A Standard Model of the Mind: Toward a Common Computational Framework Across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics

Abstract

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 human-like. The notion of a standard model has its roots in physics, where 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.

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A Standard Model of the Mind:
Toward a Common Computational Framework across Artificial Intelligence, Cognitive
Science, Neuroscience, and Robotics
Abbreviated Title: A Standard Model of the Mind
John E. Laird (University of Michigan), Christian Lebiere (Carnegie Mellon University),
and Paul S. Rosenbloom (University of Southern California)
Abstract
A standard model captures a community consensus over a coherent region of science, serving as a
cumulative reference point for the field that can provide guidance for both research and applications,
while also focusing efforts to extend or revise it. Here we propose developing such a model for human-
like minds, computational entities whose structures and processes are substantially similar to those found
in human cognition. Our hypothesis is that cognitive architectures provide the appropriate computational
abstraction for defining a standard model, although the standard model is not itself such an architecture.
The proposed standard model began as an initial consensus at the 2013 AAAI Fall Symposium on
Integrated Cognition, but is extended here via a synthesis across three existing cognitive architectures:
ACT-R, Sigma, and Soar. The resulting standard model spans key aspects of structure and processing,
memory and content, learning, and perception and motor; highlighting loci of architectural agreement as
well as disagreement with the consensus while identifying potential areas of remaining incompleteness.
The hope is that this work will provide an important step towards engaging the broader community in
further development of the standard model of the mind.
Keywords: Standard model, cognitive architecture, artificial intelligence, cognitive science, robotics,
neuroscience
1. Introduction
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 via 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 via a diversity of physical devices (a concept lately reframed as substrate independence,
Bostrom 2003), whether natural brains, traditional general-purpose 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 their 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.
Laird,!J.!E.,!Lebiere,!C.!&!Rosenbloom,!P.!S.!(2017).!A!Standard!Model!for!the!Mind:!Toward!a!Common!Computational!Framework!across!
Artificial!Intelligence,!Cognitive!Science,!Neuroscience,!and!Robotics,!AI!Magazine!38(4).!
https://doi.org/10.1609/aimag.v38i4.2744
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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/cognitively/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 human-like 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
human-like. 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 Section 5, 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, etc. – 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.

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The intent, at least for the foreseeable future, is not to develop a single implementation or model of mind
by which everyone concerned with human-like 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 human-like 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 human-like, 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. Section 2 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. Section 3 introduces three
cognitive architectures on which the effort here focused. Section 4 presents the proposed standard model
that has been developed. Section 5 summarizes what has been accomplished, including a précis of the

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proposed standard model, an analysis of where the same three cognitive architectures sit with respect to
it, and a discussion of where it will hopefully lead.
2. Background
This attempt at a standard model of the mind, although originating at the 2013 symposium, did not
spring there from nothingness; and Allen Newell was at the root of much of what came before. One
notable precursor from three decades earlier is the model human processor (Card, Moran & Newell
1983), which defines an abstract model of structural and timing regularities in human perceptual,
mental, and motor processes. It supports predicting approximate timings of human behavior, but does
not include any details of the underlying computational processes.
A second, albeit rather different, precursor is Newell’s (1990) analysis of how scale counts in cognition.
Newell observed that human activity can be classified according to different levels of processing, and
grouped by timescales at twelve different orders of magnitude, starting with 100 μs and extending up to
months. While the many disciplines that have studied the nature of the mind have focused on different
collections of levels, this analysis provides a coherent framework for integrating research into
phenomena and mechanisms at different time scales. As with the notion of a standard model, this echoes
the situation in physics, and in fact, all of the physical sciences and beyond, where the core phenomena
of interest stratify according to time (and length) scales that when combined can yield models of more
complex multi-scale phenomena.
Newell grouped these levels into four bands: biological, cognitive, rational, and social. The lowest,
biological, band corresponds to the timescale of processing for individual neurons and synapses, the
functional building blocks of the human brain that have been the focus of neuroscience research. The
next two bands up, the cognitive and rational bands span activity from approximately 100 ms to hours,
covering the levels that have been studied by cognitive science as well as traditional AI research in
reactive behavior, goal-directed decision making, natural language processing, planning, and so on. The
highest, social band includes such higher-order capabilities as Theory of Mind, organizational behavior,
and moral and ethical reasoning (as, for example, discussed from different perspectives in two articles in
this special issue – Scheutz 2017, and Bello & Bridewell 2017). What this hierarchy suggests, and what
is borne out in the diversity of research in disciplines such as neuroscience, psychology, AI, economics,
sociology, and political science, is that there are regularities at multiple time scales that are productive
for understanding the mind.
For humans, the deliberate act level, at 100 ms, is roughly at the time scale of a simple reaction,
although the roughness here obscures the fact that even simple reactions involve multiple internal
processes, including perception, cognition, and action. More broadly, the deliberate act level is where
elementary operations are selected and applied. Fundamental to this level and all levels above, is the
assumption that computational capabilities similar to a physical symbol system are available.
The physical symbol systems hypothesis states, “A physical symbol system has the necessary and
sufficient means for general intelligent action.” (Newell & Simon 1976). However, in a break with
tradition, the standard model does not assume that computation at the deliberate act level is purely or
perfectly symbolic. We know from the computational universality of symbol systems that
they are logically sufficient; however, considerable evidence suggests that many types of reasoning that
must be directly available at the deliberate act level, such as statistical and spatial, are best realized there
via non-symbolic processing.

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In the standard model, the critical feature of symbols is that they are the primitive elements over
which relations can be defined, and where their use across multiple relations enables the creation of
complex symbol structures, including (but not limited to) structures such as semantic networks,
ontologies, and taxonomies. This use mirrors the binding problem in cognitive neuroscience, which is
concerned with how multiple elements can be associated in a structured manner (Treisman 1996).
However, the model is agnostic as to whether symbols are uninterpreted labels, such as in Lisp, Soar
(Laird 2012), and ACT-R (Anderson 2007), or whether they are patterns over vectors of distributed
elements, such as semantic pointers in Spaun (Eliasmith et al. 2012) and holographic vectors in HDM
(Kelly et al. 2015), or whether both are available, such as in Clarion (Sun 2016) and Sigma
(Rosenbloom, Demski, & Ustun 2016a). What is important is that they provide the necessary
functionality to represent and manipulate relational structures.
In the standard model, non-symbolic (i.e., numeric) information has two roles. One is to represent
explicitly quantitative task information, such as distances in spatial reasoning or times in temporal
reasoning. The second is to annotate the representations of task information (symbolic and
nonsymbolic) in service of modulating how it is processed. This second type of numeric information
takes the form of (quantitative) metadata; that is, (numerical) data about data.
The mind then clearly comprises at least everything from the deliberate act level up; that is, the top three
bands in Newell’s hierarchy. Many conceptions of the mind, however, also include some portion of the
biological band as well, whether in terms of an abstract neural model, or a close cousin such as a
graphical model (Koller & Friedman 2009). Whether or not a portion of the biological band is included
in the conceptualization, a model of the fixed structure at the deliberate act level, that defines a symbol
system and more, is called a cognitive architecture. While models of the mind can be defined at
different levels, we have situated ours at the deliberate act level because we believe that it represents a
critical juncture between the neural processes that underlie it and the (boundedly) rational computations
that it gives rise to. The standard model we are striving for here amounts to a consensus on what must be
in a cognitive architecture in order to provide a human-like mind.
In a significant break from much of the early work on cognitive architectures, this standard model
involves a hybrid combination of symbolic and statistical processing to match the need introduced
earlier for statistical processing in the architecture, rather than retaining a purely symbolic model of
processing. In consequence, it also embodies forms of statistical learning, including Bayesian and
reinforcement learning. It furthermore embraces significant amounts of parallelism both within modules
and across them, while still retaining a serial bottleneck, rather than being strictly serial. Further
explanations of these shifts, along with the remaining assumptions that define the standard model, can
be found in Section 4.
Typical research efforts on cognitive architectures (Langley, Laird, & Rogers 2009) are concerned with
much more than just the architectural level – and thus may be more appropriately thought of as
developing more comprehensive cognitive systems – although none has yet spanned the entire hierarchy.
Often they start with one level, or a few, but over time expand, becoming multi-year – or even multi-
decade – research programmes (Lakatos 1970) that span larger and larger sequences of levels. However,
the standard model will not come anywhere near to providing a direct model of the entire mind. If we
again look to the situation in physics, the standard model there is also not a direct model of the entire

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physical world, focusing as it does only on the relatively low level of particles. Still it provides a critical
foundation for the levels above it, up to and including the full universe (or multiverse), while being
firmly grounded in, and constrained by, the levels below it. The standard model of the mind likewise
directly concerns only one level, but in so doing provides a critical foundation for the higher levels of
the mind, while being firmly grounded in, and constrained by, the levels below.
With respect to the higher levels of the mind, there is an ancillary hypothesis to the standard model that
they are defined purely by the knowledge and skills that are acquired and processed by the architecture.
In simple terms, the hypothesis is that intelligent behavior arises from a combination of an
implementation of a cognitive architecture plus knowledge and skills. Processing at the higher levels
then amounts to sequences of these interactions over time. Even complex cognitive capabilities – such as
natural language processing (as, for example, discussed in another article in this special issue –
McShane 2017) and planning – are hypothesized to be constructed in such a fashion, rather than existing
as distinct modules at higher levels. Specific mechanisms can sometimes be decomposed at multiple
levels: for example, Forbus and Hinrichs’ (2017, this issue) analogy process can be decomposed into a
SME mechanism located at least partly at the deliberate act level, together with attendant search
processes such as MAC/FAC and SAGE that operate at higher levels and could be decomposed into
primitive acts.
The lower levels of the mind – in the biological band or its artificial equivalent – both implement and
constrain the cognitive architecture. As the hierarchy shows, the concept of a cognitive architecture, and
thus a standard model, need not be incompatible with neural modeling. Moreover, there is potential not
only for compatibility, but also for useful complementarity. Aspects of neural processing, such as
generalization from distributed representations, have been captured in cognitive architectures in the form
of subsymbolic statistical mechanisms. Conversely, the standard model can define an architectural
structure that can be beneficial in organizing and supplementing mechanisms such as deep learning
when, for example, the need is recognized to move beyond the simple memory capabilities provided by
feedforward or recurrent neural networks (e.g., Vinokurov et al. 2012). Furthermore, the traditional
notion of a fixed cognitive architecture has always been tempered by the idea that it is fixed only relative
to the time scale of normal reasoning processes, leaving open the possibility that a symbol system could
emerge or change during development rather than necessarily being in place at birth.
The concept of cognitive architecture originated in Newell’s even earlier criticism of task-specific
models that induce a fragmented approach to cognitive science and the consequent difficulty of making
cumulative progress (Newell 1973). As a solution, he advanced the concept of an integrated model of
human cognition on top of which models of specific tasks could be developed in terms of a common set
of mechanisms and representations, with the ultimate goal of achieving Unified Theories of Cognition
(Newell 1990). Like a computer architecture, a cognitive architecture defines a general purpose
computational device capable of running programs on data. The key differences are that: (1) the kinds of
programs and data to be supported in cognitive architectures are limited to those appropriate for human-
like intelligent behavior; and (2) the programs and data are ultimately intended to be acquired
automatically from experience – that is, learned – rather than programmed, aside from possibly a limited
set of innate programs. Cognitive architectures thus induce languages, just as do computer architectures,
but they are languages geared towards yielding learnable intelligent behavior, in the form of knowledge
and skills. This is what distinguishes a cognitive architecture from an arbitrary – yet potentially quite
useful – programming language.

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From this common origin, the concept of cognitive architecture took form in multiple subfields, each
focused on different goals. In cognitive psychology, architectures such as ACT-R, Clarion, and LIDA
(Franklin & Patterson 2006) attempt to account for detailed behavioral data from controlled experiments
involving memory, problem-solving and perceptual-motor interaction. In artificial intelligence,
architectures such as Soar and Sigma focus on developing functional capabilities and applying them to
tasks such as natural language processing, control of intelligent agents in simulations, virtual humans,
and embodied robots. In neuroscience, architectures such as Leabra (O'Reilly, Hazy, & Herd 2016) and
Spaun (Eliasmith 2013) adopt mechanisms and organizations compatible with the human brain, but
primarily apply them to simple memory and decision-making tasks. In robotics, architectures such as
4D/RCS (Albus 2002) and DIARC (Schermerhorn et al. 2006) concern themselves with real-time
control of physical robots.
However, there has historically been little agreement either across or within specialties as to the overall
nature and shape of this architecture. The lack of such a consensus has hindered comparison and
collaboration across architectures, prevented the integration of constraints across disciplines, and limited
the guidance that could aid research on individual aspects of the mind. There is not even an agreed upon
term for what is being built. In addition to cognitive architectures – a term that stems from cognitive
science – relevant work also proceeds on architectures for intelligent agents, intelligent/cognitive robots,
virtual humans, and artificial general intelligence. All these terms carry significantly different goals and
requirements that span interaction with and control of, respectively, online resources, artificial physical
bodies, and artificial virtual bodies, plus generality across domains. To the extent that the human-like
components of these divergent threads can (re)converge under combined behavioral, functional, and
neural constraints, it yields a strong indication that a standard model is possible.
One recent attempt to bring several of these threads back together was work on a “generic architecture
for human-like cognition” (Goertzel, Pennachin & Geisweiller 2014a), which conceptually amalgamated
key ideas from the CogPrime (Goertzel, Pennachin & Geisweiller 2014b), CogAff (Sloman 2001),
LIDA, MicroPsi (Bach 2009), and 4D/RCS architectures, plus a form of deep learning (Arel, Rose &
Coop 2009). A number of the goals of that effort were similar to those identified for the standard model;
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 – with a bias thus also more
towards completeness than concord.
The standard model developed in this article is grounded in three other architectures and their associated
research programs: ACT-R, Soar, and Sigma. The first two are the most complete, long-standing, and
widely applied architectures in existence. ACT-R originated within cognitive science, although it has
reached out to artificial intelligence as well (e.g., Sanner et al. 2000), been mapped onto regions of the
human brain (Anderson 2007) – enabling it to be integrated with the Leabra neural architecture (Jilk et
al. 2008) – and been used to control robots (e.g., Kennedy et al. 2007). Soar originated within artificial
intelligence, although it has reached out to cognitive science (Newell 1990), and been used to control
robots (Laird & Rosenbloom 1990; Laird et al. 2012). Sigma is a more recent development, based partly
on lessons learned from the two others. It also originated within artificial intelligence, but has begun to
reach out to cognitive science (e.g., Rosenbloom 2014), is based on a generalized notion of graphical
models that has recently been extended to include neural networks (Rosenbloom, Demski, & Ustun
2016b), and been used to control virtual humans (Ustun & Rosenbloom 2016).

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We selected these three architectures because we know them well. The ultimate goal is to ground the
standard model in many more architectures and research programs, but in our experience, unless an
expert on the architecture/program is directly involved in such a process, the results can be more
problematic than useful, so our decision was to hold off on analyzing additional architectures until we
can involve others, possibly through a focused symposium or workshop, and hopefully then follow up
with a longer and more comprehensive article. Nevertheless, between these three architectures there is
significant presence across artificial intelligence and cognitive science, plus extensions into
neuroscience and robotics (& virtual humans), although it should be clear that none of the three
architectures actually originated within either of the latter two disciplines.
3. Three Cognitive Architectures
The previous section introduced the general notion of a cognitive architecture. Here we introduce the
three particular architectures we have focused on in extending the standard model beyond the initial
synthesis at the Symposium. Each architecture is described in its own terms, along with a figure that
provides a standard characterization of its structure. No attempt has been made to alter these figures to
draw out their commonalities – for example, the Soar figure explicitly shows learning mechanisms while
the other two don’t – other than to use a common color scheme for the components: brown for working
memory, red for declarative memory, blue for procedural memory, yellow for perception, and green for
motor. The core work of identifying commonalities is left to the standard model, as described in the next
section.
ACT-R is constructed as a set of modules that run asynchronously and in parallel around a central rule-
based procedural module that provides global control (Figure 1). Processing is often highly parallel
within modules, but each yields only a single result per operation, which is placed in a module-specific
Procedural
Module
(Basal Ganglia)
Matching (Striatum)
Declarative Module
(Temporal/Hippocampus)
Visual Buffer
(Parietal Cortex)
Goal Buffer
(DLPFC)
Retrieval Buffer
(VLPFC)
Manual Buffer
(Motor Cortex)
Intentional Module (aPFC)
Visual Module
(Occipital/other)
Manual Module
(Motor/Cerebellum)
Selection (Pallidum)
Execution (Thalamus)
Figure 1. ACT-R cognitive architecture.

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working memory buffer, where it can be tested as a condition by the procedural module and transferred
to other buffers to trigger further activity in the corresponding modules.
Soar is also comprised of a set of asynchronous internally parallel modules, including a rule-based
procedural memory. Soar is organized around a broader-based global working memory (Figure 2). It
includes separate episodic and semantic declarative memories, in addition to visuospatial modules and a
motor module that controls robotic or virtual effectors.
Sigma is a newer architecture that blends lessons from existing architectures such as ACT-R and Soar
with what has been learned separately about graphical models (Koller and Friedman 2009). It is less
modular architecturally, providing just a single long-term memory, which along with the working
memory and perceptual and motor components is grounded in graphical models. It instead seeks to yield
the distinct functionalities provided by the other two’s modules by specialization and aggregation above
the architecture (Figure 3). Sigma’s long-term memory, for example, subsumes a variety of both
procedural and declarative functionalities, while also extending to core perceptual aspects and
visuospatial imagery.
All three architectures structure behavior around a cognitive cycle that is driven by procedural memory,
with complex behavior arising as sequences of such cycles. In each cycle, procedural memory tests the
contents of working memory and selects an action that modifies working memory. These modifications
can lead to further actions retrieved from procedural memory, or they can initiate operations in other
modules, such as motor action, memory retrieval, or perceptual acquisition, whose results will in turn be
deposited back in working memory.
Long-Te r m Me mo r ie s
Working Memory
Procedural
Decision
Procedure
Chunking
Reinforcement
Learning
Semantic
Semantic
Learning
Episodic
Episodic
Learning
Visual Buffer
Object-based
continuous metric
Perception
Motor
Symbolic relational
graph structure
Figure 2. Soar cognitive architecture.

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4. Standard Model
In this section, we present the standard model, decomposed into structure and processing; memory and
content; learning; and perception and motor (or, to use a robotics term, action). This model represents
our understanding of the consensus that was introduced skeletally at the AAAI symposium, as fleshed
out based on our understanding of the three architectures of concern in this article. While individuals,
including the three of us, might disagree with specific aspects of what is presented here – consensus
after all does not require unanimity – it is our attempt at providing a coherent summary along with a
broadly shared set of assumptions held in the field. Specific areas of disagreement plus open issues are
discussed in the final section.
4.1 Structure and Processing
The structure of a cognitive architecture defines how information and processing are organized into
components, and how information flows between components. The standard model posits that the mind
is not an undifferentiated pool of information and processing, but is built of independent modules that
have distinct functionalities. Figure 4 shows the core components of the standard model, which include
perception and motor, working memory, declarative long-term memory, and procedural long-term
Working!Memory
Procedural!
Long-term!Memory
Declarative!
Long-term!Memory
Perception
Motor
Figure 4. The structure of the standard model.
Figure 3. Sigma cognitive architecture.
Episodic
Memory
Semantic
Memory
Procedural
Memory
Imagery
Memory
Working Memory
Long-Term Memory
Perception Motor
Procedural
Transition + Control
Declarative
Semantic/Perceptual + Episodic
Imagery

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memory. At this granularity, not a great deal of progress can be seen compared to what might have
appeared in a Standard Model several decades ago, aside from the distinction here between procedural
and declarative long-term memory. However, as will be seen in the rest of this section and summarized
in Table 1 (Section 5), there is substantial further progress when one looks deeper.
Each of the modules in Figure 4 can be seen as unitary or further decomposed into multiple modules or
sub-modules, such as multiple perceptual and motor modalities, multiple working memory buffers,
semantic vs. episodic declarative memory, and various stages of procedural matching, selection and
execution. Outside of direct connections between the perception and motor modules, working memory
acts as the inter-component communication buffer for components. It can be considered as unitary, or
consist of separate modality-specific memories (e.g., verbal, visual, etc.) that together constitute an
aggregate working memory. Long-term declarative memory, perception, and motor modules are all
restricted to accessing and modifying their associated working memory buffers, whereas procedural
memory has access to all of working memory (but no direct access to the contents of long-term
declarative memory or itself). All long-term memories have one or more associated learning
mechanisms that automatically store, modify, or tune information based on the architecture’s processing.
The heart of the standard model is the cognitive cycle. Procedural memory induces the processing
required to select a single deliberate act per cycle. Each action can perform multiple modifications to
working memory. Changes to working memory can correspond to a step in abstract reasoning or the
internal simulation of an external action, but they can also initiate the retrieval of knowledge from long-
term declarative memory, initiate motor actions in an external environment, or provide top-down
influence to perception. Complex behavior, both external and internal, arises from sequences of such
cycles. In mapping to human behavior, cognitive cycles operate at roughly 50 ms, corresponding to the
deliberate-act level in Newell’s hierarchy, although the activities that they trigger can take significantly
longer to execute.
The restriction to selecting a single deliberate act per cycle yields a serial bottleneck in performance,
although significant parallelism can occur during procedural memory’s internal processing. Significant
parallelism can also occur across components, each of which has its own time course and runs
independently once initiated. The details of the internal processing of these components are not specified
as part of the standard model, although they usually involve significant parallelism. The cognitive cycle
that arises from procedural memory’s interaction with working memory provides the seriality necessary
for coherent thought in the face of the rampant parallelism within and across components.
Although the expectation is that for a given system there can be additional perceptual and motor
modules as part of an agent’s embodiment, and additional memory modules, such as an episodic
memory, there is a strong commitment that no additional specialized architectural modules are necessary
for performing complex cognitive activities such as planning, language processing and Theory of Mind,
although architectural primitives specific to those activities (e.g., visuospatial imagery for planning, or
the phonological loop for language processing) can be included. All such activities arise from the
composition of primitive acts; that is, through sequences of cognitive cycles. The existence of a
cognitive cycle, along with an appropriate procedural memory to drive it, has become definitional for a
cognitive architecture.

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4.2 Memory and Content
The memory components store, maintain, and retrieve content to support their specific functionalities.
The core of this content is represented as relations over symbols. However, supplementing these
relational structures is quantitative metadata that annotates instances of symbols and relations for the
purpose of modulating decision making as well as the storage, retrieval, and learning of symbols and
relations. Frequency information is a pervasive form of metadata, yielding a statistical aspect to the
knowledge representation (e.g., Anderson & Schooler 1991). Other examples of metadata include
recency, co-occurrence, similarity, utility, and more general notions of activation. The inclusion of
quantitative metadata, resulting in tightly integrated hybrid symbolic-subsymbolic representations and
processing, is perhaps the most dramatic evolution from the early days of (purely) symbolic cognitive
architectures (Newell, Rosenbloom, & Laird 1989). There is a strict distinction between domain data –
symbols and relations – and such metadata. The metadata only exists in support of the symbolic
representations, and relations cannot be defined over quantitative metadata. The set of available
metadata for symbols and relations and the associated mechanisms are fixed within the architecture. In a
reflective architecture, there may be symbolic relations at a metalevel that can be used to reason about
the domain relations, but that is quite different from the architecturally maintained metadata described
here, and is not part of the current standard model. A brief summary of each of the three memory
components follows.
Working memory provides a temporary global space within which symbol structures can be dynamically
composed from the outputs of perception and long-term memories. It includes buffers for initiating
retrievals from declarative memory and motor actions, as well as buffers for maintaining the results of
perception and declarative memory retrieval. It also includes temporary information necessary for
behavior production and problem solving, such as information about goals, intermediate results of a
problem, and models of a task. All of working memory is available for inspection and modification by
procedural memory.
Procedural memory contains knowledge about actions, whether internal or external. This includes both
how to select actions and how to cue (for external actions) or execute (for internal actions) them,
yielding what can be characterized as skills and procedures. Arbitrary programs can be thought of
generically as a form of procedural memory, but they provide a rigid control structure for determining
what to do next that is difficult to interrupt, acquire, and modify. In the standard model, procedural
memory is instead based on pattern-directed invocation of actions, typically cast in the form of rules
with conditions and actions. Rule conditions specify symbolic patterns over the contents of working
memory and rule actions modify working memory, including the buffers used for cuing declarative
memory and motor actions. There is variation in how the knowledge from multiple matching rules is
integrated together, but agreement that a single deliberate act is the result, with metadata influencing the
selection.
Declarative memory is a long-term store for facts and concepts. It is structured as a persistent graph of
symbolic relations, with metadata reflecting attributes such as recency and frequency of (co-)occurrence
that are used in learning and retrieval. Retrieval is initiated by the creation of a cue in the designated
buffer in working memory, with the result being deposited in that buffer. In addition to facts, declarative
memory can also be a repository of the system’s direct experiences, in the form of episodic knowledge.
There is not yet a consensus concerning whether there is a single uniform declarative memory or
whether there are two memories, one semantic and the other episodic. The distinction between those

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13!
terms roughly maps to semantically abstract facts versus contextualized experiential knowledge,
respectively, but its precise meaning is the subject of current debate.
4.3 Learning
Learning involves the automatic creation of new symbol structures, plus the tuning of metadata, in long-
term – procedural and declarative – memories. It also involves adaptation of non-symbolic content in the
perception and motor systems. The standard model assumes that all types of long-term knowledge are
learnable, including both symbol structures and associated metadata. All learning is incremental, and
takes place online over the experiences that arise during system behavior. What is learned is typically
based on some form of a backward flow of information through internal representations of these
experiences. Learning over longer time scales is assumed to arise from the accumulation of learning
over short-term experiences. These longer time scales can include explicit deliberation over past
experiences. Learning mechanisms exist for long-term memory, and although they are not yet fully
implemented in current architectures, they are also assumed to exist for the perception and motor
modules.
There are at least two independent learning mechanisms for procedural memory: one that creates new
rules from the composition of rule firings in some form, and one that tunes the selection between
competing deliberative acts via reinforcement learning. Declarative memory also involves at least two
learning mechanisms: one to create new relations and one to tune the associated metadata.
4.4 Perception and Motor
Perception converts external signals into symbols and relations, with associated metadata, and places the
results in specific buffers within working memory. There can be many different perception modules,
each with input from a different modality – vision, audition, etc. – and each with its own perceptual
buffer. The standard model assumes an attentional bottleneck that constrains the amount of information
that becomes available in working memory, but does not embody any commitments as to the internal
representation (or processing) of information within perceptual modules, although it is assumed to be
predominantly non-symbolic in nature, and to include learning. Information flow from working memory
to perception is possible, providing expectations or possible hypotheses that can be used to influence
perceptual classification and learning.
Motor converts the symbol structures and their metadata that have been stored in their buffers into
external action through control of whatever effectors are a part of the body of the system. As with
perception, there can be multiple motor modules (arms, legs, etc.). Much is known about motor control
from the robotics and neuroscience literature, but there is at present no consensus as to the form this
should take in the standard model, largely due to a relative lack of focus on it in human-like
architectures.
!
5. Summary
Table 1 summarizes the key assumptions that underlie the standard model of human-like minds
proposed in this article. It is derived from the 2013 Symposium session plus an extensive post hoc
discussion among the authors of this article centered around ACT-R, Soar, and Sigma. In the table, the
standard model has been decomposed into A. structure and processing, B. memory and content, C.
learning, and D. perception and motor systems.

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14!
A. Structure and Processing
1. The purpose of architectural processing is to support bounded rationality, not optimality
2. Processing is based on a small number of task-independent modules
3. There is significant parallelism in architectural processing
a. Processing is parallel across modules
i. ACT-R & Soar: asynchronous; Sigma: synchronous
b. Processing is parallel within modules
i. ACT-R: rule match, Sigma: graph solution, Soar: rule firings
4. Behavior is driven by sequential action selection via a cognitive cycle that runs at ~50 ms per cycle in human
cognition!
5. Complex behavior arises from a sequence of independent cognitive cycles that operate in their local context, without
a separate architectural module for global optimization (or planning).
B. Memory and Content
1. Declarative and procedural long-term memories contain symbol structures and associated quantitative metadata
a. ACT-R: chunks with activations and rules with utilities; Sigma: predicates and conditionals with functions;
Soar: triples with activations and rules with utilities
2. Global communication is provided by a short-term working memory across all cognitive, perceptual, and motor
modules
3. Global control is provided by procedural long-term memory
a. Composed of rule-like conditions and actions
b. Exerts control by altering contents of working memory
4. Factual knowledge is provided by declarative long-term memory
a. ACT-R: single declarative memory; Sigma: unifies with procedural memory; Soar: semantic and episodic
memories
C. Learning
1. All forms of long-term memory content, whether symbol structures or quantitative metadata, are learnable
2. Learning occurs online and incrementally, as a side effect of performance and is often based on an inversion of the
flow of information from performance
3. Procedural learning involves at least reinforcement learning and procedural composition
a. Reinforcement learning yields weights over action selection
b. Procedural composition yields behavioral automatization
i. ACT-R: rule composition; Sigma: under development; Soar: chunking
4. Declarative learning involves the acquisition of facts and tuning of their metadata
5. More complex forms of learning involve combinations of the fixed set of simpler forms of learning!
D. Perception and Motor
1. Perception yields symbol structures with associated metadata in specific working memory buffers
a. There can be many different such perception modules, each with input from a different modality and its
own buffer
b. Perceptual learning acquires new patterns and tunes existing ones
c. An attentional bottleneck constrains the amount of information that becomes available in working memory
d. Perception can be influenced by top-down information provided from working memory
2. Motor control converts symbolic relational structures in its buffers into external actions
a. As with perception, there can be multiple such motor modules
b. Motor learning acquires new action patterns and tunes existing ones
!
!
Table 1: Standard model architectural assumptions

!
15!
Table 2 provides an analysis, tabulated by the assumptions in Table 1, of the extent ACT-R, Soar, and
Sigma agree in theory with the standard model and implement the corresponding capabilities. Versions
of ACT-R and Soar from the early-90s have been included to show the evolution of those architectures
in relation to the standard model. The convergence is striking. Although there was significant
disagreement (or lack of theory, especially in the case of perception and motor) in the early-90s for both
ACT-R and Soar, their current versions are in total agreement in terms of theory and only substantially
differ in the extent to which they implement perception and motor systems. Sigma is also in agreement
on most of these assumptions as well. However, because it defines some of the standard model’s
capabilities not via specialized architectural modules but via combinations of more primitive
architectural mechanisms plus specialized forms of knowledge and skills, three cells are colored blue to
indicate a partial disagreement in particular with the strong architectural distinction between procedural
and declarative memories, and the complete architectural nature of reinforcement learning.
This standard model reflects a very real consensus over the assumptions it includes, but it remains
incomplete in a number of ways. It is silent, for example, concerning metacognition, emotion, mental
imagery, direct communication and learning across modules, the distinction between semantic and
episodic memory, and mechanisms necessary for social cognition. However, even with these gaps, the
standard model captures much more than did precursors such as the model human processor, and much
more than could have been agreed upon even ten years ago. It thus reflects a significant point of
convergence, consensus, and progress.
The hope is that the presented model will yield a sound beginning upon which the field can build by
folding into the mix additional lessons from a broader set of architectures. Such an effort ideally should
focus on architectures that: (1) are under active (or recent) development and use; (2) have strong
architectural commitments that yield a coherence of assumptions rather than being just a toolkit for
construction of intelligent systems; (3) are concerned with human-like intelligence; and (4) have been
applied across diverse domains of human endeavor. Architectures worth considering for this include, but
are not limited to, CHREST (Gobet & Lane 2010), Clarion (Sun 2016), Companions (Forbus & Hinrichs
2006), EPIC (Kieras & Meyer 1997), ICARUS (Langley & Choi 2006), Leabra (O’Reilly et al. 2016),
LIDA (Franklin & Patterson 2006), MicroPsi (Bach 2009), MIDCA (Cox et al. 2013), and Spaun
(Eliasmith 2013).
Newell’s (1973) warning about trying to approach full intelligence via a pastiche of task-specific models
applies not only to cognitive science – and, in particular, psychology and AI – but also to any other
discipline that ultimately seeks or depends on such comprehensive models of intelligent behavior,
including notably neuroscience and robotics. A comprehensive standard model of the human mind could
A1 A2 A3a A3b A4 A5 B1 B2 B3a B3b B4 C1 C2 C3a C3b C4 C5 D1a D1b D1c D1d D2a D2b
ACT-R 1993
SOAR 1993
SIGMA 2016
ACT-R 2016
SOAR 2016
!"#$%&''()*&(+,#-'."/"'0(12(34'*&25
6%&''(1+3(,*3("7-8'7',3'0
6%&''(1+3(-$&3"$882("7-8'7',3'0
6%&''($,0("7-8'7',3'0
6%&''(-$&3"$882()#*7'(9'2($#-'.3#($&'($1*:'($&.4"3'.3+&'5;("7-8'7',3'0
Table 2: Analysis of Soar, ACT-R and Sigma with respect to the standard model.

!
16!
provide a blueprint for the development of robotic architectures that could act as true human
companions and teammates as well as a high-level structure for efforts to build a biologically detailed
computational reconstruction of the workings of the brain, such as the Blue Brain project. The standard
model could play an integrative role to guide research in related disciplines – for example, ACT-R is
already being applied to modeling collections of brain regions and being integrated with neural models,
and both ACT-R and Soar have been used in robotics (and Soar and Sigma in the sister discipline of
virtual humans) – but the existence of a standard model can enable more generalizable results and
guidance. Conversely, those disciplines can provide additional insights and constraints on the standard
model, leading to further progress and convergence. In addition, the standard model potentially provides
a platform for the integration of theoretical ideas without requiring realization in complete cognitive
architectures.
It is hoped that this attempt at a standard model, based as it is on extending the initial sketch from the
Symposium via a focus on three human-like architectures, will grow over time to cover more data,
applications, architectures, and researchers. This is partially a scientific process and partially a social
process. The scientific side is driven by what is learned about human-like minds from studying both
human minds and human-like artificial minds. The social side needs to be driven by spanning more and
more of the community concerned with human-like cognitive architectures, and possibly even beyond
this to other communities with related interests. This could happen incrementally, by expanding to a
single new architecture and proponent at a time, or in bursts, via symposia or workshops at which
multiple such come together to see what new consensus can be found. Community-wide surveys are also
possible, but it is our sense that by sidestepping the hard part of working out differences interactively,
this would likely not yield what is desired. Rather, it is our hope that the shared benefits of a standard
model of the mind will lead to a virtuous cycle of community contributions and incremental refinements.
Acknowledgments
The work described in this article was sponsored by the Air Force Office of Scientific Research (under
award FA9550-15-1-0157), the U.S. Army (under contract W911NF-14-D-0005), the Army Research
Laboratory (under contract W911NF-10-2- 0016), and the Office of Naval Research (under awards
N00014-15-1-2468, N00014-15-12151, N00014-12-C-0638, and N00014-08-1-0099). Statements and
opinions expressed may not reflect the position or policy of the United States Government, and no
official endorsement should be inferred.
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Author Biographies
John E. Laird is the John L. Tishman Professor of Engineering at the University of Michigan. He is one
of the original developers of the Soar architecture and leads its continued evolution. He is a founder and
Chairman of the Board of Soar Technology, Inc. and a Fellow of AAAI, AAAS, ACM, and the
Cognitive Science Society.
Christian Lebiere is Research Faculty in the Psychology Department at Carnegie Mellon University. He
is one of the original developers of the ACT-R cognitive architecture and is co-author with John R.
Anderson of The Atomic Components of Thought. He is a founding member of the Biologically Inspired
Cognitive Architectures Society.
Paul S. Rosenbloom is Professor of Computer Science at the University of Southern California and
Director for Cognitive Architecture Research at USC’s Institute for Creative Technologies. He is one of
the original developers of the Soar architecture and the primary developer of the Sigma architecture. He
is the author of On Computing: The Fourth Great Scientific Domain and a Fellow of both AAAI and the
Cognitive Science Society.