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Clarifying System 1 & 2 through the Common Model of Cognition



There have been increasing challenges to dual-system descriptions of System-1 and System-2, critiquing them as being imprecise and fostering misconceptions. We address these issues here by way of Dennett’s appeal to use computational thinking as an analytical tool, specifically we employ the Common Model of Cognition. Results show that the characteristics thought to be distinctive of System-1 and System-2 instead form a spectrum of cognitive properties. By grounding System-1 and System-2 in the Common Model we aim to clarify their underlying mechanisms, persisting misconceptions, and implications for metacognition.
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Clarifying System 1 & 2 through the Common Model of Cognition
Brendan Conway-Smith (,
Robert L. West (
Department of Cognitive Science, Carleton University
Ottawa, ON K1S5B6 Canada
There have been increasing challenges to dual-system
descriptions of System-1 and System-2, critiquing them
as imprecise and fostering misconceptions. We address
these issues here by way of Dennett’s appeal to use
computational thinking as an analytical tool, specifically
we employ the Common Model of Cognition. Results
show that the characteristics thought to be distinctive of
System-1 and System-2 instead form a spectrum of
cognitive properties. By grounding System-1 and
System-2 in the Common Model we aim to clarify their
underlying mechanisms, persisting misconceptions, and
implications for metacognition.
Keywords: dual-system; dual-process; system-1;
system-2; common model; metacognition; computational
This paper re-visits Dennett’s (1981) notion that
philosophical discussion can benefit from the use of
computational modelling. We do this by showing how
recent criticisms of the dual-systems view of the mind
(System-1 and System-2), can be clarified using the
Common Model of Cognition to ground the discussion
(Laird, Lebiere & Rosenbloom, 2017).
The terms System-1 and System-2 refer to a dual-
system model that ascribes distinct characteristics to
what are thought to be opposing aspects of cognition
(Wason & Evans, 1974; Stanovich, 1999; Strack &
Deutsch, 2004; Kahneman, 2003, 2011). System-1 is
considered to be evolutionarily old and characterized as
fast, associative, emotional, automatic, and not
requiring wo rk in g memory. System-2 i s more
evoluti onarily recent and thought to be slow,
declarative, rational, effortful, and relying on working
memory. Kahneman (2003) referred to System-1 as
“intuitive” and System-2 as “rational”, thus linking
them to higher level folk psychology concepts. The
neural correlates of System-1 and System-2 have also
been studied (e.g., Tsujii & Watanabe, 2009). System-1
and System-2 are often used in fields such as
psychology, philosophy, neuroscience, and artificial
intelligence as a means for ontologizing the functional
properties of human cognition.
Recently, however, this dual-system model has been
criticized for lacking precision and conceptual clarity
(Keren & Schul, 2009), leading to significant
misconceptions (Pennycook et al., 2018; Houwer, 2019)
and ob scur i ng t he d ynam i c c o mpl e x iti e s o f
psychological processes (Moors, 2016). One of the
originators of dual-system theory stated that an
important issue for future research is the problem that
“current theories are framed in general terms and are
yet to be developed in terms of their specific
computational architecture” (Evans, 2003).
Fo llowi ng Denne tt (1981 ) we argue t hat a
computational description is essential for clarifying
high level, psychological characterizations such as
System-1 and System-2. At the time, Dennett received
significant pushback on his view. However, we argue
that it was too e ar ly in th e development of
computational models to fully appreciate the pragmatic
value of his position.
In the spirit of this endeavour, Proust (2013) has
argued that a more precise computational definition is
needed to understand the role of System-1 and
System-2 in metacognition. Proust defined these
systems in terms of informational typologies (System-1
non-conceptual; System-2 conceptual). Similarly,
Thomson et al. (2015) argued that the expert use of
heuristics (System-1) could be defined in terms of
instance based learning in ACT-R. In fact, there are
numerous ways that cognitive models and cognitive
architectures can and have been mapped onto the
System-1 and 2 distinction. For example, dual-process
approaches to learning have been instantiated within the
CLARION architecture, modelling the interaction
between implicit and explicit processes (Sun, Terry &
Slusarz, 2005). System-1 and 2 have also been
instantiated directly into the LIDA architecture (Faghihi
et al., 2014).
While it is useful to work on modelling different
aspects of System-1 and 2, the larger question is, in
what sense is System-1 and 2 a valid construct? What
are the necessary and sufficient conditions that
precisely define System-1 and 2? And what are the
cognitive and neural alignments to System-1 and
System-2 (Evans, 2003)?
The Common Model
The Common Model of Cognition, originally the
‘Standard Model’ (Laird et al., 2017) is a consensus
architecture that integrates decades of research on how
human cognition functions computationally. The
Common Model represents a convergence across
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cognitive architectures regarding the modules and
components necessary for biological and artificial
intelligence. These modules are correlated with their
associ at ed br ai n regions and verified th ro ug h
neuroscience (Steine-Hanson et al., 2018). Neural
evidence strongly supports the Common Model as a
leading candida te for mo deling the functiona l
organization of the human brain (Stocco et al., 2021).
The computational processes of the Common Model
are categorized into five components working
memory, perception, action, declarative memory, and
procedural memory. Procedural memory is described as
a production system which contains units called
production rules (or ‘productions’). The production
system interacts with different modules through
working memory represented as buffers. While these
components are implemented differently among
Common Model-type architectures, they describe a
common functionality across implementations.
Researchers generally describe System-1 by using a
constellation of characteristics. Specifically, System-1
is described as fast, associative, emotional, automatic,
and not requiring working memory (Kahneman, 2011;
Evans, 2003; Strack & Deutsch, 2004). System-1 is
considered to be evolutionary old and present within
animals. It is composed of biologically programmed
instinctive behaviours and operations that contain
innate modules of the kind put forth by Fodor (1983).
System-1 is not comprised of a single system but is an
assembly of sub-systems that are largely autonomous
(Stanovich & West, 2000). Automatic operations are
usually described as involving minimal or no effort, and
without a sense of voluntary control (Kahneman, 2011).
Researchers generally agree that System-1 is made of
parallel and autonomous subsystems that output only
their final product into consciousness (often as affect),
which then influences human decision-making (Evans,
2003). This is one reason the system has been called
“intuitive” (Kahneman, 2003).
System-1 relies on automatic processes and shortcut
strategies called heuristics problem solving
operations or rule of thumb strategies (Simon, 1955).
The nature of System-1 is often portrayed as non
symbolic, and has been associated with reinforcement
learning (Barto et al., 1981) and neural networks
(McLeod, 1998). Affect is integral to System-1
processes (Mitchell, 2011). Affect based heuristics
result from an individual evaluating a stimulus based on
their likes and dislikes. In more complex decision-
making, it occurs when a choice is either weighed as a
net positive (with more benefits than costs), or as net
negative (less benefits than costs) (Slovic et al., 2004).
System-1 can produce what are called “cognitive
illusions” that can be harmful if left unchecked. For
example, the ‘illusion of validity’ is a cognitive bias in
which individuals overestimate their ability to
accurately predict a data set, particularly when it shows
a consistent pattern (Kahneman & Tversky, 1973).
Biases and errors of System-1 operate automatically
and cannot be turned off at will. However, they can be
offset by using System-2 to monitor System-1 and
correct it.
System-1 in the Common Model
System-1 can be associated with the production system
which is the computational instantiation of procedural
memory in the Common Model (Singley & Anderson,
1989). Procedural knowledge is represented as
production rules (“productions”) which are modeled
after computer program instructions in the form of
condition-action pairings. They specify a condition that,
when met, will perform a prescribed action. A
production can also be thought of as an if-then rule
(Anderson, 1993). If it matches a condition, then it fires
an action. Production rules transform information to
resolve problems or complete a task, and are
responsible for state-changes within the system.
Production rules fire automatically off of conditions in
working memory buffers. Their automaticity is due to
the fact that they are triggered without secondary
evaluation. Neurologically, production rules correlate
with the 50ms decision timing in the basal ganglia
(Stocco, Lebiere, & Anderson, 2010). The production
system can enact reinforcement learning in the form of
utility learning, where faster or more useful productions
are rewarded and are more likely to be used later
(Anderson, 1993). In a similar way, problem solving
heuristics can be implemented as production rules
(Payne et al., 1988).
The Common Model production system has many of
the properties associated with System-1 such as being
fast, automatic, implicit, able to implement heuristics,
and reinforcement learning. However, the Common
Model declarative memory system also has some of the
properties associated with System-1. Specifically,
associative learning and the ability to implement
heuristics that leverage associative learning (Thomson et
al., 2015). Here, it is important to understand that the
Common Model declarative memory cannot operate
without the appropriate productions firing, and without
the use of buffers (working memory). Therefore, from a
Common Model perspective, System-1 minimally
involves productions firing based on buffer conditions,
but can also involve productions directing declarative
memory retrieval, which also relies on buffers. Based
on this, System-1 cannot be defined as being uniquely
aligned with either declarative or procedural memory.
System-1 activity must involve production rules and
buffers, and can also involve declarative knowledge.
Researchers generally view System-2 as a collection of
cog n i tive prop e r ties , char a c teriz e d a s slow,
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propositional, rational, effortful, and requiring working
memory (Kahneman, 2011; Strack & Deutsch, 2004;
Fran k i s h 2 0 1 0 ). S y stem- 2 invol v e s expl i c i t
propositional knowledge that is used to guide decision-
making (Epstein & Pacini, 1999). Propositional
knowledge is associated with relational knowledge
(Halford, Wilson, & Phillips, 2010) which represents
entities (e.g.: John and Mary), the relation between
them (e.g.: loves) and the role of those entities in that
relation (e.g.: John loves Mary). Higher level rationality
in System-2 is also said to be epistemically committed
to logical standards (Tsujii & Watanabe, 2009).
System-2 processes are associated with the subjective
ex peri enc es of age ncy, cho ice , a nd eff ort ful
concentration (Frankish, 2010). The term “effortful”
encompasses the intentional, conscious, and more
strenuous use of knowledge in complex thinking.
Higher level rationality is considered responsible for
human-like reasoning, allowing for hypothetical
thinking, long-range planning, and is correlated with
overall measures of general intelligence (Evans, 2003).
Researchers have studied various ways in which
System-2’s effortful processes can intervene in
System-1 automatic operations (Kahneman, 2003).
Ordinarily, an individual does not need to invoke
System-2 unless they notice that System-1 automaticity
is insufficient or risky. System-2 can intervene when the
anticipated System-1 output would infringe on explicit
rules or potentially cause harm. For example, a scientist
early in their experiment may notice that they are
experiencing a feeling of certainty. System-2 can
instruct them to resist jumping to conclusions and to
gather more data. In this sense, System-2 can monitor
System-1 and override it by applying conceptual rules.
System-2 in the Common Model
Laird (2020) draws on Newell (1990), Legg and Hutter
(2007) and others to equate rationality with intelligence,
where “an agent uses its available knowledge to select
the best action(s) to achieve its goal(s).” Newell’s
Rationality Principle involves the assumption that
problem-solving occurs in a problem space, where
knowledge is used to navigate toward a desired end. As
Newell puts it, “an agent will use the knowledge it has
of its environment to achieve its goals” (1982, p. 17).
The prioritizing of knowledge in decision-making
corresponds with the principles of classical computation
involving symbol transformation and manipulation.
The Common Model architecture fundamentally
distingui shes between de clarative memory and
procedural memory. This maps roughly onto the
distinction between explicit and implicit knowledge
where declarative knowledge can be made explicitly
accessible in working memory, procedural knowledge
op era tes out sid e o f w ork ing me mor y a nd is
inaccessible. However, declarative knowledge can also
function in an implicit way. The presence of something
within working memory does not necessarily mean it
will be consciously accessed (Wal l ach & Lebiere, 2003).
Higher level reasoning involves the retrieval of
‘chunks’, representing propositional information, into
buffers (working memory) to assist in calculations and
problem-solving operations. This appears to correlate
with what System-2 researchers describe as “effortful”,
as this requires more computational resources (i.e.,
more productions) to manage the flow of information
through limited space in working memory (buffers). As
Kahneman points out, System-1 can involve knowledge
of simple processes such as 2+2=4. However, more
complex operations such as 17x16 require calculations
that are effortful, a characteristic that is considered
distinctive of System-2 (Kahneman, 2011).
Effort, within the Common Model, involves greater
computational resources being allocated toward a task.
Moreover, the retrieval and processing of declarative
knowledge requires more steps and more processing
time when compared to the firing of productions alone.
This longer retrieval and processing time can also
account for the characteristic of “slow” associated with
Emotion in System-1 and 2
Emotion and affect plays a vital role in the distinction
between System-1 and System-2 processes (Chaiken &
Trope, 1999; Kahneman, 2011). Decisions in System-1
are largely motivated by an individuals implicit
association of a stimulus with an emotion or affect
(feelings that something is bad or good). Behavior
motivated by emotion or affect is faster, more
automatic, and less cognitively expensive. One
evolutionary advantage of these processes is that they
allow for split-second reactions that can be crucial for
avoiding predators, catching food, and interacting with
complex and uncertain environments.
Emotions can bias or overwhelm purely rational
decision processes, but they can also be overridden by
System-2 formal rules. While emotions and affect have
historically been cast as the antithesis of reason, their
importance in decision-making is being increasingly
investigated by researchers who give affect a primary
role in motivating decisions (e.g., Zajonc, 1980; Barrett
& Salovey, 2002). Some maintain that rationality itself
is not possible without emotion, as any instrumentally
rational system must necessarily pursues desires
(Evans, 2012).
Emotion in the Common Model
Feelings and emotions have strong effects on human
performance and decision-making. However, there is
considerable disagreement over what feelings and
emotions are and how they can be incorporated into
cognitive models. However, while philosophical
explanations of affect have been debated, functional
accounts of emotions and feelings within cognitive
models have been built. Emotions have been modeled
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as amygdala states (West & Young, 2017), and somatic
markers as emotional tags attached to units of
information (Domasio, 1994). In Sigma models, low-
level appraisals have been modeled as architectural self-
reflections on factors such as expectedness, familiarity,
and desirability (Rosenbloom, et al., 2015). Core affect
theory has been modeled in ACT-R to demonstrate how
an agent may prioritize information using emotional
valuation (Juvina, Larue & Hough, 2018). Also,
feelings have also been modelled by treating them as
non propositional representations in buffers or
“metadata” (West & Conway-Smith, 2019).
Overall, the question of how to model emotion in the
Common Model remains unresolved. However, as
indicated in the research above, emotion has multiple
routes for interacting with cognition in the Common
Effort in System-1 and 2
The concept of “effort” makes up a significant and
confusing dimension of System-1 and System-2. While
it is mainly associated with System-2 rationality, a
precise definition of “effort” remains elusive and is
largely implicit in discussions of System-1 and 2.
Because System-2 is considered to have a low
processing capacity, its operations are associated with
greater effort and a de-prioritizing of irrelevant stimuli
(Stanovich, 1999).
Effort can be associated with complex calculations in
System-2 to the extent that it taxes working memory.
Alternatively, effort can be associated with System-2’s
capacity to overrule or suppress automatic processes in
System-1 (Kahneman, 2011). For example, various
System-1 biases (such as the “belief bias”) can be
subdued by instructing people to make a significant
effort to reason deductively (Evans, 1983). The
application of formal rules to “control” cognitive
processes i s also called metaco gnition the
monitoring and control of cognition (Flavell, 1979;
Fletcher & Carruthers, 2012). Researchers have
interpreted metacognition through a System-1 and
System-2 framework (Arango-Muñoz, 2011; Shea et
al., 2014). System-1 metacognition is thought to be
implicit, automatic, affect-driven, and not requiring
wo r kin g m emo ry. Syst em- 2 m eta cog niti on is
considered explicit, rule-based, and relying on working
While the concept of “effort” is considered to be the
monopoly of System-2, a computational approach
suggests that effort is a continuum with low effort
cognitive phenomena being associated with System-1,
and high effort cognitive phenomena being associated
with System-2.
Effort in the Common Model
The Common Model helps to elucidate how “effort”
can be present in System-1 type operations in the
absence of other System-2 characteristics. While neither
dual-system theories nor the Common Model contain a
clear d e f i n i t i o n of e f f o r t , computational
characteristics associated with effort can be necessary
to System-1. For instance, “effort” is often associated
with the intense use of working memory. However, the
Common Model requires working memory (along with
its processing limitations) for both System-1 and
System-2 type operations. There is no reason why
System-1 should necessarily use less working memory
than System-2 in the Common Model. Instead, it would
depend on the task duration and intensity.
System-1 and System-2 metacognition can also be
clarified by importing Proust’s (2013) more precise
account. Proust attempted to elucidate these two
systems by claiming that they should be distinguished
by their distinctive informational formats (System-1
non-conceptual; System-2 conceptual). In this sense,
System-1 metacognition can exert effortful control
wh ile s imu ltane ously be ing im plici t a nd no n
conceptual. For example, consider a graduate student
attending a conference while struggling not to fall
asleep. An example of System-1 metacognition would
involve the context implicitly prompting them to feel
nervous, noticing their own fatigue, and then attempting
to stay awake. This effort is context-driven, implicit,
non conceptual, and effortful. Alternatively, System-2
metacognition can exert effort by way of explicit
concepts, as in the case of a tired conference-attendee
repeating the verbal instruction “try to focus”. Either of
these scenarios could be modelled using the Common
Model, and to reiterate, there is little reason why
System-1 should require less effort.
Another way to think about effort is in terms of the
expense of neural energy. In this sense, effort can be
viewed as the result of greater caloric expenditure in
neurons. The neural and computational dynamics
responsible for the effortful control of internal states
have shown to be sensitive to performance incentives
(Egger et al., 2019). Research also indicates that the
allocation of effort as cognitive control is dependent on
whether a goal’s reward outweighs its costs (Shenhav,
et al., 2017). Both of these relate to reinforcement
learning, which is associated with System-1.
Examining this question through the Common Model
suggests that “effort” is not traditionally well defined,
nor is it the sole privy of System-2. Rather, effort can be
involved in processes characteristic of both System-1
and System-2.
The Common Model sheds light on the specific
mechanisms that give rise to the general traits
associated with System-1 and System-2. Interpreting
System-1 and System-2 within the Common Model
results in our conclud ing that the “a lignment
assumption” (that the two systems are opposites) is a
false dichotomy. There are, of course, cases where all
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properties of System-1 and System-2 are cleanly
bifurcated on either side. However, between these two
extremities lies a spectrum where the characteristics are
mixed. Few, if any, of these properties are ‘necessary
and sufficient’ to be sharply distinctive of either.
Evidence for this is as follows:
1. System-2 is grounded in System-1. While System-1
depends on procedural memory, so too does System-2.
System-2 cannot operate separately due to the
architectural constraints of the Common Model. Even if
a Sy stem-2 process were primarily driven by
declarative knowledge, it would still require System-1
procedural knowledge to be retrieved and acted upon.
2. System-1 and System-2 characteristics are often
mixed as they routinely act together. System-2 goal-
directed rationality often requires affect in the from of a
desired end. Also, System-2 rationality is subject to
System-1 affective biases.
3. Both System-1 and System-2 require working
memory. While conventional views claim that System-1
does not require working memory, the constraints of the
Common Model necessitate it. Production rules
(procedural knowledge) are activated by the content of
buffers (working memory) and hence are required by
both systems.
4. Effort can be directed toward both System-2
rationality and System-1 metacognitive control. The
effortful allocation of cognitive resources in System-1
can be based on an implicit cost-benefit analysis.
Regardless of whether one adopts the Common
Model architecture, researchers should be cautious of
assuming the System-1 and System-2 dichotomy within
their work. The framework is far from settled and deep
issues continue to be unresolved. Questions remain as
to whether System-1 and System-2 constitute an
ontology or a convenient epistemology.
Since before Descartes, substance dualism has
continually been reimagined as mind and soul, reason
and emotions, and opposing modes of thought. These
have been expressions of the human species’ attempt to
make sense of our own minds, its processes, and how
this understanding maps onto our personal experience.
Clearly, System-1 and System-2 captures something
deeply intuitive about the phenomenology of cognition.
However, as we have discussed Kahneman’s System-1
biases it may be worth asking is System-2 a
System-1 illusion? That is, do we assume the existence
of System-2 simply because we so often act as if it
By situating System-1 and System-2 within the
Common Model of Cognition, we have attempted to
bring light to this subject by clarifying its underlying
mechanisms, misconceptions, and the base components
needed for future research.
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