Skip to content

Cerebral networks for conscious access and decision making

Originally published in March 2019
in an electronic journal in Japanese


The purpose of this essay is to survey the relationship between decision making and large-scale cerebral networks with regard to conscious access, a purported neural correlate of consciousness, and to provide clues for computational modelling and general understanding of the issue.  The author is involved in the whole brain architecture approach, which aims to develop human-like artificial intelligence by mimicking the architecture of the entire brain.  The approach would naturally be concerned with decision-making and certain aspects of consciousness in the function of the brain. 

In computational neuroscience, simulations based on models are often carried out to interpret neural phenomena and to direct research.  Such modelling also provides clues for brain-inspired artificial intelligence development [Hassabis 2017][Kriegeskorte 2018].  Decision-making involves various brain regions and connections between them so that it would be useful to obtain an overview before getting down to detailed modelling of region function.

The issue whether ‘consciousness’ is involved in decision making has long been discussed in philosophy.  While this essay deals with “conscious” decision making, it focuses on a specific type of neural phenomenon involving cerebral large-scale networks to be explained below to avoid philosophical debates and ambiguity of the term “consciousness.”

Cerebral networks involved in decision making would include those involved in conscious access as well as those involving working memory, episodic memory, and reinforcement learning (operant conditioning). While there are ample findings on the relationship between each of those areas and decision making, it does not seem that we have an overall picture encompassing all of those areas. Thus, in summary, an overview of the mechanism based on the survey is presented.

Large-scale Cerebral Networks

It is known that there are several large-scale networks in the cerebrum.  The structure of networks could be elucidated by the anatomical investigation of neural connections and also inferred by observing the concurrence among the activities of brain regions.  The Central Executive Network (CEN), Default Mode Network (DMN), and Salience Network (SN) as well as the pathways in the perceptual information system are known as major cerebral networks [Menon 2010][Goldberg 2018].  The Arcuate Fasciculus, connecting brain areas related to language, and the Corpus Callosum, connecting the left and right hemispheres, are also large-scale networks. Below, CEN, DMN, SN and perceptual pathways will be briefly explained.  See Figure 1 for major areas of the cerebrum.

Central Executive Network (CEN)

CEN mainly connects the dorsolateral prefrontal cortex (dlPFC) and the posterior parietal lobe.  Since dlPFC deals with external information, CEN is also considered to be involved in decision making based on external information.  CEN is active when solving problems relating to the exterior environment, being led by the PFC. The posterior parietal lobe contains areas that represent positions and motions of visual objects and is thought to be involved in spatial representation.

Default Mode Network (DMN)

DMN mainly connects the ventromedial prefrontal cortex (vmPFC) and the posterior cingulate cortex (PCC), including the Angular Gyrus and medial temporal lobe.  DMN has been reported to be involved in internal conditions, the memory of the self, and social recognition [Menon 2010]. VmPFC, which is part of DMN, is thought to have the function of reward evaluation [Passingham 2012].  DMN is antagonistic with CEN: when one is active, the other is suppressed.

Figure 1. Left lateral view (left) and right medial view (right) of the human cerebrum
Modified Public domain figures from Wikimedia (Gray726 & Gray727)

Salience Network (SN)

SN mainly connects the anterior insula (AI) (Fig. 2) and the anterior cingulate cortex (ACC), and is supposed to be involved in the detection of the occurrence of events and DMN-to-CEN switching.  SN may also control attention by selecting stimuli that are important for action, as it is directly connected to motor and (value-related) limbic systems.

Figure 2. Location of the Insular Cortex (Gyrus) at the coronal plane of the human cerebrum
A public domain figure from Wikimedia (Gray717-emphasizing-insula.png)

Ventral and Dorsal Perceptual Pathways

In the cerebrum, visual information is first processed in the primary visual cortex in the occipital lobe. After that, the categories of objects are recognized through the ventral pathway in the temporal lobe (the ‘what’ pathway), and the location and motion of objects through the dorsal pathway in the parietal lobe (the ‘where’ pathway) [O’Reilly 2016].  Auditory perception is also known to have a similar pathway distinction.

“Conscious Access”

In this article, the term “conscious access” refers to specific neuroscientific phenomena involving large-scale cerebral networks.  “Conscious access” is used to refer strictly to the particular type of observable psycho-physiological phenomena reported in [Dehaene 2014]. Conscious access is the phenomenon that when attention to a perceptual object becomes large enough relative to attention to other objects, a large scale cerebral network is activated and features of the object are perceived. Conscious access is observable with the activation of a large scale cerebral network (physiological events) and the subject’s ability to discern perceptual features with a certain degree of certainty (psychological events). The activation (phase transition) of a large-scale neural circuit accompanying conscious access is also called “avalanche of consciousness.” The term “conscious access” contains the term “conscious,” because the psychological event “to discern perceptual features with a certain degree of certainty” is usually supposed to be a “conscious” event.  Here note that conscious access does not exclude the possibility of the subject’s being a philosophical zombie, as it is a merely observable psycho-physiological phenomenon.  One could also detect, in principle, conscious access in patients with total lock-in syndrome and non-human animals.  For a computational neuroscientific model (i.e., the Global Neuronal Workspace Theory), see [Dehaene 2011]. Note also that there is only one object for conscious access at a time in the whole brain, and it suggests that conscious access is done in the form of Winner-Takes-All within the large-scale cerebral network.  Since an avalanche of consciousness takes about 0.5 seconds after the presentation of stimulus, the world in which conscious access perceives is always about 0.5 seconds in the past.

Decision Making Networks

Prefrontal Cortex-Basal Ganglia-Thalamus Loops

It is hypothesized that loops starting from the prefrontal cortex, going through the basal ganglia (BG) and thalamus, and going back to PFC play an important role in decision making [O’Reilly 2016].  It is thought that reinforcement learning occurs in BG with signals coding reward prediction errors.  The decision making in this circuit is not believed to be subject to conscious access.  In decision making, there are findings that the alternative that has the maximal time integral support is chosen [Agarwal 2018].

Central Executive System and Working Memory

The term ‘central executive’ is supposedly introduced in the working memory model in [Baddeley 1974].  The mechanism of working memory in the brain has not been fully elucidated [Eriksson 2015]. As the term ‘central executing network’ comes from the term in the working memory model and since CEN is activated when performing relevant tasks, it should involve working memory.  However, decisive findings were not found in this survey. The prefrontal cortex basal ganglia working memory (PBWM) model [O’Reilly 2006], one of a few computational neuroscientific models of working memory, does not mention CEN either. Besides, [Dehaene 2014] points out that working memory requires conscious access (see below).  While the prefrontal cortex has been reported to be involved in task switching tasks such as the Wisconsin card sorting task [Nagahama 2005], there seems to be no decisive finding on the relationship between task-switching tasks and working memory either [Stratta 1997].

The Role of the Hippocampus

A well-known function of the hippocampus is episodic memory: it is known that if the hippocampus is damaged, new memories of episodes can not be formed; i.e., this implies that hippocampus is involved in the consolidation of long-term episodic memory.  The representation of an episode can be seen as that of a situation, where the disposition, movement, and relationship among objects are represented. In the brain, the representation of situations would be synthesized from information from the ‘what,’ ‘where,’ and other pathways in the entorhinal cortex near the hippocampus and directed to the hippocampus [O’Reilly 2016].  In experiments with rodents, the hippocampus is known to “replay” past activity patterns related to the task. Replay is thought to be useful for prediction and memory consolidation.  Its relationship with conscious access will be revisited in the section of learning below.

Conscious Access and Decision Making

For perception with conscious access to occur, there is a delay of about 0.4 seconds after the change in the object to be perceived.  So conscious access is useless to decide on actions to be handled faster than the delay. Furthermore, conscious access to intention is known to occur about 0.35 seconds after activities occur in the neural system for decision making [Libet 2005].  However, conscious access must influence decision making because the performance of perceptual discrimination and verbal report depends on conscious access. [Dehaene 2014] points out that conscious access is necessary for the process to link the results of routine processing ‘strategically’ (Chapter 3).  If conscious access is required to hold representations longer than one second as pointed out above, linking multi-modal representations and exploring long term memory would require conscious access, because they should take some time.

Besides, even if conscious access influences decisions, there is no need to assume “free will” or “homunculi” there.  Conscious access discussed here is a large-scale phase transition of a neural network that determines the following activity of the neural system deterministically (or stochastically).  Processes occurring before and after conscious access are not necessarily accessed to consciousness either. Attention (automatic attention) is an example of a subconscious process before conscious access [Dehaene 2014][Koch 2017](Chapter 4).  In attentional processes, neural mechanisms such as SN detect salient patterns and increases neural activities for those patterns. In the case of vision, attention is accompanied by subconscious actions (i.e., saccades) directing the gaze. When an attentional activity is elevated sufficiently, competes against the activities of other parts, and wins, it leads to conscious access.

After conscious access, if a policy to the situation has already been learned, decision is made as a routine.  If a policy (routine) is not available, long-term memory (including episodic memory) would be searched to access non-routine action options with representations added by conscious access.  Among non-routine options, that which is evaluated as the most appropriate is executed. Here, the process would be automatically performed as well. Also, task processing with working memory may involve routinization (e.g., [O’Reilly 2006]) and may not be for conscious access.  Processes that would require working memory such as parsing and solving certain familiar tasks are not for conscious access and its verbal report are also difficult.

Conscious Access and Learning

The passive consciousness model [Maeno 2005] claims that consciousness evolved to generate episodic memory.  The representation of an episode or situation would be obtained by extracting and compressing highly relevant pieces out of perceptual information.  The information sent from higher-order perception areas to the hippocampus, which generates episodic memory, is thought to be highly abstract, coding where and what objects are.  It is further converted by the hippocampus to “sparse” representation that serves as an index for the situation [O’Reilly 2016]. Such information compression is reasonable, for, if you try to make a (machine) learning system to learn high-dimensional data such that the system cannot tell which dimension should be focused, learning fails due to the “curse of dimensionality.”  Thus, extracting salient objects with conscious access would benefit learning. The extracted perceptual pattern of the object would be marked as useful by emotion (value) circuits and further registered in long term memory with indexing and replay in the hippocampus.  For the relationship between prefrontal cortex and hippocampus, see [Simons & Spiers 2003]. For the relation between consciousness and the hippocampus, see also [Behrendt 2013].

Conscious Access and Language

Since the consciousness of events and the possibility of its verbal report are often treated similarly for human beings, conscious access and language should have an overlapping mechanism. The passive consciousness model cited above assumes that the function of consciousness is to generate episodic memory.  The representations of episodes and language should both be composite representations at a higher-order or conceptual level. Besides, in the expression of linguistic functions (as well as in that of many other cognitive functions), the frontal cortex and parietal lobe (+ the upper temporal lobe), assumed to belong to CEN, show simultaneous activity (e.g., see Fig. 10.2 in [Deacon 1999]).


In this article, the relation among conscious access, decision making, and large-scale networks in the brain are explored.  Here, the author presents a hypothesis based on the survey (Fig. 3).

  • Conscious access is caused by a sufficient amount of attention directed to the object [Dehaene 2014].
  • The pattern to which attention is directed is determined by its salience [Dehaene 2014][Menon 2010] and the value assigned to it by learning [Dehaene 2014, Chapt. 2].
  • The salience of the pattern is determined by factors such as its intensity, novelty, and learned value (importance) of the pattern and by innate information such as sexual stimuli [Koch 2017 p.48] and the forms of typical enemies. [Goldberg 2018]
  • Value assignment for patterns is performed by the medial frontal lobe in DMN [Passingham 2012], while the control of access, including attention from PFC to other areas such as the parietal and temporal lobes and hippocampus, is learned in the PFC-BG-thalamic loop [O’Reilly 2016].
  • Attention is first directed to spatial representation in the parietal lobe via CEN [Menon 2010] and when the amplitude of attention grows to a certain level, conscious access occurs [Dehaene 2014].  In visual perception, attention and gaze control coordinate.
  • In order to generate episode representation by binding representations, it would be necessary to hold representations for a certain period of time, so that conscious access is required [Dehaene 2014].  Since conscious access can not have multiple objects at a time, binding representations with conscious access must be performed sequentially. For that matter, recursive neural networks are known to be able to integrate sequential information.
  • The hippocampus indexes episode representations and generates long-term episodic memory based on the indexing [O’Reilly 2016].
  • Episodic representation is used to solve the problem “offline” which requires conscious access such as planning [Dehaene 2014]. In solving these problems, primitive processes are not accessed consciously.
  • Decision is made by choosing the one with the highest time-integrated activity out of the subconsciously (without conscious access) represented options in PFC [Agarwal 2018].  Activation of options is modified by the PFC-BG-thalamic loop based on representations including those from the environment, those from episodic memory, and the result of planning [O’Reilly 2016] (where conscious access may involve).  The activation is also modified by the magnitude of the neural connections regulated by reinforcement learning (from past rewards).
  • Non-routinized decision making would involve search in episodic memory that may contain the memory of useful past actions [Maeno 2015].

Figure 3: Networks of Conscious Access and Decision Making
Conscious access is the phase transition of attention to a particular object occurring between PFC and the parietal lobe.  The information of objects sequentially accessed consciously is bound near the hippocampus and becomes episodic representation.  Episodic representation is later recalled and used by long-term memory access from PFC for decision making.

In this survey, there remain issues yet to be resolved.  With regard to task performance, no decisive finding or detailed model on the relationship between CEN, conscious access, working memory, and task switching tasks was found.  As conscious access is necessary for tasks such as discrimination, those to combine routine processing “strategically”, verbal reports, findings and models of the mechanisms to carry out them, to elucidate the issues on conscious access and decision making. Modelling conscious access and the generation of episodic representation is also an important subject, as episodic memory influences decision.

While it is an interesting problem whether conscious access occurs during introspection where DMN is at work, no conclusive finding was found in this survey ([Dehaene 2014] does not explicitly mention CEN either).  See [Song 2008] for an extension of the theory of Dehaene et al. to DMN. Note that “avalanches” seem to occur during non-CEN activities as well [Shriki 2013].


Hassabis, D., Kumaran D., Summerfield, C. & Botvinick M. (2017) Neuroscience-Inspired Artificial Intelligence. Neuron, 95(2), 245-258.

Kriegeskorte, N. & Pamela K. Douglas, P.K. (2018) Cognitive computational neuroscience. Nature Neuroscience, 21, 1148–1160.

Menon, V. (2010) Large-Scale Brain Networks in Cognition: Emerging Principles. Short Course III: Analysis and Function of Large-Scale Brain Networks, Organized by Sporns, O., Society for Neuroscience.

Goldberg, G. (2018), Creativity: The Human Brain in the Age of Innovation, Oxford University Press.

Passingham, R.E. & Wise, S.P. (2012), The Neurobiology of the Prefrontal Cortex Anatomy, Evolution, and the Origin of Insight, Oxford University Press.

O’Reilly, R.C., Frank, M.J., & al. (2016) CCNBook, .

Dehaene, S. (2014) Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts , Viking.

Dehaene, S., Changeux, J. & Naccache, L. (2011), The Global Neuronal Workspace Model of Conscious Access: From Neuronal Architectures to Clinical Applications in Research and Perspectives in Neurosciences, 18, 55-84

Agarwal, A., et al. (2018) Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning. arXiv:1809.09147 [cs.LG].

Baddeley, A. D. & Hitch, G. (1974). Working memory. In G.H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory, 8, 47–89, Academic Press.

Eriksson, J., & al. (2015) Neurocognitive Architecture of Working Memory, Neuron, 88(1) 33-46.

O’Reilly, R.C. & Frank, M.J. (2006) Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural Comput. 18(2), 283-328.

Nagahama, Y. (2005) The cerebral correlates of different types of perseveration in the Wisconsin Card Sorting Test. Journal of Neurology, Neurosurgery and Psychiatry, 76(2), 169–175.

Stratta P. (1997) Is Wisconsin Card Sorting Test performance related to ‘working memory’ capacity?. Schizophrenia Research, 27(1), 11-19.

Libet, B. (2005) Mind Time: The Temporal Factor in Consciousness, Harvard University Press.

Koch, C. (2017) Consciousness: Confessions of a Romantic Reductionist, The MIT Press.

Maeno T. (2005) How to Make a Conscious Robot (in Japanese), Journal of the Robotics Society of Japan, 23(1), 51-62.

Simons, J.S. & Spiers, H.J. (2003), Prefrontal and medial temporal lobe interactions in long-term memory, Nature reviews Neuroscience. 4(8), 637-648.

Behrendt, R.P. (2013), Conscious Experience and Episodic Memory: Hippocampus at the Crossroads, Front. Psychol.; 4: 304.

Deacon, T.W. (1998) The Symbolic Species: The Co-Evolution of Language and the Brain, W. W. Norton & Company.

Song, X.& Tang, X. (2008) An extended theory of global workspace of consciousness, in Progress in Natural Science, 18(7), 789-793.

Shriki, O., & al. (2013) Neuronal Avalanches in the Resting MEG of the Human Brain. Journal of Neuroscience, 33 (16) 7079-7090.


This article was made possible with funding from the Kakenhi project “Brain information dynamics underlying multi-area interconnectivity and parallel processing.”  The author also thanks members of Project AGI and Dwango AI Laboratory for their useful comments.

Leave a Reply