## 1. Before and After *Neurophilosophy*



Historically, neuroscientific discoveries exerted little influence on
the details of materialist philosophies of mind. The
"neuroscientific milieu" of the past half-century has made
it harder for philosophers to adopt substantive dualisms about mind.
But even the "type-type" or "central state"
identity theories that rose to brief prominence in the late 1950s
(Place 1956; Smart 1959) drew upon few actual details of the emerging
neurosciences. Recall the favorite early example of a psychoneural
identity claim: "pain is identical to C-fiber firing". The
"C-fibers" turned out to be related to only a single
aspect of pain transmission (Hardcastle 1997). Early identity
theorists did not emphasize psychoneural identity hypotheses. Their
"neuro" terms were admittedly placeholders for concepts
from future neuroscience. Their arguments and motivations were
philosophical, even if the ultimate justification of the program was
held to be empirical.



The apology offered by early identity theorists for ignoring
scientific details was that the neuroscience at that time was too
nascent to provide any plausible identities. But potential identities
were afoot. David Hubel and Torsten Wiesel's (1962)
electrophysiological demonstrations of the receptive field properties
of visual neurons had been reported with great fanfare. Using their
techniques, neurophysiologists began discovering neurons throughout
visual cortex responsive to increasingly abstract features of visual
stimuli: from edges to motion direction to colors to properties of
faces and hands. More notably, Donald Hebb had published *The
Organization of Behavior* (1949) more than a decade earlier. He
had offered detailed explanations of psychological phenomena in terms
of neural mechanisms and anatomical circuits. His psychological
explananda included features of perception, learning, memory, and even
emotional disorders. He offered these explanations as potential
identities. (See the Introduction to his 1949). One philosopher who
did take note of some available neuroscientific detail at the time was
Barbara Von Eckardt Klein (Von Eckardt Klein 1975). She discussed the
identity theory with respect to sensations of touch and pressure, and
incorporated then-current hypotheses about neural coding of sensation
modality, intensity, duration, and location as theorized by
Mountcastle, Libet, and Jasper. Yet she was a glaring exception. By
and large, available neuroscience at the time was ignored by both
philosophical friends and foes of early identity theories.



Philosophical indifference to neuroscientific detail became
"principled" with the rise and prominence of functionalism
in the 1970s. The functionalists' favorite argument was based on
multiple realizability: a given mental state or event can be realized
in a wide variety of physical types (Putnam 1967; Fodor 1974).
Consequently, a detailed understanding of one type of realizing
physical system (e.g., brains) will not shed light on the fundamental
nature of mind. Psychology is thus autonomous from any science of one
of its possible physical realizers (see the
 entry on multiple realizability
 in this Encyclopedia). Instead of neuroscience, scientifically-minded
philosophers influenced by functionalism sought evidence and
inspiration from cognitive psychology and artificial intelligence.
These disciplines abstract away from underlying physical mechanisms
and emphasize the "information-bearing" properties and
capacities of representations (Haugeland 1985). At this same time,
however, neuroscience was delving directly into cognition, especially
learning and memory. For example, Eric Kandel (1976) proposed
presynaptic mechanisms governing transmitter release rate as a
cell-biological explanation of simple forms of associative learning.
With Robert Hawkins (Hawkins and Kandel 1984) he demonstrated how
cognitivist aspects of associative learning (e.g., blocking,
second-order conditioning, overshadowing) could be explained
cell-biologically by sequences and combinations of these basic forms
implemented in higher neural anatomies. Working on the post-synaptic
side, neuroscientists began unraveling the cellular mechanisms of long
term potentiation (LTP; Bliss and Lomo 1973). Physiological
psychologists quickly noted its explanatory potential for various
forms of learning and
 memory.[1]
 Yet few "materialist" philosophers paid any attention.
Why should they? Most were convinced functionalists. They believed
that the "implementation level" details might be important
to the clinician, but were irrelevant to the theorist of mind.



A major turning point in philosophers' interest in neuroscience
came with the publication of Patricia Churchland's
*Neurophilosophy* (1986). The Churchlands (Patricia and Paul)
were already notorious for advocating eliminative materialism (see the
next section). In her (1986) book, Churchland distilled eliminativist
arguments of the past decade, unified the pieces of the philosophy of
science underlying them, and sandwiched the philosophy between a
five-chapter introduction to neuroscience and a 70-page chapter on
three then-current theories of brain function. She was unapologetic
about her intent. She was introducing philosophy of science to
neuroscientists and neuroscience to philosophers. Nothing could be
more obvious, she insisted, than the relevance of empirical facts
about how the brain works to concerns in the philosophy of mind. Her
term for this interdisciplinary method was "co-evolution"
(borrowed from biology). This method seeks resources and ideas from
anywhere on the theory hierarchy above or below the question at issue.
Standing on the shoulders of philosophers like Quine and Sellars,
Churchland insisted that specifying some point where neuroscience ends
and philosophy of science begins is hopeless because the boundaries
are poorly defined. Neurophilosophers would pick and choose resources
from both disciplines as they saw fit.



Three themes predominated Churchland's philosophical discussion:
developing an alternative to the logical empiricist theory of
intertheoretic reduction; responding to property-dualistic arguments
based on subjectivity and sensory qualia; and responding to
anti-reductionist multiple realizability arguments. These projects
remained central to neurophilosophy for more than a decade after
Churchland's book appeared. John Bickle (1998) extended the
principal insight of Clifford Hooker's (1981a,b,c)
post-empiricist theory of intertheoretic reduction. He quantified key
notions using a model-theoretic account of theory structure adapted
from the structuralist program in philosophy of science (Balzer,
Moulines, and Sneed 1987). He also made explicit a form of argument to
draw ontological conclusions (cross-theoretic identities, revisions,
or eliminations) from the nature of the intertheoretic reduction
relations obtaining in specific cases. For example, it is routinely
concluded that visible light, a theoretical posit of optics, is
electromagnetic radiation within specified wavelengths, a theoretical
posit of electromagnetism; in this case, a cross-theoretic ontological
identity. It is also routine to conclude that phlogiston does not
exist: an elimination of a kind from our scientific ontology. Bickle
explicated the nature of the reduction relation in a specific case
using a semi-formal account of "intertheoretic
approximation" inspired by structuralist results.



Paul Churchland (1996) carried on the attack on property-dualistic
arguments for the irreducibility of conscious experience and sensory
qualia. He argued that acquiring some knowledge of existing sensory
neuroscience increases one's ability to "imagine" or
"conceive of" a comprehensive neurobiological explanation
of consciousness. He defended this conclusion using a
characteristically imaginative thought-experiment based on the history
of optics and electromagnetism.



Finally, criticisms of the multiple realizability argument
flourish--and are challenged--to the present day. Although
the multiple realizability argument remains influential among
nonreductive physicalists, it no longer commands the near-universal
acceptance it once did. Replies to the multiple realizability argument
based on neuroscientific details have appeared. For example, William
Bechtel and Jennifer Mundale (1999) argue that neuroscientists use
psychological criteria in brain mapping studies. This fact undercuts
the likelihood that psychological kinds are multiply realized (for a
review of recent developments see the
 entry on multiple realizability
 in this Encyclopedia).


## 2. Eliminative Materialism and Philosophy Neuralized



Eliminative materialism (EM), in the form advocated most aggressively
by Paul and Patricia Churchland, is the conjunction of two claims.
First, our common sense "belief-desire" conception of
mental events and processes, our "folk psychology", is a
false and misleading account of the causes of human behavior. Second,
like other false conceptual frameworks from both folk theory and the
history of science, it will be replaced by, rather than smoothly
reduced or incorporated into, a future neuroscience. The
Churchlands' characterized folk psychology as the collection of
common homilies invoked (mostly implicitly) to explain human behavior
causally. You ask why Marica is not accompanying me this evening. I
reply that our grandson needed sitting. You nod sympathetically. You
understand my explanation because you share with me a generalization
that relates beliefs about taking care of grandchildren, desires to
help daughters and to spend time with grandchildren compared to
enjoying a night out, and so on. This is just one of a huge collection
of homilies about the causes of human behavior that EM claims to be
flawed beyond potential revision. Although this example involves only
beliefs and desires, folk psychology contains an extensive repertoire
of propositional attitudes in its explanatory nexus: hopes,
intentions, fears, imaginings, and more. EMists predict that a future,
genuinely scientific psychology or neuroscience will eventually eschew
all of these, and replace them with incommensurable states and
dynamics of neuro-cognition.



EM is physicalist in one traditional philosophical sense. It
postulates that some future brain science will be ultimately the
correct account of (human) behavior. It is eliminative in predicting
the future rejection of folk psychological kinds from our
post-neuroscientific ontology. EM proponents often employ scientific
analogies (Feyerabend 1963; Paul Churchland, 1981). Oxidative
reactions as characterized within elemental chemistry bear no
resemblance to phlogiston release. Even the "direction" of
the two processes differ. Oxygen is gained when an object burns (or
rusts), phlogiston was said to be lost. The result of this theoretical
change was the elimination of phlogiston from our scientific ontology.
There is no such thing. For the same reasons, according to EM,
continuing development in neuroscience will reveal that there are no
such things as beliefs, desires, and the rest of the propositional
attitudes as characterized by common sense.



Here we focus only on the way that neuroscientific results have shaped
the arguments for EM. Surprisingly, only one argument has been
strongly influenced. (Most arguments for EM stress failures of folk
psychology as an explanatory theory of behavior.) This argument is
based on a development in cognitive and computational neuroscience
that might provide a genuine alternative to the representations and
computations implicit in folk psychological generalizations. Many
eliminative materialists assume that folk psychology is committed to
propositional representations and computations over their contents
that mimic logical inferences (Paul Churchland 1981; Stich 1983;
Patricia Churchland
 1986).[2]
 Even though discovering an alternative to this view has been an
eliminativist goal for some time, some eliminativists hold that
neuroscience only began delivering this alternative over the past
thirty years. Points in and trajectories through vector spaces, as an
interpretation of synaptic events and neural activity patterns in
biological and artificial neural networks are the key features of this
alternative. The differences between these notions of cognitive
representation and transformations, and those of the propositional
attitudes of folk psychology, provide the basis for one argument for
EM (Paul Churchland 1987). However, this argument will be opaque to
those with no background in cognitive and computational neuroscience,
so we present a few details. With these details in place, we will
return to this argument for EM (five paragraphs below).



At one level of analysis, the basic computational element of a neural
network, biological or artificial, is the nerve cell, or neuron.
Mathematically, neurons can be represented as simple computational
devices, transforming inputs into output. Both inputs and outputs
reflect biological variables. For our discussion, we assume that
neuronal inputs are frequencies of action potentials (neuronal
"spikes") in the axons whose terminal branches synapse
onto the neuron in question, while neuronal output is the frequency of
action potentials generated in its axon after processing the inputs. A
neuron thereby computes its total input, usually treated
mathematically as the sum of the products of the signal strength along
each input line times the synaptic weight on that line. It then
computes a new activation state based on its total input and current
activation state, and a new output state based on its new activation
value. The neuron's output state is transmitted as a signal
strength to whatever neurons its axon synapses on. The output state
reflects systematically the neuron's new activation
 state.[3]



Analyzed in this fashion, both biological and artificial neural
networks are interpreted naturally as *vector-to-vector
transformers*. The input vector consists of values reflecting
activity patterns in axons synapsing on the network's neurons
from outside (e.g., from sensory transducers or other neural
networks). The output vector consists of values reflecting the
activity patterns generated in the network's neurons that
project beyond the net (e.g., to motor effectors or other neural
networks). Given that each neuron's activity depends partly upon
their total input, and its total input depends partly on synaptic
weights (e.g., presynaptic neurotransmitter release rate, number and
efficacy of postsynaptic receptors, availability of enzymes in
synaptic cleft), the capacity of biological networks to change their
synaptic weights make them *plastic* vector-to-vector
transformers. In principle, a biological network with plastic synapses
can come to implement any vector-to-vector transformation that its
composition permits (number of input units, output units, processing
layers, recurrency, cross-connections, etc.) (discussed in Paul
Churchland, 1987, with references to the primary scientific
literature).



![anatomical organization of the cerebellum](figure1-crop.gif)
Figure 1.




The anatomical organization of the cerebellum provides a clear example
of a network amenable to this computational interpretation. Consider
 Figure 1.
 The cerebellum is the bulbous convoluted structure dorsal to the
brainstem. A variety of studies (behavioral, neuropsychological,
single-cell electrophysiological) implicate this structure in motor
integration and fine motor coordination. Mossy fibers (axons) from
neurons outside the cerebellum synapse on cerebellar granule cells,
which in turn project to parallel fibers. Activity patterns across the
collection of mossy fibers (frequency of action potentials per time
unit in each fiber projecting into the cerebellum) provide values for
the input vector. Parallel fibers make multiple synapses on the
dendritic trees and cell bodies of cerebellular Purkinje neurons. Each
Purkinje neuron "sums" its post-synaptic potentials (PSPs)
and emits a train of action potentials down its axon based (partly) on
its total input and previous activation state. Purkinje axons project
outside the cerebellum. The network's output vector is thus the
ordered values representing the pattern of activity generated in each
Purkinje axon. Changes to the efficacy of individual synapses on the
parallel fibers and the Purkinje neurons alter the resulting PSPs in
Purkinje axons, generating different axonal spiking frequencies.
Computationally, this amounts to a different output vector to the same
input activity
 pattern--plasticity.[4]



![synaptic weight error space](figure2-crop.gif)
Figure 2.




This interpretation puts the useful mathematical resources of
*dynamical systems* into the hands of computational
neuroscientists. *Vector spaces* are an example. Learning can
then be characterized fruitfully in terms of changes in synaptic
weights in the network and subsequent reduction of error in network
output. (This approach to learning goes back to Hebb 1949, although
the vector-space interpretation was not part of Hebb's account.)
A useful representation of this account uses a *synaptic
weight-error space*. One dimension represents the global error in
the network's output to a given task, and all other dimensions
represent the weight values of individual synapses in the network.
Consider
 Figure 2.
 Points in this multi-dimensional state space represent the global
performance error correlated with each possible collection of synaptic
weights in the network. As the weights change with each performance,
in accordance with a biologically-inspired learning algorithm, the
global error of network performance continually decreases. The
changing synaptic weights across the network with each training
episode reduces the total error of the network's output vector,
compared to the desired output vector for the input vector. Learning
is represented as synaptic weight changes correlated with a descent
along the error dimension in the space (Churchland and Sejnowski
1992). Representations (concepts) can be portrayed as
*partitions* in multi-dimensional vector spaces. One example is
a *neuron activation* vector space. See
 Figure 3.
 A graph of such a space contains one dimension for the activation
value of each neuron in the network (or some specific subset of the
network's neurons, such as those in a specific layer). A point
in this space represents one possible pattern of activity in all
neurons in the network. Activity patterns generated by input vectors
that the network has learned to group together will cluster around a
(hyper-) point or subvolume in the activity vector space. Any input
pattern sufficiently similar to this group will produce an activity
pattern lying in geometrical proximity to this point or subvolume.
Paul Churchland (1989) argued that this interpretation of network
activity provided a quantitative, neurally-inspired basis for
prototype theories of concepts developed in late-twentieth century
cognitive psychology.



![neuron activation vector space](figure3-crop.gif)
Figure 3.




Using this theoretical development, and in the realm of
neurophilosophy, Paul Churchland (1987, 1989) offered a novel,
neuroscientifically-inspired argument for EM. According to the
interpretation of neural networks just sketched, activity vectors are
the central kind of representations, and vector-to-vector
transformations are the central kind of computations, in the brain.
This contrasts sharply with the *propositional* representations
and *logical/semantic* computations postulated by folk
psychology. Vectorial content, an ordered sequence of real numbers, is
unfamiliar and alien to common sense. This cross-theoretic conceptual
difference is at least as great as that between oxidative and
phlogiston concepts, or kinetic-corpuscular and caloric fluid heat
concepts. Phlogiston and caloric fluid are two "parade"
examples of kinds eliminated from our scientific ontology due to the
nature of the intertheoretic relation obtaining between the theories
with which they are affiliated and the theories that replaced them.
The structural and dynamic differences between the folk psychological
and then-emerging cognitive neuroscientific kinds suggested that the
theories affiliated with the latter will likewise replace the theory
affiliated with the former. But this claim was the key premise of the
eliminativist argument based on predicted intertheoretic relations.
And with the rise of neural networks and parallel distributed
processing, intertheoretic contrasts with folk-psychological
explanatory kinds were no longer just an eliminativist's future
hope. Computational and cognitive neuroscience was delivering an
alternative kinematics for cognition, one that provided no structural
analogue for folk psychology's propositional attitudes or
logic-like computations over propositional contents.



Certainly the vector-space alternatives of this interpretation of
neural networks are alien to folk psychology. But do they justify EM?
Even if the propositional contents of folk-psychological posits find
no analogues in one theoretical development in cognitive and
computational neuroscience (that was hot three decades ago), there
might be other aspects of cognition that folk psychology gets right.
Within the scientific realism that informed early neurophilosophy,
concluding that a cross-theoretic identity claim is true (e.g., folk
psychological state F is identical to neural state N) or that an
eliminativist claim is true (there is no such thing as folk
psychological state F) depended on the nature of the intertheoretic
reduction obtaining between the theories affiliated with the posits in
question (Hooker 1981a,b,c; Churchland 1986; Bickle, 1998). But the
underlying account of intertheoretic reduction also recognized a
spectrum of possible reductions, ranging from relatively
"smooth" through "significantly revisionary"
to "extremely
 bumpy".[5]
 Might the reduction of folk psychology to a "vectorial"
computational neuroscience occupy some middle ground between
"smooth" and "bumpy" intertheoretic reduction
endpoints, and hence suggest a "revisionary" conclusion?
The reduction of classical equilibrium thermodynamics-to-statistical
mechanics provided a potential analogy here. John Bickle (1992, 1998,
chapter 6) argued on empirical grounds that such an outcome is likely.
He specified conditions on "revisionary" reductions from
historical examples and suggested that these conditions are obtaining
between folk psychology and cognitive neuroscience as the latter
develops. In particular, folk psychology appears to have gotten right
the grossly-specified functional profile of many cognitive states,
especially those closely related to sensory inputs and behavioral
outputs. It also appears to get right the "intentionality"
of many cognitive states--the object that the state is of or
about--even though cognitive neuroscience eschews its implicit
linguistic explanation of this feature. Revisionary physicalism
predicts significant *conceptual change* to folk psychological
concepts, but denies total elimination of the caloric fluid-phlogiston
variety.



The philosophy of science is another area where vector space
interpretations of neural network activity patterns has impacted
philosophy. In the Introduction to his (1989) book, *A
Neurocomputational Perspective*, Paul Churchland asserted,
distinctively neurophilosophically, that it will soon be impossible to
do serious work in the philosophy of science without drawing on
empirical work in the brain and behavioral sciences. To justify this
claim, in Part II of the book he suggested neurocomputational
reformulations of key concepts from the philosophy of science. At the
heart of his reformulations is a neurocomputational account of the
structure of scientific theories (1989: chapter 9). Problems with the
orthodox "sets-of-sentences" view of scientific theories
have been well-known since the 1960s. Churchland advocated replacing
the orthodox view with one inspired by the "vectorial"
interpretation of neural network activity. Representations implemented
in neural networks (as sketched above) compose a system that
corresponds to important distinctions in the external environment, are
not explicitly represented as such within the input corpus, and allow
the trained network to respond to inputs in a fashion that continually
reduces error. According to Churchland, these are functions of
theories. Churchland was bold in his assertion: an individual's
theory-of-the-world is a specific point in that individual's
error-synaptic weight vector space. It is a configuration of synaptic
weights that partitions the individual's activation vector space
into subdivisions that reduce future error messages to both familiar
and novel inputs. (Consider again
 Figure 2
 and
 Figure 3.)
 This reformulation invites an objection, however. Churchland boasts
that his theory of theories is preferable to existing alternatives to
the orthodox "sets-of-sentences" account--for
example, the *semantic* view (Suppe 1974; van Fraassen
1980)--because his is closer to the "buzzing brains"
that use theories. But as Bickle (1993) noted, neurocomputational models based on the
mathematical resources described above are a long way into the realm
of mathematical abstraction. They are little more than novel (albeit
suggestive) application of the mathematics of quasi-linear dynamical
systems to simplified schemata of brain circuitries. Neurophilosophers
owe some account of identifications across ontological categories
(vector representations and transformation to what?) before the
philosophy of science community will treat theories as points in
high-dimensional state spaces implemented in biological neural
networks. (There is an important methodological assumption lurking in
Bickle's objection, however, which we will discuss toward the
end of the next paragraph.)



Churchland's neurocomputational reformulations of other
scientific and epistemological concepts build on this account of
theories. He sketches "neuralized" accounts of the
theory-ladenness of perception, the nature of concept unification, the
virtues of theoretical simplicity, the nature of Kuhnian paradigms,
the kinematics of conceptual change, the character of abduction, the
nature of explanation, and even moral knowledge and epistemological
normativity. Conceptual redeployment, for example, is the activation
of an already-existing prototype representation--the centerpoint
or region of a partition of a high-dimensional vector space in a
trained neural network--by a novel type of input pattern.
Obviously, we can't here do justice to Churchland's many
and varied attempts at reformulation. We urge the intrigued reader to
examine his suggestions in their original form. But a word about
philosophical methodology is in order. Churchland is *not*
attempting "conceptual analysis" in anything resembling
its traditional philosophical sense. Neither, typically, are
neurophilosophers in any of their reformulation projects. (This is why
a discussion of neurophilosophical reformulations fits with a
discussion of EM.) There are philosophers who take the
discipline's ideal analyses to be a relatively simple set of
necessary and sufficient conditions, expressed in non-technical
natural language, governing the application of important concepts
(like justice, knowledge, theory, or explanation). These analyses
should square, to the extent possible, with pretheoretical usage.
Ideally, they should preserve synonymy. Other philosophers view this
ideal as sterile, misguided, and perhaps deeply mistaken about the
underlying structure of human knowledge (Ramsey 1992).
Neurophilosophers tend to reside in the latter group. Those who
dislike philosophical speculation about the promise and potential of
developing science to reformulate
("*reform*-ulate") traditional philosophical
concepts have probably already discovered that neurophilosophy is not
for them. But the familiar charge that neurocomputational
reformulations of the sort Churchland attempts are
"philosophically uninteresting" or
"irrelevant" because they fail to provide
"analyses" of theory, explanation, and the like will fall
on deaf ears among many contemporary "naturalistic"
philosophers, who have by and large given up on traditional
philosophical "analysis".



Before we leave the topic of proposed neurophilosophical applications
of this theoretical development from "neural
networks"-style cognitive/computational neuroscience, one final
point of actual scientific detail bears mention. This approach did not
remain state-of-the-art computational *neuro*science for long.
Many *neural* modelers quickly gave up this approach to
modeling the brain. *Compartmental modeling* enabled
computational *neuro*scientists to mimic activity in and
interactions between patches of neuronal membrane (Bower and Beeman
1995). This approach permitted modelers to control and manipulate a
variety of subcellular factors that determine action potentials per
time unit, including the topology of membrane structure in individual
neurons, variations in ion channels across membrane patches, and field
properties of post-synaptic potentials depending on the location of
the synapse on the dendrite or soma. By the mid-1990s modelers quickly
began to "custom build" the neurons in their target
circuitry. Increasingly powerful computer hardware still allowed them
to study circuit properties of modeled networks. For these reasons,
many serious computational neuroscientists switched to working at a
level of analysis that treats neurons as structured rather than simple
computational devices. With compartmental modeling, vector-to-vector
transformations came to be far less useful in serious neurobiological
models, replaced by differential equations representing ion currents
across patches of neural membrane. Far more biological detail came to
be captured in the resulting models than "connectionist"
models permitted. This methodological change across computational
neuroscience meant that a neurophilosophy guided by
"connectionist" resources no longer drew from the state of
the art of the scientific field.



Philosophy of science and scientific epistemology were not the only
areas where neurophilosophers urged the relevance of neuroscientific
discoveries for traditionally philosophical topics. A decade after
*Neurophilosophy*'s publication, Kathleen Akins (1996)
argued that a "traditional" view of the senses underlies a
variety of sophisticated "naturalistic" programs about
intentionality. (She cites the Churchlands, Daniel Dennett, Fred
Dretske, Jerry Fodor, David Papineau, Dennis Stampe, and Kim Sterelny
as examples.) But then-recent neuroscientific work on the mechanisms
and coding strategies implemented by sensory receptors shows that this
traditional view is mistaken. The traditional view holds that sensory
systems are "veridical" in at least three ways. (1) Each
signal in the system correlates with a small range of properties in
the external (to the body) environment. (2) The structure in the
relevant external relations that the receptors are sensitive to is
preserved in the structure of the internal relations among the
resulting sensory states. And (3) the sensory system reconstructs
faithfully, without fictive additions or embellishments, the external
events. Using then-recent neurobiological discoveries about response
properties of thermal receptors in the skin (i.e.,
"thermoreceptors") as an illustration, Akins showed that
sensory systems are "narcissistic" rather than
"veridical". All three traditional assumptions are
violated. These neurobiological details and their philosophical
implications open novel questions for the philosophy of perception and
for the appropriate foundations for naturalistic projects about
intentionality. Armed with the known neurophysiology of sensory
receptors, our "philosophy of perception" or account of
"perceptual intentionality" will no longer focus on the
search for correlations between states of sensory systems and
"veridically detected" external properties. This
traditional philosophical (and scientific!) project rests upon a
mistaken "veridicality" view of the senses.
Neuroscientific knowledge of sensory receptor activity also shows that
sensory experience does not serve the naturalist well as a
"simple paradigm case" of an intentional relation between
representation and world. Once again, available scientific detail
showed the naivety of some traditional philosophical projects.



Focusing on the anatomy and physiology of the pain transmission
system, Valerie Hardcastle (1997) urged a similar negative implication
for a popular methodological assumption. Pain experiences have long
been philosophers' favorite cases for analysis and theorizing
about conscious experiences generally. Nevertheless, every position
about pain experiences has been defended: eliminativism, a variety of
objectivist views, relational views, and subjectivist views. Why so
little agreement, despite agreement that pain experiences are the
place to start an analysis or theory of consciousness? Hardcastle
urged two answers. First, philosophers tend to be uninformed about the
neuronal complexity of our pain transmission systems, and build their
analyses or theories on the outcome of a single component of a
multi-component system. Second, even those who understand some of the
underlying neurobiology of pain tend to advocate gate-control
 theories.[6]
 But the best existing gate-control theories are vague about the
neural mechanisms of the gates. Hardcastle instead proposed a
dissociable dual system of pain transmission, consisting of a pain
sensory system closely analogous in its neurobiological implementation
to other sensory systems, and a descending pain inhibitory system. She
argued that this dual system is consistent with neuroscientific
discoveries and accounts for all the pain phenomena that have tempted
philosophers toward particular (but limited) theories of pain
experience. The neurobiological uniqueness of the pain inhibitory
system, contrasted with the mechanisms of other sensory modalities,
renders pain processing atypical. In particular, the pain inhibitory
system dissociates pain sensation from stimulation of nociceptors
(pain receptors). Hardcastle concluded from the neurobiological
uniqueness of pain transmission that pain experiences are atypical
conscious events, and hence not a good place to start theorizing about
or analyzing the general type.


## 3. Neuroscience and Psychosemantics



Developing and defending theories of content is a central topic in
contemporary philosophy of mind. A common desideratum in this debate
is a theory of cognitive representation consistent with a physical or
naturalistic ontology. We'll here describe a few contributions
neurophilosophers have made to this project.



When one perceives or remembers that he is out of coffee, his brain
state possesses intentionality or "aboutness". The percept
or memory is about one's being out of coffee; it represents one
as being out of coffee. The representational state has content. A
psychosemantics seeks to explain what it is for a representational
state to be about something, to provide an account of how states and
events can have specific representational content. A physicalist
psychosemantics seeks to do this using resources of the physical
sciences exclusively. Neurophilosophers have contributed to two types
of physicalist psychosemantics: the Functional Role approach and the
Informational approach. For a description of these and other theories
of mental content, see the entries on
 causal theories of mental content,
 mental representation, and
 teleological theories of mental content.



The core claim of a functional role semantics is that a representation
has its specific content in virtue of relations it bears to other
representations. Its paradigm application is to concepts of
truth-functional logic, like the conjunctive "and" or
disjunctive "or". A physical event instantiates the
"and" function just in case it maps two true inputs onto a
single true output. Thus it is the relations an expression bears to
others that give it the semantic content of "and".
Proponents of functional role semantics propose similar analyses for
the content of all representations (Block 1995). A physical event
represents birds, for example, if it bears the right relations to
events representing feathers and others representing beaks. By
contrast, informational semantics ascribe content to a state depending
upon the causal relations obtaining between the state and the object
it represents. A physical state represents birds, for example, just in
case an appropriate causal relation obtains between it and birds. At
the heart of informational semantics is a causal account of
information (Dretske 1981, 1988). Red spots on a face carry the
information that one has measles because the red spots are caused by
the measles virus. A common criticism of informational semantics holds
that mere causal covariation is insufficient for representation, since
information (in the causal sense) is by definition always veridical
while representations can misrepresent. A popular solution to this
challenge invokes a teleological analysis of "function". A
brain state represents *X* by virtue of having the function of
carrying information about being caused by *X* (Dretske 1988).
These two approaches do not exhaust the popular options for a
psychosemantics, but are the ones to which neurophilosophers have most
contributed.



Paul Churchland's allegiance to functional role semantics goes
back to his earliest views about the semantics of terms in a language.
In his (1979) book, he insisted that the semantic identity (content)
of a term derives from its place in the network of sentences of the
entire language. The functional economies envisioned by early
functional role semanticists were networks with nodes corresponding to
the objects and properties denoted by expressions in a language. Thus
one node, appropriately connected, might represent birds, another
feathers, and another beaks. Activation of one of these would tend to
spread activation to the others. As "connectionist" neural
network modeling developed (as discussed in the previous section
above), alternatives arose to this one-representation-per-node
"localist" approach. By the time Churchland (1989)
provided a neuroscientific elaboration of functional role semantics
for cognitive representations generally, he too had abandoned the
"localist" interpretation. Instead, he offered a
"state-space semantics".



We saw in the previous section how (vector) state spaces provide an
interpretation for activity patterns in neural networks, both
biological and artificial. A state-space semantics for cognitive
representations is a species of a functional role semantics because
the individuation of a particular state depends upon the relations
obtaining between it and other states. A representation is a point in
an appropriate state space, and points (or subvolumes) in a space are
individuated by their relations to other points (locations,
geometrical proximity). Paul Churchland (1989, 1995) illustrated a
state-space semantics for neural states by appealing to sensory
systems. One popular theory in sensory neuroscience of how the brain
codes for sensory qualities (like color) is the *opponent process
account* (Hardin 1988). Churchland (1995) describes a
three-dimensional activation vector state-space in which every color
perceivable by humans is represented as a point (or subvolume). Each
dimension corresponds to activity rates in one of three classes of
photoreceptors present in the human retina and their efferent paths:
the red-green opponent pathway, yellow-blue opponent pathway, and
black-white (contrast) opponent pathway. Photons striking the retina
are transduced by photoreceptors, producing an activity rate in each
of the segregated pathways. A represented color is hence a triplet of
neuronal activation frequency rates. As an illustration, consider
again
 Figure 3.
 Each dimension in that three-dimensional space will represent average
frequency of action potentials in the axons of one class of ganglion
cells projecting out of the retina. Each color perceivable by humans
will be a region of that space. For example, an orange stimulus
produces a relatively low level of activity in both the red-green and
yellow-blue opponent pathways (*x*-axis and *y*-axis,
respectively), and middle-range activity in the black-white (contrast)
opponent pathway (*z*-axis). Pink stimuli, on the other hand,
produce low activity in the red-green opponent pathway, middle-range
activity in the yellow-blue opponent pathway, and high activity in the
black-white (contrast) opponent
 pathway.[7]
 The location of each color in the space generates a "color
solid". Location on the solid, and geometrical proximity between
these locations, reflect structural similarities between the perceived
colors. Human gustatory representations are points in a
four-dimensional state space, with each dimension coding for activity
rates generated by gustatory stimuli in each type of taste receptor
(sweet, salty, sour, and bitter) and their segregated efferent
pathways. When implemented in a neural network with structural
resources, and hence computational resources as vast as the human
brain, the state space approach to psychosemantics generates a theory
of content for a huge number of cognitive
 states.[8]



Jerry Fodor and Ernest LePore (1992) raised an important challenge to
Churchland's psychosemantics. Location in a state space alone
seems insufficient to fix a state's representational content.
Churchland never explains why a point in a three-dimensional state
space represents a *color*, as opposed to any other quality,
object, or event that varies along three
 dimensions.[9].
 So Churchland's account achieves its explanatory power by the
interpretation imposed on the dimensions. Fodor and LePore alleged
that Churchland never specified how a dimension comes to represent,
e.g., degree of saltiness, as opposed to yellow-blue wavelength
opposition. One obvious answer appeals to the stimuli that form the
"external" inputs to the neural network in question. Then,
for example, the individuating conditions on neural representations of
colors are that opponent processing neurons receive input from a
specific class of photoreceptors. The latter in turn have
electromagnetic radiation (of a specific portion of the visible
spectrum) as their activating stimuli. However, this appeal to
"external" stimuli as the ultimate individuating
conditions for representational content makes the resulting approach a
version of informational semantics. Is this approach consonant with
other neurobiological details?



The neurobiological paradigm for informational semantics is the
*feature detector*: one or more neurons that are (i) maximally
responsive to a particular type of stimulus, and (ii) have the
function of indicating the presence of that stimulus type. Examples of
such stimulus-types for visual feature detectors include high-contrast
edges, motion direction, and colors. A favorite feature detector among
philosophers is the alleged fly detector in the frog. Lettvin et al.
(1959) identified cells in the frog retina that responded maximally to
small shapes moving across the visual field. The idea that these
cells' activity functioned to detect flies rested upon knowledge
of the frogs' diet. (Bechtel 1998 provides a useful discussion.)
Using experimental techniques ranging from single-cell recording to
sophisticated functional imaging, neuroscientists discovered a host of
neurons that are maximally responsive to a variety of complex stimuli.
However, establishing condition (ii) on a feature detector is much
more difficult. Even some paradigm examples have been called into
question. David Hubel and Torsten Wiesel's (1962) Nobel
Prize-winning work establishing the receptive fields of neurons in
striate (visual) cortex is often interpreted as revealing cells whose
function is edge detection. However, Lehky and Sejnowski (1988)
challenged this interpretation. They trained an artificial neural
network to distinguish the three-dimensional shape and orientation of
an object from its two-dimensional shading pattern. Their network
incorporates many features of visual neurophysiology. Nodes in the
trained network turned out to be maximally responsive to edge
contrasts, but did not appear to have the function of edge detection.
(See Churchland and Sejnowski 1992 for a review.)



Kathleen Akins (1996) offered a different neurophilosophical challenge
to informational semantics and its affiliated feature-detection view
of sensory representation. We saw in the previous section that Akins
argued that the physiology of thermoreception violates three necessary
conditions on "veridical" representation. From this fact
she raised doubts about looking for feature-detecting neurons to
ground a psychosemantics generally, including for thought contents.
Human thoughts about flies, for example, are sensitive to numerical
distinctions between particular flies and the particular locations
they can occupy. But the ends of frog nutrition are well served
without a representational system sensitive to such ontological
niceties. Whether a fly seen now is numerically identical to one seen
a moment ago need not, and perhaps cannot, figure into the
frog's feature detection repertoire. Akins' critique cast
doubt on whether details of sensory transduction will scale up to
provide an adequate unified psychosemantics for all concepts. It also
raised new questions for human intentionality. How do we get from
activity patterns in "narcissistic" sensory receptors,
keyed not to "objective" environmental features but rather
only to effects of the stimuli on the patch of tissue innervated, to
human ontologies replete with enduring objects with stable
configurations of properties and relations, types and their tokens (as
the "fly-thought" example presented above reveals), and
the rest? And how did the development of a stable, rich ontology
confer survival advantages to human ancestors?


## 4. Consciousness Explained?



Consciousness re-emerged over the past three decades as a topic of
research focus in philosophy of mind, and in the cognitive and brain
sciences. Instead of ignoring it, many physicalists sought to explain
it (Dennett 1991). Here we focus exclusively on ways that
neuroscientific discoveries have impacted philosophical debates about
the nature of consciousness and its relation to physical mechanisms.
(See links to other entries in this encyclopedia below in
 Related Entries
 for broader discussions about consciousness and physicalism.)



Thomas Nagel (1974) argued famously that conscious experience is
subjective, and thus permanently recalcitrant to objective scientific
understanding. He invited us to ponder "what it is like to be a
bat" and urged the intuitive judgment that no amount of
physical-scientific knowledge, including neuroscientific, supplies a
complete answer. Nagel's intuition pump has generated extensive
philosophical discussion. At least two well-known replies made direct
appeal to neurophysiology. John Biro (1991) suggested that part of the
intuition pumped by Nagel, that bat experience is substantially
different from human experience, presupposes systematic relations
between physiology and phenomenology. Kathleen Akins (1993a) delved
deeper into existing knowledge of bat physiology and reports much that
is pertinent to Nagel's question. She argued that many of the
questions about bat subjective experience that we still consider open
hinge on questions that remain unanswered about neuroscientific
details. One example of the latter is the function of various cortical
activity profiles in the active bat.



David Chalmers (1996) famously argued that any possible brain-process
account of consciousness will leave open an "explanatory
gap" between the brain process and properties of the conscious
 experience.[10]
 This is because no brain-process theory can answer the
"hard" question: Why should that particular brain process
give rise to that particular conscious experience? We can always
imagine ("conceive of") a universe populated by creatures
having those brain processes but completely lacking conscious
experience. A theory of consciousness requires an explanation of how
and why some brain process causes a conscious experience, replete with
all the features we experience. The fact that the hard question
remains unanswered shows that we will probably never get a complete
explanation of consciousness at the level of neural mechanism. Paul
and Patricia Churchland (1997) offered the following diagnosis and
reply. Chalmers offers a *conceptual argument*, based on our
ability to imagine creatures possessing active brains like ours but
wholly lacking in conscious experiences. But the more one learns about
how the brain produces conscious experience--and such a
literature has emerged (for some early work, see Gazzaniga
1995)--the harder it becomes to imagine a universe consisting of
creatures with brain processes like ours but lacking consciousness.
This is not just bare assertion. The Churchlands appeal to some
neurobiological detail. For example, Paul Churchland (1995) develops a
neuroscientific account of consciousness based on recurrent
connections between thalamic nuclei, particularly between
"diffusely projecting" nuclei like the intralaminar nuclei
and the
 cortex.[11]
 Churchland argues that thalamocortical recurrency accounts for the
selective features of consciousness, for the effects of short-term
memory on conscious experience, for vivid dreaming during REM
(rapid-eye movement) sleep, and other "core" features of
conscious experience. In other words, the Churchlands are claiming
that when one learns about activity patterns in these recurrent
circuits, one can no longer "imagine" or "conceive
of" this activity occurring without these core features of
conscious experience occurring. (Other than just mouthing the
expression, "I am now imagining activity in these circuits
without selective attention/the effects of short-term memory/vivid
dreaming/...").



A second focus of skeptical arguments about a complete neuroscientific
explanation of consciousness is on sensory *qualia*: the
introspectable qualitative aspects of sensory experience, the features
by which subjects discern similarities and differences among their
experiences. The colors of visual sensations are a philosopher's
favorite example. One famous puzzle about color qualia is the alleged
conceivability of spectral inversions. Many philosophers claim that it
is conceptually possible (if perhaps physically impossible) for two
humans not to differ neurophysiologically, while the color that fire
engines and tomatoes appear to have to one subject is the color that
grass and frogs appear to have to the other (and vice versa). A large
amount of neuroscientifically-informed philosophy has addressed this
question. (C.L. Hardin 1988 and Austen Clark 1993 are noteworthy
examples.) A related area where neurophilosophical considerations have
emerged concerns the metaphysics of colors themselves (rather than
color experiences). A longstanding philosophical dispute is whether
colors are objective properties existing external to perceivers or
rather identifiable as or dependent upon minds or nervous systems.
Some neuroscientific work on this problem begins with characteristics
of color experiences: for example, that color similarity judgments
produce color orderings that align on a circle (Clark 1993). With this
resource, one can seek mappings of phenomenology onto environmental or
physiological regularities. Identifying colors with particular
frequencies of electromagnetic radiation does not preserve the
structure of the hue circle, whereas identifying colors with activity
in opponent processing neurons does. Such a tidbit is not decisive for
the color objectivist-subjectivist debate, but it does convey the type
of neurophilosophical work being done on traditional metaphysical
issues beyond the philosophy of mind. (For more details on these
issues, see the
 entry on color
 in this Encyclopedia.)



We saw in the discussion of Hardcastle (1997) two sections above that
neurophilosophers have entered disputes about the nature and
methodological import of pain experiences. Two decades earlier, Dan
Dennett (1978) took up the question of whether it is possible to build
a computer that feels pain. He compares and notes tension between
neurophysiological discoveries and common sense intuitions about pain
experience. He suspects that the incommensurability between scientific
and common sense views is due to incoherence in the latter. His
attitude is wait-and-see. But foreshadowing Churchland's reply
to Chalmers, Dennett favors scientific investigations over
conceivability-based philosophical arguments.



Neurological deficits have attracted philosophers interested in
consciousness. For nearly fifty years philosophers have debated
implications for the unity of the self of the Nobel Prize-winning
experiments with commissurotomy patients who, for clinical reasons,
had their corpus callosum surgically ablated (Nagel
 1971).[12]
 The corpus callosum is the huge bundle of axons connecting neurons
across the left and right mammalian cerebral hemispheres. In carefully
controlled experiments, commissurotomy patients seemingly display two
dissociable "seats" of consciousness. Elizabeth Schechter
(2018) has recently greatly updated philosophical treatment of the
scientific details of these "split-brain" patients,
including their own experiential reports, and has traced implications
for our understanding of the self. 



In chapter 5 of her (1986) book, Patricia Churchland extended both the
range and philosophical implications of neurological deficits. One
deficit she discusses in detail is blindsight. Some patients with
lesions to primary visual cortex report being unable to see items in
regions of their visual fields, yet perform far better than chance in
forced guess trials about stimuli in those regions. A variety of
scientific and philosophical interpretations have been offered. Ned
Block (1995) worried that many of these interpretations conflate
distinct notions of consciousness. He labels these notions
"phenomenal consciousness" ("P-consciousness")
and "access consciousness"
("A-consciousness"). The former is the "what it is
like"-ness of conscious experiences. The latter is the
availability of representational content to self-initiated action and
speech. Block argued that P-consciousness is not always
representational, whereas A-consciousness is. Dennett (1991, 1995) and
Tye (1993) are skeptical of non-representational analyses of
consciousness in general. They provide accounts of blindsight that do
not depend on Block's distinction.



We break off our brief overview of neurophilosophical work on
consciousness here. Many other topics are worth neurophilosophical
pursuit. We mentioned commissurotomy and the unity of consciousness
and the self, which continues to generate discussion. Qualia beyond
those of color and pain experiences quickly attracted
neurophilosophical attention (Akins 1993a,b, 1996; Austen Clark 1993),
as did self-consciousness (Bermudez 1998).


## 5. Locating Cognitive Functions: From Lesion Studies to Functional Neuroimaging



One of the first issues to arise in neurology, as far back as the
nineteenth century, concerned the localization of specific cognitive
functions to specific brain regions. Although the
"localization" approach had dubious origins in the
phrenology of Gall and Spurzheim, and had been challenged strenuously
by Flourens throughout the early nineteenth century, it re-emerged
late in the nineteenth century in the study of aphasia by Bouillaud,
Auburtin, Broca, and Wernicke. These neurologists made careful studies
(when possible) of linguistic deficits in their aphasic patients,
followed by brain autopsies post
 mortem.[13]
 Broca's initial study of twenty-two patients in the
mid-nineteenth century confirmed that damage to the left cortical
hemisphere was predominant, and that damage to the second and third
frontal convolutions was necessary to produce speech production
deficits. Although the anatomical coordinates Broca postulated for the
"speech production center" do not correlate exactly with
damage producing production deficits, both this area of frontal cortex
and speech production deficits still bear his name
("Broca's area" and "Broca's
aphasia"). Less than two decades later Carl Wernicke published
evidence for a second language center. This area is anatomically
distinct from Broca's area, and damage to it produced a very
different set of aphasic symptoms. The cortical area that still bears
his name ("Wernicke's area") is located around the
first and second convolutions in temporal cortex, and the aphasia that
bears his name ("Wernicke's aphasia") involves
deficits in language comprehension. Wernicke's method, like
Broca's, was based on lesion studies produced by natural trauma:
a careful evaluation of the behavioral deficits, followed by post
mortem autopsies to find the sites of tissue damage and atrophy. More
recent and more careful lesion studies suggest more precise
localization of specific linguistic functions, and remain a
cornerstone to this day in aphasia research.



Lesion studies have also produced evidence for the localization of
other cognitive functions: for example, sensory processing and certain
types of learning and memory. However, localization arguments for
these other functions invariably include studies using animal models.
With an animal model, one can perform careful behavioral measures in
highly controlled settings, then ablate specific areas of neural
tissue (or use a variety of other techniques to block or enhance
activity in these areas) and re-measure performance on the same
behavioral tests. Since we lack widely accepted animal models for
human language production and comprehension, this additional evidence
isn't available to the neurologist or neurolinguists. This
limitation makes the neurological study of language a paradigm case
for evaluating the logic of the lesion/deficit method of inferring
functional localization. Barbara Von Eckardt (Von Eckardt Klein 1978)
attempted to make explicit the steps of reasoning involved in this
common and historically important method. Her analysis begins with
Robert Cummins' well-known analysis of functional explanation,
but she extends it into a notion of *structurally adequate*
functional analysis. These analyses break down a complex capacity C
into its constituent capacities *c*1,
*c*2,..., *c**n*,
where the constituent capacities are consistent with the underlying
structural details of the system. For example, human speech production
(complex capacity C) results from formulating a speech intention, then
selecting appropriate linguistic representations to capture the
content of the speech intention, then formulating the motor commands
to produce the appropriate sounds, then communicating these motor
commands to the appropriate motor pathways (all together, the
constituent capacities*c*1,
*c*2,..., *c**n*). A
functional-localization hypothesis has the form: brain structure S in
organism (type) O has constituent capacity
*c**i*, where *c**i*
is a function of some part of O. An example might be: Broca's
area (S) in humans (O) formulates motor commands to produce the
appropriate sounds (one of the constituent capacities
*c**i*). Such hypotheses specify aspects of
the structural realization of a functional-component model. They are
part of the theory of the neural realization of the functional
model.



Armed with these characterizations, Von Eckardt Klein argues that inference
to a functional-localization hypothesis proceeds in two steps. First,
a functional deficit in a patient is hypothesized based on the
abnormal behavior the patient exhibits. Second, localization of
function in normal brains is inferred on the basis of the functional
deficit hypothesis plus the evidence about the site of brain damage.
The structurally-adequate functional analysis of the capacity connects
the pathological behavior to the hypothesized functional deficit. This
connection suggests four adequacy conditions on a functional deficit
hypothesis. First, the pathological behavior P (e.g., the speech
deficits characteristic of Broca's aphasia) must result from
failing to exercise some complex capacity C (human speech production).
Second, there must be a structurally-adequate functional analysis of
how people exercise capacity C that involves some constituent capacity
*c**i* (formulating motor commands to produce
the appropriate sounds). Third, the operation of the steps described
by the structurally-adequate functional analysis minus the operation
of the component performing *c**i*
(Broca's area) must result in pathological behavior P. Fourth,
there must not be a better available explanation for why the patient
does P. Arguments to a functional deficit hypothesis on the basis of
pathological behavior is thus an instance of argument to the best
available explanation. When postulating a deficit in a normal
functional component provides the best available explanation of the
pathological data, we are justified in drawing the inference.



Von Eckardt Klein applies this analysis to a neurological case study
involving a controversial reinterpretation of
 agnosia.[14]
 Her philosophical explication of this important neurological method
reveals that most challenges to localization arguments either argue
only against the localization of a particular type of functional
capacity or against generalizing from localization of function in one
individual to all normal individuals. (She presents examples of each
from the neurological literature.) Such challenges do not impugn the
validity of standard arguments for functional localization from
deficits. It does not follow that such arguments are unproblematic.
But they face difficult factual and methodological problems, not
logical ones. Furthermore, the analysis of these arguments as
involving a type of functional analysis and inference to the best
available explanation carries an important implication for the
biological study of cognitive function. Functional analyses require
functional theories, and structurally adequate functional analyses
require checks imposed by the lower level sciences investigating the
underlying physical mechanisms. Arguments to best available
explanation are often hampered by a lack of theoretical imagination:
the available alternative explanations are often severely limited. We
must seek theoretical inspiration from any level of investigation or
explanation. Hence making explicit the "logic" of this
common and historically important form of neurological explanation
reveals the necessity of joint participation from all scientific
levels, from cognitive psychology down to molecular neuroscience. Von
Eckardt Klein (1978) thus anticipated what came to be heralded as the
"co-evolutionary research methodology", which remains a
centerpiece of neurophilosophy to the present day (see
 section 6).



Over the last three decades, new evidence for localizations of
cognitive functions has come increasingly from a new source, the
development and refinement of neuroimaging techniques. However, the
logical form of localization-of-function arguments appears not to have
changed from those employing lesion studies, as analyzed by Von
Eckardt Klein. Instead, these new neuroimaging technologies resolve some of
the methodological problems that plagued lesion studies. For example,
researchers do not need to wait until the patient dies, and in the
meantime probably acquires additional brain damage, to find the lesion
sites. Two functional imaging techniques have been prominent in
philosophical discussions: positron emission tomography, or PET, and
functional magnetic resonance imaging, or fMRI. Although these measure
different biological markers of functional activity, PET approved for
human use now has spatial resolution down to the single mm range,
while fMRI has resolution down to less than
 1mm.[15]
 As these techniques increased spatial and temporal resolution of
functional markers, and continued to be used with sophisticated
behavioral methodologies, arguments for localizing specific
psychological functions to increasingly specific neural regions
continued to grow. Stufflebeam and Bechtel provided an early and
philosophically useful discussion of PET. Bechtel and Richardson
(1993) provided a general framework for "localization and
decomposition" arguments, which anticipated in many ways the
coming "new mechanistic" perspective in philosophy of
science and philosophy of neuroscience (see
 sections 7 and 8 below).
 Bechtel and Mundale (1999) further refined philosophical arguments
for localization of function specific to neuroscience.



More recent philosophical discussion of these functional imaging
techniques has tended to urge more caution resting localization claims
on their results. Roskies (2007), for example, points out the tendency
to think of the evidential force of functional neuroimages (especially
fMRI) on an analogy of that of photographs. Drawing on work in
aesthetics and the visual arts, Roskies argues that many of the
features that give photographs their evidential force are not present
in functional neuroimages. So while neuroimages do serve as evidence
for claims about neurofunctions, and even for localization hypotheses,
details of their proper interpretation are far more complicated than
philosophers sometimes assume. More critically, Klein (2010) argues
that images of "brain activity" resulting from functional
neuroimaging, especially fMRI are poor evidence for functional
hypotheses. For these images present the results of null hypothesis
significance testing on fMRI data, and such testing alone cannot
provide evidence about the functional structure of a causally dense
system, which the human brain is. Instead, functional neuroimages are
properly interpreted as indicating regions where further data and
analysis are warranted. But these data will typically require more
than simple significance testing, so skepticism about the evidential
force of neuroimages does not warrant skepticism more generally about
fMRI. 



Localization of function remains to this day a central topic of
discussion in philosophy of neuroscience. We will cover more recent
work in later sections.


## 6. A Result of the Co-evolutionary Research Ideology: Philosophy's Emphasis on Cognitive and Computational Neuroscience



What neuroscience has now discovered about the cellular and molecular
mechanisms of neural conductance and transmission is spectacular.
These results constitute one of the crowning achievements of
scientific inquiry. (For those in doubt, simply peruse for five
minutes a recent volume of *Society for Neuroscience
Abstracts*.) Less comprehensive, yet still spectacular, are
discoveries at "higher" levels of neuroscience: circuits,
networks, and systems. All this is a natural outcome of increasing
scientific specialization. We develop the technology, the experimental
techniques, and ultimately the experimental results-driven theories
within specific disciplines to push forward our understanding. Still,
a crucial aspect of the total picture sometimes gets neglected: the
relationship between the levels, the "glue" that binds
knowledge of neuron activity to subcellular and molecular mechanisms
"below", and to circuit, network, and systems activity
patterns "above". This problem is especially glaring when
we try to relate "cognitivist" psychological theories,
postulating information-bearing representations and processes
operating over their contents, to neuronal activities.
"Co-evolution" between these explanatory levels still
seems more a distant dream than an operative methodology guiding
day-to-day scientific research.



It is here that some philosophers and neuroscientists turned to
computational methods (Churchland and Sejnowski 1992). One hope was
that the way computational models have functioned in more developed
sciences, like in physics, might provide a useful model. One
computational resource that has usefully been applied in more
developed sciences to similar "cross-level" concerns are
*dynamical systems*. Global phenomena, such as large-scale
meteorological patterns, have been usefully addressed as dynamical,
nonlinear, and often chaotic interactions between lower-level physical
phenomena. Addressing the interlocking levels of theory and
explanation in the mind/brain using computational resources that have
worked to bridge levels in more mature sciences might yield comparable
results. This methodology is necessarily interdisciplinary, drawing on
resources and researchers from a variety of levels, including higher
ones like experimental psychology, artificial intelligence, and
philosophy of science.



The use of computational methods in neuroscience itself is not new.
Hodgkin, Huxley, and Katz (1952) incorporated values of
voltage-dependent sodium and potassium conductance they had measured
experimentally in the squid giant axon into an equation from physics
describing the time evolution of a first-order kinetic process. This
equation enabled them to calculate best-fit curves for modeled
conductance versus time data that reproduced the changing membrane
potential over time when action potentials were generated. Also using
equations borrowed from physics, Rall (1959) developed the cable model
of dendrites. This model provided an account of how the various inputs
from across the dendritic tree interact temporally and spatially to
determine the input-output properties of single neurons. It remains
influential today, and was incorporated into the GENESIS software
for programming neurally realistic networks (Bower and Beeman 1995;
see discussion in
 section 2 above).
 David Sparks and his colleagues showed that a vector-averaging model
of activity in neurons of superior colliculi correctly predicts
experimental results about the amplitude and direction of saccadic eye
movements (Lee, Rohrer, and Sparks 1988). Working with a more
sophisticated mathematical model, Apostolos Georgopoulos and his
colleagues predicted direction and amplitude of hand and arm movements
based on averaged activity of 224 cells in motor cortex. Their
predictions were borne out under a variety of experimental tests
(Georgopoulos, Schwartz, and Kettner 1986). We mention these
particular studies only because these are ones with which we are
familiar. No doubt we could multiply examples of the fruitful
interaction of computational and experimental methods in neuroscience
easily by one-hundred-fold. Many of these extend back before
"computational neuroscience" was a recognized research
endeavor.



We've already seen one example, the vector transformation
account of neural representation and computation, once under active
development in cognitive neuroscience (see
 section 2 above).
 Other approaches using "cognitivist" resources were, and
continue to be,
 pursued.[16]
 Some of these projects draw upon "cognitivist"
characterizations of the phenomena to be explained. Some exploit
"cognitivist" experimental techniques and methodologies.
Some even attempt to derive "cognitivist" explanations
from cell-biological processes (e.g., Hawkins and Kandel 1984). As
Stephen Kosslyn (1997) put it, cognitive neuroscientists employ the
"information processing" view of the mind characteristic
of cognitivism without trying to separate it from theories of brain
mechanisms. Such an endeavor calls for an interdisciplinary community
willing to communicate the relevant portions of the mountain of detail
gathered in individual disciplines with interested nonspecialists.
This requires more than people willing to confer with others working
at related levels, but also researchers trained explicitly in the
methods and factual details of a variety of disciplines. This is a
daunting need, but it offers hope to philosophers wishing to
contribute to actual neuroscience. Thinkers trained in both the
"synoptic vision" afforded by philosophy, and the
scientific and experimental basis of a genuine (graduate-level)
science would be ideally equipped for this task. Recognition of this
potential niche was slow to dawn on graduate programs in philosophy,
but a few programs have taken steps to fill it (see, e.g.,
 Other Internet Resources
 below).



However, one glaring shortcoming remains. Given philosophers'
training and interests, "higher-level"
neurosciences--networks, cognitive, systems, and the fields of
computational neuroscience which ally with these--tend to attract
the most philosophical attention. As natural as this focus might be,
it can lead philosophers to a misleading picture of neuroscience.
Neurobiology remains focused on cellular and molecular mechanisms of
neuronal activity, and allies with the kind of behavioral neuroscience
that works with animal models. This is still how a majority of members
of the Society for Neuroscience, now more than 37,000 members strong,
classify their own research; this is where the majority of grant money
for research goes; and these are the areas whose experimental
publications most often appear in the most highly cited scientific
journals. (The link to the Society for Neuroscience's web site
in
 Other Internet Resources below
 leads to a wealth of data on these numbers; see especially the
Publications section.) Yet philosophers have tended not to pay much
attention to cellular and molecular neuroscience. Fortunately this
seems to be changing, as we will document in sections 7 and 8 below.
Still, the preponderant attention philosophers pay to
cognitive/systems/computational neuroscience obscures the wetlab
experiment-driven focus of ongoing neurobiology.


## 7. Developments in the Philosophy of Neuroscience



The distinction between "philosophy of neuroscience" and
"neurophilosophy" came to be better clarified over the
first decade of the twenty-first century, due primarily to more
questions being pursued in both areas. Philosophy of neuroscience
still tends to pose traditional questions from philosophy of science
specifically about neuroscience. Such questions include: What is the
nature of neuroscientific explanation? And, what is the nature of
discovery in neuroscience? Answers to these questions are pursued
either descriptively (how does neuroscience proceed?) or normatively
(how should neuroscience proceed)? Some normative projects in
philosophy of neuroscience are "deconstructive",
criticizing claims about the topic made by neuroscientists. For
example, philosophers of neuroscience have criticized the conception
of personhood assumed by researchers in cognitive neuroscience (cf.
Roskies 2009). Other normative projects are constructive, proposing
new theories of neuronal phenomena or methods for interpreting
neuroscientific data. Such projects often integrate smoothly with
theoretical neuroscience itself. For example, Chris Eliasmith and
Charles Anderson developed an approach to constructing
neurocomputational models in their book *Neural Engineering*
(2003). In separate publications, Eliasmith has argued that the
framework introduced in *Neural Engineering* provides both a
normative account of neural representation and a framework for
unifying explanation in neuroscience (e.g., Eliasmith 2009).



Neurophilosophy continued to apply findings from the neurosciences to
traditional, philosophical questions. Examples include: What is an
emotion? (Prinz 2004) What is the nature of desire? (Schroeder 2004)
How is social cognition made possible? (Goldman 2006) What is the neural basis of
moral cognition? (Prinz 2007) What is the neural basis of happiness?
(Flanagan 2009) Neurophilosophical answers to these questions are
constrained by what neuroscience reveals about nervous systems. For
example, in his book *Three Faces of Desire*, Timothy Schroeder
(2004) argued that our commonsense conception of desire attributes to
it three capacities: (1) the capacity to reinforce behavior when
satisfied, (2) the capacity to motivate behavior, and (3) the capacity
to determine sources of pleasure. Based on evidence from the
literature on dopamine function and reinforcement learning theory,
Schroeder argued that reward processing is the basis for all three
capacities. Thus, reward is the essence of desire.



During the first decade of the twenty-first century a trend arose in
neurophilosophy to look toward neuroscience for guidance in moral
philosophy. That should be evident from the themes we've just
mentioned. Simultaneously, there was renewed interest in moralizing
about neuroscience and neurological treatments (see Levy 2007; Roskies
2009). This new field, *neuroethics*, thus combined both
interest in the relevance of neuroscience data for understanding moral
cognition, and the relevance of moral philosophy for acquiring and
regulating the application of knowledge from neuroscience. The
regulatory branch of neuroethics initially focused explicitly on the
ethics of treatment for people who suffer from neurological
impairments, the ethics of attempts to enhance human cognitive
performance (Schneider 2009), the ethics of applying "mind
reading" technology to problems in forensic science (Farah and
Wolpe 2004), and the ethics of animal experimentation in neuroscience
(Farah 2008). More recently both of these fields of neuroethics has
seen tremendous growth. The interested reader should consult the
 neuroethics entry
 in this Encyclopedia.



Trends during the first decade of the twenty-first century in
philosophy of neuroscience included renewed interest in the nature of
mechanistic explanations. This was in keeping with a general trend in
philosophy of science (e.g., Machamer, Darden, and Craver 2000). The
application of this general approach to neuroscience isn't
surprising. "Mechanism" is a widely-used term among
neuroscientists. In his book, *Explaining the Brain* (2007),
Carl Craver contended that mechanistic explanations in neuroscience
are causal explanations, and typically multi-level. For example, the
explanation of the neuronal action potential involves the action
potential itself, the cell in which it occurs, electro-chemical
gradients, and the proteins through which ions flow across the
membrane. Thus we have a composite entity (a cell) causally
interacting with neurotransmitters at its receptors. Parts of the cell
engage in various activities, e.g., the opening and closing of
ligand-gated and voltage-gated ion channels, to produce a pattern of
changes, the depolarizing current constituting the action potential. A
mechanistic explanation of the action potential thus countenances
entities at the cellular, molecular, and atomic levels, all of which
are causally relevant to producing the action potential. This causal
relevance can be confirmed by altering any one of these variables,
e.g., the density of ion channels in the cell membrane, to generate
alterations in the action potential; and by verifying the consistency
of the purported invariance between the variables. For challenges to
Craver's account of mechanistic explanation in neuroscience,
specifically concerning the action potential, see Weber 2008, and
Bogen 2005.



According to epistemic norms shared implicitly by neuroscientists,
good explanations in neuroscience are good mechanistic explanations;
and good mechanistic explanations are those that pick out invariant
relationships between mechanisms and the phenomena they control. (For
fuller treatment of invariance in causal explanations throughout
science, see James Woodward 2003. Mechanists draw extensively on
Woodward's "interventionist" account of cause and
causal explanations.) Craver's account raised questions about
the place of reduction in neuroscience. John Bickle (2003) suggested
that the working concept of reduction in the neurosciences consists of
the discovery of systematic relationships between interventions at
lower levels of biological organization, as these are pursued in
cellular and molecular neuroscience, and higher level behavioral
effects, as they are described in psychology. Bickle called this
perspective "reductionism-in-practice" to contrast it with
the concepts of intertheoretic or metaphysical reduction that have
been the focus of many debates in the philosophy of science and
philosophy of mind. Despite Bickle's reformulation of reduction,
however, mechanists generally resist, or at least relativize, the
"reductionist" label. Craver (2007) calls his view the
"mosaic unity" of neuroscience. Bechtel (2009) calls his
"mechanistic reduction(ism)". Both Craver and Bechtel
advocate multi-leveled "mechanisms-within-mechanisms",
with no level of mechanism epistemically privileged. This is in
contrast to reduction(ism), ruthless or otherwise which privileges
lower levels. Still we can ask: Is mechanism a kind of
reductionism-in-practice? Or does mechanism, as a position on
neuroscientific explanation, assume some type of autonomy for
psychology? If it assumes autonomy, reductionists might challenge
mechanists on this assumption. On the other hand, Bickle's
reductionism-in-practice clearly departs from inter-theoretic
reduction, as the latter is understood in philosophy of science. As
Bickle himself acknowledges, his latest reductionism was inspired
heavily by mechanists' criticisms of his earlier "new
wave" account. Mechanists can challenge Bickle that his
departure from the traditional accounts has also led to a departure
from the interests that motivated those accounts. (See Polger 2004 for
a related challenge.) As we will see in
 section 8 below,
 these issues surrounding mechanistic philosophy of neuroscience have
grown more urgent, as mechanism has grown to dominate the field.



The role of temporal representation in conscious experience and the
kinds of neural architectures sufficient to represent objects in time
generated interest. In the tradition of Husserl's phenomenology,
Dan Lloyd (2002, 2003) and Rick Grush (2001, 2009) have separately
drawn attention to the tripartite temporal structure of phenomenal
consciousness as an explanandum for neuroscience. This structure
consists of a subjective present, an immediate past, and an
expectation of the immediate future. For example, one's
conscious awareness of a tune is not just of a time-slice of
tune-impression, but of a note that a moment ago was present, another
that is now present, and an expectation of subsequent notes in the
immediate future. As this experience continues, what was a moment ago
temporally immediate is now retained as a moment in the immediate
past; what was expected either occurred or didn't in what has
now become the experienced present; and a new expectation has formed
of what will come. One's experience is not static, even though
the experience is of a single object (the tune). These earlier works
found increased relevance with the rise of "predictive
coding" models of whole brain function, developed by
neuroscientists including Karl Friston (2009) less than a decade
later, and brought to broader philosophical attention by Jakob Hohwy
(2013) and Andy Clark (2016). 



According to Lloyd, the tripartite structure of consciousness raises a
unique problem for analyzing fMRI data and designing experiments. The
problem stems from the tension between the sameness in the object of
experience (e.g., the same tune through its progression) and the
temporal fluidity of experience itself (e.g., the transitions between
heard notes). At the time Lloyd was writing, one standard means of
analyzing fMRI data consisted in averaging several data sets and
subtracting an estimate of baseline activation from the composites.
 [17]
 This is done to filter noise from the task-related hemodynamic
response. But as Lloyd points out, this then-common practice ignores
much of the data necessary for studying the neural correlates of
consciousness. It produces static images that neglect the
relationships between data points over the time course. Lloyd instead
applies a multivariate approach to studying fMRI data, under the
assumption that a recurrent network architecture underlies the
temporal processing that gives rise to experienced time. A simple
recurrent network has an input layer, an output layer, a hidden layer,
and an additional layer that copies the prior activation state of
either the hidden layer or the output layer. Allowing the output layer
to represent a predicted outcome, the input layer can then represent a
current state and the additional layer a prior state. This assignment
mimics the tripartite temporal structure of experience in a network
architecture. If the neuronal mechanisms underlying conscious
experience are approximated by recurrent network architecture, one
prediction is that current neuronal states carry information about
immediate future and prior states. Applied to fMRI, the model predicts
that time points in an image series will carry information about prior
and subsequent time points. The results of Lloyd's (2002)
analysis of 21 subjects' data sets, sampled from the publicly
accessible National fMRI Data Center, support this prediction.



Grush's (2001, 2004) interest in temporal representation is part
of his broader systematic project of addressing a semantic problem for
computational neuroscience, namely: how do we demarcate study of the
brain as an information processor from the study of any other complex
causal process? This question leads back into the familiar territory
of psychosemantics (see
 section 3 above),
 but now the starting point is internal to the practices of
computational neuroscience. The semantic problem is thereby rendered
an issue in philosophy of neuroscience, insofar as it asks: what does
(or should) "computation" mean in computational
neuroscience?



Grush's solution drew on concepts from modern control theory. In
addition to a controller, a sensor, and a goal state, certain kinds of
control systems employ a *process model* of the actual process
being controlled. A process model can facilitate a variety of
engineering functions, including overcoming delays in feedback and
filtering noise. The accuracy of a process model can be assessed
relative to its "plug-compatibility" with the actual
process. Plug-compatibility is a measure of the degree to which a
controller can causally couple to a process model to produce the same
results it would produce by coupling with the actual process. Note
that plug-compatibility is not an information relation.



To illustrate a potential neuroscientific implementation, Grush
considers a controller as some portion of the brain's motor
systems (e.g., premotor cortex). The sensors are the sense organs
(e.g., stretch receptors on the muscles). A process model of the
musculoskeletal system might exist in the cerebellum (see Kawato
1999). If the controller portion of the motor system sends spike
trains to the cerebellum in the same way that it sends spikes to the
musculoskeletal system, and if in return the cerebellum receives spike
trains similar to real peripheral feedback, then the cerebellum
emulates the musculoskeletal system (to the degree that the mock
feedback resembles real peripheral feedback). The proposed unit over
which computational operations range is the neuronal realization of a
process model and its components, or in Grush's terms an
"emulator" and its "articulants".



The details of Grush's framework are too sophisticated to
present in short compass. (For example, he introduces a host of
conceptual devices to discuss the representation of external objects.)
But in a nutshell, he contends that understanding temporal
representation begins with understanding the emulation of the timing
of sensorimotor contingencies. Successful sequential behavior (e.g.,
spearing a fish) depends not just on keeping track of where one is in
space, but where one is in a temporal order of movements and the
temporal distance between the current, prior, and subsequent
movements. Executing a subsequent movement can depend on keeping track
of whether a prior movement was successful and whether the current
movement is matching previous expectations. Grush posits
emulators--process models in the central nervous
system--that anticipate, retain, and update mock sensorimotor
feedback by timing their output proportionally to feedback from an
actual process (Grush 2005).



Lloyd's and Grush's approaches to studying temporal
representation are varied in their emphases. But they are unified in
their implicit commitment to localizing cognitive functions and
decomposing them into subfunctions using both top-down and bottom-up
constraints. (See Bechtel and Richardson 1993 for more details on this
general explanatory strategy.) As we mentioned a few paragraphs above,
both anticipated in important and interesting ways more recent
neuroscientific and philosophical work on predictive coding and the
brain. Both developed mechanistic explanations that pay little regard
to disciplinary boundaries. One of the principal lessons of
Bickle's and Craver's work is that neuroscientific
practice in general is structured in this fashion. The ontological
consequences of adopting this approach continue to be debated.


## 8. Developments over the Second Decade of the Twenty-First Century



Mechanism, first introduced in section 7 above, came to dominate the
philosophy of neuroscience throughout the second decade of the
twenty-first century. One much-discussed example is Gualtiero
Piccinini and Carl Craver (2011). The authors employ two popular
mechanistic notions. Their first is the multi-level, nested
hierarchies of mechanisms-within-mechanisms perspective, discussed in
section 7 above, that traces back to Craver and Darden (2001). Their
second is that of "mechanism sketch", suggested initially
in Machamer, Darden, and Craver (2000) and developed in detail in
Craver (2007). Piccinini and Craver's goal is to
"seamlessly" situate psychology as part of an
"integrated framework" alongside neuroscience. They
interpret psychology's familiar functional analyses of cognitive
capacities as relatively incomplete mechanism-sketches, which leave
out many components of the mechanisms that ultimately will fully
explain the system's behavior. Neuroscience in turn fills in
these missing components, dynamics, and organizations, at least ones
found in nervous systems. This filling-in thereby turns
psychology's mechanism-sketches into full-blown mechanistic
explanations. So even though psychology proceeds via functional
analyses, so interpreted it is nonetheless mechanistic. Piccinini and
Craver realize that their "integrated" account clashes
with classical "autonomy" claims for psychology vis-a-vis
neuroscience. Nevertheless, they insist that their challenge to
classical "autonomy" does not commit them to
"reductionism", in either its classical or more recent
varieties. Their commitment to nested hierarchy of
mechanisms-within-mechanisms to account for a system's behavior
acknowledges the importance of mechanisms and intralevel causation at
all levels constituting the system, not just at lower (i.e., cellular,
molecular) levels.



David Kaplan and Craver (2011) focus the mechanist perspective
critically on dynamical systems mathematical models popular in recent
systems and computational neuroscience. They argue that such models
are explanatory only if there exists a "plausible mapping"
between elements in the model and elements in the modeled system. At
bottom is their Model-to-Mechanism-Mapping (3M) Constraint on
explanation. The variables in a genuinely explanatory model correspond
to components, activities, or organizational features of the system
being explained. And the dependencies posited among variables in the
model, typically expressed mathematically in systems and computational
neuroscience, correspond to causal relations among the system's
components. Kaplan and Craver justify the 3M Constraint on grounds of
explanatory norms, common to both science and common sense. All other
things being equal, they insist, explanations that provide more
relevant details about a system's components, activities, and
organization, more likely will answer more questions about how the
system will behave in a variety of circumstances, than will an
explanation that provides fewer (mechanistic) details.
"Relevant" here pertains to the functioning of the
specific mechanism. Models from systems and computational neuroscience
that violate the 3M Constraint are thus more reasonably thought of as
mathematical descriptions of phenomena, not explanations of some
"non-mechanistic" variety.



Kaplan and Craver challenge their own view with one of the more
popular dynamical/mathematical models in all of computational
neuroscience, the Haken-Kelso-Bunz (1985) model of human bimanual
finger-movement coordination. They point to passages in these
modelers' publications that suggest that the modelers only
intended for their dynamical systems model to be a mathematically
compact description of the temporal evolution of a "purely
behavioral dependent variable". The modelers interpreted none of
the model's variables or parameters as mapping onto components
or operations of any hypothetical mechanism generating the behavioral
data. Nor did they intend for any of the model's mathematical
relations or dependencies among variables to map onto hypothesized
causal interactions among components or activities of any mechanism.
As Kaplan and Craver further point out, after publishing their
dynamicist model, these modelers themselves then began to investigate
how the behavioral regularities their model described might be
produced by neural motor system components, activities, and
organization. Their own follow-up research suggests that these
modelers saw their dynamicist model as a heuristic, to help
neuroscientists move toward "how-possibly", and ultimately
to a "how-actually" mechanistic explanation. 



At bottom, Kaplan and Craver's 3M constraint on explanation
presents a dilemma for dynamicists. To the extent that dynamical
systems modelers intend to model hypothesized neural mechanisms for
the phenomenon under investigation, their explanations will need to
cohere with the 3M Constraint (and other canons of mechanistic
explanation). To the extent that this is not a goal of dynamicist
modelers, their models do not seem genuinely explanatory, at least not
in one sense of "explanation" prominent in the history of
science. Furthermore, when dynamicist models are judged to be
successful, they often prompt subsequent searches for underlying
mechanisms, just as the 3M Constraint and the general mechanist
account of the move from "how-possibly" to "how
actually" mechanisms recommends. Either horn gores dynamicists
who claim that their models constitute a necessary additional kind of
explanation in neuroscience to mechanistic explanation, beyond any
heuristic value such models might offer toward discovering
mechanisms.



Kaplan and Craver's radical conclusion, that dynamicist
"explanations" are genuine explanations only to the degree
that they respect the (mechanist's) 3M Constraint, needs more
defense. The burden of proof always lies on those whose conclusions
strike at popular assumptions. More than the discussion of a couple of
landmark dynamicist models in neuroscience is needed (in their 2011,
Kaplan and Craver also discuss the difference-of-Gaussians model of
receptive field properties of mammalian visual neurons). Expectedly,
dynamicists have taken up this challenge. Michael Silberstein and
Anthony Chemero (2013), for example, argue that localization and
decomposition strategies characterize mechanistic explanation, and
that some explanations in systems neuroscience violate one of these
assumptions, or both. Such violations in turn create a dilemma for
mechanists. Either they must "stretch" their account of
explanation, beyond decomposition and localization, to capture these
recalcitrant cases, or they must accept "counterexamples"
to the generality of mechanistic explanation, in both systems
neuroscience and systems biology more generally.



Lauren Ross (2015) and Mazviita Chirimuuta (2014) independently appeal
to Robert Batterman's account of minimal model explanation as an
important kind of non-mechanistic explanation in neuroscience. Minimal
models were developed initially to characterize a kind of explanation
in the physical sciences (see, e.g., Batterman and Rice 2014).
Batterman's account distinguishes between two different kinds of
scientific "why-questions": why a phenomenon manifests in
particular circumstances; and why a phenomenon manifests generally, or
in a number of different circumstances. Mechanistic explanations
answer the first type of why-question. Here a "more details the
better" (MDB) assumption (Chirimuuta 2014), akin to Kaplan and
Craver's "all things being equal" assumption about
better explanations (mentioned above), holds force. Minimal models,
however, which minimize over the presented implantation details and
hence violate MDB, are better able to answer the second type of
scientific why-questions. Ross (2015), quoting from computational
neuroscientists Rinzel and Ermentrout, insists that models containing
more details than necessary can obscure identification of critical
elements by leaving too many open possibilities, especially when one
is trying to answer Batterman's second kind of why-questions
about a system's behavior.



Chirimuuta and Ross each appeal to related resources from
computational neuroscience to illustrate the applicability of
Batterman's minimal model explanation strategy. Ross appeals to
"canonical models", which represent "shared
qualitative features of a number of distinct neural systems"
(2015: 39). Her central example is the derivation of the
Ermentrout-Kopell model of class I neuron excitability, which uses
"mathematical abstraction techniques" to "reduce
models of molecularly distinct neural systems to a single ...
canonical model". Such a model "explains why molecularly
diverse neural systems all exhibit the same qualitative
behavior", (2015: 41) clearly a Batterman second-type
why-question. Chirimuuta's resource is "canonical neural
computations" (CNCs): 



> 
> 
> computational modules that apply the same fundamental operations in a
> variety of contexts ... a toolbox of computational operations
> that the brain applies in a number of different sense modalities and
> anatomic regions and which can be described at higher levels of
> abstraction from their biophysical implementation. (Chirimuuta 2014:
> 138) 
> 
> 
> 



Examples include shunting inhibition, linear filtering, recurrent
amplification, and thresholding. Rather than being mechanism-sketches,
awaiting further mechanistic details to be turned into full-blown
how-actually mechanisms, CNCs are invoked in a different explanatory
context, namely, ones posing Batterman's second type of
why-questions. Ross concurs concerning canonical models: 



> 
> 
> Understanding the approach dynamical systems neuroscientists take in
> explaining [system] behavior requires attending to their explanandum
> of interest and the unique modeling tools [e.g., canonical models]
> common in their field. (2015: 52) 
> 
> 
> 



In short, both Chirimuuta's and Ross's replies to Kaplan
and Craver's challenge is a common one in philosophy: save a
particular form of explanation from collapsing into another by
splitting the explanandum.



Finally, to wrap up this discussion of mechanism ascendant, an
analogue of Craver's (2007) problem of accounting for
"constitutive mechanistic relevance", that is, for
determining which active components of a system are actually part of
the mechanism for a given system phenomenon, has also re-emerged in
recent discussions. Robert Rupert (2009) suggests that
"integration" is a key criterion for determining which set
of causally contributing mechanisms constitute the system for a task,
based on the relative frequency with which sets of mechanisms
co-contribute to causing task occurrences. He cashes frequency of
co-contribution as the probability of the set for causing the
cognitive task, conditional to every other co-occurring causal set.
Felipe De Brigard (2017) challenges Rupert's criterion, arguing
that it cannot account for cognitive systems displaying two features,
"diachronic dynamicity" along with "functional
stability". The frequency with which a given mechanism causally
contributes to the same cognitive task (functional stability) can
change over time (diachronic dynamicity). Although De Brigard
emphasizes the critical importance of these features for
Rupert's integration criterion via a fanciful thought
experiment, he also argues that they are a widespread phenomenon in
human brains. Both features are found, for example, in evidence
pertaining to the "Hemispheric Asymmetry Reduction in Older
Adults", in which tasks that recruit hemispherically localized
regions of prefrontal cortex in younger adults show a reduction in
hemispheric asymmetry in older adults. And both are found in
"Posterior-Anterior Shift with Aging", where a task
increases activity in anterior brain regions while decreasing activity
in posterior regions in older adults, relative to activity invoked by
the same task in younger adults.



To replace Rupert's notion of integration as a criterion for
determining which sets of mechanisms constitute a cognitive system, De
Brigard points to two promising recent developments in network
neuroscience which potentially allow for parametrized time.
"Scaled inclusivity" is a method for examining each node
in a network and identifying its membership in "community
structures" across different iterations of the network.
"Temporal-dynamic network analyses" is a way to quantify
changes in community structures or modules between networks at
different time points. Both methods thereby identify "modular
alliances", which convey both co-activation and dynamic change
information in a single model. De Brigard suggests that these are thus
the candidates with which cognitive systems could be identified.



Clearly, much remains to be discussed regarding the impact mechanism
has come to wield in philosophy of neuroscience over the last decade.
But while mechanism has become the most dominant general perspective
in the field, work in other areas continues. Michael Anderson defends
the relevance of cognitive neuroscience for determining
psychology's taxonomy, independent of any commitment to
mechanism. The most detailed development of his approach is in his
(2014) book, *After Phrenology*, based on his influential
"neural reuse" hypothesis. Each region of the brain, as
recognized by the standard techniques of cognitive neuroscience
(especially fMRI), engages in cognitive functions that are highly
various, and form different "neural partnerships" with one
another under different circumstances. Psychological categories are
then to be reconceived along lines suggested by the wide-ranging
empirical data in support of neural reuse. A genuine
"post-phrenological" science of the mind must jettison the
assumption that each brain region performs its own fundamental
computation. In this fashion Anderson's work explicitly
continues philosophy of neuroscience's ongoing interest in
localizations of cognitive functions.



In shorter compass, Anderson (2015) investigates the relevance of
cognitive neuroscience for reconceiving psychology's basic
categories, starting from a consequence of his neural reuse
hypothesis. Attempts to map cognitive processes onto specific neural
processes and brain regions reveal "many-to-many"
relations. Not only do these relations show that combined
anatomical-functional labels for brain regions (e.g., "fusiform
face area") are deceptive; they also call into question the
possibility of deciding between alternative psychological taxonomies
by appealing to cognitive neuroscientific data.



For all but the strongest proponents of psychology's autonomy
from neuroscience, these many-to-many mappings will suggest that the
psychological taxonomy we bring to this mapping project needs
revision. One need not be committed to any strong sense of
psychoneural reduction, or the epistemological superiority of
cognitive neuroscience to psychology, to draw this conclusion. The
mere relevance of cognitive neuroscience for psychology's
categories is enough. This debate is thus "about the
requirements for a unified science of the mind, and the proper role of
neurobiological evidence in the construction of such an
ontology" (2015: 70), not about the legitimacy of either.



Anderson divides revisionary projects for psychology into three kinds,
based on the degree of revision each kind recommends for psychology,
and the extent of one-to-one function-to-structure mappings the
proposed revisions predicts will be available.
"Conservatives" foresee little need for extensive
revisions of psychology's basic taxonomy, even as more
neuroscientific evidence is taken into account than current standard
practices pursue. "Moderates" insist that our knowledge of
brain function "can (and should) act as one arbiter of the
psychologically real" (2015: 70), principally by
"splitting" or "merging" psychological
concepts that currently are in use. "Radicals" project
even more drastic revisions, even to the most primitive concepts of
psychology, and even after such revisions they still do not expect
that many one-to-one mappings between brain regions and the new
psychological primitives will be found. Although Anderson does not
stress this connection (eliminative materialism has not been a
prominent concern in philosophy of mind or neuroscience for two
decades), readers will notice similar themes discussed in
 section 2
 above, only now with scientific, not folk psychology the target of
the radical revisionists. A key criterion for any satisfactory
reformulation of a cognitive ontology is the degree to which it
supports two kinds of inferences: "forward inferences",
from the engagement of a specific cognitive function to the prediction
of brain activity; and "reverse inferences", from the
observation that a specific brain region or pattern occurs to the
prediction that a specific cognitive operation is engaged. In light of
this explicit criterion, Anderson usefully surveys the work of a
number of prominent psychologists and cognitive neuroscientists in
each of his revisionist groups. Given his broader commitment to neural
reuse, and the trek it invites into "evolutionarily-inspired,
ecological, and enactive terms", Anderson's own sentiments
lie with the "radicals": 



> 
> 
> language and mathematics, for instance, are best understood as
> extensions of our basic affordance processing capacities augmented
> with public symbol systems ... The psychological science that
> results from this reappraisal may well look very different from the
> one we practice today. (2015: 75)
> 
> 
> 



Landmark neuroscientific hypotheses remain a popular focus in recent
philosophy of neuroscience. Berit Brogaard (2012), for example, argues
for a reinterpretation of the standard "dissociation"
understanding of Melvin Goodale and David Milner's (1992)
celebrated "two visual processing streams", a landmark,
now "textbook" result from late-twentieth century
neuroscience. Two components of the standard dissociation are key. The
first is that distinct brain regions compute information relevant for
visually guided "on-the-fly" actions, and for object
recognition, respectively, the dorsal stream (which runs from primary
visual cortex through the medial temporal region into the superior and
inferior parietal lobules) and the ventral stream (which runs from
primary visual cortex through V4 and into inferior temporal cortex).
And second, that only information relevant for visual object
recognition, processed in the ventral stream, contributes to the
character of conscious visual experiences.



Brogaard's concern is that this standard understanding
challenges psychofunctionalism, our currently most plausible
"naturalistic" account of mental states.
Psychofunctionalism draws its account of mind directly from our best
cognitive psychology. If ph is some mental state type that has
inherited the content of a visual experience, then according to
cognitive psychology a wide range of visually guided beliefs and
desires, different kinds of visual memories, and so on, satisfy
ph's description. But by the standard
"dissociation" account of Goodale and Milner's two
visual streams, only dorsal-stream states, and not ventral-stream
states, represent truly egocentric visual properties, namely
"relational properties which objects instantiate from the point
of view of believers or perceivers", (Brogaard 2012: 572). But
according to cognitive psychology, dorsal-stream states do not play
this wide-ranging ph-role. So according to psychofunctionalism
"ph-mental states cannot represent egocentric
properties" (2012: 572). But it seems "enormously
plausible" that some of our perceptual beliefs and visual
memories represent egocentric properties. So either we reject
psychofunctionalism, and so our most plausible naturalization project
for determining whether a given mental state is instantiated, or we
reject the standard dissociation interpretation of Goodale and
Milner's two visual streams hypothesis, despite the wealth of
empirical evidence supporting it. Neither horn of this dilemma looks
comfortably graspable, although the first horn might be thought to be
more so, since psychofunctionalism as a general theory of mind lacks
the kind of strong empirical backing that the standard interpretation
of Goodale and Milner's hypothesis enjoys.



Nevertheless, Brogaard recommends retaining psychofunctionalism, and
instead rejecting "a particular formulation" of Goodale
and Milner's two visual stream hypothesis. The interpretation to
reject insists that "dorsal-stream information cannot contribute
to the potentially conscious representations computed by the ventral
stream" (2012: 586-587). Egocentric representations of
visual information computed by the dorsal stream contribute to
conscious visual stream representations "via feedback
connections" from dorsal- to ventral-stream neurons (2012: 586).
This isn't to deny dissociation: 



> 
> 
> Information about the egocentric properties of objects is processed by
> the dorsal stream, and information about allocentric properties of
> objects is processed by the ventral stream. (2012: 586) 
> 
> 
> 



But this dissociation hypothesis "has no bearing on what
information is passed on to parts of the brain that process
information which correlated with visual awareness" (2012: 586).
With this re-interpretation, psychofunctionalism is rendered
consistent with Goodale and Milner's two stream, dorsal and
ventral, "what" and "where/how" hypothesis and
the wealth of empirical evidence that supports it. According to
Brogaard, psychofunctionalism can thereby "correctly treat
perceptual and cognitive states that carry information processed in
the ventral visual stream as capable of representing egocentric
properties" (2012: 586).



Despite philosophy of neuroscience's continuing focus on
cognitive/systems/computational-neuroscience (see the discussion in
 section 7 above),
 interest in neurobiology's cellular/molecular mainstream
appears to be increasing. One notable paper is Ann-Sophie Barwich and
Karim Bschir's (2017) historical-cum-philosophical study of
G-protein coupled receptors (GPCRs). Work on the structure and
functional significance of these proteins has dominated molecular
neuroscience for the past forty years; their role in the mechanisms of
a variety of cognitive functions is now empirically documented beyond
question. And yet one finds little interest in, or even notice of this
shift in mainstream neuroscience among philosophers. Barwich and
Bschir's yeoman history research on the discovery and
development of these objects pays off philosophically. The role of
manipulability as a criterion for entity realism in the
science-in-practice of wet-lab research becomes meaningful "only
once scientists have decided how to conceptually coordinate measurable
effects distinctly to a scientific object" (2017: 1317).
Scientific objects like GPCRs get assigned varying degrees of reality
throughout different stages of the discovery process. Such an
object's role in evaluating the reality of "neighboring
elements of enquiry" becomes a part of the criteria of its
reality as well.



The impact of science-in-practice on philosophy of science generally
has been felt acutely in the philosophy of neuroscience, most notably
in increased philosophical interest in neuroscientific
experimentation. In itself this should not surprise. Neuroscience
relies heavily on laboratory experimentation, especially within its
cellular and molecular, "Society for Neuroscience"
mainstream. So the call to understand experiment should beckon any
philosopher who ventures into neuroscience's cellular/molecular
foundations. Two papers by Jacqueline Sullivan (2009, 2010) have been
important in this new emphasis. In her (2009) Sullivan acknowledges
both Bickle's (2003) and Craver's (2007) focus on cellular
and molecular mechanisms of long-term potentiation, and
experience-driven form of synaptic plasticity. But she insists that
broader philosophical commitments, which lead Bickle to ruthless
reductionist and Craver to mosaic unity "global" accounts,
obscure important aspects of real laboratory neuroscience practice.
She emphasizes the role of "subprotocols", which specify
how data are to be gathered, in her model of "the experimental
process", and illustrates these notions with a number of
examples. Her analysis reveals an important underappreciated tension
among a pair of widely-accepted experiment norms. Pursuing
"reliability" drives experimenters more deeply into
extensive laboratory controls. Pursuing "external
validity" drives them toward enriched experimental environments
that more closely represent the messy natural environment beyond the
laboratory. These two norms commonly conflict: in order to get more of
one, scientists introduce conditions that give them less of the other.




In her (2010) Sullivan offers a detailed history of the Morris water
maze task, tracing her account back to Morris's original
publications. Philosophers of neuroscience have uncritically assumed
that the water maze is a widely-accepted neuroscience protocol for
rodent spatial learning and memory, but the detailed scientific
history is not so clear on this interpretation. Scientific commentary
over time on what this task measures, including some from Morris
himself, reveals no clear consensus. Sullivan traces the source of
this scientific inconsistency back to the impact of 1980s-era
cellular-molecular reductionism driving experimental behavioral
neurobiology protocols like the Morris water maze.



A different motivation drives neurobiologist Alcino Silva,
neuroinformaticist Anthony Landreth, and philosopher of neuroscience
John Bickle's (2014) focus on experimentation. All contemporary
sciences are growing at a vertiginous pace; but perhaps none more so
than neuroscience. It is no longer possible for any single scientist
to keep up with all the relevant published literature in even his or
her narrow research field, or fully to comprehend its implications. An
overall lack of clarity and consensus about what is known, what
remains doubtful, and what has been disproven creates special problems
for experiment planning. There is a recognized and urgent need to
develop strategies and tools to address these problems. Toward this
explicit end, Silva, Landreth, and Bickle's book describes a
framework and a set of principles for organizing the published record.
They derive their framework and principles directly from landmark case
studies from the influential neuroscientific field of molecular and
cellular cognition (MCC), and describe how their framework can be used
to generate maps of experimental findings. Scientists armed with these
research maps can then determine more efficiently what has been
accomplished in their fields, and where the knowledge gaps still
reside. The technology needed to automate the generation of these maps
already exists. Silva, Landreth, and Bickle sketch the transformative,
revolutionary impact these maps can have on current science.



Three goals motivate Silva, Landreth, and Bickle's approach.
First, they derive their framework from the cellular and molecular
neurobiology of learning and memory. This choice was due strictly to
familiarity with the science. Silva was instrumental in bringing gene
targeting techniques applied to mammals into behavioral neuroscience,
and Bickle's focus on ruthlessly reductive neuroscience was
built on these and other experimental results. And while each of their
framework's different kinds of experiments and evidence have
been recognized by others, theirs purports to be the first to
systematize this information explicitly toward the goal of
facilitating experimental planning by practicing scientists. Silva,
Landreth and Bickle insist that important new experiments can be
identified and planned by methodically filling in the different forms
of evidence recognized by their framework, and applying the different
forms of experiments to the gaps in the experimental record revealed
by this process.



Second, Silva, Landreth, and Bickle take head-on the problem of the
growing amount, complexity and integration of the published literature
for experiment planning. They show how graphic weighted
representations of research findings can be used to guide research
decisions; and how to construct these. The principles for constructing
these maps are the principles for integrating experimental results,
derived directly from landmark published MCC research. Using a case
study from recent molecular neuroscience, they show how to generate
small maps that reflect a series of experiments, and how to combine
these small maps to illustrate an entire field of neuroscience
research.



Finally, Silva, Landreth and Bickle begin to develop a science of
experiment planning. They envision the causal graphs that compose
their research maps to play a role similar to that played by
statistics in the already-developed science of data analysis. Such a
resource could have profound implications for further developing
citation indices and other impact measures for evaluating
contributions to a field, from those of individual scientists to those
of entire institutions.



More recently Bickle and Kostko (2018) have extended Silva, Landreth
and Bickle's framework beyond the neurobiology of learning and
memory. Their case study comes from developmental and social
neuroscience, Michael Meaney and Moshe Szyf's work on the
epigenetics of rodent maternal nursing behaviors on offspring stress
responses. Using the details of this case study they elaborate on a
notion that Silva, Landreth and Bickle leave underdeveloped, that of
experiments designed explicitly for their results, if successful, to
be integrated directly into an already-existing background of
established results. And they argue that such experiments
"integratable by design" with others are aimed not at
establishing evidence for individual causal relations among
neuroscientific kinds, but rather at formulating entire causal
pathways connecting multiple phenomena. Their emphasis on causal paths
relates to that of Lauren Ross (forthcoming). Ross's work is
especially interesting in this context because she uses her causal
pathway concept to address "causal selection", which has
to do with distinguishing between background conditions and
"true" (triggering) causes of some outcome of interest.
For Silva, Landreth, and Bickle (2014), accounting for this
distinction is likewise crucial, and they rely on a specific kind of
connection experiment, "positive manipulations", to draw
it. Bickle and Kostko's appeal to causal paths in a detailed
case study from recent developmental neurobiology might help bridge
Silva, Landreth and Bickle's broader work on neurobiological
experimentation with Ross's work drawn from biology more
generally.