such a methodology are briefly touched upon in the concluding section of the paper.

My aim here is to raise a few questions concerning the problem of representation in scientific discovery computer programs. Representation, as Simon says in his paper, "imposes constraints upon the phenomena that allow the mechanisms to be inferred from the data". The issue is obviously barely outlined by Simon in his paper, while it is addressed in detail in the book by Langley, Simon, Bradshaw and Zytkow (1987), to which I shall refer in this note. Nevertheless, their analysis would appear to leave open certain issues related to the nature of the "constraints" imposed by representation on problem solving strategies.

The notion of loop seems to be ubiquitous in the study of organisms, the human mind and symbolic systems. With the possible exception of quantum-mechanical approaches, the treatments of consciousness we are acquainted with crucially appeal to the concept of loop. The uses of loops in this context fall within two broad classes. In the first one, loops are used to express the control of the organism’s interaction with the environment; in the second one, they are used to express self-reference. Both classes are tied with investigations which aim at accounting for symbolic capabilities, and ultimately for subjectivity. Neurophysiological research detects loops in the animal nervous system since its very inception (e.g., all the work on the reflex arc). Recently, Gray proposed a model purporting to explain the mechanism supporting the contents of consciousness in the human CNS, in ways that are practically indistinguishable from models formulated within the cybernetical point of view. Both types of models apply loops in strikingly similar ways. While there is no conclusive evidence that loops are necessary to support consciousness, they are nonetheless as good a candidate as today can be found for inclusion in the list of essential ingredients for subjectivity to arise. In the first two sections we discuss the above mentioned uses by means of significant examples. In the third section, we compare Gray’s recent model to MacKay’s early cybernetical model of a selfobserving system, in the setting of a broader discussion on loops for consciousness.

Since the early eighties, computationalism in the study of the mind has been “under attack” by several critics of the so-called “classic” or “symbolic” approaches in AI and cognitive science. Computationalism was generically identified with such approaches. For example, it was identified with both Allen Newell and Herbert Simon’s Physical Symbol System Hypothesis and Jerry Fodor’s theory of Language of Thought, usually without taking into account the fact ,that such approaches are very different as to their methods and aims. Zenon Pylyshyn, in his influential book Computation and Cognition, claimed that both Newell and Fodor deeply influenced his ideas on cognition as computation. This probably added to the confusion, as many people still consider Pylyshyn’s book as paradigmatic of the computational approach in the study of the mind. Since then, cognitive scientists, AI researchers and also philosophers of the mind have been asked to take sides on different “paradigms” that have from time to time been proposed as opponents of (classic or symbolic) computationalism. Examples of such oppositions are: computationalism vs. connectionism, computationalism vs. dynamical systems, computationalism vs. situated and embodied cognition, computationalism vs. behavioural and evolutionary robotics. Our preliminary claim in section 1 is that computationalism should not be identified with what we would call the “paradigm (based on the metaphor) of the computer” (in the following, PoC). PoC is the (rather vague) statement that the mind functions “as a digital computer”. Actually, PoC is a restrictive version of computationalism, and nobody ever seriously upheld it, except in some rough versions of the computational approach and in some popular discussions about it. Usually, PoC is used as a straw man in many arguments against computationalism. In section 1 we look in some detail at PoC’s claims and argue that computationalism cannot be identified with PoC. In section 2 we point out that certain anticomputationalist arguments are based on this misleading identification. In section 3 we suggest that the view of the levels of explanation proposed by David Marr could clarify certain points of the debate on computationalism. In section 4 we touch on a controversial issue, namely the possibility of developing a notion of analog computation, similar to the notion of digital computation. A short conclusion follows in section 5.

This Research Topic aimed at deepening our understanding of the levels and explanations that are of interest for cognitive sci- entists, neuroscientists, psychologists, behavioral scientists, and philosophers of science. Indeed, contemporary developments in neuroscience and psy- chology suggest that scientists are likely to deal with a multiplicity of levels, where each of the different levels entails laws of behavior appropriate to that level (Berntson et al., 2012). Also, gathering and modeling data at the different levels of analysis is not suffi- cient: the integration of information across levels of analysis is a crucial issue. Given such state of affairs, a number of interesting questions arise. How can the autonomy of explanatory levels be properly understood in behavioral explanation? Is reductionism a satis- factory strategy? How can high-level and low-level models be constrained in order to be actually explanatory of both behav- ioral and neurological or molecular evidence? What is the kind of relationship between those models?

The year 1943 is customarily considered as the birth of cybernetics. Artificial Intelligence (AI) was officially born thirteen years later, in 1956. This chapter is about two theories on human cognitive processes developed in the context of cybernetics and early AI. The first theory is that of the cyberneticist Donald MacKay, in the framework of an original version of self-organizing systems; the second is that of Allen Newell and Herbert Simon (initially with the decisive support of Clifford Shaw) and is known as information-processing psychology (IPP). The latter represents the human-oriented tendency of early AI, in which the three authors were pioneers. I shall not discuss this topic with the intention, common in this type of reconstruction, of seeking contrasts between opposing paradigms (classical AI vs. cybernetics, symbolic vs. subsymbolic, symbolic vs. situated, and so forth). Randall Beer, referring to one of these battles, the ‘‘battle between computational and dynamical ideologies,’’ decried the fact that the subjects usually examined are not ‘‘experimentally testable predictions, but rather competing intuitions about the sort of theoretical framework that will ultimately be successful in explaining cognition.’’ He concluded, ‘‘the careful study of concrete examples is more likely to clarify the key issues than abstract debate over formal definitions’’. I believe this is a conclusion that should be endorsed.

A number of contributions are been given in recent years to illustrate Herbert Simon’s multidisciplinary approach to the study of behaviour. In this chapter, I give a brief picture of the origins of Simon’s bounded rationality in the framework of rising AI. I show then how seminal it was Simon’s insight on the unifying role of bounded rationality in different fields, from evolutionary theory to domains traditionally difficult for AI decision-making, such as those of real-life and real-world problems.

In this paper some applications and methodological developments of mechanical models in psychology in the 1950s are examined. During that period, a new conception of the theory-model relationship in psychology become evident, which had been proposed earlier by the mechanistic trend in psychology in the 1930s. Such a conception allowed psychologists a new approach to many problems in theoretical psychology, such as the role of hypotheses and neurophysiology in psychological explanation and the positions of psychologists concerning neobehavioristic theories of behaviour and the operational method. The ideas of psychologists who accepted mechanical model (and had also been influenced by the advancement of cybernetics) were not homogeneous in that period. Two trend are examined in this paper: the first (in turn not a homogeneous one), which prevailed during the previous decade and was more influenced by cybernetics, was developed during the first half of the 1950s (Deutsch’ machine with insight, Wyckoff model for learning, Broadbent’s model for attention); the second, which originated around the middle of the 1950’s and was dominant in in the next decades, is Newell and Simon’s Information Processing Psychology. Several methodological principles that characterize those two trends are discussed in this paper, along with their similarities and above all their differences.

Aaron Sloman remarks that a lot of present disputes on consciousness are usually based, on the one hand, on re-inventing “ideas that have been previously discussed at lenght by others”, on the other hand, on debating “unresolvable” issues, such as that about which animals have phenomenal consciousness. For what it’s worth I would make a couple of examples, which are related to certain topics that Sloman deals with in his paper, and that might be useful for introducing some comments in the following of this brief note

In this paper I put forward a reconstruction of the evolution of certain explanatory hypotheses on the neural basis of association and learning that are the premises of connectionism in the cybernetic age and of present-day connectionism. The main point of my reconstruction is based on two little-known case studies. The first is the project, published in 1913, of a hydraulic machine through which its author believed it was possible to simulate certain essential elements of the plasticity of nervous connections. The author, S. Bent Russell, was an engineer deeply influenced by the neurological hypotheses on nervous conduction of Herbert Spencer, Max Meyer and Edward L. Thorndike. The second is the project, published in 1929, of an electromechanical machine in which the author, the psychologist J.M. Stephens, believed it was possible to embody Thorndike's law of effect. Thus both Bent Russell and Stephens referred to the principles of learning that Thorndike defined as connectionist . Their attempt was that of simulating by machines at least certain simple aspects of inhibition, association and habit formation that are typical of living organisms. I propose to situate their projects within the frame of thediscovery of a simulative (modelling) methodology which I believe might be considered an important topic of the Culture of the Artificial . Certain more recent steps toward such a methodology made by both connectionism of the 1950s and present-day connectionism are briefly pointed out in the paper

Since the second half of the XXth century, researchers in cybernetics and AI, neural nets and connectionism, Artificial Life and new robotics have endeavoured to build different machines that could simulate functions of living organisms, such as adaptation and development, problem solving and learning. In this book these research programs are discussed, particularly as regard the epistemological issues of the behaviour modelling. One of the main novelty of this book consists of the fact that certain projects involving the building of simulative machine models before the advent of cybernetics are been investigated for the first time, on the basis of little known, and sometimes completely forgotten or unpublished, texts and figures. These pre-cybernetics projects can be considered as steps toward the “discovery” of a modelling methodology that has been fully developed by those more recent research programs, and that shares some of their central goals and key methodological proposals. More info in Springer link: http://www.springer.com/new+%26+forthcoming+titles+%28default%29/book/978-1-4020-0606-7 This book is the English translation of La scoperta dell'artificiale, Dunod/Masson, Milan, 1998.

Heuristic programming was the first area in which AI methods were tested. The favourite case-studies were fairly simple toy- problems, such as cryptarithmetic, games, such as checker or chess, and formal problems, such as logic or geometry theorem-proving. These problems are well-defined, roughly speaking, at least in comparison to real-life problems, and as such have played the role of Drosophila in early AI. In this chapter I will investigate the origins of heuristic programming and the shift to more knowledge-based and real-life problem solving.

The expression ‘‘artificial intelligence’’ (AI) was introduced by John McCarthy, and the official birth of AI is unanimously considered to be the 1956 Dartmouth Conference. Thus, AI turned fifty in 2006. How did AI begin? Several differently motivated analyses have been proposed as to its origins. In this paper a brief look at those that might be considered steps towards Dartmouth is attempted, with the aim of showing how a number of research topics and controversies that marked the short history of AI were touched on, or fairly well stated, during the year immediately preceding Dartmouth. The framework within which those steps were taken was the development of digital computers. Earlier computer applications in areas such as complex decision making and management, at that time dealt with by operations research techniques, were important in this story. The time was ripe for AI’s intriguingly tumultuous development, marked as it has been by hopes and defeats, successes and difficulties.

In this paper we will illustrate some of the technological, ethical and political issues, under discussion at present. These were raised by Wiener from the 1940s on, and include the responsibility of the scientist in war, decisions on the exploitation of technological innovations, copyright issues raised by communication technologies, the social control made possible by digital devices, and the inspirational role of research in some critical areas of social development. We will try to obtain some insights into these issues from Wiener’s viewpoint

In this paper several reformulations of William Ross Ashby and Norbert Wiener’s classical claims on purposive behavior are examined. Next restatements of this issue are then discussed, particularly as regards the following question: is it possible to extend the concepts and methods of mechanical (physical) explanation to psychological explanation, in order to explain human (and animal) purposive behavior? This question was restated in the 1950s as follows: are negative feedback and homeostatic mechanisms really explanatory of adaptive and purposive behavior, or are they tautological re-formulations of older notions? Another notion of adaptive and purposive behavior is also examined in this paper, which was developed in that very period by Herbert A. Simon.

The early examples of self-directing robots attracted the interest of both scientific and military communities. Biologists regarded these devices as material models of animal tropisms. Engineers envisaged the possibility of turning self-directing robots into new “intelligent” torpedoes during World War I. Starting from World War II, more extensive interactions developed between theoretical inquiry and applied military research on the subject of adaptive and intelligent machinery. Pioneers of Cybernetics were involved in the development of goal-seeking warfare devices. But collaboration occasionally turned into open dissent. Founder of Cybernetics Norbert Wiener, in the aftermath of World War II, argued against military applications of learning machines, by drawing on epistemological appraisals of machine learning techniques. This connection between philosophy of science and techno-ethics is both strengthened and extended here. It is strengthened by an epistemological analysis of contemporary machine learning from examples; it is extended by a reflection on ceteris paribus conditions for models of adaptive behaviours.

In this article, I shall examine some of the issues and questions involved in the technology of autonomous robots, a technology that has developed greatly and is advancing rapidly. I shall do so with reference to a particularly critical field: autonomous military robotic systems. In recent times, various issues concerning the ethical implications of these systems have been the object of increasing attention from roboticists, philosophers and legal experts. The purpose of this paper is not to deal with these issues, but to show how the autonomy of those robotic systems, by which I mean the full automation of their decision processes, raises difficulties and also paradoxes that are not easy to solve. This is especially so when considering the autonomy of those robotic systems in their decision processes alongside their reliability. Finally, I would like to show how difficult it is to respond to these difficulties and paradoxes by calling into play a strong formulation of the precautionary principle