## 1. Fundamentals


### 1.1 A Map of the Brain



It will be helpful to grasp the basic anatomy of the brain. A central
distinction concerns the difference between the cerebral
*cortex* and the *subcortex*. The cortex is divided into
two hemispheres, left and right, each of which can be divided into
four lobes: frontal, parietal, temporal and occipital.



![a diagram of the brain. Major clockwise sections labeled are the 'Frontal Lobe' with the PFC marked in it. 'Parietal Lobe' with S1 between it and the Frontal Lobe; also includes the SPL and IPL. 'Dorsal Stream' with, in a circle, MST and MT, V6, V3A. 'Occipital Lobe' with V1 and then in concentric circles around V1 are V2 then V3. 'Ventral Stream' with V4. 'Temporal Lobe' with IT. V1 has an arrow pointing to V4 then IT. V1 also has an arrow pointing to V3A which in turn has arrows pointing to V6 and MST/MT. V6 has arrows pointing to SPL and MST/MT. MST/MT has an arrow pointing to IPL.](figure1.png)

Figure 1: The Cerebral Cortex and Salient
Areas
  

Figure Legend: The four lobes of the primate brain, shown for the left
hemisphere. Some areas of interest are highlighted. Abbreviations:
PFC: prefrontal cortex; IT: inferotemporal cortex; S1: primary
somatosensory cortex; IPL and SPL: Inferior and Superior Parietal
Lobule; MST: medial superior temporal visual area; MT: middle temporal
visual area (also called V5 in humans); V1: primary visual cortex;
V2-V6 consist of additional visual areas.




The discussion that follows will highlight specific areas of cortex
including the *prefrontal cortex* that will figure in
discussions of confidence
 (section 2.2),
 the global neuronal workspace
 (section 3.1)
 and higher order theories
 (section 3.3);
 the *dorsal visual stream* that projects into parietal cortex
and the *ventral visual stream* that projects into temporal
cortex including visual areas specialized for processing places,
faces, and word forms (see sections
 2.6
 on places,
 4.1
 on visual agnosia and
 5.3.3
 on seeing words); *primary somatosensory cortex* S1 (see
section
 5.3.2
 on tactile sensation); and early visual areas in the occipital cortex
including the *primary visual area, V1* (see sections
 4.2
 on blindsight and
 5.2
 on binocular rivalry) and a motion sensitive area *V5*, also
known as the *middle temporal area* (MT;
 section 5.3.1
 on seeing motion). Beneath the cortex is the subcortex, divided into
the forebrain, midbrain, and hindbrain, which covers many regions
although our discussion will largely touch on the *superior
colliculus* and the *thalamus*, two areas that play an
important role in visual processing.


### 1.2 Neurons and Brain



A neuroscientific explanation of consciousness adduces properties of
the brain, typically the brain's electrical properties. A
salient phenomenon is neural signaling through *action
potentials* or *spikes.* A spike is a large change in
electrical potential across a neuron's cellular membrane which
can be transmitted between neurons that form a neural circuit. For a
sensory neuron, the spikes it generates are tied to its *receptive
field.* For example, in a visual neuron, its receptive field is
understood in spatial terms and corresponds to that area of external
space where an appropriate stimulus triggers the neuron to spike.
Given this correlation between stimulus and spikes, the latter carries
information about the former. Information processing in sensory
systems involves processing of information regarding stimuli within
receptive fields.



Which electrical property provides the most fruitful explanatory basis
for understanding consciousness remains an open question. For example,
when looking at a single neuron, neuroscientists are not interested in
spikes per se but the spike rate generated by a neuron per unit time.
Yet spike rate is one among many potentially relevant neural
properties. Consider the blood oxygen level dependent signal (BOLD)
measure in functional magnetic resonance imaging (fMRI). The BOLD
signal is a measure of changes in blood flow in the brain when neural
tissue is active and is postulated to be a function of electrical
properties at a different part of a neuron than that part tied to
spikes. Specifically, given a *synapse* which is the connection
between two neurons to form a basic circuit motif, spikes are tied to
the *pre*synaptic side while the BOLD signal is thought to be a
function of electrical changes on the *post*synaptic side
(signal flow is from pre to post). Furthermore, neuroscientists are
typically not interested in the response of a single neuron but rather
that of a population of neurons, of whole brain regions, and/or their
interactions. Higher order properties of brain regions include the
*local field potential* generated by populations of neurons and
correlated activity such as *synchrony* between activity in
different areas of the brain (neural oscillations were postulated to
be central to consciousness by Crick & Koch 1990).



The number of neural properties potentially relevant to explaining
mental phenomena is dizzying. This review focuses on the facts that
neural sensory systems carry information about the subject's
environment and that neural information processing can be tied to a
notion of *neural representation*. How precisely to understand
neural representation is itself a vexed question (Cao 2012, 2014; Shea
2014), but we will deploy a simple assumption with respect to spikes
which can be reconfigured for other properties: where a sensory neuron
generates spikes when a stimulus is placed in its receptive field, the
spikes carry information about the stimulus (strictly speaking, about
a random variable). *Information* as used in neuroscience is
typically not a semantic notion, but bearing in mind that caveat, it
will simplify matters to speak of a sensory neuron's activity as
*representing* the relevant aspect of the stimulus that drives
the neuron's response (e.g., direction of motion or intensity of
a
 sound).[2]
 This way of speaking is imprecise, so we shall return to neural
representation in the final section when discussing how neural
representations might explain conscious contents.


### 1.3 Access Consciousness and Phenomenal Consciousness



An important distinction separates *access consciousness* from
*phenomenal consciousness* (Block 1995). "Phenomenal
consciousness" refers to those properties of experience that
correspond to *what it is like* for a subject to have those
experiences (Nagel 1974 and the entry on
 qualia).
 These features are apparent to the subject from the inside, so
tracking them arguably depends on one's having the relevant
experience. For example, one understands what it is like to see red
only if one has visual experiences of the relevant type (Jackson
1982).



As noted earlier, introspection is the first source of evidence about
consciousness. Introspective reports bridge the subjective and
objective. They serve as a behavioral measure that expresses the
subject's own take on what it is like for her in having an
experience. While there have been recent concerns about the
reliability or empirical usefulness of introspection (Schwitzgebel
2011; Irvine 2012a), there are plausibly many contexts where
introspection is reliable (Spener 2015; see Irvine 2012b for an
extended discussion of introspection in consciousness science; for
philosophical theories, see Smithies & Stoljar 2012).



Introspective reports demonstrate that the subject can access the
targeted conscious state. That is, the state is
*access*-conscious: it is accessible for use in reasoning,
report, and the control of action. Talk of access-consciousness must
keep track of the distinction between *actual* access versus
*accessibility*. When one reports on one's conscious
state, one accesses the state. Thus, access consciousness provides
much of the evidence for empirical theories of consciousness. Still,
it seems plausible that a state can be conscious even if one does not
access it in report so long as that state is *accessible*. One
*can* report it. Access-consciousness is usually defined in
terms of this dispositional notion of accessibility. 



We must also consider the type of access/accessibility. Block's
original characterization of access-consciousness emphasized
accessibility in terms of the *rational* control of behavior,
so we can summarize his account as follows:



> 
> 
> A representation is access-conscious if it is poised for free use in
> reasoning and for direct "rational" control of action and
> speech.
> 
> 
> 



Rational access contrasts with a broader conception of
*intentional* access that takes a mental state to be
access-conscious if it can inform goal-directed or intentional
behavior including behavior that is not rational or done for a reason.
This broader notion allows for additional measurable behaviors as
relevant in assessing phenomenal consciousness especially in
non-linguistic animals. So, if access provides us with evidence for
phenomenal consciousness, this can be (a) through introspective
reports; (b) through rational behavior, (c) through intentional
behavior including nonrational behavior. Indeed, in certain contexts,
reflexive behavior provides measures of consciousness
 (section 2.3).


### 1.4 Generic and Specific Consciousness



Explanations answer specific questions. Two questions regarding
phenomenal consciousness frame this entry: Generic and Specific. The
first focuses on a mental state's *being conscious* in
general as opposed to not being conscious. Call this property
*generic consciousness*, a property shared by specific
conscious states such as seeing a red rose, feeling a touch, or being
angry. Thus:



> 
> 
> *Generic Consciousness:* What conditions/states *N* of
> nervous systems are necessary and (or) sufficient for a mental state,
> *M*, to be conscious as opposed to not?
> 
> 
> 



If there is such an *N*, then the presence of *N* entails
that an associated mental state *M* is conscious and (or) its
absence entails that *M* is unconscious.



A second focus will be on the content of consciousness, say that
associated with a perceptual experience's being of some
perceptible *X*. This yields a question about specific contents
of consciousness such as experiencing the motion of an object (see
 section 5.3.1)
 or a vibration on one's finger (see
 section 5.3.2):



> 
> 
> *Specific Consciousness:* What neural states or properties are
> necessary and/or sufficient for a conscious perceptual state to have
> content *X* rather than *Y*?
> 
> 
> 



Expanding a bit, perceptual states have intentional content and
specifying that content is one way of describing what that state is
like. In introspectively accessing her conscious states, a subject
reports what her experience is like by reporting what she experiences.
Thus, the subject can report seeing an object moving, changing color,
or being of a certain kind (e.g., a mug) and thus specify the content
of the perceptual state. Discussion of specific consciousness will
focus on perceptual states described as consciously perceiving
*X* where *X* can be a particular such as a face, a property
such as the frequency of a vibration or a proposition, say seeing that
an object moves in a certain direction.



Many philosophers take perceiving *X* to be perceptually
representing *X*. Intentional content on this reading is a
semantic notion, and this suggests a linking principle tying conscious
content to the brain: Perceptually representing *X* is based on
neural representations of *X*. The "based on"
locution hedges on the precise relation between neural contents and
conscious contents, but a simple relation is identity: neural content
is perceptual
 content.[3]
 This principle expresses a type of *first-order
representationalism* about phenomenal content, a topic we return
to in
 section 5;
 see also the entry on
 representational theories of consciousness.
 The principle explains specific consciousness by appeal to neural
representational content.



Posing a clear question involves grasping its possible answers and in
science, this is informed by identifying experiments that can provide
evidence for such answers. The emphasis on necessary and sufficient
conditions in our two questions indicates how to empirically test
specific proposals. To test sufficiency, one would aim to produce or
modulate a certain neural state and then demonstrate that
consciousness of a certain form arises. To test necessity, one would
eliminate a certain neural state and demonstrate that consciousness is
abolished. Notice that such tests go beyond mere correlation between
neural states and conscious states (see
 section 1.6
 on neural correlates and sections
 2.2,
 4 and
 5
 for tests of necessity and sufficiency).



In many experimental contexts, the underlying idea is *causal*
necessity and sufficiency. However, if \(A=B\), then *A*'s
presence is also necessary and sufficient for *B*'s
presence since they are identical. Thus, a brain lesion that
eliminates *N* and thereby eliminates conscious state *S*
might do so either because *N* is causally necessary for *S*
or because \(N=S\). An intermediate relation is that *N*
*constitutes* or *grounds* *S* which does not imply
that \(N=S\) (see the entry on
 metaphysical grounding).
 Whichever option holds for *S*, the first step is to find
*N*, a neural correlate of consciousness
 (section 1.6).



In what follows, to explain generic consciousness, various global
properties of neural systems will be considered
 (section 3)
 as well as specific anatomical regions that are tied to conscious
versus unconscious vision as a case study
 (section 4).
 For specific consciousness, fine-grained manipulations of neural
representations will be examined that plausibly shift and modulate the
contents of perceptual experience
 (section 5).


### 1.5 The Hard Problem



David Chalmers presents the hard problem as follows:



> 
> 
> It is undeniable that some organisms are subjects of experience. But
> the question of how it is that these systems are subjects of
> experience is perplexing. Why is it that when our cognitive systems
> engage in visual and auditory information-processing, we have visual
> or auditory experience: the quality of deep blue, the sensation of
> middle C? How can we explain why there is something it is like to
> entertain a mental image, or to experience an emotion? It is widely
> agreed that experience arises from a physical basis, but we have no
> good explanation of why and how it so arises. Why should physical
> processing give rise to a rich inner life at all? It seems objectively
> unreasonable that it should, and yet it does. If any problem qualifies
> as *the* problem of consciousness, it is this one. (Chalmers
> 1995: 212)
> 
> 
> 



The Hard Problem can be specified in terms of generic and specific
consciousness (Chalmers 1996). In both cases, Chalmers argues that
there is an inherent limitation to empirical explanations of
phenomenal consciousness in that empirical explanations will be
fundamentally either structural or functional, yet phenomenal
consciousness is not reducible to either. This means that there will
be something that is left out in empirical explanations of
consciousness, a missing ingredient (see also the *explanatory
gap* [Levine 1983]).



There are different responses to the hard problem. One response is to
sharpen the explanatory targets of neuroscience by focusing on what
Chalmers calls *structural features* of phenomenal
consciousness, such as the spatial structure of visual experience, or
on the contents of phenomenal consciousness. When we assess
explanations of specific contents of consciousness, these focus on the
neural representations that fix conscious contents. These explanations
leave open exactly what the secret ingredient is that shifts a state
with that content from unconsciousness to consciousness. On
ingredients explaining generic consciousness, a variety of options
have been proposed (see
 section 3),
 but it is unclear whether these answer the Hard Problem, especially
if any answer to that the Problem has a necessary condition that the
explanation must conceptually close off certain possibilities, say the
possibility that the ingredient could be added yet consciousness not
ignite as in a zombie, a creature without phenomenal consciousness
(see the entry on
 zombies).
 Indeed, some philosophers deny the hard problem (see Dennett 2018 for
a recent statement). Patricia Churchland urges: "Learn the
science, do the science, and see what happens" (Churchland 1996:
408).



Perhaps the most common attitude for neuroscientists is to set the
hard problem aside. Instead of explaining the existence of
consciousness in the biological world, they set themselves to
explaining generic consciousness by identifying neural properties that
can turn consciousness on and off and explaining specific
consciousness by identifying the neural representational basis of
conscious contents.


### 1.6 Neural Correlates of Consciousness



Modern neuroscience of consciousness has attempted to explain
consciousness by focusing on *neural correlates of
consciousness* or NCCs (Crick & Koch 1998, 2003). Identifying
correlates is an important first step in understanding consciousness,
but it is an early step. After all, correlates are not necessarily
explanatory in the sense of answering specific questions posed by
neuroscience. That one does not want a mere correlate was recognized
by Chalmers who defined an NCC as follows:



> 
> 
> An NCC is a minimal neural system *N* such that there is a
> mapping from states of *N* to states of consciousness, where a
> given state of *N* is sufficient under conditions *C*, for
> the corresponding state of consciousness. (Chalmers 2000: 31)
> 
> 
> 



Similarly, Christof Koch and others speak of "the minimal neural
mechanisms jointly sufficient for any one specific conscious
experience" (Koch et al. 2016: 307). One wants a minimal neural
system since, crudely put, the brain is sufficient for consciousness
but to point this out is hardly to explain consciousness even if it
provides an answer to questions about sufficiency. There is, of
course, much more to be said that is informative even if one does not
drill down to a "minimal" neural system which is tricky to
define or operationalize (see Chalmers 2000 for discussion; for
criticisms of the NCC approach, see Noe & Thompson 2004; for
criticisms of Chalmers' definition, see Fink 2016).



The emphasis on sufficiency goes beyond mere correlation, as
neuroscientists aim to answer more than the question: What is a neural
correlate for conscious phenomenon *C*? For example,
Chalmers' and Koch's emphases on sufficiency indicate that
they aim to answer the question: What neural phenomenon is sufficient
for consciousness? Perhaps more specifically: What neural phenomenon
is causally sufficient for consciousness? Accordingly, talk of
"correlate" is unfortunate since sufficiency implies
correlation but not vice versa. For example, correlation does not
imply causal sufficiency, so not every correlate will be explanatory
in the sense of answering Chalmers' and Koch's question.
After all, assume that the NCC is type identical to a conscious state.
Then many neural states will correlate with the conscious state: (1)
the NCC's typical effects, (2) its typical causes, and (3)
states that are necessary for the NCCs obtaining (e.g., the presence
of sufficient oxygen). Thus, some correlated effects will not be
explanatory. For example, citing the effects of consciousness will not
provide causally sufficient conditions for consciousness.



While many theorists are focused on explanatory correlates, it is not
clear that the field has always grasped this, something recent
theorists have been at pains to emphasize (Graaf, Hsieh, & Sack
2012; Aru et al. 2012; Koch et al. 2016). In other contexts,
neuroscientists speak of the neural *basis* of a phenomenon
where the basis does not simply correlate with the phenomenon but also
explains and possibly grounds it. However, talk of correlates is
entrenched in the neuroscience of consciousness, so one must remember
that the goal is to find the subset of neural correlates that are
explanatory, in answering concrete questions. Reference to neural
correlates in this entry will always mean neural explanatory correlate
of consciousness (on occasion, I will speak of these as the neural
*basis* of consciousness). That is, our two questions about
specific and generic consciousness focus the discussion on
neuroscientific theories and data that contribute to explaining them.
This project allows that there are limits to neural explanations of
consciousness, precisely because of the explanatory gap (Levine
1983).


## 2. Methods for Tracking Consciousness



Since studying consciousness requires that scientists track its
presence, it will be important to examine various methods used in
neuroscience to isolate and probe conscious states.


### 2.1 Introspection and Report



Scientists primarily study phenomenal consciousness through subjective
reports. We can treat reports in neuroscience as conceptual in that
they express how the subject recognizes things to be, whether
regarding what they perceive (perceptual or observational reports, as
in psychophysics) or regarding what mental states they are in
(introspective reports). A report's conceptual content can be
conveyed in words or through another overt behavior whose significance
is fixed within an experimental context (e.g., pressing a button to
indicate that a stimulus is present or that one sees it). Subjective
reports of conscious states draw on distinctively first-personal
access to that state. The subject *introspects*.



Introspection raises questions that science has only recently begun to
address systematically in large part because of longstanding suspicion
regarding introspective methods. Early modern psychology relied on
introspection to parse mental processes but ultimately abandoned it
due to worries about introspection's reliability (Feest 2012;
Spener forthcoming). Introspection was judged to be an unreliable
method for addressing questions about mental processing. To address
these worries, we must understand how introspection works, but unlike
many other psychological capacities, we lack detailed models of
introspection of consciousness (Feest 2014; for theories of
introspecting propositional attitudes, see Nichols & Stich 2003;
Goldman 2006; Heal 1996; Carruthers 2011). This makes it difficult to
address long-standing worries about introspective reliability
regarding consciousness.



In science, questions raised about the reliability of a method are
answered by calibrating and testing the method. This calibration has
not been done with respect to the type of introspection commonly
practiced by philosophers. Such introspection has revealed many
phenomenal features that are targets of active investigation such as
the phenomenology of mineness (Ehrsson 2009), sense of agency (Bayne
2011; Vignemont & Fourneret 2004; Marcel 2003; Horgan, Tienson,
& Graham 2003); transparency (Harman 1990; Tye 1992),
self-consciousness (Kriegel 2003: 122), cognitive phenomenology (Bayne
& Montague 2011); phenomenal unity (Bayne & Chalmers 2003)
among others. A scientist might worry that philosophical introspection
merely recycles rejected methods of a century ago, indeed without the
stringent controls or training imposed by earlier psychologists. How
can we ascertain and ensure the reliability of introspection in the
empirical study of consciousness?



One way to address the issue is to connect introspection to attention.
Philosophical conceptions of introspective attention construe it as
capable of directly focusing on phenomenal properties and experiences.
As this idea is fleshed out, however, it is clearly not a form of
attention studied by cognitive science, for the posited direct
introspective attention is neither perceptual attention nor what
psychologists call internal attention (e.g., the retrieval of thought
contents as in memory recollection; (Chun, Golomb, & Turk-Browne
2011)). Calibrating introspection as it is used in the science of
consciousness would benefit from concrete models of introspection,
models we lack (see Spener 2015, for a general form of
calibration).



One philosophical tradition links introspection to perceptual
attention, and this allows construction of concrete models informed by
science. The intuitive idea is expressed in Harman's
observation:



> 
> 
> Look at a tree and try to turn your attention to intrinsic features of
> your visual experience. I predict you will find that the only features
> there to turn your attention to will be features of the presented
> tree, including relational features of the tree "from
> here". (Harman 1990: 39) 
> 
> 
> 



This is related to a proposal inspired by Gareth Evans (1982): in
introspecting perceptual states, say judging that one sees an object,
one draws on the same perceptual capacities used to answer the
question whether the object is present. In introspection, one then
appends a further concept of "seeing" to one's
perceptual
 report.[4]
 Thus, instead of simply reporting that a red stimulus is present, one
reports that one sees the red stimulus. Paradoxically, introspection
relies on externally directed perceptual attention, but as noted
earlier, identifying what one perceives is a way of characterizing
what one's perception is like, so this "outward"
perspective can provide information about the inner. Further, the
advantage of this proposal is that questions of reliability come down
to questions of the reliability of psychological capacities that can
be empirically assessed, say perceptual, attentional and conceptual
reliability. For example, Peters and Lau (2015) showed that accuracy
in judgments about the visibility of a stimulus, the introspective
measure, coincided with accuracy in judgments about stimulus
properties, the objective measure (see also Rausch & Zehetleitner
2016).



Introspection can be reliable. Successful clinical practice relies on
accurate introspection as when dealing with pain or correcting blurry
vision in optometry. The success of medical interventions suggests
that patient reports of these phenomenal states are reliable. Further,
in many of the examples to be discussed, the perceptual
attention-based account provides a plausible cognitive model of
introspection. Subjects report on what they perceptually experience by
attending to the object of their experience, and where perception and
attention are reliable, a plausible hypothesis is that their
introspective judgments will be reliable as well. Accordingly, I
assume the reliability of introspection in the empirical studies to be
discussed. Still, given that no scientist should assert the
reliability of a method without calibration, introspection must be
subject to the same standards. There is more work to be done.


### 2.2 Access as a Condition on Phenomenal Consciousness



Introspection illustrates a type of cognitive access, for a state that
is introspected is access conscious. This raises a question that has
epistemic implications: is access consciousness necessary for
phenomenal consciousness? If it is not, then there can be phenomenal
states that are not access conscious, so are in principle not
reportable. That is, phenomenal consciousness can *overflow*
access consciousness (Block 2007).



Access is tied to attention. For example, the Global Workspace theory
of consciousness understands consciousness in terms of access
 (section 3.1)
 where the accessibility of a perceived object requires attention to
the object (attention puts an object, namely a representation of it,
in the global workspace). So, the necessity of attention for
phenomenal consciousness is entailed by the necessity of access for
phenomenal
 consciousness.[5]
 In contrast, recurrent processing theory holds that there can be
phenomenal states that are not accessible
 (section 3.2).



Many scientists of consciousness take there to be evidence for no
phenomenal consciousness without access and little if no evidence of
phenomenal consciousness outside of access. An important set of
studies focuses on the thesis that attention is a necessary gate for
phenomenal consciousness, where attention is tied to access. Call this
the *gatekeeping* thesis. To assess that evidence, we must ask:
what is attention? An uncontroversial conception of attention is that
it is subject selection of a target to inform task performance (Wu
2014b). The experimental studies thought to support the necessity of
attention for consciousness draw on this
 conception.[6]
 For example, in *inattentional blindness* paradigms (Mack
& Rock 1998), attention is directed by asking subjects to perform
a task on target *T* while a surprising stimulus *S* is
presented. This approach tests necessity by ensuring through task
performance that the subject is not attending to *S*. One then
measures whether the subject is aware of *S* by observing whether
the subject reports it. If the subject does not report *S*, then
the hypothesis is that failure of attention to *S* explains the
failure of conscious awareness of *S* and hence the failure of
report.



A well-known experiment asks subjects to attend to the number of
passes of a ball thrown by players in white shirts while ignoring a
second ball passed by players in black shirts (Simons & Chabris
1999). During the task, a person in a gorilla costume walks across the
scene. Half of the subjects fail to notice and report the gorilla,
this being construed as evidence for the absence of visual awareness
of the gorilla. Hence, failure to attend to the gorilla is said to
render subjects phenomenally blind to it. Similar claims are made in
*change blindness* where subjects fail to detect the difference
between two similar pictures (Simons & Ambinder 2005), the
*attentional blink* where subjects fail to detect a stimulus
presented immediately after detecting a prior stimulus (Dux &
Marois 2009; Martens & Wyble 2010), and *hemispatial
neglect* where patients fail to report objects in a part of their
visual field that they cannot attend to due to brain lesions.



The gatekeeping thesis holds that attention is necessary for
consciousness, so that removing it from a target eliminates
consciousness of it. Yet there is a flaw in the methodology. To report
a stimulus, one must attend to it, i.e., select it for report. The
experimental logic requires eliminating attention to a stimulus
*S* to test if attention is a necessary condition for
consciousness (e.g., eliminating attention to the gorilla by
distracting the subject with the ball). Yet even if the subject were
conscious of *S*, when attention to *S* is eliminated, one
can *predict* that the subject will fail to act (report) on
*S* since attention is necessary for report. The observed results
are actually consistent with the subject being conscious of *S*
without attending to it, and thus are neutral between overflow and
gatekeeping. Instead, the experiments concern parameters for the
capture of attention and not consciousness.



While those antagonistic to overflow have argued that it is not
empirically testable (M.A. Cohen & Dennett 2011), gatekeeping
might be equally untestable. After all, to test the necessity of
attention for consciousness, we must eliminate attention to a target
while gathering evidence for the absence of consciousness. Yet if
gathering evidence for consciousness requires attention, then in
fulfilling the conditions for testing the necessity of attention, we
undercut the access needed to substantiate the absence of
consciousness (Wu 2017; for a monograph length discussion of attention
and consciousness, see Montemayor & Haladjian 2015). How then can
we gather the required evidence to assess competing theories?


### 2.3 No Report Paradigms



One response to is to draw on *no-report paradigms* which
measure reflexive behaviors correlated with conscious states to
provide a window on the phenomenal that is independent of access
(Lumer & Rees 1999; Tse et al. 2005). For example, Frassle et
al. (2014) demonstrate that certain occular reflexes are correlated
with perceptual experience in binocular rivalry (Naber, Frassle,
& Einhauser 2011). They presented subjects either with
stimuli moving in opposite directions or stimuli of different
luminance values, one stimulus in each pair presented separately to
each eye. This induces binocular rivalry, an alternation in which of
the two stimuli is visually experienced (see
 section 5.2).
 Where the stimuli involved motion, subjects demonstrated
*optokinetic nystagmus* where the eye slowly moves in the
direction of the stimulus and then makes a fast, corrective saccade
(ballistic eye movement) in the opposite direction. Frassle et
al. observed that optokinetic nystagmus tracked the perceived
direction of the stimulus as reported by the subject. Similarly, for
stimuli of different luminance, the pupils would dilate, being wider
for dimmer stimuli, and narrower for brighter stimuli, again
correlating with subjective reports of the intensity of the
stimulus.



No-report paradigms use reflexive responses to track the
subject's perceptual experience in the absence of explicit
(conceptualized) report. They seem to provide a way to track
phenomenal consciousness even when access is eliminated. This would
broaden the evidential basis for consciousness beyond introspection
and indeed, beyond intentional behavior (our "broad"
conception of access). Yet no-report paradigms do not circumvent
introspection (Overgaard & Fazekas 2016). For example, optokinetic
nystagmus's usefulness depends on validating its correlation
with alternating experience *given subjective reports.* Once it
is validated, monitoring this reflex can provide a way to substitute
for subjective reports within that paradigm. One cannot, however,
simply extend the use of no-report paradigms outside the behavioral
contexts within which the method is validated. With each new
experimental context, we must revalidate the measure with
introspective report.



Can we use no report paradigms to address whether access is necessary
for phenomenal consciousness? A likely experiment would be one that
validates no-report correlates for some conscious phenomenon *P*
in a concrete experimental context *C*. With this validation in
hand, one then eliminates accessibility and attention with respect to
*P* in *C*. If the no-report correlate remains, would this
clearly support overflow? Perhaps, though gatekeeping theorists likely
will respond that the result does not rule out the possibility that
phenomenal consciousness disappears with access consciousness
*despite* the no-report correlate remaining. For example, the
reflexive response and phenomenal consciousness might have a common
cause that remains even if phenomenal consciousness is selectively
eliminated by removing access.


### 2.4 Confidence and Metacognitive Approaches



Given worries about calibrating introspection, researchers have asked
subjects to provide a different metacognitive assessment of conscious
states via reports about *confidence* (Grimaldi, Lau, &
Basso 2015; Pouget, Drugowitsch, & Kepecs 2016). A standard
approach is to have subjects perform a task, say perceptual
discrimination of a stimulus, and then indicate how confident they are
that their perceptual judgment was accurate. This judgment about
perception can be assessed for accuracy by comparing the metacognitive
judgment with perceptual performance (for discussion of formal methods
such as metacognitive signal detection theory, see Maniscalco &
Lau 2012). Related paradigms include post-decision wagering where
subjects place wagers on specific responses as a way of estimating
their confidence (Persaud, McLeod, & Cowey 2007; but see Dienes
& Seth 2010).



How is metacognitive assessment of performance tied to consciousness?
The metacognitive judgment reflects introspective assessment of the
quality of perceptual states and can provide information about the
presence of consciousness. For example, Peters and Lau (2015) tested
whether normal subjects have unconscious vision under conditions of
visual masking, a method that seems to eliminate conscious vision to a
stimulus but allows for accurate visually guided behavior to it
(Breitmeyer & Ogmen 2006). They presented stimuli in two temporal
"windows" under masking but where the stimulus was present
in only one window and a "blank" present in the other. If
subjects accurately respond to the stimulus but showed no difference
in metacognitive confidence in respect of the quality of perception of
the target versus of the blank, this would provide evidence of the
absence of consciousness in vision (effectively, blindsight in normal
subjects;
 section 4.2).
 Interestingly, Peters and Lau found no evidence for unconscious
vision in their specific paradigm.



One concern with metacognitive approaches is that they also rely on
introspection (Rosenthal 2018; see also Sandberg et al. 2010; Dienes
& Seth 2010). If metacognition relies on introspection, does it
not accrue all the disadvantages of the latter? One advantage of
metacognition is that it allows for psychophysical analysis. There has
also been work done on metacognition and its neural basis. Studies
with non-human primates and rodents have begun to shed light on neural
processing for metacognition (for a review, see Grimaldi, Lau, &
Basso 2015; Pouget, Drugowitsch, & Kepecs 2016). From animal
studies, one theory is that metacognitive information regarding
perception is already present in perceptual areas that guide
observational judgments, and these studies implicate parietal cortex
(Kiani & Shadlen 2009; Fetsch et al. 2014) or the superior
colliculus (Kim & Basso 2008; but see Odegaard et al. 2018).
Alternatively, information about confidence might be read out by other
structures, say prefrontal cortex (see
 section 3.3
 on Higher-Order Theory; also the entry on
 higher order theories of consciousness).


### 2.5 The Intentional Action Inference



Metacognitive and introspective judgments result from intentional
action, so why not look at intentional action, broadly construed, for
evidence of consciousness? Often, when subjects perform perception
guided actions, we infer that they are relevantly conscious. It would
be odd if a person cooks dinner and then denies having seen any of the
ingredients. That they did something intentionally provides evidence
that they were consciously aware of what they acted on. An emphasis on
intentional action embraces a broader evidential basis for
consciousness. Consider the *Intentional Action Inference* to
phenomenal consciousness:



> 
> 
> If some subject acts intentionally, where her action is guided by a
> perceptual state, then the perceptual state is phenomenally
> conscious.
> 
> 
> 



An epistemic version takes the action to provide good evidence that
the state is conscious. Notice that introspection is typically an
intentional action so it is covered by the inference. In this way, the
Inference effectively levels the evidential playing field:
introspective reports are simply one form among many types of
intentional actions that provide evidence for consciousness. Those
reports are not privileged.



The intentional action inference and no-report paradigms highlight the
fact the science of consciousness has largely restricted its
behavioral data to one type of intentional action, introspection. What
is the basis of privileging one intentional action over others?
Consider the calibration issue. For many types of intentional action
deployed in experiments, scientists can calibrate performance by
objective measures such as accuracy. This has not been done for
introspection of consciousness, so scientists have privileged an
uncalibrated measure over a calibrated one. This seems empirically
ill-advised. On the flip side, one worry about the intentional action
inference is that it ignores guidance by unconscious perceptual states
(see sections
 4
 and
 5.3.1).


### 2.6 Vegetative State and the Intentional Action Inference



The Intentional Action Inference is operative when subjective reports
are not available. For example, it is deployed in arguing that some
patients diagnosed as being in the vegetative state are conscious
(Shea & Bayne 2010; see also Drayson 2014).



> 
> 
> A patient in the vegetative state appears at times to be wakeful, with
> cycles of eye closure and eye opening resembling those of sleep and
> waking. However, close observation reveals no sign of awareness or of
> a 'functioning mind': specifically, there is no evidence
> that the patient can perceive the environment or his/her own body,
> communicate with others, or form intentions. As a rule, the patient
> can breathe spontaneously and has a stable circulation. The state may
> be a transient stage in the recovery from coma or it may persist until
> death. (Working Party RCP 2003: 249)
> 
> 
> 



Vegetative state patients are not clinically comatose but fall short
of being in a "minimally conscious state". Unlike
vegetative state patients, minimally conscious state patients
seemingly perform intentional actions.



Recent work suggests that some patients diagnosed as in the vegetative
state are conscious. Owen et al. (2006) used fMRI to demonstrate
correlated activity in such patients in response to commands to deploy
imagination. In an early study, a young female patient was scanned by
fMRI while presented with three auditory commands: "imagine
playing tennis", "imagine visiting the rooms in your
home", "now just relax". The commands were presented
at the beginning of a thirty-second period, alternating between
imagination and relax commands. The patient demonstrated similar
activity when matched to control subjects performing the same task:
sustained activation of the supplementary motor area (SMA) was
observed during the motor imagery task while sustained activation of
the parahippocampal gyrus including the parahippocampal place area
(PPA) was observed during the spatial imagery task. Later work
reproduced this result in other patients and in one patient, the tasks
were used as a proxy for "yes"/ "no" responses
to questions (Monti et al. 2010; for a review, see
Fernandez-Espejo & Owen 2013). Note that these tasks probe
*specific* contents of consciousness by monitoring neural
correlates of conscious imagery.



Several authors (Greenberg 2007; Nachev & Husain 2007) have
countered that the observed activity was an automatic, non-intentional
response to the command sentences, specifically to the words
"tennis" and "house". In normal subjects,
reading action words is known to activate sensorimotor areas
(Pulvermuller 2005). Owen et al. (2007), responded that the
sustained activity over thirty-seconds made an automatic response less
likely than an intentional response. One way to rule out automaticity
is to provide the patient with different sentences such as "do
*not* imagine playing tennis" or "*Sharlene*
was playing tennis". Owen et al. (2007) demonstrated that
presenting "Sharlene was playing tennis" to a normal
subject did not induce the same activity as when the subject obeyed
the command "imagine playing tennis", but the result was
not duplicated in patients. 



Owen et al. draw on a neural correlate of imagination, a mental
action. Arguing that the neural correlate provides evidence of the
patient's executing an intentional action, they invoke a version
of the Intentional Action Inference to argue that performance provides
evidence for specific consciousness tied to the information carried in
the brain areas activated. Of note, experiments stimulating the
parahippocampal place area induces seeming hallucinations of places
(Megevand et al.
 2014).[7]


## 3. Neurobiological Theories of Consciousness



Recall that the Generic Consciousness question asks:



> 
> 
> What conditions/states *N* of nervous systems are necessary and/or
> sufficient for a mental state, *M*, to be conscious as opposed to
> not?
> 
> 
> 



Victor Lamme notes:



> 
> 
> Deciding whether there is phenomenality in a mental representation
> implies putting a boundary--drawing a line--between
> different types of representations...We have to start from the
> intuition that consciousness (in the phenomenal sense) exists, and is
> a mental function in its own right. That intuition immediately implies
> that there is also *un*conscious information processing. (Lamme
> 2010: 208)
> 
> 
> 



It is uncontroversial that there is unconscious information
processing, say processing occurring in a computer. What Lamme means
is that there are conscious and unconscious mental states
(representations). For example, there might be visual states of seeing
*X* that are conscious or not
 (section 4).



In what follows, the theories discussed provide higher level neural
properties that are necessary and/or sufficient for generic
consciousness of a given state. To provide a gloss on the hypotheses:
For the Global Neuronal Workspace, entry into the neural workspace is
necessary and sufficient for a state or content to be consciousness.
For Recurrent Processing Theory, a type of recurrent processing in
sensory areas is necessary and sufficient for perceptual
consciousness, so entry into the Workspace is not necessary. For
Higher-Order Theories, the presence of a higher-order state tied to
prefrontal areas is necessary and sufficient for phenomenal
experience, so recurrent processing in sensory areas is not necessary
nor is entry into the workspace. For Information Integration Theories,
a type of integration of information is necessary and sufficient for a
state to be conscious.


### 3.1 The Global Neuronal Workspace



One explanation of generic consciousness invokes the *global
neuronal workspace*. Bernard Baars first proposed the global
workspace theory as a cognitive/computational model (Baars 1988), but
we will focus on the neural version of Stanislas Dehaene and
colleagues: a state is conscious when and only when it (or its
content) is present in the global neuronal workspace making the state
(content) globally *accessible* to multiple systems including
long-term memory, motor, evaluational, attentional and perceptual
systems (Dehaene, Kerszberg, & Changeux 1998; Dehaene &
Naccache 2001; Dehaene et al. 2006). Notice that the previous
characterization does not commit to whether it is phenomenal or access
consciousness that is being defined. 



*Access* should be understood as a relational notion:



A system *X* accesses content from system *Y* iff *X*
uses that content in its computations/processing.



The *accessibility* of information is then defined as its
potential access by other systems. Dehaene (Dehaene et al. 2006)
introduces a threefold distinction: (1) neural states that carry
information that is not accessible (*subliminal* information);
(2) states that carry information that is accessible but not accessed
(not in the workspace; *preconscious* information); and (3)
states whose information is accessed by the workspace
(*conscious* information) and is globally accessible to other
systems. So, a necessary and sufficient condition for a state's
being conscious rather than not is the access of a state or content by
the workspace, making that state or content accessible to other
systems. Hence, only states in (3) are conscious.



![see legend. The top figure is a series of dotted concentric circles with a network of lines and nodes imposed on top; the innermost circle is labeled 'Global Workspace'. The circles are divided into five sectors [except the sector radii don't cross the innermost circle]; each sector has a labels in an arrow pointing in [unless otherwise noted] clockwise from the top as, 'Evaluative Systems (VALUE)', 'Attentional Systems (FOCUSING)', 'Motor systems (FUTURE)' [the only one with an arrow pointing out], 'Perceptual systems (PRESENT)', 'Long-Term Memory (PAST)'. The bottom figure is the same network of lines and nodes (minus circles, arrows, and labels but with on the left a picture labeled 'frontal' and on the right a picture labeled 'sensory'. ](figure2.png)

Figure 2. The Global Neuronal
Workspace



Figure Legend: The top figure provides a neural architecture for the
workspace, indicating the systems that can be involved. The lower
figure sets the architecture within the six layers of the cortex
spanning frontal and sensory areas, with emphasis on neurons in layers
2 and 3. Figure reproduced from Dehaene, Kerszberg, and Changeux 1998. Copyright
(1998) National Academy of Sciences.




The global neuronal workspace theory ties access to brain
architecture. It postulates a cortical structure that involves
workspace neurons with long-range connections linking systems:
perceptual, mnemonic, attentional, evaluational and motoric.



What is the global workspace in neural terms? Long-range workspace
neurons within different systems can constitute the workspace, but
they should not necessarily be identified with *the* workspace.
A subset of workspace neurons becomes the workspace when they
exemplify certain neural properties. What determines which workspace
neurons constitute the workspace at a given time is the activity of
those neurons given the subject's current state. The workspace
then is not a rigid neural structure but a rapidly changing neural
network, typically only a proper subset of all workspace neurons.



Consider then a neural population that carries content *p* and is
constituted by workspace neurons. In virtue of being workspace
neurons, the content *p* is accessible to other systems, but it
does not yet follow that the neurons then constitute the global
workspace. A further requirement is that workspace neurons are (1) put
into an active state that must be sustained so that (2) the activation
generates a *recurrent* activity between workspace systems.
Only when these systems are recurrently activated are they, along with
the units that access the information they carry, constituents of the
workspace. This activity accounts for the idea of global
*broadcast* in that workspace contents are accessible to
further systems. Broadcasting explains the idea of consciousness as
*for the subject*: globally broadcasted content is accessible
for the subject's use in informing behavior.



The global neuronal workspace theory provides an account of
*access* consciousness but what of phenomenal consciousness?
The theory predicts widespread activation of a cortical workspace
network as correlated with phenomenal conscious experience, and
proponents often appeal to imaging results that reveal widespread
activation when consciousness is reported (Dehaene & Changeux
2011). There is, however, a potential confound. We track phenomenal
consciousness by access in introspective report, so widespread
activity during reports of conscious experience correlates with both
access and phenomenal consciousness. Correlation cannot tell us
whether the observed activity is the basis of phenomenal consciousness
or of access consciousness in report (Block 2007). This remains a live
question for as discussed in
 section 2.2,
 we do not have empirical evidence that overflow is false.



To eliminate the confound, experimenters ensure that performance does
not differ between conditions where consciousness is present and where
it is not. Where this was controlled, widespread activation was not
clearly observed (Lau & Passingham 2006). Still, the absence of
observed activity by an imaging technique does not imply the absence
of actual activity for the activity might be beyond the limits of
detection of that technique. Further, there is a general concern about
the significance of null results given that neuroscience studies
focused on prefrontal cortex are typically underpowered (for
discussion, see Odegaard, Knight, & Lau 2017).


### 3.2 Recurrent Processing Theory



A different explanation ties perceptual consciousness to processing
independent of the workspace, with focus on *recurrent*
activity in sensory areas. This approach emphasizes properties of
first-order neural representation as explaining consciousness. Victor
Lamme (2006, 2010) argues that recurrent processing is necessary and
sufficient for consciousness. Recurrent processing occurs where
sensory systems are highly interconnected and involve feedforward and
feedback connections. For example, forward connections from primary
visual area V1, the first cortical visual area, carry information to
higher-level processing areas, and the initial registration of visual
information involves a forward sweep of processing. At the same time,
there are many feedback connections linking visual areas (Felleman
& Van Essen 1991), and later in processing, these connections are
activated yielding dynamic activity within the visual system.



Lamme identifies four stages of normal visual processing:


* Stage 1: Superficial feedforward processing: visual signals are
processed locally within the visual system.
* Stage 2: Deep feedforward processing: visual signals have
travelled further forward in the processing hierarchy where they can
influence action.
* Stage 3: Superficial recurrent processing: information has
traveled back into earlier visual areas, leading to local, recurrent
processing.
* Stage 4: Widespread recurrent processing: information activates
widespread areas (and as such is consistent with global workspace
access).



Lamme holds that recurrent processing in Stage 3 is necessary and
sufficient for consciousness. Thus, what it is for a visual state to
be conscious is for a certain recurrent processing state to hold of
the relevant visual circuitry. This identifies the crucial difference
between the global neuronal workspace and recurrent processing theory:
the former holds that recurrent processing at Stage 4 is necessary for
consciousness while the latter holds that recurrent processing at
Stage 3 is sufficient. Thus, recurrent processing theory affirms
phenomenal consciousness without access by the global neuronal
workspace. In that sense, it is an overflow theory (see
 section 2.2).



Why think that Stage 3 processing is sufficient for consciousness?
Given that Stage 3 processing is not accessible to introspective
report, we lack introspective evidence for sufficiency. Lamme appeals
to experiments with brief presentation of stimuli such as letters
where subjects are said to report seeing more than they can identify
in report (Lamme 2010). For example, in George Sperling's
partial report paradigm (Sperling 1960), subjects are briefly
presented with an array of 12 letters (e.g., in 300 ms presentations)
but are typically able to report only three to four letters even as
they claim to see more letters (but see Phillips 2011). It is not
clear that this is strong motivation for recurrent processing, since
the very fact that subjects can report seeing more letters shows that
they have some access to them, just not access to letter identity.



Lamme also presents what he calls *neuroscience arguments.*
This strategy compares two neural networks, one taken to be sufficient
for consciousness, say the processing at Stage 4 as per Global
Workspace theories, and one where sufficiency is in dispute, say
recurrent activity in Stage 3. Lamme argues that certain features
found in Stage 4 are also found in Stage 3 and given this similarity,
it is reasonable to hold that Stage 3 processing suffices for
consciousness. For example, both stages exhibit recurrent processing.
Global neuronal workspace theorists can allow that recurrent
processing in stage 3 is correlated, even necessary, but deny that
this activity is explanatory in the relevant sense of identifying
sufficient conditions for consciousness.



It is worth reemphasizing the empirical challenge in testing whether
access is necessary for phenomenal consciousness (sections
 2.1-3).
 The two theories return different answers, one requiring access, the
other denying it. As we saw, the methodological challenge in testing
for the presence of phenomenal consciousness independently of access
remains a hurdle for both theories.


### 3.3 Higher-Order Theory



A long-standing approach to conscious states holds that one is in a
conscious state if and only if one relevantly represents oneself as
being in such a state. For example, one is in a conscious visual state
of seeing a moving object if and only if one suitably represents oneself
being in that visual state. This higher-order state, in representing
the first-order state that represents the world, results in the first
order state's being conscious as opposed to not. The intuitive
rationale for such theories is that if one were in a visual state but
in no way aware of that state, then the visual state would not be
conscious. Thus, to be in a conscious state, one must be aware of it,
i.e., represent it (see the entry on
 higher order theories of consciousness;
 Rosenthal 2002). Higher-order theories merge with empirical work by
tying high-order representations with activity in prefrontal cortex
which is taken to be the neural substrate of the required higher-order
representations. On certain higher-order theories, one can be in a
conscious visual state even if there is no visual system activity, so
long as one represents oneself as being in that state.



The focus on prefrontal cortex allows for empirical tests of the
higher-order theory as against other accounts (Lau & Rosenthal
2011). For example, on the higher-order theory, lesions to prefrontal
cortex should affect consciousness (Kozuch 2014), testing the
necessity of prefrontal cortex for consciousness. Against higher-order
theories, some reports claim that patients with prefrontal cortex
surgically removed maintain preserved perceptual consciousness (Boly
et al. 2017). This would lend support to recurrent processing theories
that hold that prefrontal cortical activity is not necessary for
consciousness. It is not clear, however, that the interventions
succeeded in removing *all* of prefrontal cortex, leaving
perhaps sufficient frontal areas needed to sustain consciousness
(Odegaard, Knight, & Lau 2017). Bilateral suppression of
prefrontal activity using transcranial magnetic stimulation also seems
to selectively impair visibility as evidenced by metacognitive report
(Rounis et al. 2010). Furthermore, certain syndromes and experimental
manipulations suggest consciousness in the absence of appropriate
sensory processing as predicted by some higher-order accounts (Lau
& Brown forthcoming), a claim that coheres with the theory's sufficiency
claims.


### 3.4 Information Integration Theory



*Information Integration Theory of Consciousness* (IIT) draws
on the notion of *integrated information*, symbolized by Ph, as a way to explain generic consciousness (Tononi 2004,
2008). IIT defines integrated information in terms of the effective
information carried by the parts of the system in light of its causal
profile. For example, we can focus on a part of the whole circuit, say
two connected nodes, and compute the effective information that can be
carried by this microcircuit. The system carries *integrated
information* if the effective informational content of the whole
is greater than the sum of the informational content of the parts. If
there is no partitioning where the summed informational content of the
parts equals the whole, then the system as a whole carries integrated
information and it has a positive value for Ph. Intuitively, the
interaction of the parts adds more to the system than the parts do
alone.



IIT holds that a non-zero value for Ph implies that a neural
system is conscious, with more consciousness going with greater values
for Ph. For example, Tononi has argued that the human cerebellum
has a low value for Ph despite there being four to five times
the number of neurons in the cerebellum versus in human cortex. On
IIT, what matters is the presence of appropriate connections and not
the number of neurons.



A potential problem for IIT is that it treats many things to be
conscious which are *prima facie* not (in
 Other Internet Resources,
 see Aaronson 2014a; for striking counterexamples and Aaronson 2014b
with a response from
 Tononi).[8]
 That said, the idea of integrated information might be useful for
neuroscience, but we must show that invoking Ph can explain
generic
 consciousness.[9]



### 3.5 Frontal or Posterior?



In recent years, one way to frame the debate between theories of
generic consciousness is whether the "front" or the
"back" of the brain is crucial. Using this rough
distinction allows us to draw the following contrasts: Recurrent
processing theories focus on sensory areas (in vision, the
"back" of the brain) such that where processing achieves a
certain recurrent state, the relevant contents are conscious even if
no higher-order thought is formed or no content enters the global
workspace. Similarly, proponents of IIT have recently emphasized a
"posterior hot zone" covering parietal and occipital areas
(Boly et al. 2017) as a neural correlate for consciousness, as they
speculate that this zone may have the highest value for Ph. For
certain higher-order thought theories, having a higher-order state,
supported by prefrontal cortex, without corresponding sensory states
can suffice for conscious states. In this case, the front of the brain
would be sufficient for consciousness. Finally, the global neuronal
workspace, drawing on workspace neurons that are present across brain
areas to form the workspace, might be taken to straddle the
difference, depending on the type of conscious state involved. They
require entry into the global workspace such that neither sensory
activity nor a higher order thought on its own is sufficient, i.e.,
neither just the front nor the back of the brain.



The point of talking coarsely of brain anatomy in this way is to
highlight the neural focus of each theory and thus, of targets of
manipulation as we aim for explanatory neural correlates in terms of
what is necessary and/or sufficient for generic consciousness. What is
clear is that once theories make concrete predictions of brain areas
involved in generic consciousness, neuroscience can test them.


## 4. Neuroscience of Generic Consciousness: Unconscious Vision as Case Study



Since generic consciousness is a matter of a state's being
conscious or not, we can examine work on specific types of mental
state that shift between being conscious or not and isolate neural
substrates. Work on unconscious vision provides an informative
example. In the past decades, scientists have argued for unconscious
seeing and investigated its brain basis especially in
*neuropsychology*, the study of subjects with brain damage.
Interestingly, if there is unconscious seeing, then the intentional
action inference must be restricted in scope since some intentional
behaviors might be guided by unconscious perception
 (section 2.5).
 That is, the existence of unconscious perception blocks a direct
inference from perceptually guided intentional behavior to perceptual
consciousness. The case study of unconscious vision promises to
illuminate more specific studies of generic consciousness along with
having repercussions for how we attribute conscious states.


### 4.1 Unconscious Vision and the Two Visual Streams



Since the groundbreaking work of Leslie Ungerleider and Mortimer
Mishkin (1982), scientists divide primate cortical vision into two
streams: dorsal and ventral (for further dissection, see Kravitz et
al. 2011). The dorsal stream projects into the parietal lobe while the
ventral stream projects into the temporal lobe (see
 Figure 1).
 Controversy surrounds the functions of the streams. Ungerleider and
Mishkin originally argued that the streams were functionally divided
in terms of *what* and *where*: the ventral stream for
categorical perception and the dorsal stream for spatial perception.
David Milner and Melvyn Goodale (1995) have argued that the dorsal
stream is for *action* and the ventral stream for
"*perception*", namely for guiding thought, memory
and complex action planning (see Goodale & Milner 2004 for an
engaging overview). There continues to be debate surrounding the
Milner and Goodale account (Schenk and McIntosh 2010) but it has
strongly influenced philosophers of mind.



Substantial motivation for Milner and Goodale's division draws
on lesion studies in humans. Lesions to the dorsal stream do not seem
to affect conscious vision in that subjects are able to provide
accurate reports of what they see (but see Wu 2014a). Rather, dorsal
lesions can affect visual-guidance of action with *optic
ataxia* being a common result. Optic ataxic subjects perform
inaccurate motor actions. For example, they grope for objects, yet
they can accurately report the object's features (for reviews,
see Andersen et al. 2014; Pisella et al. 2009; Rossetti, Pisella,
& Vighetto 2003). Lesions in the ventral stream disrupt normal
conscious vision, yielding visual agnosia, an inability to see visual
form or to visually categorize objects (Farah 2004).



Dorsal stream processing is said to be unconscious. If the dorsal
stream is critical in the visual guidance of many motor actions such
as reaching and grasping, then those actions would be guided by
unconscious visual states. The visual agnosic patient DF provides
critical support for this
 claim.[10]
 Due to carbon monoxide poisoning, DF suffered focal lesions largely
in the ventral stream spanning the *lateral occipital complex*
that is associated with processing of visual form (high resolution
imaging also reveals small lesions in the parietal lobe, [James et al.
2003]). Like other visual agnosics with similar lesions, DF is at
chance in reporting aspects of form, say the orientation of a line or
the shape of objects. Nevertheless, she retains color and texture
vision. Strikingly, DF can generate accurate visually guided action,
say the manipulation of objects along specific parameters: putting an
object through a slot or reaching for and grasping round stones in a
way sensitive to their center of mass. Simultaneously, DF denies
seeing the relevant features and, if asked to verbally report them,
she is at chance. In this dissociation, DF's verbal reports give
evidence that she does not visually experience the features to which
her motor actions remain sensitive.



What is uncontroversial is that there is a division in explanatory
neural correlates of visually guided behavior with the dorsal stream
weighted towards the visual guidance of motor movements and the
ventral stream weighted towards the visual guidance of conceptual
behavior such as report and reasoning (see
 section 5.3.3
 on manipulation of seeing words via ventral stream stimulation). A
substantial further inference is that consciousness is segregated away
from the dorsal stream to the ventral stream. How strong is this
inference?



Recall the intentional action inference. In performing the slot task,
DF is doing something intentionally and in a visually guided way. For
control subjects performing the task, we conclude that this visually
guided behavior is guided by conscious vision. Indeed, a
folk-psychological assumption might be that consciousness informs
mundane action (Clark 2001; for a different perspective see Wallhagen
2007). Since DF shows similar performance on the same task, why not
conclude that she is also visually conscious? Presumably, one
hesitates because DF's introspective reports clash with the
intentional action inference. DF denies seeing features she is
visually sensitive to in action. Should introspection then trump
intentional action in attributing consciousness?



Two issues are worth considering. The first is that introspective
reports involve a specific type of intentional action guided by the
experience at issue. One type of intentional behavior is being
prioritized over another in adjudicating whether a subject is
conscious. What is the empirical justification for this
prioritization? The second issue is that DF is possibly unique among
visual agnosics. It is a substantial inference to move from DF to a
general claim about the dorsal stream being unconscious in
neurotypical individuals (see Mole 2009 for arguments that
consciousness does not divide between the streams and Wu 2013 for an
argument for unconscious visually guided action in normal subjects).
What this shows is that the methodological decisions that we make
regarding how we track consciousness are substantial in theorizing
about the neural bases of conscious and unconscious vision.


### 4.2 Blindsight



A second neuropsychological phenomenon also highlighting putative
unconscious vision is *blindsight* which results from lesions
in primary visual cortex (V1) typically leading to blindness over the
part of visual space contralateral to the sight of the lesion
(Weiskrantz 1986). For example, left hemisphere V1 deals with right
visual space, so lesions in left V1 lead to deficits in seeing the
right side of space. Subjects then report that they cannot see a
visual stimulus in the affected visual space. Strikingly, these
clinically blind subjects can draw on information from the
"unseen" stimulus to visually inform behavior regarding
it, often in striking ways. For example, a blindsight patient with
bilateral damage to V1 (i.e., in both hemispheres) who is blind across
the visual field can walk down a hallway around obstacles he reports
being unable to see (Gelder et al. 2008). Blindsight patients see in
the sense of visually discriminating the stimulus to act on it yet
deny that they see it. The contrast between behavior and report leads
to the paradoxical term, "blindsight". Like DF,
blindsighters show a dissociation between certain actions and report,
but unlike DF, they do not spontaneously respond to relevant features
but must be encouraged to generate behaviors towards
 them.[11]



The neuroanatomical basis of blindsight capacities remains unclear.
Certainly, the loss of V1 deprives later cortical visual areas of a
normal source of visual information. Still, there are other ways that
information from the eye bypasses V1 to provide inputs to later visual
areas. Alternative pathways include the superior colliculus (SC), the
lateral geniculate nucleus (LGN) in the thalamus, and the pulvinar as
likely sources.



![see legend, to the left is a collection of 5 ovals labeled 'retina', 'LGN', 'pulvinar', 'SC', 'amygdala'; to the right are 5 rectangles labeled 'V1' through 'V5'. Above V3 and to its left, V5, which are on the top is a black arrow going right to left labeled 'dorsal stream'; below V1 and to its left V4 is a black arrow going right to left labeled 'ventral stream'. A thick organge arrow connects retina to LGN to V1 and thick blue arrows connect V1 to V2 then from V2 to both V3 and V4 and from V3 to V5. Thin blue arrows connect V1 to V4, V5, and V3 and well as V2 to V5. Thin orange arrows connect retina to pulvinar and SC ; LGN to SC, V5, V2, and V4; SC to LGN, pulvinar, and amygdala; pulvinar to V5, V2, and amygdala.](figure3.png)

Figure 3: Subcortical Pathways and their
Connection to Cortical Vision (from Urbanski, Coubard, &
Bourlon 2014)



Figure Legend: The front of the head is to the left, the back of the
head is to the right. One should imagine that the blue-linked regions
are above the orange-linked regions, cortex above subcortex. V4 is
assigned to the base of the ventral stream; V5, called area MT in
nonhuman primates, is assigned to the base of the dorsal stream.




The latter two have direct *extrastriate* projections
(projections to visual areas in the occipital lobe outside of V1)
while the superior colliculus synapses onto neurons in the LGN and
pulvinar which then connect to extrastriate areas (Figure 3). Which of
these provide for the basis for blindsight remains an open question
though all pathways might play some role (Cowey 2010; Leopold 2012).
If blindsight involves nonphenomenal, unconscious vision, then these
pathways would be a substrate for it, and a functioning V1 might be
necessary for normal conscious vision.



Campion et al. (1983) raised an important alternative explanation:
blindsight subjects in fact have severely degraded conscious vision
but merely report on them with low confidence. In their reports,
blindsight subjects feel like they are guessing about stimuli they can
objectively discriminate. Campion et al. drew on *signal detection
theory*, which emphasizes two determinants of detection behavior:
*perceptual sensitivity* and *response criterion*. A
subject's ability to visually detect a signal will depend partly
on how well her visual system extracts the signal from noise (sensitivity) but also
on the criterion that is set as a threshold for response. Consider
trying to detect something moving in the brush at twilight versus at
noon. In the latter, the signal will be greatly separated from noise
(the object will be easier to detect) while in the former, the signal
will not be (the object will be harder to detect). Yet in either case,
one might operate with a conservative response criterion, say because
one is afraid to be wrong. Thus, even if the signal is detectable, one
might still opt not to report on it given a *conservative*
bias (criterion), say if one is in the twilight scenario and would be ridiculed
for "false alarms", i.e., claiming the object to be
present when it is not.



Campion et al. hypothesized that blindsight patients are conscious in
that they are aware of visual signal where discriminability is low
(cf. the twilight condition). Further, blindsight patients are more
conservative in their response so will be apt to report the absence of
a signal by saying that they do not see the relevant stimulus even
though the signal is there, and they can detect it, as verified by
their above chance visually guided behavior. This possibility was
explicitly tested by Azzopardi and Cowey (1997) with the well-studied
blindsight patient, GY. They compared blindsight performance with
normal subjects at threshold vision using signal detection measures
and found that with respect to motion stimuli, the difference between
discrimination and detection used to argue for blindsight can be
explained by changes in response criterion, as Campion et al.
hypothesized. That is, GYs claim that he does not see the stimulus is
due to a conservative criterion and not to a detection incapacity.
Interestingly, for static stimuli, his response criterion did not
change but his sensitivity did, as if he was tapping into two
different visual processing mechanisms in each task (for an
alternative explanation based on shifting response criterion, see Ko
& Lau 2012).



In introspecting, what concepts are available to subjects will
determine their sensitivity in report. In many studies with
blindsight, subjects are given a binary option: do you see the
stimulus or do you not see it? The concern is that the *do not
see* option would cover cases of degraded consciousness that
subjects might be unwilling to classify as seeing due to a conservative
response criterion. So, what if subjects are given more options for
report? Ramsoy and Overgaard (2004; see also Overgaard et al.
2006) provided subjects with four categories for introspective report:
*no experience; brief glimpse; almost clear experience; clear
experience.* Using this *perceptual awareness scale*, they
found that subjects' objective performance tracked their
introspective reports where performance was at chance when subjects
reported no visual experience. As visibility increased, so did
performance. When the scale was used with a blindsight patient
(Overgaard et al. 2008), no above chance performance was detected when
the subject reported no visual experience (see also Mazzi, Bagattini,
& Savazzi 2016 for further evidence). A live alternative
hypothesis is that blindsight does not present a case of unconscious
vision, but of degraded conscious vision with a conservative response
bias that affects introspection. At the very least, the issue depends
on how introspection is deployed, a topic that deserves further
attention (see Phillips 2016 for further discussion of
blindsight).


### 4.3 Unconscious Vision and the Intentional Action Inference



Blindsight and DF show that damage to specific regions of the brain
disrupts normal visual processing, yet subjects can access visual
information in preserved visual circuits to inform behavior despite
failing to report on the relevant visual contents. The received view
is that these subjects demonstrate unconscious vision. One implication
is that the normal processing in the ventral stream, tied to normal V1
activity, plays a necessary role in normal conscious vision. Another
is that dorsal stream processing or visual stream processing that
bypasses V1 via subcortical processing yields only unconscious visual
states. This points to a set of networks that begin to provide an
answer to what makes visual states conscious or not. An important
further step will be to integrate these results with the general
theories noted earlier
 (section 3).



Still, the complexities of the empirical data bring us back to
methodological issues about tracking consciousness and the following
question: What behavioral data should form the basis of attributions
of phenomenal consciousness? The intentional action inference is used
in a variety of cases to attribute conscious states, yet the results
of the previous sections counsel us to be wary of applying that
inference widely. After all, some intentional behavior might be
unconsciously guided.



In the case of DF, we noted that unlike many other visual agnosics,
she can direct motor actions towards stimuli that she cannot
explicitly report and which she denies seeing. In her case, we
prioritize introspective reports over intentional action as evidence
for unconscious vision. Yet, one might take a broader view that vision
for action is always conscious and that what DF vividly illustrates is
that some visual contents (dorsal stream) are tied directly to
performance of intentional motor behavior and are not directly
available to conceptual capacities deployed in report. In contrast, other
aspects of conscious vision, supported by the ventral stream, are directly available to
guide reports. This functional divergence is explained by the
anatomical division in cortical visual processing.



For some time now, these striking cases have been taken as clear cases
of unconscious vision and if this hypothesis is correct, the work has
begun to identify visual areas critical for creating seeing, sometimes
conscious and sometimes not. The neuroanatomy demonstrates that
visually-guided behavior has a complex neural basis involving cortical
and subcortical structures that demonstrate a substantial level of
specialization. Understanding consciousness and unconsciousness in
vision will need to be sensitive to the complexities of the underlying
neural substrate.


## 5. Specific Consciousness



We turn to experimental work on specific consciousness:



> 
> 
> *Specific Consciousness:* What neural states or properties are
> necessary and/or sufficient for a conscious perceptual state to have
> content *X* rather than *Y*?
> 
> 
> 



In this section, we examine attempts to address claims about
necessity and sufficiency by manipulation of the contents of
consciousness through direct modulation of neural representational
content.


### 5.1 Neural Representationalism



In thinking about neural explanations of specific consciousness,
namely the contents of consciousness, we will provisionally assume a
type of first-order representationalism about phenomenal content,
namely that such content supervenes on neural content (see the entry
on
 representational theories of consciousness).
 One strong position would be that phenomenal content is
*identical* to appropriate neural content. A weaker correlation
claim affirms only supervenience: no change in phenomenal content
without a change in neural content. This neural representationalism
allows us to link phenomenal properties to the brain via linking
neural contents to perceptual contents.


### 5.2 The Contrast Strategy: Binocular Rivalry



A common approach, the *contrast strategy*, enjoins
experimentalists to identify relevant correlates for some phenomenon
*P* by contrasting cases where *P* is present from cases
where *P* is not. Work on binocular rivalry illustrates this
strategy (among many reviews, see Tong, Meng, & Blake 2006; Blake,
Brascamp, & Heeger 2014). When each eye receives a different image
simultaneously, the subject does not see both, say one stimulus
overlapping the other. Rather, visual experience alternates between
them. Call this *phenomenal alternation*. An initial
restatement of our question about specific consciousness in respect of
binocular rivalry is:



> 
> 
> *Specific Rivalry*: What neural property is necessary and/or
> sufficient for phenomenal alternation in binocular rivalry in
> condition *C*?
> 
> 
> 



That is, empirical theories aim to explain how visual content
alternates in binocular
 rivalry.[12]
 Notice that this is a question about specific rather than generic
consciousness, as the contrast is not between a state's being
conscious versus not but about the contrast between two conscious
states with different contents.



Neural explanations of binocular rivalry concern competition at some
level of visual processing: (a) "interocular" competition
between *monocular* neurons early in the visual system, namely
visual neurons that receive input from only one eye or (b) competition
between *binocular* neurons later in the visual system, namely
neurons that receive input from both eyes. The winner of competition
fixes which stimulus the subject experiences at a given time. Some of
the earliest electrophysiological studies (Leopold & Logothetis
1996; Logothetis, Leopold, & Sheinberg 1996) on awake behaving
monkeys supported later binocular processing as the neural basis of
binocular rivalry. Processing in later (inferotemporal cortex, IT; see
 figure 1)
 rather than earlier visual areas (V1 or V2) were observed to be best
correlated to the monkey's reported perception based on the
monkey's stimuli-specific response. In contrast, imaging studies
in humans suggested that neural activity in V1 did correlate with
alternation. For example, Polonsky et al. used fMRI to demonstrate
that V1 activity to competing stimuli tracked perception (Polonsky et
al. 2000; but see Maier et al. 2008).



Recent accounts have taken binocular rivalry as resulting from
processes at multiple levels (Wilson 2003; Freeman 2005; Tong, Meng,
& Blake 2006). For example, when the two competing stimuli have
parts that can be fused into a coherent stimulus, as when half of a
picture is presented to each eye, the subject can perceive the fusion,
integrating content from each eye (Kovacs et al. 1996; Ngo et
al. 2000). This suggests that binocular rivalry can be sensitive to
global properties of the stimulus (see Baker & Graf 2009). What
unifies the mechanisms, perhaps, is the function of resolving a
conflict generated by the stimuli.



Assume that some neural process *R* resolves interocular
competition: when *R* resolves competition between stimuli
*X* and *Y* in favor of *X*, then the subject is
phenomenally conscious of *X* rather than *Y* and vice
versa. Notice that *R* has the same "gating" function
for any stimuli *X* and *Y* that are subject to binocular
rivalry. So, while the presence of *R* can explain why the
subject is having one conscious visual experience rather than another,
*R* is not tied to a specific content. This suggests that in
answering the question about rivalry, we will at best be identifying a
necessary but not sufficient condition for a conscious visual state
having a content *X*. *R* is a general gate for
consciousness (cf. attention in global workspace theory).



A narrower explanation of specific consciousness would identify the
specific neural representations that explain a conscious state's
having the specific content *X* (rather than *Y*). By the
representationalism assumption, this will involve identifying neural
representations with the same content, *X*. Focusing on a gate in
explaining alternation in rivalry stops short of identifying those
representations. Still, binocular rivalry can provide a useful method
for isolating neural populations that carry relevant content. In
principle, for any stimulus type of interest, *X* (e.g., faces,
words, etc.), so long as *X* is subject to binocular rivalry, we
can use rivalry paradigms to isolate brain areas that carry the
relevant information that correlate with the subject's
perceiving *X*. That would allow us to identify potential
candidates for the neural basis of conscious content.


### 5.3 Neural Stimulation



There are limited opportunities to manipulate human brain activity in
a targeted way. Recent use of transcranial magnetic stimulation to
activate or suppress neural activity has provided illumination, but
such interventions are coarse-grained. Ultimately, to locate an
explanatory correlate for specific conscious contents, we will need
more fine-grained interventions in brain tissue. In humans, such
opportunities are generally confined to manipulation before surgical
interventions, say for brain tumors or epilepsy (see
 section 5.3.3
 for work with epilepsy patients).



In the middle of the last century, neurosurgeon Wilder Penfield and
colleagues performed a set of direct electrical microstimulations
during preoperative procedures (Penfield & Perot 1963), and in
certain cases induced hallucinations by stimulating primary sensory
cortices such as V1 or S1 (see
 figure 1).
 This provided evidence that endogenous activity could be causally
sufficient for phenomenal experiences. Penfield's interventions,
however, were not based on fine-grained targeting of specific neural
representations. As Cohen and Newsome note, 



> 
> 
> Penfield's approach failed to generate substantial new insights
> into the neural basis of perception and cognition...because the
> gross electrical activation elicited by surface electrodes could not
> be related mechanistically to the information being processed within
> the excited neural tissue. (Cohen & Newsome 2004: 1)
> 
> 
> 



A different approach begins with a more detailed understanding of
underlying neural representations tied to different brain regions. For
example, the fusiform face (FFA) area appears to be necessary for
normal human face experience in that lesions in FFA lead to
*prosopagnosia*, the inability to see faces even if one can see
their parts. FFA is part of a larger network that is important in
visual processing of faces (Behrmann & Plaut 2013). Recently,
microstimulation of FFA in an awake human epilepsy patient induced
visual distortions of actual faces as opposed to other objects
(Parvizi et al. 2012). Alterations of visual experience were also
reported during microstimulating the parahippocampal place (PPA) area
in an awake pre-operative epileptic patient that induced visual
hallucinations of scenes (Megevand et al. 2014). PPA is the
same area that showed activation in vegetative state patients when
they putatively imagined walking around their home
 (section 2.5).



![See legend. The cortex picture is labeled at the top as 'Anterior' and the bottom as 'Posterior', the left is labeled 'RH' and the right is labeled 'LH'. A picture of a face is labeled 'FFA' in red with and arrow to a region in red of the cortex picture on the far left and about a third up from the bottom, other regions in red are on both sides of the cortex and to the top and about half way up on the left. A picture of a cup is labeled 'LO' in blue and points to a largish blue region in the lower right hand side of the cortex picture; another largish blue region is on the lower left hand side of the picture. A picture of a house is labeled 'PPA' in green with arrows pointing to two smallish green regions above and slightly overlapping the two blue regions](figure4.png)

Figure 4. Ventral Stream Areas 



A view from the bottom of cortex with location of areas FFA, PPA and
LO identified. Occipital cortex is on the bottom. LO is lesioned in the visual agnosic patient, DF (see
 section 4.1).
 This figure is modified from figure 1 of Behrmann and Plaut 2013, kindly provided by Marlene
Behrmann and used with her permission.




It is worth noting that many neuroscientists of vision take themselves
to be investigating seeing in the ordinary sense, one that implies
consciousness, but very few of them would characterize their work as
about consciousness. That said, their work is of direct relevance to
our understanding of specific consciousness even if it is not always
characterized as such.



An important approach in visual neuroscience was articulated by A.J.
Parker and William Newsome in "Sense and the Single
Neuron" (1998) via "principles" to connect
electrophysiological data about information processing to perception
(for a recent discussion, see Ruff & Cohen 2014). To probe the
neural basis of perception, neuroscientists need to explanatorily link
neural data to the subject's perception that guides behavior.
The experimenter must ensure that recorded neural content correlates
with perceptual content and not just response. Further, manipulation
of the neurons carrying information should affect perception: inducing
appropriate neural activity should shift perceptual response while
abolishing or reducing that activity should eliminate or reduce
perceptual response as measured in behavior. These proposals address
concerns about necessity and sufficiency.



The intentional action inference is applicable (or at least its
evidential version):



> 
> 
> If some subject acts intentionally, where her action is guided by a
> perceptual state, then that state is phenomenally conscious.
> 
> 
> 



We will consider the strength of this inference in three cases. The
first case, visual motion perception, introduces the principles that
guide the manipulation of neural content while the second case
concerns tactile experience of vibration. These cases involve
experiments with non-human primates, so we lack introspective reports.
The final case concerns direct manipulation of the human brain along
with introspective reports.



These experiments involve microstimulation of small populations of
neurons that are targeted precisely because of their informational
content. Microstimulation involves injecting a small current from the
tip of an electrode inserted into brain tissue that directly
stimulates nearby neurons or, through synaptic connections to other
neurons, indirectly activates more distant neurons (see Histed, Ni,
& Maunsell 2013 for a review). It is assumed that neurons
*tuned* in similar ways, that is neurons that respond to
similar stimuli, tend to be interconnected, so microstimulation is
taken to largely drive similarly tuned neurons.


#### 5.3.1 Visual Motion Perception



We begin with visual motion perception in primates. Since the
principles introduced here are central to much perceptual neuroscience
and provide the basis for probing the link between neural
representations and perceptual content, I examine it carefully. The
salient question will be whether conscious experience is changed by
the manipulations.



The work we shall discuss was done in awake behaving macaque monkeys.
Visual area MT in the monkey brain (called V5 in humans) plays an
important role in the visual experience of motion. MT is taken to lie
in the dorsal visual stream
 (figure 1).
 Lesions that disrupt MT are known to cause *akinetopsia*, the
inability to see motion. One patient with an MT (V5) lesion reported
the following phenomenology: "people were suddenly here or there
but I have not seen them moving" (Zihl, Von Cramon, & Mai
1983: 315). MT processing looks to be necessary for normal visual
motion experience. Furthermore, MT neurons represent (carry
information regarding) the direction of motion of visible stimuli: MT
neurons are *tuned* for motion in specific directions with the
highest firing rate for a specific direction of motion (for other
functions and responses of MT, see Born & Bradley 2005). By
placing motion stimuli in a neuron's receptive field, scientists
can map its tuning:



![a graph with a y-axis going from 0 to 120 and labeled 'Responses (spikes/s) and a x-axis from -180 to 180 labeled 'Directions of motion (deg)'. There are three lines on the graph: a horizontal dot-dash line almost at the 0 point of the y-axis; a solid line starting about the 15 point of the y-axis rising in a curve to about 90 at the 0 point of the x-axis before symetrically falling back to 15 at the 180 point of the x-axis; a dashed line that starts and and ends at the same points as the solid line but rises higher to about 110 at the 0 point of x-axis.](figure5.png)

Figure 5. MT Neuron Tuning Curve



Figure Legend: Tuning of a neuron in MT showing a peak response in
spiking rate at 0 degrees of motion. The dashed curve is generated when the animal is
attending to the motion stimulus while it is in the receptive field
(we shall not discuss the neural basis of attention, but see Wu 2014b,
chap. 2, for a summary of the neuroscience of attention). The solid curve shows MT response when the animal is not attending to the motion stimulus in the receptive field. Figure from
Lee & Maunsell 2009.




What is plotted is the activity of an MT neuron, in spikes per second,
to a specific type of motion stimulus placed within its receptive
field. How to relate a tuning curve to a determinate content is
complicated. Since the neural response is not simply to one stimulus
value, it is not obvious that the neuron should be taken to represent
0 degrees of motion, namely the value at its peak response. Indeed,
theorists have noted that the tuning curve looks like a probability
density function, and many now take neurons to have
*probabilistic* content
 (section 5.4).



Experimenters have trained macaque monkeys to perform discrimination
tasks reporting direction of motion. Typically, the monkey maintains
fixation while the moving stimulus is placed within the receptive
field of the recorded neuron. The monkey reports the direction of the
stimulus by moving its eyes to a target that stands for either
leftward or rightward motion (other behavioral reports can be
generated such as moving a joystick). Provisionally, we apply the
intentional action inference, so we assume that such reports are
guided by conscious visual experience of the stimuli. Thus,
*changes in behavior will be evidence for changes in conscious
content.*



Early work suggested that the activity of a single neuron provides a
strong correlate of the animal's visually guided performance.
This can be seen by plotting both the animal and the neuron's
performance across different stimulus values. In these experiments,
the value concerns the percent *coherence* of motion of a set
of dots defined as the number of dots moving in the same direction (0%
coherence being random motion; 100% being all dots moving in the same
direction). In the first case, we construct a *psychometric
curve* that plots the animal's percent correct reports
relative to percent coherence of motion of the stimuli. As one might
expect, percent correct reports drop as coherence drops, and the
inflection point reflects where the subject is equally likely to
indicate left or right motion. We can do the same for the neural
activity of the neuron across the same stimulus values, a
*neurometric curve*.



The experimentalist's window onto conscious experience is
through behavior, the assumption being that report about motion
correlates with perceptual experience. Correlation is assessed by
asking the following question: would an ideal observer, using the
activity of the neuron in question, be able to *predict* the
animal's visually guided performance? Essentially, do the
psychometric and neurometric curves overlap? Strikingly, yes. MT
neurons were observed to predict the animal's behavior (Britten
et al. 1992).



![A graph labeled 'Neurometric and psychometric curves' with a y-axis going from 0.5 to 1 and labeled 'Proposition correct' and a x-axis labeled 'Motion coherence (%)' going from an unknown point to 10 at the halfway point and 100 at the far right. An elongated S curve goes from the lower left (about 0.5 on the y and a quarter of the way from the origin to 10 on the x) to 1 on the y-axis and 100 on the x-axis. A close look shows there are two lines, one black which seems to end before the other, gray, line. The legend says the black line is 'psychometric' and the gray line 'neurometric'.](figure6.png)

Figure 6



Figure Legend: Psychometric and Neurometric curves for a single MT
neuron during performance of a motion direction detection task. Percent correct performance is plotted on the y-axis while percent motion coherence is plotted in a log scale on the x-axis. Figure
modified from Ruff & Cohen 2014 and kindly provided by Doug
Ruff.




This shows that the activity of a single MT neuron provides a neural
correlate of the animal's visual discrimination of motion. Note
that this is just a neural correlate of behavior. No one suggested
that this neuron was causally sufficient for the behavior or for
perception. Later results have suggested that individual neurons are
not quite as sensitive as Britten suggested, but that small groups of
MT neurons are sufficient to predict behavior (Cohen &
Newsome 2009).



Earlier, we worried about mere correlates. To get causal or
explanatory purchase, the content of the MT neurons correlated to the
animal's behavior must be shown to contribute to perceptual
guidance. This predicts that if we manipulate the content of the
neurons, i.e., manipulate neural representations, then we should
manipulate the content of the animal's visual experience of
motion as reflected by predicted changes in behavior. This would be to
test sufficiency with respect to specific consciousness.



Newsome and colleagues demonstrated that microstimulation of MT
neurons shifted the animal's performance in predictable ways.
Assume that neural population *P*, by encoding information about
stimulus motion, can inform the subject's report of motion
direction. This information is accessible for the control of behavior.
Activation of *P* by microstimulation should shift behavior in a
motion selective way correlated with the direction that *P* is
tuned to (represents). Metaphorically, if a downstream control system
is sensitive to the response of *P* when it generates behavior,
then if we change *P* in a specific way, say amplifying its
signal, we should change behavior in a way biased by *P*'s
content. This was first demonstrated by Salzman et al. (1990). They
inserted electrodes into MT and identified neurons tuned to a
particular orientation. During a motion discrimination task,
microstimulation of neurons with that tuning led to a shift in the
psychometric curve as if that neuron was given more weight in driving
behavior.



In conditions of microstimulation relative to its absence, the monkey
was more likely to report that there was motion in the stimulated
neuron's preferred direction. In the original experiment, the
psychophysical effect of microstimulation was equivalent to the
addition of 7-20% coherence in the stimulus with respect to the
neuron's preferred direction, depending on the experimental
conditions. Further, as a test of necessity, a selective lesion of MT
disrupted motion discrimination though the animals were able to
recover some function suggesting that other visual information streams
could be tapped so as to support performance (Newsome &
Pare 1988).



Adopting the intentional action inference, one can conclude that the
microstimulation shifted perceptual content (or again, that we have
good evidence for this shift). That said, given our discussion of
unconscious vision
 (section 4),
 another possibility is that MT microstimulation only changes
unconscious visual representations. Newsome himself asked:



> 
> 
> What is the conscious experience that accompanies the stimulation and
> the monkey's decision? Even if you knew everything about how the
> neurons encode and transmit information, you may not know what the
> monkey experiences when we stimulate his MT. (Singer 2006)
> 
> 
> 



Clearly, having the monkey provide an introspective report would add
evidential weight, but obtaining such reports from non-linguistic
creatures is difficult. How can we get the animal to turn attention
*inward* to their perceptual states in an experimental
 context?[13]



#### 5.3.2 Tactile Vibration



What of microstimulation in the absence of a stimulus? Might we induce
*hallucinations* as Penfield did in his patients? Rather than
the work from the Newsome group that modulated ongoing perceptual
processing, the issue here is to create an internal signal that mimics
perception. Romo et al. (1998) demonstrated that monkeys can carry out
sensory tasks via activation triggered by microstimulation. The
monkeys' task was to discriminate the frequency of two
sequential "flutters" on their fingertips, that is,
mechanical vibrations on the skin at specific frequencies. In an
experimental trial, an initial *sample* flutter was presented
for 500 ms and after a gap of 1-3 seconds, a second *test*
flutter of either higher or lower frequency was presented. The animal
reported whether the second test frequency was higher or lower than
the sample.



The experimenters examined whether direct microstimulation in the
absence of a stimulus could tap into the same neural representations
that guided the animal's report. They isolated
neurons in primary somatosensory cortex responsive to vibration
frequency on the fingers (S1, the somatosensory homunculus discovered
by Penfield [Penfield & Boldrey 1937]; see
 figure 1).
 The investigators then stimulated the same neurons in S1 in the
absence of the test flutter, so used stimulation as a substitute for
an actual vibration. Thus, the animal had to make a comparison between
the frequency of a mechanical sample to either a subsequent (1) real
mechanical test vibration (i.e., the good case with an actual
stimulus) or (2) to a microstimulation test stimulus (i.e., the
"hallucinatory" case where direct activation of the S1
neurons occurred in the absence of a stimulus). Romo et al.
demonstrated that discrimination performance based either on
mechanical stimulation or microstimulation was *equivalent*. In
other words, the animals could match either mechanical or
microstimulation to a remembered mechanical
 sample.[14]



In subsequent work (Romo et al. 2000), the investigators inverted the
experiment, using the microstimulation as the *sample.* In this
case, the animals had to remember the information conveyed by the
microstimulation (effectively, a hallucination) and then compare it to
either a subsequent (a) mechanically generated stimulation on the
finger (actual test stimulus) or (b) a microstimulation of S1 as test
(i.e., no stimulus). In both cases, performance was similar to earlier
results. The striking finding is that behavior could be driven
entirely by microstimulation. At least for the tactile stimulations at
issue, the animal might have been in the *Matrix*!



One might think that the intentional action inference is stronger in
this paradigm, given the elegant flipping of stimuli in Romo et al.
2000. Still, the authors comment:



> 
> 
> This study, therefore, has directly established a strong link between
> neural activity and perception. However, we do not know yet whether
> microstimulation of the QA circuit in S1 elicits a subjective flutter
> sensation in the fingertips. This can only be explored by
> microstimulating S1 in an attending human observer. (Romo et al. 2000:
>  277)[15]
> 
> 
> 



Like Newsome, the authors reach for introspection. Yet they might
undersell their result, for it seems that the animals are having a
tactile hallucination: (a) Penfield showed that stimulation of primary
sensory cortices like S1 induces hallucinations in humans; (b) action
is engaged not at low stimulation of S1 in monkeys but only at higher
level stimulation; (c) at that point, when the stimulation grabs their
attention, the monkeys do what they were trained to do, namely
discriminate stimuli, either with (d) just mechanical stimulation
(normal experience), or (e) with a mix of mechanical and
microstimulation or with just microstimulation; (f) given the
behavioral equivalence of these three cases, one might then argue that
if performance in the mechanical stimulation cases involves conscious
tactile experience, then that same experience is involved in the other
cases.


#### 5.3.3 Visual Word Form



In humans, language is lateralized to the left hemisphere, and in
visual word recognition, the left midfusiform gyrus (lmFG; sometimes
referred to as the *visual word form area*, VWFA) is important
for normal processing of visual word forms during reading. For
example, damage to lmFG affects reading in adults (Gaillard et al.
2006; Behrmann & Shallice 1995) while learning novel words
sharpens representations therein (Glezer et al. 2015). Nevertheless,
it is possible that lmFG is tuned to general visual form and is not
specific to visual word forms. This is a common point of contention in
addressing the function of various areas in the human visual system,
notably the fusiform face area (FFA): is it a content specific area or
is it more a general visual expertise area (Kanwisher 2000; Tarr &
Gauthier 2000)?



In a recent study, Hirshorn et al. (2016) used microstimulation to
disrupt processing in lmFG in human epileptic patients with
preoperatively implanted electrodes spanning that area. These
electrodes are used to map sites that will be surgically removed to
relieve intractable seizures. Subjects read words or letters during
actual or sham microstimulation. Crucially, stimulation in lmFG
selectively disrupted word and letter reading but *not* general
form perception. During lmFG stimulation, one subject when presented
with "illegal" reported not seeing the word (see 
 Movie S1
in Other Internet Resources). Rather, she reported thinking of
different words (still, she did not report
*seeing* different words). With "message" she
reported thinking that an "*n*" was present. In a
second patient, the identification of letters was completely
disrupted (see 
 Movie S2 in Other Internet Resources). 
The patient reported seeing an "*A*" when presented
with an "*X*", and then an "*F*" and
"*H*" when presented with "*C*". The
results suggest that normal word reading requires lmFG processing to
parse linguistic forms. A plausible hypothesis is that
microstimulation disrupted visual experience of specific types of
stimuli, a test of necessity. That said, the first patient's
introspective reports of thinking rather than seeing words complicates
matters.



Techniques for decoding information processing (using machine
learning) suggests that processing in lmFG becomes more finely tuned
to word form. Initially, lmFG represents a more *gist*-like
representation but then develops more precise representations that
individuate words of similar form. These converging results provide
evidence that the areas stimulated carry information about word form
such that in disrupting that activity, word perception was selectively
disrupted.



Taken together, the three cases provide examples of detailed
manipulations in different sensory modalities, animals, and contents
that test for causal sufficiency and necessity across different levels
of the sensory processing hierarchy, from early levels (e.g., S1) to
mid-levels (MT) and finally to higher levels (lmFG or FFA). Working
backwards, the experimental strategy is as follows: Given a perceptual
experience with a specific content, one identifies neural correlates
that carry equivalent content, say one's seeing motion, feeling
vibration, or parsing words linked to neural correlates processing
information about motion, vibrational frequency and word forms. Next,
fine-grained manipulations of the neural content are then correlated
with related effects on perceptual experience. One issue that remains
open is whether in tapping into neural processing by microstimulation,
one has simply identified an earlier causal node in the neural
processes that generate perceptual experience, there being more
informative neural correlates later in the causal pathway. An
important question is how one might identify the neural basis of the
experience as opposed to its cause.



A couple of salient methodological challenges stand in the way of
explaining specific consciousness. The first is that much of the
detailed work will for the foreseeable future be done on non-human
animals where introspective report is not easily available and where
the intentional action inference will be essential. To strengthen that
inference, we will need more detailed models that make plausible that
conscious experience figures in the generation of the observed
behavior. There is an experiment-theory circle that we must break
into: we need a theory that supports the role of consciousness in
behavior but the theory itself will be supported by behavioral data.
How will we break into this cycle? The intentional action inference
will be an important means of tracking consciousness, and the limits
of its applicability must be investigated. The second challenge is
that there is a need to individuate different kinds of explanatory
correlates of consciousness, for some will be causes (upstream of
conscious states), some will be enabling conditions, and some will be
constituents of the state itself. Dividing these cases involves not
just gathering data, but having a clear conceptual framework to draw
distinctions in a principled way. Clearly joint philosophical and
experimental work is needed.


### 5.4 Neural Representation and Probabilistic Coding



In invoking neural content, the assumption is that neural content
mirrors perceptual content, so that if one is experiencing dots moving
in a certain direction, there is a neural representation with the same
content. This is a simplification and does not cohere with a common
current approach to neural content that takes it to be
*probabilistic.* Consider again the tuning curve from an MT
neuron, *M*
 (figure 5).



If asked to assign a determinate content to *M*, one might choose
the value that corresponds to its peak response, here,
 0o.[16]
 This would be the most natural option if the tuning curve were
essentially a sharp line at 0o, i.e., were the neuron only
active for its preferred stimulus but otherwise not. Of course, that
is not the neuron's response profile, so what is the content of
the neural representation?



One approach that converges on a determinate content considers the
activity of a neural population. To evaluate more information, the
brain might integrate MT response by giving each MT neuron, \(M\_n\),
one vote weighted according to the *strength* of its response.
Thus, the tuning curve represents how strongly the neuron votes for
its preferred value (the value at the peak). The votes are tallied by
a downstream system, and the result can be represented as a
*population vector* whose direction is understood to be the
direction represented by the neural population. This specific approach
was taken by Georgopoulos and colleagues to decode the direction of
bodily movement from the activity of a population of motor neurons
(Georgopoulos et al. 1982; for discussion of different coding
approaches, see Pouget, Dayan, & Zemel 2003).



In recent perceptual neuroscience, an alternative picture of neural
representational content often tied to *Bayesian approaches* to
perceptual computation has gained traction (for accessible
discussions, see Colombo & Series 2012; Rescorla 2015). On
Bayesian models, extracting information from populations of (say) MT
neurons does not yield a specific value of motion direction but rather
a *probability density function*, across the space of possible
motion directions (for a philosophical discussion of neural
probabilistic codes, see Shea 2014). A key idea is not the generation
of specific values as what neurons represent, say 0 degree motion as
proposed earlier, but rather the conceptualization of the population
response as reflecting uncertainty inherent in neural activity given
noise.



If we plot the activity of all MT neurons responding to a specific
motion stimulus, one hypothesis is that the population response codes
the *likelihood*, \(\pP(r\mid s)\), namely the conditional
probability that one has the observed MT response *r* given the
stimulus *s*. A Bayesian approach to neural population codes then
understands neural processing to involve computation of the
*posterior probability*, \(\pP(s\mid r)\) from the likelihood
and prior knowledge of the probability of the stimulus \(\pP(s)\) in
accordance with Bayes Theorem (this is normalized so that the
probabilities sum to one). The details of Bayesian computation need
not concern us since our main concern is with the possibility of
neural content as probabilistic, something that seems counterintuitive
relative to the approach illustrated by Georgopoulos and
colleagues.



Knill and Pouget contrast the two approaches:



> 
> 
> This is the basic premise on which Bayesian theories of cortical
> processing will succeed or fail--that the brain represents
> information probabilistically, by coding and computing with
> probability density functions or approximations to probability density
> functions...The opposing view is that neural representations are
> deterministic and discrete, which might be intuitive but also
> misleading. This intuition might be due to the apparent
> 'oneness' of our perceptual world and the need to
> 'collapse' perceptual representations into discrete
> actions, such as decisions or motor behaviors. (Knill & Pouget
> 2004)
> 
> 
> 



How might a probabilistic account of neural representation affect our
thinking about phenomenal consciousness via neural
representationalism? Consider this possibility: What if probabilistic
content is pervasive? Pouget, Dayan and Zemel note:



> 
> 
> decoding is not an essential neurobiological operation because there
> is almost never a reason to decode the stimulus explicitly. Rather,
> the population code is used to support computations involving
> *s*, whose outputs are represented in the form of yet more
> population codes over the same or different collections of neurons.
> (2003: 385)
> 
> 
> 



Put another way, the determinacy is apparent only at the output stage,
the goal of processing. In the case of motor action, neural content is
probabilistic until the actual movement when a determinate path is
implemented in a specific movement trajectory.



Yet perceptual content does not *seem* probabilistic. This
emphasizes a *prima facie* disconnect between current theories
of neural content and those of phenomenal content. The linking
principles we have deployed assume a specific view of neural content
that might not cohere with current approaches to neural coding,
leaving us with the challenge of explanatorily linking probabilistic
content at the neural level with more determinate, nonprobabilistic
content at the phenomenal level. One option is to find
nonprobabilistic content at the neural level (e.g., as in the
population vector approach). The other is to find probabilistic
content at the phenomenal level (for related ideas, see Morrison 2017
and response by Denison 2017; also Block 2018). Either way,
explanations of specific content will need to deal with this *prima
facie* disconnect between phenomenal content as revealed by
introspection and current theories of neural content.


## 6. The Future



Talk of the neuroscience of consciousness has, thus far, focused on
the neural correlates of consciousness. Not all neural correlates
are explanatory, so finding correlates is a first step in the
neuroscience of consciousness. The next step involves manipulation of
relevant correlates to test claims about sufficiency and necessity, as
isolated in our two questions:



> 
> 
> *Generic Consciousness:* What conditions/states *N* of
> nervous systems are necessary and (or) sufficient for a mental state,
> *M*, to be conscious as opposed to not?
> 
> 
> 
> *Specific Consciousness:* What neural states or properties are
> necessary and/or sufficient for a conscious perceptual state to have
> content *X* rather than *Y*?
> 
> 
> 



A productive neuroscience of consciousness requires that we understand
the relevant neural properties at the right level of analysis. For
generic consciousness, this will involve manipulation of relevant
properties in a way that can avoid the access/phenomenal confound, and
recent work focuses on pitting the many theories we have considered
against each other. For specific consciousness, the critical issue
will be to understand neural representational content and to find ways
to link experimentally and explanatorily neural content to phenomenal
content. We have tools to manipulate neural contents to affect
phenomenal content, and in doing so, we can begin to uncover the
neural basis of conscious contents. There is much interesting work yet
to be done, philosophically and empirically, and we can look forward
to a productive interdisciplinary research program.