A mind is a functional entity that can think, and thus support intelligent behavior. Humans possess minds, as do many other animals. In natural systems such as these, minds are implemented through brains, one particular class of physical device. However, a key foundational hypothesis in artificial intelligence is that minds are computational entities of a special sort -that is, cognitive systems -that can be implemented through a diversity of physical devices (a concept lately reframed as substrate independence [Bostrom 2003 ]), whether natural brains, traditional generalpurpose computers, or other sufficiently functional forms of hardware or wetware. Artificial intelligence, cognitive science, neuroscience, and robotics all contribute to our understanding of minds, although each draws from a different perspective in directing its research. Artificial intelligence concerns building artificial minds, and thus cares most for how systems can be built that exhibit intelligent behavior. Cognitive science concerns modeling natural minds, and thus cares most for understanding cognitive processes that generate human thought. Neuroscience concerns the structure and function of brains, and thus cares most for how minds arise from brains. Robotics concerns building and controlling artificial bodies, and thus cares most for how minds control such bodies. Will research across these disciplines ultimately converge on a single understanding of mind, or will the result be a large but structured space of possibilities, or even a cacophony of approaches? This is a deep scientific question to which there is as yet no answer. However, there must at least be a single answer for cognitive science and neuroscience, as they are both investigating the same mind, or narrow class of minds, albeit at different levels of abstraction. Biologically, or cognitively, or psychologically inspired research in artificial intelligence and robotics also may fit within this particular class of minds, particularly if the class is slightly abstracted; but so may other work that has no aspiration to such inspiration yet still finds itself in the same neighborhood for functional reasons. This broader class comprises what can be called humanlike minds, with an overall focus more on the bounded rationality hypothesized to be central to human cognition (Simon 1957; Anderson 1990 ) than on the optimality that is the focus in much of artificial intelligence and robotics. The class is broader than the more familiar one of naturally inspired minds, as it also includes both natural minds and some artificial minds that are not necessarily naturally inspired yet functionally related. However, it is narrower in scope than human-level intelligence, as it excludes minds that are sufficiently inhuman in how they achieve this level of intelligence. The purpose of this article is to begin the process of engaging the international research community in developing what can be called a standard model of the mind, where the mind we have in mind here is humanlike. The notion of a standard model has its roots in physics, where for over more than a half-century the international community has developed and tested a standard model that combines much of what is known about particles. This model is assumed to be internally consistent, yet still have major gaps. Its function is to serve as a cumulative reference point for the field while also driving efforts to both extend and break it. As with physics, developing a standard model of the mind could accelerate work across the relevant disciplines by providing a coherent baseline that facilitates shared cumulative progress. For integrative researchers concerned with modeling entire minds, a standard model can help focus work on differences between particular approaches and the standard model, and on how to both extend and break the model. Also, instead of each such researcher needing to describe all the assumptions and constraints of their particular approach from scratch, given the standard model they can simply state how their own approach differs from it. Tables 1 and 2 in the summary of this article, for example, specify the standard model developed in this article and the standing of three distinct approaches with respect to it. In this process, the standard model itself could serve as something of an interlingua or shared ontology, providing a vehicle for mapping the common aspects, and possibly uncommon terminology, of disparate architectures onto a common base. For theoretical and systems researchers who model/build specific components of mind -whether learning, memory, reasoning, language -a standard model can provide guidance when they seek to expand to include aspects of other components. For experimental researchers who tease out the details of how natural minds and brains work, a standard model can provide top-down guidance in interpreting the results, as well as suggesting new experiments that may be worth trying. For all researchers, a standard model can serve as a framework around which data that is used in evaluating single components or combinations of components may be organized and made available for use by the community; potentially growing to yield standard tests and testbeds. A standard model can also provide a sound basis for guiding practitioners in constructing a broad range of intelligent applications. The intent, at least for the foreseeable future, is not to develop a single implementation or model of mind by which everyone concerned with humanlike minds would abide, or even a theory in which all of the details are agreed to as correct. What is sought though is a statement of the best consensus given the community's current understanding of the mind, plus a sound basis for further refinement as more is learned. Much of the existing work on integrative models of mind focuses on implementations rather than theory, with too little interchange or synthesis possible across these implementations. The development of a standard model provides an opportunity for the community to work together at a more abstract level, where such interchange and synthesis should be more practicable. For this to transpire though will depend on researchers within the community being interested in relating their own approaches to the standard model and participating in its further evolution. In the process, it is fully expected that they will disagree with some aspects of the standard model presented here, leading ideally to efforts to either disprove or improve parts of it. It is also expected that the standard model will be incomplete in significant ways, not because those parts that are left out are unimportant, but because an adequate consensus on them has not yet been achieved. Omission from the standard model is thus often a statement of where a consensus is needed, rather than a consensus on a lack of either existence or importance. Although the boundary around the class of humanlike minds is ill defined, at least at present, we do anticipate an evolving dialogue around this, driven by a sequence of challenges from ideas and data that conflict in substantive ways with the standard model. For each such challenge, it will be critical to determine whether the consensus is ultimately that the standard model should be altered -either changed to eliminate the conflict or abstracted to cover both old and new approaches -or that the new ideas or data should be deemed insufficiently humanlike, and thus outside of the class of interest. These will not necessarily be easy decisions, nor will the process as a whole be smooth, but the potential rewards for succeeding are real. This article grew out of the 2013 AAAI Fall Symposium on Integrated Cognition that was initiated by two of us to bring together researchers across a set of disparate perspectives and communities concerned with an integrated view of human-level cognition (Burns et al. 2014) . The full organizing committee included representatives from cognitive science, cognitively and biologically inspired artificial intelligence, artificial general intelligence, and robotics. The final activity during the symposium was a panel on Consensus and Outstanding Issues, at which two of us presented and the third participated. One of these presentations led to the startling finding that the wide range of researchers in the room at the time agreed that the content of the presentation was an appropriate consensus about the current state of the field. Given the field's history of stark differences between competing approaches, neither of the initiators of the symposium had anticipated this as a realistic outcome, and when it occurred, it startled those in attendance. It implied that a consensus had implicitly begun to emerge -perhaps signaling the dawning maturity of the field -and that an attempt to make it explicit could provide significant value. This attempt is what fills the remainder of this article. The next section covers important background that largely predates the 2013 symposium and this effort, including several notable precursors to the concept of a standard model of the mind plus the critical notion of a cognitive architecture -a hypothesis about the fixed structure of the mind -which is at the heart of this attempt. We then introduce three cognitive architectures on which the effort here focused. That section is followed by a presentation of the proposed standard model that has been developed. In the summary section, we review what has been accomplished, including a précis of the proposed standard model, an analysis of where the same three cognitive architectures sit with respect to it, and a discussion of where we hope it will lead.
A number of the goals of that effort were similar to those identified for the CMC; however, the result was more of a pastiche than a consensus assembling disparate pieces from across these architectures rather than identifying what is common among them REF .
A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics
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