# Rejecting Cognitivism: Computational Phenomenology for Deep Learning

Pierre Beckmann  Université de Lausanne (UNIL), Department of Philosophy
Guillaume Köstner  Université de Lausanne (UNIL), Department of Philosophy
Inês Hipólito  Humboldt-Universität zu Berlin, Department of Philosophy &
Berlin School of Mind and Brain  University of Amsterdam, Faculty of Social
and Behavioural Sciences

###### Abstract

We propose a non-representationalist framework for deep learning relying on a
novel method: computational phenomenology, a dialogue between the first-person
perspective (relying on phenomenology) and the mechanisms of computational
models. We thereby reject the modern cognitivist interpretation of deep
learning, according to which artificial neural networks encode representations
of external entities. This interpretation mainly relies on neuro-
representationalism, a position that combines a strong ontological commitment
towards scientific theoretical entities and the idea that the brain operates
on symbolic representations of these entities. We proceed as follows: after
offering a review of cognitivism and neuro-representationalism in the field of
deep learning, we first elaborate a phenomenological critique of these
positions; we then sketch out computational phenomenology and distinguish it
from existing alternatives; finally we apply this new method to deep learning
models trained on specific tasks, in order to formulate a conceptual framework
of deep-learning, that allows one to think of artificial neural networks’
mechanisms in terms of lived experience.

##  1 Introduction

In the past years, deep learning (DL) has achieved impressive feats, its
artificial neural networks (ANNs) competing with human performance on tasks
involving the understanding or the generation of text (Devlin et al., 2019;
Brown et al., 2020; Schulman et al., 2022), images (Radford et al. 2021;
Ramesh et al. 2021) or speech (Baevski et al. 2020; Hsu et al., 2021). DL has
had a revolutionary impact in the industry and society, as well as in
scientific research (DeVries et al., 2018; Davies et al., 2021; Jumper et al.,
2021). Specifically, ANNs ability to successfully mimic a number of human
cognitive processes, has fueled comparative research between DL and cognitive
sciences or neurosciences (Yamins, 2016; Kumar et al., 2022; McClelland, 2022;
Millet et al., 2022). Recently, DL also motivated renewed philosophical
perspectives upon old questions relating to the mind, brain and behavior
(Buckner, 2019; Sloman, 2019; Fazi 2021; Perconti & Plebe, 2020).

Despite its successes, ANNs are notoriously hard to interpret, in the sense
that we cannot exactly understand how they solve their tasks (Boge, 2022). For
this reason they are sometimes referred to as “black boxes” (Castelvecchi,
2016). This opacity makes DL models susceptible to diverse interpretations
through different conceptual frameworks. The most prominent framework for the
interpretation of DL has been cognitivism, the first research program in
cognitive sciences (MacKay et al., 1956; Lees & Chomsky, 1957; Minsky, 1961).
Relying on the functioning of the Turing machine, cognitivism defends
cognition in terms of _symbol_ manipulation: cognitive processes are thought
to rely on _representation_ of entities111We use the ontologically neutral
term of “entity” as it does not matter for our purpose whether the ontology of
the world is conceived in terms of objects, properties, relations, processes,
events, tropes, or other metaphysical categories. What is important is the
representative relation holding between those entities and the mental symbols
according to cognitivism. It should also be noted that this relation does not
imply that the grammar of those symbols has to match the ontology of the
world. For example, the representation _of_ a process does not need to
represent it _as a process_. of an external pregiven world. This approach has
been influential both in cognitive sciences and philosophy of mind, with
Fodor’s computational theory of mind being the most prominent (Fodor, 1983).

DL originates within connectionism, a computationalist framework that disputes
that cognitive, computational processes are leveraged by symbol manipulation
(McCulloch & Pitts, 1943; Rosenblatt, 1958; Rumelhart et al., 1986). Aiming
for a more “biological” resemblance of the distributed operations of the
brain, connectionism brings forth the ancestors of DL models, such as
Rosenblatt’s perceptron (Rosenblatt, 1958). Unlike cognitivism’s Turing
machine, these mathematical models do not need to be implemented fully: they
can learn how to solve tasks by slowly adjusting to new inputs. With these
models, connectionism promotes a new conceptual framework to think about human
cognition as _emergence_ of global _states_ to fulfill cognitive functions. It
therefore initially opposes cognitivist’s symbol-like representations : “one-
to-one mappings” between entity and representation (Rosenblatt,
1958)222However, as Bechtel (1991) notes, from the 1980s, connectionists try
to reconcile their theory with the predominant cognitivism, insisting that
their models “should be embraced as a subsymbolic alternative to symbolic
models of cognition”..

Although it stems from connectionism, DL is today largely thought in terms of
_representation_ -based operations – relying on the cognitivist toolkit. In
fact, connectionist models’ self-organizational capabilities happen to be of
use for the convinced cognitivist, because they can provide the otherwise
unanswered explanation of how a system can _learn_ symbols. ANNs learn
symbolic representations of external properties on the basis of which they can
execute further computations to solve tasks. This stance is quickly associated
with neuro-representationalism (NR), combining a strong realism towards
scientific entities with the idea that we experience a brain generated model
of these entities (Churchland and Sejnowski, 1990; Milkowski, 2013; Mrowca et
al., 2018; Sitzmann et al., 2020).

NR with the notion of representation is pervasive both in computational
neuroscience (Piantadosi, 2021; Poldrack, 2021) and DL itself (LeCun et al.,
2015; Ha & Schmidhuber, 2018; Gidaris et al., 2018; Chen et al., 2020; Goh et
al., 2021; Matsuo et al., 2022). In _Nature_ ’s most cited paper, “Deep
Learning” for example, ANNs are described from the first paragraph, as
machines that can “be fed with raw data” and “automatically discover the
_representations_ needed for detection or classification” (LeCun et al.,
2015). To the best of our knowledge, this framework is implicitly prevalent in
deep learning literature. Often, deep learning researchers motivate their use
of the cognitivist concept of representation by relying on NR; see this recent
deep learning textbook for example:

> More often than not, hidden layers have fewer neurons than the input layer
> to force the network to learn compressed representations of the original
> input. For example, while our eyes obtain raw pixel values from our
> surroundings, our brain thinks in terms of edges and contours. This is
> because the hidden layers of biological neurons in our brain force us to
> come up with better representations for everything we perceive. (Buduma et
> al., 2022)

Furthermore, DL researchers commonly interpret ANNs as learning “world
models”, that mimic external world structures and dynamics to plan ahead (Ha &
Schmidhuber, 2018; Matsuo et al., 2022). When used to understand the mind,
these “world models” are oftentimes reduced to perception; the idea being the
following: because perception is indirect, the brain must build internal
models in an attempt to represent what could potentially be the perceptual
space state of affairs (Von der Malsburg, 1995; Ashby, 2014; Saddler, Gonzalez
& McDermott, 2022). This introduction of intermediate representations posits
the existence of an external reality, the ontological structure of which can
(arguably) be known independently of the way the mind relates to it
(metaphysical realism), but that we can never directly access through our
perception (representationalism). In philosophy, this form of
representationalism is famously opposed by phenomenology, which puts on hold
the question of the existence of an external reality in favor of a rigorous
description of lived experience (Husserl, 1931; Merleau-Ponty, 1945). Today,
in cognitive science, and under the influence of phenomenology,
representationalism is further challenged in the Embodied and Enactive
Cognitive Science (EECS) research program (Varela et al, 1991; Hutto & Myin,
2012; Chemero, 2011; Di Paolo et al., 2017; Gallagher, 2017).

Taking into consideration these critiques of representationalism, this paper
aims to provide a conceptual framework of DL that does not rely on symbols as
the basic units of cognition. As such, we chose to rely on phenomenology,
privileging insights from the careful description of reality as it appears to
us in first-person perspective. Furthermore, following Lutz and Thompson
(2003), we will leverage three levels of enquiry, or sources of exploration of
cognition : _the neurophysiological source_ , _the phenomenological source_
and _the computational source_. Historically, the computational source was
used to formalize and link the findings obtained from the two other sources.
With DL successfully mimicking some of our cognitive processes, the
computational source now generates new _data_ and becomes a new source of
exploration. This observation opens up the possibility of _computational
phenomenology (CP)_ : an exclusive dialogue between the _computational_ and
the _phenomenological_ source that puts on hold the question of the material
basis of cognitive processes (which belongs to the neurophysiological source).
The point of such a dialogue is not to disqualify the neurophysiological
source, but rather to provisionally let the two other sources free of any
constraint or import coming from the third one. As such, this dialogue is more
faithful to phenomenology (relying on first-person descriptions of experience)
than EECS approaches that tend to recast phenomenology in a more naturalist,
third-person point of view. Turning to DL, we find that from lived experience,
the apparent non-decomposability of ANN operations – their “black box” aspect
– is not surprising as the underlying mechanics of many of our cognitive
processes are unclear, or opaque, to us. This observation allows us to both
propose new phenomenology-drawn concepts to think of ANN operations, and to
embrace the opaque processual nature of cognitive processes. The contribution
of this paper is threefold, we propose computational phenomenology, a new
methodology and conceptual framework for philosophers and cognitive scientists
to conceive (1) of the mind and its relation to (2) task-solving computational
models; such a conceptual framework provides (3) DL engineers with a new
experience-based toolkit by applying this methodology to the operations of
ANNs.

##  2 Cognitivism and neuro-representationalism in deep learning

DL is concerned with the design and the training of ANNs. An ANN sequentially
connects _layers_ of (non-linear) threshold-activated nodes with linear
operations according to a set of _weights_ to transform an input into an
output. Layers between the input layer and the output layer are called _hidden
layers_ ; the “deep” in DL refers to the multiple hidden layers in the ANNs.

The weights of an ANN are not fixed but are gradually adjusted to better solve
a precise task. To enable learning, the deep learning researcher picks three
main ingredients, given some particular data: the network’s _architecture_
(the way in which the different nodes are connected through the weights), a
_loss function_ (that models the objective of the task) and a _learning rule_
(that dictates the way the weights are updated according to the loss function
– and is almost always based on an algorithm called _backpropagation_
(Rumelhart et al., 1986). Once the optimization of the weights – also called
_training_ – is done, ANNs allow _inference_ : the propagation of new input
all the way to output layers, causing the nodes of the hidden layers to
_activate_ in a particular way. In short, once the ANN is trained, the
_weights_ are fixed and allow the processing of new inputs; on the other hand,
the _activations_ are obtained through inference and always correspond to one
given input. These weight-determined successive activations, or patterns of
activations, are not easily interpretable. Importantly, this means that it is
not possible – so far at least – to clearly decompose them into explainable
steps, or symbol-based operations. The original connectionist framework sees
them as nothing more than _emerging states_ and is already satisfied that they
allow solving a particular cognitive task (Varela et al., 1991, p. 98).
However, some researchers reject the apparent weaker explanatory power of this
interpretation in favor of reading of DL in terms of cognitivist’s mind-
computer metaphor : i.e. conceiving cognition as symbolic computation
(Shinozaki, 2021; Zbontar et al., 2021; Matsuo et al., 2022; Ceccon et al.,
2022; Leek, 2022).

A cognitivist reading of an ANN relies on interpreting its pattern of
activations as symbol-based operations. We argue that this reading isn’t
motivated by technical reasons but that it is grounded in a philosophical
worldview. Cognitivism, from its birth in the 1950s, relies on the mechanisms
of the Turing machine to interpret human cognition (Putnam, 1967; Fodor,
1983). When one implements a Turing machine, one decides what a one or a zero
in a given cell _means,_ that is to say, to what it relates to in our world.
This physically manipulable entry then carries a human-assigned meaning; it
becomes a _symbol_ that _represents_ a given external entity, and on the basis
of which Turing machines can carry out meaningful operations. In a second
step, cognitivism transposes this functioning to the mind. The mind is thought
to run on the basis of _mental_ _representations_ , that semantically _encode_
properties of an external pregiven world analogously to the symbols of a
Turing machine. Cognitivism therefore implies a _metaphysical realism_ , the
view that there exists an external world, with entities, independently of our
perception or thoughts of it. Cognitivism can be systematized around the
answers given to three major questions:

  1. 1.

What is the world?

An external reality that exists independently of our cognition of it.

  2. 2.

What is a representation?

A symbol that stands for an entity of the world.

  3. 3.

What is the mind?

A system capable of rule-based operations to carry out cognitive processes.

Metaphysical realism is however a philosophical assumption or standpoint,
which cannot be proven by cognitivism: after all, by cognitivist lights, we
only ever have access to our mental representations of the external world.
Furthermore, in this setting it isn’t clear how the correspondence between the
entity and the representation is grounded. There is no possible way to step
outside our mental representations and observe the real world and the way it
relates to our representations of it in the same way the implementer of a
Turing machine knows what symbol relates to what entity.

As a mathematical model capable – after training – to solve tasks without
relying on the assignment of symbols (except for input and output nodes), the
ANN is of special interest to cognitivism. Rather than questioning her
conceptual framework – given that computation without representation suddenly
seems possible –, the convinced cognitivist supposes that ANNs do in fact rely
on internal symbols to solve tasks. Henceforth, she obtains a model that does
not need symbol assignment as it _learns_ them. Following this position, ANNs
offer cognitivism’s missing element and can be thought of as some sort of
elaborate Turing machine that self-adapts to obtain symbol-like
representations of some external properties. They are thought to
simultaneously learn how to _represent_ important properties – _for_
themselves (as if they were the implementer of the Turing machine) – and how
to use these representations to solve their task. This stance however relies
on the supposition that ANNs do indeed learn Turing-like symbols333At this
point the reader might argue that the fact that ANNs can be implemented on
Turing machines does show that they do rely on symbols. The reason this
argument fails is that, in such a case, the numbers on the tape of the Turing
machine don’t explicitly stand for anything, since it was not the implementer
who chose what they stand for.. Furthermore, it is not clear if cognitivism’s
initial problem is solved as even by relying on the ANN it is not clear how
our representations relate to the external world.

In today’s cognitive sciences and deep learning literature, this idea of a
self-organizing Turing machine is frequently taken up in a new version of
cognitivism: neuro-representationalism (NR). In NR, cognitivism’s metaphysical
realism takes the specific form of a strong scientific realism: the external
entities that exist independently of us become the theoretical entities of
modern natural sciences. Additionally the cognitivist-interpreted ANN becomes
a model of the brain; in other words, the brain is thought to learn Turing-
like mental representations of the world. Finally, NR takes cognitivism’s
representationalism to its extreme by making a claim about our conscious
experience: we experience a brain generated model of these entities
(Churchland and Sejnowski, 1990; Mrowca et al., 2018; Sitzmann et al., 2020);
for criticism see (Zahavi, 2018; Hipólito 2022). Frith puts it in slogan form:
“my perception is not of the world, but of my brain’s model of the world”
(Frith, 2007). According to NR, agents do not directly perceive a photon and
its wavelength but only a mental representation of it – in this case, a
certain color (Metzinger, 2009). NR too can be systematized around three major
questions:

  1. 1.

What is the world?

The physical world, composed of the scientific entities discovered by science.

  2. 2.

What is a representation?

A symbol learned by the brain that encodes an external entity of the

  3. 3.

What is the mind?

Composed of the cognitive processes responsible for mental phenomena – which
consist in neural computations on the basis of the representations – and of
the content of these phenomena, consciousness – which is given only by the
representations.

NR’s concept of representation is pervasive in the DL field. In _Nature_ ’s
most cited paper, “Deep Learning”, we find a common cognitivist interpretation
of ANNs from the first paragraph, describing them as machines that can “be fed
with raw data” and “automatically discover the _representations_ needed for
detection or classification” (LeCun et al., 2015). Following this view, the
first layers of the ANN operate a _feature extraction_ , computing relevant
features, or representations, while the last layers adequately combine these –
carrying out some form of “reasoning”. It is also common to consider the
layers as extracting features hierarchically, where the first layers compute
“low-level” representations – such as edges in an image – and subsequent
layers compute “high-level” representations – such as larger motifs in an
image (LeCun et al., 2015). An analogous interpretation can be found in
computational neurosciences, when describing, for example, early vision as
extracting low-level representations on the basis of which higher-level
reasoning is carried out in the brain (LeCun et al., 2015). As mentioned in
the introduction this implicit framework is a given in modern deep learning
research and frequently specifically relies on NR and its understanding of
human cognition.

A fundamental condition for the acceptance of the construct of NR is the
existence of a pre-given world. Reenacting such an agent/world setting
corresponds to a large field of DL called deep reinforcement learning (DRL)
(Mnih et al., 2015; Silver et al., 2016; Eppe et al. 2022; Wang et al, 2022).
DRL trains as ANN, considered as an agent, to select the right actions based
on the observations (or states) of an external environment in order to
maximize potential reward (Fig. 1). In this setting the ANN is typically
thought to learn an internal _world model_ that _represents_ the dynamics of
the environment and allows it to predict, reason, and plan (Ha & Schmidhuber,
2018; Goyal et al., 2022; Mazzaglia et al., 2022; Driess et al. 2022). The
concept of “world model” is very important among researchers: it is often
thought to be the key element to human-level intelligence (Matsuo et al.,
2022). While, in the literature the term “world model” can refer to different
things, in the context of DL it means that the ANN does not rely directly on
the external environment but operates on the basis of its “world model”. Once
again, the motivation comes from the cognitivist assumption that an agent does
not have direct access to the world and therefore has to “wonder” and
“interact” with it through the mediation of internal representations. If this
internal character is taken as far as making the world model the realm of
perceived things, we get the key ingredient for NR: conscious experience,
then, is (limited to) our model of the world. Subsequently, when the ANN
learns to extract features from its environment, it is thought to be analogous
to the brain learning to “come up” with internal representations of the hidden
physical world. By this reasoning, DRL becomes a direct mathematical
formalization of NR. In the Machine Learning research community, DRL – a
growing field in neuroscience (Botvinick et al., 2020) – is almost exclusively
interpreted in this NR setting – in fact, the brain-in-the-world analogy is
typically used to introduce the field of DRL (Fig. 2).

Figure 1: Model-free deep reinforcement learning. The ANN, or DNN (deep neural
network), learns to pick an action based on observations of an external
environment, in order to maximize reward. Taken from (Mao et al., 2016) Figure
2: Figure from deep mind’s introduction to reinforcement learning course
(Silver, 2015). To introduce deep reinforcement learning, the brain-in-world
analogy is used to explain the situation of the ANN in its environment.

In short, although DL stems from connectionism, a program compelled to reject
the challenges that come with assuming (Turing) symbol computation mechanisms
on the neurobiological level, the majority of research today carried out in DL
still incorporates the cognitivist toolkit, employing concepts (such as world
models, representation, etc.) which, then, returns cognition as reducing to
NR.

The DL researcher might however object that the concept of representation is
of technical use in numerous applications. Indeed, the term representation is
often used as a synonym of the term _embedding_ , a low-dimensional feature
typically obtained by extracting node activations at a particular layer of a
trained ANN. When working with images for example, an engineer can extract the
pen-ultimate layer’s activations of an ANN trained on a first task and use it
as a representation, or embedding, on a downstream task such as image
classification (Chen et al., 2020). Because this procedure works, the DL
engineer might argue that ANNs do indeed rely on representations. This line of
thought can be challenged. First of all, these embeddings correspond to one
given datum (one precise image for example), not a certain general property of
the data. Secondly, they are representations that the engineer chooses for
downstream tasks, they are representations for the scientist; they do not
stand one-to-one for the datum – in fact, no perfect inverse mapping, that
would allow retrieving the datum from an embedding alone, is technically
possible. The technical utility of embeddings only shows that patterns of
activations at a particular level contain relevant material – that might be,
in essence, non-decomposable – to solve a number of tasks. Should this
reasoning hold, embeddings – just as the activations of an ANN – are hardly
representations in the sense of Turing machine symbols.

In conclusion, little technical or empirical reasons plead in favor of the
employment of Turing-like representations _for_ _the ANN_. While in some cases
it can be established that some layers are more sensitive to particular
patterns (for example edges in an image), the weights and activations of ANNs
are notoriously hard to interpret (Zhang et al., 2021). Even when some
interpretation is possible (Olah, 2015; Goh et al., 2021), the particular
patterns could simply represent particular properties of some data _for_ _the
engineer/scientist_ that interprets them given a certain task (Boge, 2022). In
any case, if some form of interpretation is possible decomposition into
symbol-based operations seems nowhere near. This is why we will henceforth
employ the concept of ANN “non-decomposability” – as in non-decomposable into
symbol-based operations – rather than vaguer “uninterpretability” that is
sometimes used in DL literature.

In the next sections we will propose a conceptual framework for DL that
doesn’t rely on the cognitivist notion of representation. We will do so by
first taking some insights from phenomenology and then applying them to deep
learning.

##  3 The phenomenological critique of cognitivism: implications for Deep
Learning

As seen in the previous section, NR takes a robust scientific realist stance,
i.e. existence of an external pre-given objective world (that can be described
by science and represented by cognitive processes); from then on, the question
of consciousness becomes: how does it arise from natural processes? This is
notoriously difficult and referred to as the hard problem of consciousness
(Chalmers, 1995).

Phenomenology flips the problem around444It is impossible for us to offer an
all-encompassing account of the tremendously rich phenomenological tradition
or to engage with exegetical issues pertaining to the thought of Husserl and
other important thinkers in said tradition. We will limit ourselves to
highlighting the points that are essential for the approach we want to sketch
in these pages. For detail, see (Zahavi, 2008; Gallagher and Zahavi, 2020)..
Instead of positing the existence of an outside world and questioning the
emergence of consciousness, phenomenology seeks to describe how the world
appears to us in lived phenomena (Merleau-Ponty, 1945). It puts on hold any
question regarding the existence of the external world (_i.e._ , whether
something is _really_ out there or if we are simply hallucinating our
reality), its ontology (_i.e._ , the types of things that really exist), and
the mind-world relationship (idealism, realism, _etc_.). Husserl (1931, §32:
59-60) calls this bracketing of judgment “ _epoché_ ”, which aims at
neutralizing what he calls the “natural attitude”. This attitude is
characterized by a common-sense belief in the reality of external, discrete,
ordinary objects. Merleau-Ponty (2012: p. 69) calls this natural attitude
“objective thought” and interprets it as the shared assumption of idealism and
realism, and as the unquestioned metaphysics of modern natural sciences555It
is important to note that phenomenology is not _per se_ incompatible with a
moderate scientific realism which claims that natural sciences discovers real
objective features of the world and produces true statements about it (see,
for example, Dreyfus 1992), although it rejects the idea that the scientific
image of the world is a complete and exhaustive descriptive-explanatory
framework.. Once every judgment pertaining to the natural attitude/objective
thought has been put into brackets, phenomenology takes as a starting point a
passive stance with regards to phenomena. That is, it lets things appear as
they appear spontaneously to the mind that is directed towards the world
without trying to categorize them. The task of phenomenology is _then_ to
describe the structures of manifestation, producing an understanding of the
mind and its interactions with the world that differs strongly from the views
exposed in the previous section. One of the most fundamental features of the
mind that phenomenology emphasizes is _intentionality_ , that is the fact that
mental states are _directed towards_ something. For example, an episode of
perception is always a perception _of_ something. Following Husserl (1900;
1931, §37), we will call the thing towards which the mind is directed the
“intentional object”, and the conscious mental state directed towards the
intentional object an “intentional act”.

Phenomenology and NR diverge significantly in the way they describe cognitive
processes. For NR, which operates on a clear separation between subject and
external world, a cognitive process can be cast as a sequence of symbol-based
operations, from perception (sense data inputs) to a particular action (motor
output), or storage of a new useful representation. With its bracketing,
phenomenology considers cognitive processes from a different point of view
where it makes no sense to distinguish an external entity from our
representation of it; there are simply intentional objects that appear to me:
consciousness and the world are given in one stroke. Therefore, cognitive
processes are not considered as an algorithmic processing of perceptual
inputs, but rather as habits that underlie and structure our lived experience,
“that simultaneously [delimit] our field of vision and our field of action”
(Merleau-Ponty, 1945, p. 153).

Merleau-Ponty (1945, p. 143) offers a useful illustration of the problem posed
to those conceiving of perception as indirect: the blind man’s cane. He
depicts and opposes the position of the intellectualist (which seems identical
to NR’s position), according to which the blind man infers the shape of
external objects in two steps: first, by deducing the cane’s position given
its pressure on the hand and then, by inferring the shape given this position
(Merleau-Ponty, 1945, p. 153). He argues that once that the blindman gets used
to the cane, once he has it “in hand”, the habit precisely “relieves [him] of
this very task” (Merleau-Ponty, 1945, p. 153). The cane becomes “an instrument
_with_ which he perceives”, its tip is “transformed into a sensitive zone”,
expanding his perceived world (Merleau-Ponty, 1945, p. 154). The acquired
cane-sensing skill is simultaneously a perceptual habit and a motor habit
(there is no perception without movement of the cane) that structures
conscious experience. It grounds an “organic relation between the subject and
the world” that does not rely on symbol-like representations (Merleau-Ponty,
1945, p. 154).

Could this different perspective on cognition open up a different way to
interpret computational models? Dreyfus, drawing upon Heidegger’s
phenomenology, considers cognitive processes as acquired habits (and
consequently opposes cognitivism’s representationalism). He argues that we do
not acquire skills by storing representations but by a gradual refinement of
our perception that offers new solicitations in given situations in the world;
therefore, “the best model of the world is the world itself” (Dreyfus, 2007).
He, for example, rejects the existence of an internal map: “what we have
learned from our experience of finding our way around in a city is
‘sedimented’ in how that city _looks_ to us” (Dreyfus, 2007, p. 1144). Dreyfus
insists that basic cognitive processes (he gives the examples of driving a car
or playing chess) do not rely explicitly on symbols but are just the result of
a gradual adaptation – they are representation-less (Dreyfus, 2002).
Therefore, he sees the advent of ANNs as a strong blow against cognitivism’s
commitment to representations as they “provide a model of how the past can
affect present perception and action without the brain needing to store
specific memories at all” (Dreyfus, 2002, p. 374). From the phenomenological
standpoint, ANNs seeming non-decomposabilty is particularly interesting
because it evokes the opacity of our implicit habits (i.e. that do not seem to
rely on representations).

However, the use of the term “brain” in Dreyfus’s previous citation marks an
important conceptual shift that shouldn’t go unnoticed. Important observations
that were acquired from the phenomenological source are mapped onto the
functioning of the brain – a system understood in terms of and by scientific
investigation that belongs to the neurophysiological source. Dreyfus’s
implicit supposition of an overlap between the phenomenological and the
neurophysiological is in tension with phenomenology’s initial ambition to put
on hold the question of existence of external objects. And this supposition
can be taken a step further by trying to reduce the phenomenological to the
natural – shifting from cognition explained from first-person perspective to
cognition explained from third-person perspective.

This _naturalization_ of phenomenology is constitutive of Varela’s formulation
of enactivism, proposed as an alternative to cognitivism (Varela et al.,
1991). His enactivism retains all major principles uncovered by the
phenomenological source and uses them to think the coupling processes of an
agent with its environment, considered in a naturalistic setting. It deems
that, through sensorimotor activity, organisms become structurally coupled to
their environment which allows them to _enact_ a world. The “organic relation”
between consciousness and world from first-person perspective becomes a
“structural coupling” between organism and environment (Varela et al., 1991,
p. 206). When considering the relation between mind and computational models,
this enactivism is interested, for example, in robots that can navigate
autonomously in the external world; Varela et al. (1991) for example turn
towards Brooks’s finite state machines (Brooks, 1987). This brings it closer
to the understanding of cognitive processes as a way to interact with the
external world, rather than understanding them as habits that underlie our
lived experience. Because enactivism aims to bridge phenomenology and
neurophysiology, it operates upon a third-person separation between agent and
environment; phenomenology doesn’t rely on this dissociation and considers
experiences (that are structured by habits) in which the I and the world are
intertwined. We therefore insist on the necessity of a clear epistemic
separation between the phenomenological source and the neurophysiological
source (in the tradition of Husserl’s epoche) to approach the question of the
relationship between cognition and computation. This distinction opens up the
possibility of establishing parallels between cognitive processes seen as
_habits_ and computational models in a way that is more faithful to
phenomenology’s initial project. Parallels that will be useful to develop a
representation-less toolkit for DL.

##  4 Computational phenomenology and deep learning

In the previous section, we have highlighted the need for a method that
considers computational models from phenomenology without reducing it to
structures uncovered by third-person sciences, such as by
neurophysiology666Note that mathematical formalizations tend to be seen only
(possibly wrongly) as tools to describe objective natural processes.
Phenomenology has therefore traditionally been skeptical of any attempts of
mathematical formalization. Husserl for example, famously described Galileo as
“at once a discovering and a concealing genius” because his mathematization of
natural phenomena ends up covering up its origin: lived phenomena (Husserl,
1936, p. 53).. The first such method that distinguished phenomenology,
neurophysiology and mathematical/computational modeling is neurophenomenology
(Varela, 1996). Neurophenomenology aims to establish “reciprocal constraints”
between first-person data from phenomenology and measured data of
physiological processes, in order to allow a dialogue, or a “circulation”,
between the internal (phenomenological) and external (scientific) accounts of
a given cognitive process (Varela, 1996, p. 343); a third component is then
used to provide a “neutral ground” between “these two kinds of accounts:
formal (or computational) models (Lutz & Thompson, 2003). Some approaches
specifically focus on mathematically formalizing the structures of lived
experience directly, referred to as the “CREA proposal” (Petitot & Smith,
1996; Petitot 1999). However their obtained mathematical formalizations
correspond to a “physical behavior” from which the “qualitative structure of a
phenomenon” _emerges_ (Petitot & Smith, 1996, p. 241); the computational model
describes neurophysiological processes from which phenomenological events
emerge (similar to connectionism).

More recently, Yoshimi (2011) proposed to associate phenomenological
structures with neuro-computational structures, considering connectionist
models. The first approach to propose computational models of the structures
of lived experience _without_ necessarily assuming that they belong to the
neurophysiological is Ramstead et al.’s (2022) computational phenomenology.
This active inference-inspired approach seeks to build generative models that
explain data from “our first-person phenomenology _itself_ ” (Ramstead et al.,
2022). In a second step, such models (of perception, or of action for example)
can also be used in simulations (Sandved-Smith et al., 2021).

What all these approaches have not accounted for fully is the “trainability”
aspect: computational models can now be _trained_ , and therefore generate
their own kind of data, in the same way as the neurophysiological and the
phenomenological can. In our computational phenomenology account, we propose
to use the term _source_ to designate a distinct level of inquiry that can
provide its own type of data777A source is therefore defined epistemologically
and not ontologically: it doesn’t designate a level of reality but a set of
methods from which we can obtain a specific type of data. We owe the term to
Bitbol (2006).. Following the three fields of knowledge of Lutz & Thompson
(2003), we therefore propose to distinguish three different _sources_ to
explore cognition: _the neurophysiological source_ , _the phenomenological
source_ and _the computational source_ – insisting that computational models
are not simply tools to formalize data from other fields of knowledge but that
they can generate their own.

In fact, neuroscience researchers have long ago recognized the computational
as a source of exploration for cognition. Notably, the advent of this new
source has strengthened the tendency of neuroscientific research to cast aside
the phenomenological source by implicitly assuming a close correspondence
between physiological processes and conscious experience. In DL-based
computational neuroscience, numerous research completely dismisses the
phenomenological source and compares ANN processes to brain processes
(comparison which does seem quite natural given DL’s initial structural
inspiration being the brain); examples include Yamins’ paradigmatic research
on vision in brains and in ANNs (Yamins & DiCarlo, 2016), other recent
investigations (Kumar et al., 2022; Millet et al., 2022) and even proposed
research programs (Doerig et al., 2022; Cohen et al., 2022).

To restore the phenomenological source and give it an epistemic role in DL
research, we propose a new formulation of _computational phenomenology_ (CP),
defining it as an epistemic dialogue between the phenomenological source and
computational source (in an analogous way to neurophenomenology).
Specifically, we consider circulation of knowledge between phenomenological
descriptions of a given cognitive process (and its corresponding habit) and
the mechanisms of trained computational models on a corresponding task. In
line with the phenomenological tradition, we bracket the question of the
existence of the external world and provisionally let the two sources free of
any constraint or import coming from the neurophysiological source. Therefore
CP doesn’t help itself to the third-person separation between agent and
environment (which neurophenomenology as well as enactivism rely on) and
considers cognitive processes, and corresponding habits, in which
consciousness and the world are intertwined. CP’s goal is to uncover
structural invariants and guiding constraints between phenomenology and
computational modeling. However, it also provides a ground to develop a new
framework to think computational models different from the existing ones
(cognitivism, connectionism or NR)888Our CP approach can be considered a
“cousin” of Ramstead et al.’s active inference formulation (2022). The main
difference being that Ramstead’ et al.s CP mathematizes the underlying
structures of experience directly, whereas our version identifies common
mechanisms between AI systems and corresponding first-person experiences of
cognitive processes in the perspective to formulate an alternative to
cognitivism. (See Fig. 3.).

Figure 3: The three sources of exploration of cognition. Modern computational
sciences promote a circulation of knowledge between neuroscience and
computational sciences ; in consequence they tend to exclude the
phenomenological aspect of cognition. In response, we propose computational
phenomenology, an epistemic dialogue between the phenomenological and the
computational, in a similar vein than neurophenomenology, a dialogue between
the neurophysiological and the phenomenological.

Once the computational is recast as a _source_ (because it generates data), it
makes sense to select the models that yield the most impressive results, that
are best at solving cognitive tasks. Therefore it is natural for CP to turn
towards DL. In the next sections, we will engage in a dialogue between
phenomenology and deep learning systematically covering important dimensions
of cognition: perception, action, imagination and language. We will see that
ANNs non-decomposaibilty is particularly interesting when considering
phenomenological descriptions. However, we will not limit our investigation to
highlight a shared opacity in DL and in phenomenology. We will rely on common
DL interpretation techniques999For a review of such interpretation techniques
see (Räuker et al., 2022). to highlight similarities between specific
cognitive processes, cast as habits, and ANNs trained on an analogous task.
This investigation will notably allow us to redefine the term of
_representation_. Specifically, we abandon the idea of symbol-like
representations underlying our cognition and will characterize the
decomposable phenomenal content of our cognitive processes – that means, our
conscious representations. In what follows, we offer clear directions within
our account of computational phenomenology to its employment within specific
DL/cognition areas: perception (4.1), imagination (4.2), and language (4.3).

###  4.1 Merleau-pontian perception: a new setting in which to consider
learning

Merleau-Ponty’s phenomenology gives a central role to the body (Merleau-Ponty,
1945). In his theoretical framework, the functional unit that allows the
imbrication of consciousness and world through action and perception is the
_corps propre_ (one’s own body). The _corps propre_ is not the body considered
as an object, but the body I live as, and through which I have a world. It is
the body I discover by investigating the underlying structures of the first-
person perspective; as opposed to the biological body discovered by “objective
thought” that is “unaware of the subject of perception” (Merleau-Ponty, 1945,
p. 214). It does not rely on the concept of brain, as the brain cannot be
identified as the origin of perception from the lived phenomena themselves
just as “it would be absurd to say that I see with my eyes or that I hear with
my ears” (Merleau-Ponty, 1945, p. 214). When it comes to explaining cognition,
the biological body (including the brain) is the functional unit of the
neurophysiological source whereas the _corps propre_ is the functional unit of
the phenomenological source. Considering perception by modeling the former as
an ANN tends to fall into NR. An alternative is given by investigating the
similarities between the latter and DL.

It turns out the ANN is a good candidate in modeling the way the _corps
propre_ adjusts its grip on the world. The _corps propre_ enables learning
from past experiences by _sedimentation_ into _habits_. Merleau-Ponty uses
this term to stress that experiences aren’t stored separately, as symbols in a
Turing machine or entries on a hard disk, but rather consolidate into new
habits in an analogous way solid material settles (sediments) at the bottom of
a liquid. The seemingly uninterpretable way the particles settle resembles the
adjustment of weights during the training of an ANN. The sedimented habits
form a “contracted knowledge” that isn’t “an inert mass at the foundation of
our consciousness” but is “taken up” in every “new movement” towards the world
(Merleau-Ponty, 1945, p. 132) in the same way that at inference, all the
weights of an ANN are mobilized to propagate the content of each input. In
accordance with ANNs that do not store all the examples in the form of
representations but rather benefit from them by weight adjustment, the _corps
propre_ ’s intentional acts are not based on a superposition, but a
sedimentation of experiences. Intentional acts allowed by the _corps propre_
include the _perceptual synthesis_ : the detachment of a privileged object
from an indifferent background. As such our perceptions are grounded on a
perceptual tradition, that contracts the history of previous experiences in a
way that remains wholly opaque to the subject of perception – opacity, or non-
decomposability, that is also characteristic of ANNs.

That perception is grounded on the sedimentation of past experiences means
that what we learn is directly transcribed in the way the world shows up to
us. The adjustment of perception corresponds to the search for an _optimal
grip_ on the lived world that allows confident action. This means that the
features of the world that appear to us do not correspond to some encoded
properties of external realities. This finding turns some of NR’s most
elementary assumptions completely on their head: the space that I experience
in perception, for example, is now conceived as the result of my grip on the
world. The orientation of the whole of our perception – what we consider as
being “top”, “bottom”, “left” and “right” – at any given time, translates a
certain equilibrium I reached in my lived world, rather than the encoding of
some universally given directions.

But how does this redefinition of perception – as a dynamically adjusted
foundation of our experiences rather than a faithful reconstruction of
external entities – allow a dialogue with DL models? Let’s consider two
related cases where perception is perturbed by rotation of our visual field.
When I look at an upside-down face for some time, it becomes “monstrous”: I
see a “pointed and hairless head” with a “blood-red orifice” on its forehead
and “two moving eyeballs” where the mouth should be (Merleau-Ponty, 1945, p.
263). My lack of interaction with inverted faces translates into an unstable
_grip_. An ANN trained to recognize faces that would be presented to it
systematically the "right side up", would fail if they were suddenly turned
around. In such cases, the neural network is said to have difficulty
generalizing, i.e., adapting to a new type of data. Analogously to the _corps
propre_ , an ANN is constantly trying to adjust its “grip” on the data, and
doesn’t rely on a translation into symbols – that, in this case, would have
allowed it to instantly revert the inflicted rotation.

The _corps propre_ can also readapt in cases of spatial level shifting. In one
of Wertheimer’s experiments, a subject observing his room for a few minutes
through a mirror that tilts it by 45 degrees, suddenly sees his visual spatial
level shift when he projects himself into this new setting. The “spectacle”
offered by the tilted room is a call to a new “virtual” _corps propre_. That
is, the body with “the legs and arms that it would take to walk and act in”
the room, to open the cupboard or sit at the table (Merleau-Ponty, 1945, p.
289). At that point perception adjusts in a way to guide actions in this new
setting: the spatial level shifts. What exactly triggers this tilting? Before
the shift, the orientation of the (intentional) objects in the mirror is not
natural and does not allow the usual interaction with them. It is therefore
the objects – and particularly faces, according to Merleau-Ponty – that are
the sign of a tilted visual field and serve as anchor points to switch to a
new oriented phenomenal space in which they are the “right side up”. It turns
out an ANN learns a similar “trick” to identify rotated images: _RotNet_ ,
trained to identify the rotation angle (0, 90, 180, or 270 degrees) of rotated
images, also relies on the orientation of objects and faces present in the
scene (Gidaris et al., 2018). Indeed, attention maps – which overlay the input
image with network activations – reveal a high attention attributed to faces
in rotation angle classification (Fig. 4). The orientation of the objects
themselves, which allows them to serve as anchor points, is given by a
“perceptual itinerary”, a learned order in which I visit its important
features. _DeepFace_ (Taigman et al., 2014), a neural network that learns to
identify a specific person from their face, also relies on certain learned key
points as shown by higher activations at pixels corresponding to the eyes and
mouth (Fig. 5)101010However, these key points are not visited in a certain
order. The ANNs inference lacks the temporal dimension of our lived perceptual
synthesis..

Figure 4: Attention maps of the artificial neural network Rotnet that learns
to recognize the angle of rotation of an image (Gidaris, 2018). Figure 5:
Architecture of the _DeepFace_ model and activations corresponding to a given
input. First blue square from the left is the most telling (Taigman, 2014).

We have first highlighted similarities between the _corps propre_ ’s grip
adjustment and the training of an ANN; we have then shown a case where an ANN
and corps propre learn a similar “trick”. But what do these correspondences
actually show? First, it is interesting to note that simple mathematical
functions fitted with a simple learning rule (in this case, gradient descent)
end up relying on similar tricks than the _corps propre_ to solve analogous
tasks. Furthermore, they show how computational learning can be conceptualized
in the lived world, in conscious experience, rather than with respect to an
external world. Indeed, the input to the spatial level adjustment process are
not some photons or other scientific entities but our previous lived
experience (which calls for an adjustment if it does not allow an optimal
grip). Thinking these kinds of processes in terms of ANNs paves the way for a
new framework to conceive DL, in which learning happens in the lived world.
Furthermore, ANNs’ non-decomposability is good news in this setting because it
opens up the idea that some processes could, by nature, be non-decomposable
into operations on symbols – in the same way adjustment processes are _opaque_
to the subject of perception. They allow us to interpret cognition from the
phenomenological source without relying on unconscious representations. As for
conscious representations (phenomenal contents), we have seen with the case of
the orientation of space or of intentional objects, that they are not the
encoding of some external entity but the result of an optimal grip on the
world. We will seek to further characterize them in the two following
sections.

###  4.2 Sartrean imagination: conscious re-presentation of sedimented
experiences

If ANNs are thought to model processes of the _corps propre_ , their training
is recast as a _sedimentation_ of past experiences in order to obtain an
_optimal grip_ on the _lived world_ by refining the _perceptual synthesis_
which determines the way things appear to us. But how can the world be “the
best model of itself” when I _imagine_ something? Do we not store some
representations of things to be able to later form mental images of them?
Indeed, cognitivism might turn to this form of explanation relying on the
Turing-machine’s functioning, positing that we save some representations to be
able to later access them. This explanation corresponds to Hume’s
understanding of mental images as faint copies of past perceptions. In _The
imaginary_ , Sartre rejects Hume’s copy principle because it falls under what
he calls the “illusion of immanence”. He holds that mental images are not
copies _contained_ in consciousness and recasts them as _imaging
consciousnesses_ (Sartre, 2004). If Hume’s copy principle is best explained
with the Turing-machine, we will show that Sartre’s _imaging consciousness_
better resembles the cascade of activations of an ANN.

Sartre rejects the idea of the mental image as a copy; in fact, he considers
it as incompatible with the dynamic nature of consciousness: indeed, it would
be “impossible to slip these material portraits into a conscious synthetic
structure without destroying the structure, cutting the contacts, stopping the
current, breaking the continuity.” (Sartre, 2004, p. 6). The act of imagining
something cannot rely on a symbol, or any object it would be heterogeneous to;
rather, the act of imagining and the mental image are one. “The majority of
psychologists [mistakenly] think that they find the image in taking a cross-
section through the current of consciousness”, Sartre says (Sartre, 2004, p.
15); this error is repeated by anyone trying to interpret DL using the
cognitivism framework by trying to find representation in the “current” of
successful activations of an ANN inference. Therefore, we should rethink the
mental image – that is an _imaging consciousness_ – as the complete inference
of an ANN.

What does it mean for a mental image to be an imaging consciousness? And why
cannot it be a representation that would be retrieved by consciousness? Just
like the _perceptual synthesis_ , the _imaging consciousness_ is an
intentional act. And in both processes, the object is aimed at as a corporeal
object while never _entering_ my consciousness. Indeed, when I perceive the
chair, it would be absurd to say that the chair _enters_ into my
consciousness; in the same way an _imagining consciousness_ does not rely on a
copy of a chair that would be _in_ my consciousness. Both intentional acts do
not rely on separable symbols. However, even if it relies on a perceptual
tradition, the _perceptual synthesis_ “encounters” the object it aims at
(Sartre, 2004, p. 7). This does not seem to be the case for the _imagining
consciousness_. On what knowledge can it rely? How can it aim at the sensitive
elements of objects it first encountered in perception?

To picture something as possessing certain sensible qualities, to aim at my
friend as “blond, tall, with a snub or aquiline nose, etc.”, my _imaging
consciousness_ “must aim through a certain layer of consciousness that we can
call the layer of knowledge” (Sartre, 2004, p. 57). From Sartre’s
descriptions, we can formulate a hypothesis according to which the _imaging
consciousness_ re-employs certain tracks of the _perceptual synthesis_. In DL
mechanisms, this could mean that the imaging consciousness uses the sedimented
weights of processes belonging to the _perceptual synthesis_ (such as the
process that allows the orientation of an object). In fact, there exists a
technique in DL – called _DeepDream_ – that allows one to generate images from
a trained image classification network: for example, by tweaking random noise
in order to maximize the activation of a final output node corresponding to
one particular label (Mordvintsev et al., 2015) (Fig. 6). Recently, (Fei et
al., 2022) have employed a similar technique to visualize what they call the
“imagination” of a model trained to bring closer an image and a matching
textual description in a higher-level feature space. In a similar way, my
_imaging consciousness_ could try to strongly re-activate the processes that
allow me to recognize (in perception) my friend as being “blond, tall, with a
snub or aquiline nose, etc.”. This, of course, would be only one possible way
to do it; the general idea we want to convey is that past perceptual
experiences might _sediment_ into perceptual _habits_ , and that in a second
phase some structures, or some “regions”, of these habits might be used, or
_re-activated_ , by _imaging consciousnesses_ (which are themselves are
particular type of habits). As such, an imagined object isn’t really red
because we stored its color in symbol form, it is red because we employ a
certain red-making process (that relies on the process used to recognize
objects as red in perception): we imagine _redly_.

Figure 6: _DeepDream_ images that maximize activations corresponding to
particular image labels. (Mordvintsev, 2015).

Once the mental image recast as an _imaging consciousness_ , Sartre
establishes two characteristics of the aimed object in the way it appears to
us. These also apply, analogously, to images generated by DL. Mental images
are given as an “undifferentiated whole”, meaning that they can possess
qualitative properties but be quantitatively undefined: it is possible for
example to have a mental image of the Pantheon but to be unable to count the
columns – it can have an undetermined number of them (Sartre, 2004, p. 87).
Furthermore, they do not obey the “principle of individuation”: when I imagine
my friend for example, she can appear simultaneously from the front and the
profile. Both the qualitative undefinition and the superposition of various
viewpoints can be observed in images generated by _DeepDream_ (Fig. 6) or
other DL image generations techniques (Fei et al., 2022; Ramesh et al., 2021).
These properties, or failure cases, of imagination from the phenomenological
source can be observed analogously in DL based image generation. The TM, on
the other hand, cannot explain an “undifferentiated” Pantheon; it is more in
line with Hume’s copy principle, where one either has a stored copy of the
Pantheon and can retrieve or one does not and cannot output anything.
Dedicated DL models better explain borderline cases of mental images
considered from the phenomenological source than the TM, cognitivism’s go-to
model, does.

In conclusion, Sartre’s concept of _imaging consciousness_ can be modeled by
the inference of an ANN taken as a whole process, wherein one does not look
for any symbol-like representations. It relies on previously _sedimented_
perceptions allowing it to _re-present_ (show again) properties aimed at by
the _perceptual synthesis_ by _re-activating_ them. Examples of such
properties include the color and even the orientation of an object. We use the
term _re-presentation_ to stress that previous experiential content is _re-
activated_ , rather than symbolically represented: we imagine _redly_ instead
of accessing the symbol red in our internal database. Therefore, our analysis
of imagination culminates in a new understanding of phenomenal content as
_conscious re-presentations_.

###  4.3 Language: concepts as conscious re-presentations

Computational phenomenology also has to address a major aspect of cognition
that is also an important research domain in deep learning: language. Language
and concept-based reasoning heavily rely on individuated units or conscious
symbols. These units provide the ability to group and recognize things by
subsuming them under the same concept. These concepts can be employed even in
the absence of any instance of it, for example in a propositional act. In such
a case a concept is linked with a series of sounds, to a word. These
“meaningful cores” can also be explored from the phenomenological source: with
Merleau-Ponty, a word is a “phonetic gesture”, generated through my body and
its vocal cords, that “produces a certain structuring of experience” in the
same way than my perceptual habits do (Merleau-Ponty, 1945, p. 199). As they
crystallize into words, concepts become communicable: in the same movement
“the body opens itself to a new behavior and renders that behavior
intelligible to external observers” (Merleau-Ponty, 1945, p. 199). We can
consider concepts as _conscious re-presentations_ by extending the sartrean
conception of imagination.

As we did for perception and imagination, we could consider concepts as being
cognitive processes that rely on conceptual _habits_. This thesis can be
mapped to ANN mechanisms : the inference of the ANN can stand for the concept
as a cognitive process (or _conscious re-presentation_), the training – or
sedimentation of past experiences – can stand for the formation of the
_conceptual_ _habit_. Similarly to mental images concepts might also reemploy
some sedimented tracks of other habits (perceptual or imaginative111111Sartre
for example speaks of “illustrations” and “symbolic schemas”, that are two
different kinds of mental images that can accompany our recollection of a
particular concept (Sartre, 2004, p. 87).). Here particular ANNs can be used
to reason about different functions of concepts understood as cognitive
processes. Panaccio proposes two fundamental roles of the concept: its
_representational role_ and its _inferential role_ (Panaccio, 2011).

The _representational role_ refers to a concept’s relationship to perception
allowing recognition and re-presentation. The concept _red_ allows me to
perceive objects as red in my lived world and also to re-present _redly_ in
imagination. As such concepts, and associated words, are structuring lived
experience; they are not name tags of external properties. Merleau-Ponty
stresses this point by analyzing a study of Gelb and Goldstein where patients
with color name amnesia were incapable of grouping objects of the same color
(Merleau-Ponty, 1945, p. 197). From DL, models of image classification – where
a concept (a word) is associated with an image – would then be of interest to
investigate the _representational role_ of concepts. In such cases, the task
of image recognition sediments into weights which determine the pattern of
successive activations at inference (perception) and allow re-activation
(imagination). The aspect of experience-structuring can also be observed
analogously: a recent DL study, for example, showed that semantic segmentation
emerges when training a network to caption images (Xu et al., 2022).

The _inferential role_ refers to a concept’s relationship to language.
Inherent to my concept of a _cat_ is the ability it gives me to draw
inferences from it: “a cat is a mammal”, “a cat has four legs”, etc.
Interestingly, I can possess only the inferential dimension of a concept: even
if I never saw a platypus, and even if I have no idea what it looks like, I
might still know that it is a mammal. In such cases, we first encounter the
word and then form the corresponding concept based on the things we learn it
implies (Panaccio, 2011, p. 61). To model the _inferential role_ of concepts
drawing from deep learning, we can turn to DL language modeling (Brown et al.,
2020; Devlin et al., 2019). Under masked language modeling, sentences are fed
word by word to an ANN; however, certain words are randomly masked, and the
task of the model is to predict them. Once trained, the ANN allows the
generation of highly coherent text. Recent DL research has shown promising
results for approaches relying on both of these concept roles (Fei et al.,
2022; Li et al., 2020; Radford et al., 2021) – two roles that most of the
concepts of everyday use play simultaneously.

This last section on mental language and language allows us to generalize our
proposal. In the lived world we can isolate some cognitive processes that
structure our experience, for example that give the objects their orientation
or allow me to recognize them as falling under the concept of _red_. These
processes continuously rely on a _sedimentation_ of past experiences, which
allows an _optimal action-orienting grip_ on the world. _Habits_ , resulting
from the sedimentation, also allow re-presentation, in the form of imagination
or recollection: I can form mental images and I can draw inferences from my
concepts (even those I only “encountered” through language and not through
direct perception). These re-presentations can be thought of as re-activations
of sedimented weights of an ANN. Finally, in all these processes, conscious
representation (or concept) and cognitive process are one: cognitive processes
are not to be decomposed into symbols and their content is to be thought of as
a process and not as an object. Both the mechanism of an ANN (processual) and
its seeming non-decomposability support this thesis.

##  5 A new toolkit for deep learning and a novel mathematization of cognition

Computational phenomenology is a novel approach to work towards a
computational interpretation of cognitive processes as described from first-
person perspective. Rather than mathematizing cognition considered as emerging
from the external world as described by science, it takes a new background of
exploration: conscious experience. The CP recipe is the following: (1) place
oneself in the lived world, (2) isolate a particular cognitive process and (3)
compare it to the mechanism that a computational model uses to solve an
analogous task. We believe CP has potential for practical applications in both
phenomenology and deep learning: it could provide the phenomenologists some
clues as to what aspects to investigate given the way an ANN solves a given
task (like the mechanism of RotNet indicates that faces might help to
determine any tilt of the perceptive field); and it could provide the deep
learning engineer some new ideas to design DL setups (for example by trying to
generate images using the weights of trained recognition networks in an
analogous way to the mechanism of sartrean imagination). From phenomenology to
deep learning, one could even hope for some insights to tackle some more
fundamental aspects of modern DL – could be investigated: a first-person-
perspective credible learning rule (an alternative to _backpropagation_) or a
way to make the inference of an ANN more temporal (like the habits of the
_corps propre_ are)121212This aspect was raised in footnote 10: the perceptual
synthesis (one of the habits of the _corps propre_) is temporal – we
constantly adjust what we see in time – whereas the inference of an ANN isn’t
(the sequentiality of its operations can’t be equated to lived temporality)..
We hope our theoretical groundwork will enable such transdisciplinary
approaches, like there have been many for neurophenomenology.

Applying CP to DL we have arrived at a conceptual framework that lies in stark
opposition to cognitivism and neuro-representationalism. We take as a starting
point the lived world rather than the world interpreted in physicalist terms.
From this perspective, we reject the existence of unconscious symbols
underlying our cognitive processes and propose a theory of conscious re-
presentations as re-activations of sedimented habits that are fused to their
associated processes. Finally, this perspective also does not allow us to draw
a boundary between mind and world, like a naturalist can separate an organism
from its environment. Consciousness remains the background of our
investigation, and even in the investigated cognitive processes a “subject of
the cognition” is not easily found – Merleau-Ponty for example, says that
perception is better described by “ _one_ perceives in me” than “I perceive”
(Merleau-Ponty, 1945, p. 223). Still a functional unit, the _corps propre_ ,
can be isolated to better explain the way I adjust my grip on the world. This
has an interesting consequence: from CP the quest for an autonomous agent, or
even an AGI, is not necessarily the ultimate goal, as we rather isolate and
study particular cognitive processes (of the _corps propre_). Finally, to
summarize:

  1. 1.

What is the world?

The lived world, that can be investigated from first-person perspective.

  2. 2.

What is a representation?

A conscious representation is an act that is inextricably linked to its
corresponding process. This act is realized by _re-activation_ of _habits_
obtained by _sedimentation_ of past experiences.

  3. 3.

What is the mind?

The mind is not delimitable. However, cognitive processes can be isolated and
explored through consciousness. All the cognitive processes belong to the
_corps propre_ and have a common objective: obtaining an _optimal grip_ on the
world.

Unsurprisingly, this framework that we have obtained by applying CP to DL, is
better modeled using the mechanisms of ANNs than those of a Turing Machine. We
introduced the CP toolkit and its novel vocabulary. We propose to consider the
training of ANNs as a way to constitute an _optimal grip_ on some data, just
as the _corps propre_ adjusts its grip on the _lived world_. As such it allows
the _sedimentation_ of experiences into _habits_. These _habits_ can further
be reemployed in other tasks by _re-activation_ , allowing _conscious re-
presentations_. When thought of as modeling processes of the _corps propre_ ,
the ANN’s inference can be seen as an _intentional act_ – such as the
_perceptual synthesis_ , the _imaging consciousness_ , or even the use of a
_lexical_ _concept_. The fact that DL is non-decomposable is not seen as a
weakness in this framework, as conscious representations, or concepts, are
inextricably linked to their corresponding cognitive processes. In the end,
the DL engineer, or computational neuroscientist, might rely on the CP
framework or the NR framework depending on the cognitive process he is
considering. If she considers learning as happening in lived experience – as
when someone is learning to drive a car or play chess for example – she might
rely on the former; if she considers it as happening in the physical world –
as with the perception of a certain wavelength for example – she could rely on
the latter.

We have proposed a new methodology to move the field of DL and phenomenology
forward as well as an alternative conceptual toolkit to consider DL. It
considers the computational from lived experience and therefore puts on hold
the question of the material basis of consciousness. Therefore, it
distinguishes itself from cognitivism and neuro-representationalism; but also
from connectionism and some forms of enactivism (like Varela’s) that think of
consciousness in terms of emergence. Notably, it has led us to a novel type
mathematization that cannot be conceived from the scientific-realism-grounded
NR position. Indeed, by considering ANNs to model the processes structuring
our experience, we oppose Galilean mathematization, criticized by Husserl,
that necessarily implies a commitment towards the existence of external
objects: for example, of physical bodies to which the law of gravitation can
apply. As such, CP opens up a novel form of computational modeling – that
“precedes” the laws that govern objects – of the processes by which I learn to
perceive, imagine, or think things. Become mathematizable not only the
physical laws applying to objects but also the mechanisms that allow me to
perceive an (intentional) object as a delimited object distinguished from its
background – perception of objects from which I can start to formulate the
hypothesis of the existence of an external physical world.

In such cases, and more generally for all processes considered by
computational phenomenology, what do the mathematical models tell us? What do
they correspond to? Two interpretations are possible. Either we remain
faithful to classical phenomenology by keeping the neurophysiological source
bracketed. In that case, we consider that we are modeling mechanisms of the
spontaneous mind that are neither identical nor reducible to the physical
states with which they are correlated, but rather allow the creation of a
world around us. Furthermore, qualia are part of the starting point of our
investigation and their emergence does not need to be explained: the hard
problem of consciousness is thus avoided. Otherwise, we return to naturalism
and consider that the cognitive processes involved in the unveiling of the
physical world are themselves reducible to physical events131313After all, the
order in which we learn to conceive things does not necessarily entail an
order of existence. As Sellars puts it: “we must distinguish between primacy
in the order of being and primacy in the order of conceiving” (Sellars, 1971,
p. 408).. The invariant mechanisms we have isolated by applying computational
phenomenology – that rely on reactivating sedimented lived experiences – could
be confronted with the neural operations of the brain. Such an informed return
to the neurophysiological source could provide an opportunity to surpass
cognitivism and neuro-representationalism in modern
neuroscience141414Following this second interpretation, a CP-informed
neuroscience would reject the notion of neural representations, following
neuroscientists that find it misleading (Freeman and Skarda, 1990; Brette,
2019), having identified it as the well-known map-territory fallacy (confusing
models of reality with reality itself) (Korzybski, 1933). It might find common
grounds with anti-representationalist theories such as the dynamical systems
approach (Van Gelder, 1995; Freeman, 2000; Favela, 2021; Hipólito, 2022) or
instrumentalist accounts of the Free Energy Principle (Friston 2021; Andews,
2021; van Es, 2021; Parr Pezzulo and Friston, 2022)..

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