## 1. Defining Perceptual Learning



In 1963, the psychologist Eleanor Gibson wrote a landmark survey
article on perceptual learning in which she purported to define the
term. According to Gibson, perceptual learning is "[a]ny
relatively permanent and consistent change in the perception of a
stimulus array, following practice or experience with this
array..." (1963:
 29).[1]
 Gibson's definition has three basic parts. First, perceptual
learning is long-lasting. Second, it is perceptual. Third, it is the
result of practice or experience. This entry expands on each of these
features of the definition.


### 1.1 Perceptual Learning as *Long-Term* Perceptual Changes



Perceptual learning involves *long-term* changes in perception.
This criterion rules out short term perceptual changes due to sensory
adaptation (for more on sensory adaptation see Webster 2012). In the
waterfall illusion, for instance, a person who looks at a waterfall
for a minute, and then looks away at some rocks, sees the rocks as
moving even though they are not. This is a short-term change in
perception, lasting perhaps for fifteen to thirty seconds. Since it is
not a long-term change in perception, however, it does not count as
perceptual learning. In another short term adaptive change, a person
who goes indoors after walking through a blizzard may have trouble as
her eyes adjust to the new lighting. There is a change in her
perception as a result of her experience in the blizzard. But it is
not a long-term change, and so it does not count as perceptual
learning.



While there are clear cases of long-term experience-induced perceptual
changes and clear cases of short-term experience-induced perceptual
changes, there may be intermediary cases where it is difficult to tell
whether they count as long-term or not. In such cases, in order to
determine whether the case is a genuine case of perceptual learning,
it may be necessary to look at the mechanisms involved (see
 section 2
 below on the mechanisms of perceptual learning). If the mechanisms
involved are characteristic of other cases of perceptual learning,
then that is a reason to count the case as an instance of perceptual
learning. If the mechanisms involved are uncharacteristic of
perceptual learning, then that is a reason not to count the case as an
instance of perceptual learning.


### 1.2 Perceptual Learning as *Perceptual* Changes



Perceptual learning involves changes in
 *perception*.[2]
 This rules out mere changes in aesthetic taste, among other things.
For instance, imagine a contrarian who likes things only insofar as
other people do not like those things. Suppose he finds out that
everyone else has come to like his favorite microbrew. This might
cause him to change how he judges that beer aesthetically. However,
the beer may well taste the same to him. So, it is not a case of
perceptual learning, but a mere change in the person's aesthetic
judgment. The fact that perceptual learning involves changes in
perception also rules out mere changes in belief. Suppose someone
acquires the belief that the symphony movement they are hearing is a
scherzo. If nothing changes in that person's perception, this is
not a case of perceptual learning. It is a change in the
person's belief, not a change in the person's
perception.



It is important here to distinguish perceptual learning from learning
that is simply based on perception (see Dretske 2015: fn.
 6).[3]
 Perceptual learning involves changes in perception, while learning
that is based on perception need not. Looking at my table, I might
learn that the cup is on the table. However, this does not involve any
long-term changes in perception. It is learning that is based on
perception, but it is not perceptual learning. Furthermore, I might
learn to put the cup on the table into the dishwasher every time it is
empty. Again, this is learning that is based on perception (I need to
perceive the cup in order to move it). However, it is not perceptual
learning. 



One of the main reasons for holding that improvements in perceptual
discrimination can be genuinely perceptual is due to somewhat recent
evidence from neuroscience. As Manfred Fahle puts it, during the 1970s
and 1980s, it tended to be the case that improvements in perceptual
discrimination were thought to be cognitive rather than perceptual
(2002: xii). However, during the 1990s, pressure was put on the
cognitive interpretation due to new neuroscientific evidence in
perceptual learning studies. In particular, studies found that
learning-induced plasticity occurs in the adult primary sensory
cortices much more than researchers had previously thought (Fahle
2002: xii). Neurological evidence of plasticity in adult primary
sensory cortices due to learning provides some evidence that changes
in perceptual discrimination can be due to perceptual learning. (See
also Garraghty & Kass 1992: 522; Gilbert 1996: 269; Goldstone
2003: 238; Gilbert & Li 2012: 250; and Sagi 2011:
1552-53).


### 1.3 Perceptual Learning as *Resulting from Practice or Experience*



Perceptual learning involves perceptual changes of a particular kind,
namely, those that result from practice or experience. For this
reason, laser eye surgery or cataracts removal do not count as
instances of perceptual learning. They are not really cases of
learning because they do not result from practice or experience. So,
while such cases involve long-term changes in perception, they do not
count as cases of perceptual learning.



To be authentic cases of learning, perceptual changes have to be the
result of a learning process. As a contrast case, suppose someone
undergoes a long-term change in their perception due to a brain
lesion. Such a change in perception does not result from a learning
process, since the change in perception comes from the lesion, rather
from practice or experience. Because of this, the case does not count
as an instance of perceptual learning, even though it involves a
long-term change in perception.


### 1.4 Potential Further Criteria for Defining Perceptual Learning



The conversation above roughly follows Eleanor Gibson's
definition of perceptual learning. However, there are also other
accounts in the psychology literature. Robert Goldstone's
account of perceptual learning, for instance, agrees with
Gibson's account in many respects, but it additionally offers a
story of why perceptual changes occur. On his account, 



> 
> 
> Perceptual learning involves relatively long-lasting changes to an
> organism's perceptual system that improve its ability to respond
> to its environment *and are caused by this environment*. (1998:
> 587, italics added for emphasis) 
> 
> 
> 



This definition offers an answer to the question as to why perceptual
learning occurs at all. On Goldstone's account, perceptual
learning occurs to improve an organism's ability to respond to
the environment.



Goldstone's account admits of two different interpretations. On
one interpretation, the account places a condition on perceptual
learning: that to count as an instance of perceptual learning, a
long-term perceptual change has to improve an organism's ability
to respond to the environment. Such an account gains plausibility if
one thinks of "learning" as a success-term. The idea then
is that each genuine instance of perceptual learning leads to success
for the organism, namely, it improves the organism's ability to
respond to the environment. On a second interpretation of
Goldstone's account, however, it is not that each instance of
perceptual learning has to improve an organism's ability to
respond to the environment. Rather, it is that perceptual learning is
a general capacity for improving an organism's ability to
respond to the environment, even if perceptual learning fails to do so
in some instances. Why might organisms have such a capacity? One
possibility is that the capacity is a trait that improves fitness and
is the product of natural selection. However, the biological origin of
perceptual learning is an area of research that still needs to be
carefully explored. 


### 1.5 Contrast Classes


#### 1.5.1 Perceptual Development



How much of the perceptual development we undergo as infants and young
children is the result of learning? There are many difficulties
distinguishing development from learning, conceptually (for some
discussion, see Carey 2009, especially pp. 11-14). The issue of
how to distinguish development from learning bears on the traditional
philosophical debate between nativists and empiricists (see Markie
2015, for a summary of that debate). In the perceptual learning
literature, for instance, Kellman and Garrigan reject the view that
all perceptual development is the result of learning, a view that they
consider to be empiricist (2009: 57). Specifically, they think that
data on infant perception collected in and around the 1980s provide
evidence that at least some perceptual development is innate:



> 
> 
> What this research has shown is that the traditional empiricist
> picture of perceptual development is incorrect. Although perception
> becomes more precise with age and experience, basic capacities of all
> sorts - such as the abilities to perceive objects, faces,
> motion, three-dimensional space, the directions of sounds, coordinate
> the senses in perceiving events, and other abilities - arise
> primarily from innate or early-maturing mechanisms (Bushnell, Sai,
> & Mullin 1989; Gibson et al., 1979; Held 1985; Kellman &
> Spelke 1983; Meltzoff & Moore 1977; and Slater, Mattock, &
> Brown 1990). (Kellman & Garrigan 2009: 57)
> 
> 
> 



In short, according to Kellman and Garrigan, evidence on infant
perception--including evidence about object perception, the
perception of faces, and the perception of three-dimensional
space--tells against the view that all perceptual development is
learned.



If not all perceptual development is learned, while all perceptual
learning is learned, then there is a distinction between perceptual
development and perceptual learning. One way to draw the distinction
more fully is the following. Perceptual development involves
perceptual learning. However, it does not just involve perceptual
learning. It also involves what is called *maturation*. For
instance, the abilities that Kellman and Garrigan describe above
(object perception, the perception of faces, the perception of
three-dimensional space, etc.) fall under the category of
maturation.



There are many ways to try to draw the further distinction between
perceptual maturation and perceptual learning. Some such ways are
found in the debate between nativism and empiricism (see Samet 2008
and Markie 2015) and specifically in the difference between innate and
acquired characteristics (see Griffiths 2009 and Cowie 2016). One
potential criterion here is that cases of perceptual maturation
involve perceptual abilities that are typical of the species, while
cases of perceptual learning involve perceptual abilities that are not
typical of the species. This criterion seems to get it right for some
instances of perceptual learning, say, for those involved in
birdwatching. After all, the perceptual abilities acquired in
birdwatching are unique to birdwatchers, not typical of the entire
human species. However, the criterion seems to get it wrong for other,
more universal, instances of perceptual learning. For instance, since
human faces are both ubiquitous and important to humans, the
perceptual learning involved in face perception is in fact typical of
the species.



In the literature on perceptual learning, by contrast, the distinction
between perceptual learning and perceptual maturation is often drawn
in terms of the role of the environment. On Goldstone's account
of perceptual learning, to count as perceptual learning, perceptual
changes must be caused by the environment. It is important to
understand why exactly Goldstone thinks that *caused by the
environment* is a crucial feature of the definition. He thinks it
is crucial since this criterion distinguishes between perceptual
changes that are simply the result of maturation, and perceptual
changes that are the result of learning. As Goldstone puts it,
"If the changes are not due to environmental inputs, then
maturation rather than learning is implicated" (1998: 586).
Manfred Fahle puts it similarly by saying that the term
*maturation* "ascribe[s] the main thrust of the changes
in a behavior to genetics, not the environment" (2002: xi). For
Fahle, this is what distinguishes it from perceptual learning. 


#### 1.5.2 Perception-Based Skills



A further point of contrast with perceptual learning is
perception-based skills, such as dart-throwing or racecar driving. To
understand the relationship between perceptual learning and
perception-based skills, start by considering the following case.
Williams and Davids (1998) reported that when expert soccer players
defend opponents, they focus longer on their opponent's hips
than non-experts do. This tuned attention is a long-term change in
perception that results from practice or experience. That is, it is an
instance of perceptual learning (see
 section 2.3
 below). Such changes certainly serve to enable perception-based
skills. For instance, attending to the hips is part of what enables
the soccer players to defend well. Since the hips provide a cue for
what the offensive player will do next, when the defender attends
there, it helps them to do all sorts of things: to keep the offensive
player from dribbling by them; to keep the offensive player from
completing a pass; and to keep them from shooting and scoring. Without
the attentional tuning, the expert soccer players would not be able to
perform as high above baseline as they do.



Perceptual learning can enable perception-based skills, yet it is
important to distinguish these skills from perceptual learning. In
fact, arguably, as Stanley and Krakauer (2013) claim, perceptual
learning does not in itself give you a skill, properly speaking. One
reason why, drawing on Stanley and Krakauer, is that skills quite
plausibly require instruction (at least initially), or observation of
someone else (2013: 3). Perceptual learning, by contrast, can at times
be unsupervised learning (see Goldstone 2003: 241 and Goldstone &
Byrge 2015: section 3). Long-term, learning-induced changes in
perception sometimes happen through mere exposure to stimuli, and
without any instruction whatsoever. Furthermore, arguably, as Stanley
and Krakauer put it, "our skilled actions are always under our
rational control..." (2013: 3; see also Stanley &
Williamson forthcoming: 6). Yet, there is an important sense in which
one cannot control a tuned attentional pattern like that of the expert
soccer players mentioned above. Goldstone, for instance, cites a study
on attentional tuning by Shiffrin and Schneider (1977). In that study,
letters were used first as targets in the experiment, but later
letters were used as distractors to be ignored (Goldstone 1998: 589).
Due to their prior training with the letters, the subjects'
attention became automatic with respect to the letters in the scene,
even though they were trying to deliberately ignore them. More
generally, after training, it is difficult to rationally control a
tuned attentional pattern because the attention is automatic toward
particular properties.


#### 1.5.3 Cognitive Penetration



Perceptual learning involves changes in perception that are
*long-term*. This long-term criterion rules out some cases of
cognitive penetration, that is, cases where one's beliefs,
thoughts, or desires influence one's perception (see Macpherson
2012: 24). For instance, to borrow a case from Susanna Siegel (2012),
if Jill sees Jack as angry because she just now believes Jack is
angry, this need not be a case of perceptual learning, since it need
not be a long-term change. After all, if Jill changes her belief that
Jack is angry shortly after, she will no longer see his neutral face
as angry. It would be a short-term change in her perception, not a
long-term one. And so it would not be a case of perceptual
learning.



Simply because some cases of cognitive penetration are not cases of
perceptual learning, however, it does not follow that no cases of
cognitive penetration are cases of perceptual learning. Jerry Fodor
distinguishes between synchronic penetration and diachronic
penetration, where only the latter involves "experience and
training" (1984: 39). The case of Jack and Jill is a case of
synchronic penetration, one where the penetration does not involve
experience and training. However, at least some cases of perceptual
learning might more plausibly fit into the category of diachronic
penetration. (For more on the relationship between perceptual learning
and cognitive penetration, see
 section 3.2)


#### 1.5.4 Machine Learning



Machine perception seeks "to enable man-made machines to
perceive their environments by sensory means as human and animals
do" (Nevatia 1982: 1). Standard cases of machine perception
involve computers that are able to recognize speech, faces, or types
of objects. Some types of machine perception are simply programmed
into the device. For instance, some speech recognition devices
(especially older ones) are simply programmed to recognize speech, and
do not learn beyond what they have been programmed to do. Other types
of machine perception involve "machine learning" where the
device learns based on the inputs that it receives, often involving
some kind of feedback.



Like cases of perceptual learning, machine learning can be either
supervised or unsupervised, although these distinctions mean something
very specific in the machine case. In supervised learning, builders
test the machine's initial performance on, say, the recognition
of whether a given image contains a face. They then measure the
performance error and adjust the parameters of the machine to improve
performance (LeCun, Bengio, & Hinton 2015: 436). Importantly, in
cases of supervised learning, engineers program into the machine which
features it should look for when, say, identifying a face. In cases of
unsupervised learning, by contrast, the machine does not have
information about its target features. The machine merely aims to find
similarities in the given images, and if it is successful, the machine
comes to group all the faces together according to their similarities
(Dy & Brodley 2004: 845).



In machine learning, one major difficulty is that machines can develop
racist and sexist patterns (for several examples, see Crawford 2016).
The problem is often that engineers input a biased set of images (such
as a set of images that include too many white people) into the
machine, from which the machine builds its model (Crawford 2016). This
suggests a potential corresponding source of bias in human perceptual
learning, based on the inputs that humans receive through media. 


## 2. Varieties of Perceptual Learning



The psychology literature provides ample evidence of perceptual
learning. Goldstone (1998) helpfully distinguishes between four
different types of perceptual learning in the literature:
differentiation, unitization, attentional weighting, and stimulus
imprinting. This section surveys these four types of perceptual
learning (for further review, see Goldstone 2003; Goldstone,
Braithwaite, & Byrge 2012; and Goldstone & Byrge 2015).


### 2.1 Differentiation



When most people reflect on perceptual learning, the cases that tend
to come to mind are cases of *differentiation*. In
differentiation, a person comes to perceive the difference between two
properties, where they could not perceive this difference before. It
is helpful to think of William James' case of a person learning
to distinguish between the upper and lower half of a particular kind
of wine. Prior to learning, one cannot perceive the difference between
the upper and lower half. However, through practice one becomes able
to distinguish between the upper and lower half. This is a paradigm
case of differentiation.



Psychologists have studied differentiation in lab environments. In one
such study, experimenters took six native Japanese speakers who had
lived in the United States from between six months and three years
(Logan, Lively, & Pisoni 1991). The subjects were not native English speakers. The
experimenters found that they were able to train these subjects to
better distinguish between the phonemes /r/ and /l/. This is a case of
improved differentiation, where the subjects became better at
perceiving the difference between two properties, which they had more
trouble telling apart before.


### 2.2 Unitization



Unitization is the counterpart to differentiation. In unitization, a
person comes to perceive as a single property, what they previously
perceived as two or more distinct properties. One example of
unitization is the perception of written words. When we perceive a
written word in English, we do not simply perceive two or more
distinct letters. Rather, we perceive those letters as a single word.
Put another way, we perceive written words as a single unit (see Smith
& Haviland 1972). This is not the case with non-words. When we
perceive short strings of letters that are not words, we do not
perceive them as a single unit. Goldstone and Byrge provide a list of
items for which there is empirical evidence of such unitization: 



> 
> 
> birds, words, grids of lines, random wire structures, fingerprints,
> artificial blobs, and three-dimensional creatures made from simple
> geometric components. (2015: 823)
> 
> 
> 



While unitization and differentiation are converses, the one unifying
and the other distinguishing, Goldstone and Byrge also conceive of
them as "flip sides of the same coin" (2015: 823). This is
because, as they put it, both unitization and differentiation
"involve creating perceptual units..." (2015: 823).
Regardless of whether the unit arises from the fusion or the
differentiation of two other units, both instances of perceptual
learning involve the creation of new perceptual units.


### 2.3 Attentional Weighting



In attentional weighting, through practice or experience people come
to systematically attend toward certain objects and properties and
away from other objects and properties. Paradigm cases of attentional
weighting have been shown in sports studies, where it has been found,
for instance, that expert fencers attend more to their
opponents' upper trunk area, while non-experts attend more to
their opponents' upper leg area (Hagemann et al., 2010). Practice or experience
modulates attention as fencers learn, shifting it towards certain
areas and away from other areas.



In the case of the expert fencer, a shift in the weight of attention
to the opponents' upper trunk area facilitates the
expert's fencing skills. However, shifts in attentional
weighting can also fail to facilitate skills or even stifle them. For
example, a new golfer with inadequate coaching might develop the bad
habit of attending to their putter while putting, rather than learning
to keep their "eye on the ball." This unhelpful shift in
attentional weighting may well stifle the new golfer's ability
to become a skillful putter.



One way to understand weighted attention is as attention that has
become *automatic* with respect to particular properties. In
other words, when the expert fencer attends to the upper trunk area,
this attention is no longer governed by her intention (see Wu 2014:
33, for more on this account of automaticity). Rather, as the result
of practice, the expert fencer's attention is now automatic
*with respect to the trunk area*. This italicized part is
important. On Wayne Wu's account of attention, for instance, one
might ask whether attention is automatic with respect to different
features of the process of attention: "where attention is
directed and in what sequence, how long it is sustained, to what
specific features in the scene, and so on" (p. 34). In the case
of the expert fencer, plausibly her attention is automatic with
respect to the trunk area, even if it is not automatic in other
respects. This automaticity is the product of her learning
process.


### 2.4 Stimulus Imprinting



Recall that in unitization, what previously looked like two or more
objects, properties, or events later looks like a single object,
property, or event. Cases of "stimulus imprinting" are
like cases of unitization in the end state (you detect a whole
pattern), but there is no need for the prior state--no need for
that pattern to have previously looked like two or more objects,
properties, or events. This is because in stimulus imprinting, the
perceptual system builds specialized detectors for whole stimuli or
parts of stimuli to which a subject has been repeatedly exposed
(Goldstone 1998: 591). Cells in the inferior temporal cortex, for
instance, can have a heightened response to particular familiar faces
(Perrett et al., 1984, cited in Goldstone 1998: 594). One area where
these specialized detectors are helpful is with unclear or quickly
presented stimuli (Goldstone 1998: 592). Stimulus imprinting happens
entirely without guidance or supervision (Goldstone 2003: 241).


## 3. The Philosophical Significance of Perceptual Learning



Perceptual learning is philosophically significant both in itself, and
for the role that it has played in prior philosophical discussions.
Sections
 3.1-3.4
 will focus on the latter. However, there are good reasons to see
perceptual learning as philosophically significant in itself,
independently from the role that it has played in prior philosophical
discussions.



Why is perceptual learning philosophically significant? One reason is
that it says something about the very nature of perception--that
perception is more complex than it might seem from the first-person
point of view. Specifically, the fact that perceptual learning occurs
means that the causes of perceptual states are not just the objects in
our immediate environment, as it seems at first glance. Rather, given
the reality of perceptual learning, there is a long causal history to
our perceptions that involves prior perception. When the expert
wine-taster tastes the Cabernet Sauvignon, for example, that glass of
wine alone is not the sole cause of her perceptual state. Rather, the
cause of her perceptual state includes prior wines and prior
perceptions of those wines. One way to put this is to say that
perception is more than the immediate inputs into our senses. It is
tied to our prior experiences.



Another way in which perceptual learning is philosophically
significant is because it shows how perception is a product of both
the brain and the world. In this respect, there are some similarities
between the role of constancy mechanisms and the role of perceptual
learning, in that both involve the brain playing a role in structuring
perception in a way that goes beyond the perceptual input. Constancy
mechanisms, such as those involved in shape, size, and color
constancy, are brain mechanisms that allow us to perceive shapes,
sizes, and colors more stably across variations in distance or
illumination. In cases of constancy, the brain manipulates the input
from the world, and this allows the perceiver to track the shape,
size, or color more easily. Similarly, in cases of perceptual
learning, the brain manipulates the input from the world. In many
cases, this may actually make the perception more helpful, as when
through learning the perceptual system weights attention in a
particular way, say, towards the features relevant for identifying a
Cabernet Sauvignon. Perceptual learning might upgrade the epistemic
status of perception, putting the perceiver in a better position with
respect to knowledge (see Siegel 2017). At the same time, people can
learn incorrectly, leading to perceptions that are unhelpful, as when
a new golfer with inadequate coaching develops the bad habit of
attending to their putter while putting, rather than attending to the
golf ball.



Perceptual learning is philosophically significant in itself. In
addition, the rest of section 3 goes on to explore the role that
perceptual learning has played in prior philosophical discussions.


### 3.1 The Contents of Perception



In the philosophy literature, cases of perceptual learning have often
been used to show that through learning we come to represent new
properties in perception, which we did not represent prior to
learning. Siegel (2006, 2010), for instance, asks us to suppose that
we have been tasked to cut down all and only the pine trees in a
particular grove of trees. After several months pass, she says, pine
trees might begin to look different to us. This is a case of
perceptual learning, a long-term change in our perception following
practice or experience with pine trees. Siegel uses the case to argue
that perception comes to represent kind properties, like the property
of *being a pine tree*. The idea is that the best way to
explain the change in perception is that perception represents the
property of *being a pine tree* after, but not before, learning
takes place. That property becomes part of the content of perception:
it comes to be presented in perceptual experience (for more background
on the contents of perception, see Siegel 2016).



Thomas Reid's notion of *acquired perception* has
recently been interpreted in a way similar to Siegel's pine tree
case. According to Reid, some of our perceptions, namely acquired
perceptions, are the result of prior experience. For instance, Reid
writes about how through experience we might come to "perceive
that this is the taste of cyder," or "that this is the
smell of an apple," or that "this [is] the sound of a
coach passing" ([1764] 1997: 171). Rebecca Copenhaver (2010,
2016) has interpreted Reid as claiming that through experience
properties like *being a cider*, *being an apple*, and
*being a coach* can come to be part of the content of our
perception. 



Cases of perceptual learning might also be used to show that through
learning we come to represent new properties in perception, even if
those properties are simply low-level properties like colors, shapes,
textures, and bare sounds, rather than high-level kind properties like
*being a pine tree* or *being a cider*. For instance, in
discussing the perceptual expertise of jewelers, the
14th-century Hindu philosopher Vedanta Desika
writes, 



> 
> 
> [T]he difference among colours [of a precious stone], which was first
> concealed by their similarity, is eventually made apparent as
> something sensual.... (Freschi [trans.] manuscript,
> Other Internet Resources, pp. 12-13)
> 
> 
> 



In this case, the jeweler comes to perceive new colors in the
gemstone, which others cannot perceive. This is a case where through
learning someone comes to perceive a new low-level property.



The cases from both Reid and Vedanta Desika both speak to
the internal complexity of perception mentioned in the previous
section. If Vedanta Desika's description of the
jeweler case is accurate, then perception is more than the inputs into
our senses, since both an expert jeweler and a non-expert can have the
same visual inputs, but have different perceptions. Similarly, to take
a new example from Reid, suppose that a farmer acquires the ability to
literally see the rough amount corn in a heap ([1764] 1997: 172).
Since both a farmer and a non-farmer can have the same visual inputs,
but have different perceptions, the causes of their perceptions are
not just restricted to the immediate objects out in their environment.
Perception is more complex than that.



One of the most detailed contemporary discussions of cases of
perceptual learning is found in Siewert (1998: section 7.9). Siewert
discusses in detail the role that learning plays in altering
perceptual phenomenology, although he stops short of saying that this
affects the high-level contents of perception. He writes, for
instance, that there is a difference in perceptual phenomenology
between just seeing "something shaped, situated, and colored in
a certain way," and recognizing that thing as a sunflower (or
another type) (1998: 255). Siewert also writes that a person might
look different to you after you know them for a long time than they
did the first time you met them, and that your neighborhood might look
different to you after you have lived there for a long time than the
first time you moved in (pp. 256, 258). Furthermore, he writes about
how a chessboard in midgame might look differently to a chess player
than to a novice, and how a car engine might look differently to a
mechanic than to someone unfamiliar with cars (1998: 258). These are
all examples where learning affects one's sensory
phenomenology.



Several cases of perceptual learning in the philosophical literature
involve language learning, both in the case of written and spoken
language. As an example of the former, Christopher Peacocke writes
that there is a difference 



> 
> 
> between the experience of a perceiver completely unfamiliar with
> Cyrillic script seeing a sentence in that script and the experience of
> one who understands a language written in that script. (1992: 89) 
> 
> 
> 



With regard to spoken language, as Casey O'Callaghan (2011)
points out, several philosophers have made the claim that after a
person learns a spoken language, sounds in that language comes to
sound different to them (O'Callaghan cites Block 1995: 234;
Strawson 2010: 5-6; Tye 2000: 61; Siegel 2006: 490; Prinz 2006:
452; and Bayne 2009: 390). Ned Block, for instance, writes,
"[T]here is a difference in what it is like to hear sounds in
French before and after you have learned the language" (1995:
234). It is tempting to think that this difference is explicable in
terms of the fact that, after learning a language, a person hears the
meanings of the words, where they do not before learning the language.
On such a view, meanings would be part of the contents of auditory
perception. However, O'Callaghan (2011) denies this (see also
O'Callaghan 2015 and Reiland 2015). He argues that the
difference is in fact due to a kind of perceptual learning.
Specifically, through learning we come to hear phonological features
specific to the new language. As O'Callaghan argues, these
phonological features, not the meanings, explain what it's like
to hear a new language.



By contrast, Brogaard (forthcoming) argues that meanings are in fact
part of the content of perception (see also Pettit 2010). After
offering arguments against the opposing view, she relies on evidence
about perceptual learning to help make the positive case for her view.
In particular, she uses evidence about perceptual learning to rebut
the view that we use background information about context and combine
it with what we hear, in order to get meanings. Instead, she argues,
language learning is perceptual in nature. She points to changes in
how we perceive utterances, more in chunks rather than in parts, as a
result of learning. Background information directly influences what we
hear, she argues, altering how language sounds to us.



Both Siegel's pine tree case and the case of hearing a new
language fundamentally involve phenomenal contrasts. That is, the
motivating intuition in both cases is that there is a contrast in
sensory phenomenology between two perceptual experiences.
Interestingly, in both cases the phenomenal contrast is due to
learning. The question in both the pine tree case and the new language
case is what explains the difference in sensory phenomenology. Siegel
argues that the best explanation in the pine tree case is that the
property of *being a pine* (and, more generally, natural kind
properties) can come to be represented in perception.
O'Callaghan (2011) argues that the best explanation for the
difference in sensory phenomenology in the new language case is that
we come to hear phonological features specific to the new language.
Brogaard (forthcoming) argues that the best explanation in that case
is that we come to hear meanings in the new language.


### 3.2 Cognitive Penetration



Recall that cases of cognitive penetration are cases where one's
beliefs, thoughts, or desires influence one's perception (see
Macpherson 2012: 24). One role of perceptual learning in the
philosophical literature has been to explain away putative cases of
cognitive penetration. For instance, it might seem at first glance
that Siegel's pine tree case is a case of cognitive penetration,
a case where one's newly acquired concept of a pine tree
influences one's perception. Connolly (2014b) and Arstila (2016), however, have
both argued that the best way to understand Siegel's pine tree
case is not as a case of cognitive penetration, but rather through the
particular mechanisms of perceptual learning. Connolly counts it as a
case of attentional weighting, while Arstila understands it as
involving both unitization and differentiation. 



One reason why perceptual learning is a good instrument for explaining
away putative cases of cognitive penetration is the following. In
cases of perceptual learning, it is the external environment that
drives the perceptual changes. As Raftopoulos puts it,
"perceptual learning does not necessarily involve cognitive
top-down penetrability but only data-driven processes" (2001:
493). For putative cases of cognitive penetration, the strategy for
the perceptual learning theorist is to show how the perceptual changes
involved may have been data-driven instead of top-down. Several
philosophers have used this strategy at times, including Pylyshyn
(1999: section 6.3), Brogaard and Gatzia (2015: 2), as well as Stokes
(2015: 94), and Deroy (2013) might be interpreted in that way as
well.



One exception to the trend of explaining away putative cases of
cognitive penetration in terms of perceptual learning is Cecchi
(2014). Cecchi argues that a particular case of perceptual
learning--that found in Schwartz, Maquet, and Frith
(2002)--should count as a case of cognitive penetration. The
study in question found changes in the primary visual cortex due to
learning, and also that these changes were brought about by higher
areas in the brain influencing the primary visual cortex. Because the
perceptual changes were the result of top-down influence, Cecchi
argues that this case of perceptual learning should count as a case of
cognitive penetration.


### 3.3 The Theory-Ladenness of Observation



One traditional debate in the philosophy of science is whether
scientific observation is permeated with the theory of the scientist,
or *theory-laden* (see the entry on 
 theory and observation in science). 
 As Raftopoulos and Zeimbekis point out, when
asking whether observation is theory-laden, the answer will depend in
part on what it means for a subject to possess a theory (2015: 18). On
their view, theories can be tacit, rather than just "having a
set of beliefs and concepts" (p. 18).



Assuming that theories can be held tacitly, perceptual learning might
plausibly play a role in making observation theory laden. Raftopoulos
and Zeimbekis, for instance, ask us to imagine a scientist who has
undergone perceptual learning in her expert domain (2015: 19).
Specifically, through repeated exposure to items in her expert domain,
she has developed perceptual sensitivity to certain features, in
accordance with her professional needs. This includes learned
attention to particular dimensions, and involves physical changes
early in her visual system (p. 19). As a result, the scientist might
quite literally see the world differently within her expert domain
than someone from outside her expertise would see it.



Such a case suggests that perceptual learning can make observation
theory-laden. The scientist's perceptual system comes to shape
the kind of visual information that makes it into the
scientist's conscious perception, and does so based on her
professional needs. As Raftopoulos and Zeimbekis put it, the case
suggests 



> 
> 
> that non-cognitive, clearly perceptual influences on incoming visual
> information can be indirect bearers of the kinds of theoretical
> commitments that we usually think of as the content of conceptually
> couched theories. (2015: 19) 
> 
> 
> 



Although the case does not involve explicit beliefs directly
influencing perception, arguably it involves a theory being held
tacitly and appropriated into one's perceptual system.


### 3.4 Modularity



According to the modular view of the mind (Fodor 1983), the basic systems involved
in perception are encapsulated from information outside of it,
excluding its inputs (see Robbins 2015, for a summary of modularity).
It might then seem at first glance that cases of perceptual learning
challenge the view that the mind is modular, at least insofar as they
involve the modulation of perception through any background theory
that the subject has. However, it is important to note that Fodor
himself seems to allow for such cases of perceptual learning. While he
thinks that perception is synchronically impenetrable, he allows for
the possibility of diachronic penetration, that is, cases where
"experience and training can affect the accessibility of
background theory to perceptual mechanisms" (1984: 39).



Why think that a modular view of the mind should allow for diachronic
penetration? When Fodor allows for diachronic penetration, he does so
because the alternative is to say that all modular systems are
specified endogenously (1984: 39). Fodor admits that this alternative
would be too extreme, and he points out, for instance, that children
learn something from hearing a language. In other words, the modules
for language are not just specified endogenously. However, Fodor is
conservative about the scope of diachronic penetration, suggesting
that it may only happen within strict limits, perhaps limits that are
themselves endogenously defined (1984: 39-40).



Other philosophers have argued that diachronic penetration of
perception undermines modularity. Churchland (1988), for instance,
sees Fodor's allowance of diachronic penetration as
"grudgingly conceded," and he argues that diachronic
penetration is in fact widespread, rather than something that happens
within strict limits (p. 176). One such case, raised by Churchland, is
the case of perceiving music. Churchland argues that a person who
knows the relevant music theory and vocabulary "perceives, in
any composition whether great or mundane, a structure, development,
and rationale that is lost on the untrained ear" (1988: 179).
Fodor replies that it is unclear whether such cases are genuinely
perceptual (1988: 195). He suggests another possibility, which is that the
person who knows the relevant music theory does not perceive it
differently, but rather forms different beliefs about the music.
Furthermore, even if the case is genuinely perceptual, Fodor replies
that it could be that a trained ear results simply from repeated
exposure to the relevant music, rather than through knowledge of
theory (1988: 195).