# Report on Candidate Computational Indicators for Conscious Valenced
Experience

Andres Campero
AI Futures Fellowship, Research Affiliate at Massachusetts Institute of
Technology
<EMAIL_ADDRESS>
March 2024

###### Abstract

This report enlists 13 functional conditions cashed out in computational terms
that have been argued to be constituent of conscious valenced experience.
These are extracted from existing empirical and theoretical literature on,
among others, animal sentience, medical disorders, anaesthetics, philosophy,
evolution, neuroscience, and artificial intelligence.

This report falls within the broad research program of understanding
phenomenological consciousness. As spelled out by Nagel[1] the relevant
question is what characteristics of a system make it such that the system
feels like being something. Other terms that have been used interchangeably to
characterize versions of this question include the "hard problem of
consciousness", the problem of qualia, subjective experience, and sentience.

While this question is philosophically puzzling, difficult to define, and
difficult to study empirically, the most endorsed possibility is that the
answer has to do with the functional organization of the internal structure of
the system. This functionalist thesis often comes accompanied by
computationalism, which implicitly or explicitly implies that it is in virtue
of a certain computational organization that the system has experiences, feels
like being something, or has phenomenological consciousness.

In this report I focus specifically on valenced experience (specially the
experience of pain and pleasure). Inspired by Butlin et al. [2], I enlist 13
functional indicators111Following [2] and as elaborated by [3], I focus on
criteria that are proposed or can be cashed out in terms of the functional
computational organization of the system instead of other criteria such as
behavioural or anatomical/physiological traits. that have been proposed by
empirical and theoretical literature to be constituent of conscious valenced
experience, which can be spelled out in computational terms.

In the following I make some background notes about consciousness research,
valenced experience, computational functionalism, digital sentience, and the
value of this project. The list of indicators is summarized in page 3 before
presenting each indicator along with some of its properties as an appendix.

#### Consciousness Research

Recent years have seen a revived interest in the study of consciousness which
has resulted in a resurgence of scientific theories, both computational and
non computational [2, 4, 5].

Different methods and approaches to study it include evolutionary accounts [6,
7, 8, 9], search of neural correlates of consciousness [10, 11, 12],
philosophical analysis [13], and several others for the experience of pain and
pleasure more specifically. For example, pathologies such as Pain
Asymbolia[14, 15], Akinetic Mutism [16], Schizophrenia[17], Depersonalisation
Syndrome [15, 14], Congenital Insensitivity to Pain, and others have provided
various insights. Similarly, the nascent field of Animal Sentience has been
another source of empirical evidence and suggested the possibility that
different types of phenomenological consciousness (such as perceptual,
evaluative, and self-consciousness) might have different evolutionary origins
and underlying computational functions [18, 6].

#### Components of Valence

Valenced experience includes pain and pleasure, emotions, as well as other
experiences such as thirst, nausea, hunger, etc. Unlike perceptual
consciousness, valenced experience has been less studied and has less clearly
established or even stated theories.

Several characteristics of valenced experience have been posed which relate it
with sensing, cognition, behavioral and bodily responses, learning, and
decision-making; explored dimensions include the relevant appraisal
mechanisms, and duration properties such as sensitization, arousal, and
sustaining. With risk of oversimplification and overgeneralization, there seem
to be four functional components for the role of valence with broad
applicability across much of literature: motivational role, commensurability,
self-reflexivity, and sense of mineness. The first two are perhaps most
explicitly articulated in [19, 20], the third is defended in [21], and the
fourth is widely explored including in [22, 7, 23]:

Motivational Role. Valence is directly motivating and seems to have intrinsic
motivational force.

Commensurability. Valence allows for commensurability of different experiences
through trade-offs; we can often compare states as well as whether they get
more or less intense overtime. Several studies provide evidence that animals
willingly undergo one negative state in order to avoid another even more
negative one [24, 25, 26, 27]. Carruthers[20] even makes the assumption "that
valence is a natural kind, the same across all different forms of affective
state".

Self-reflexivity. As noted by [21, 19], valenced experiences do not just
motivate us to end some bodily disturbance but rather motivate us to end the
experience itself. One example is the use of analgesics, even injured fish
have been showed to self-administer analgesics and prefer situations where a
local anaesthetic is available [28, 29].

Mineness. Refers to the feeling that experiences belong to you. It has been
related to a self-representation, to a sense of subjective presence, to
personalization, and to a subjective point of view.

### Computational Functionalism

Computational functionalism about consciousness is the encompassing thesis
that a physical system is conscious in virtue of a certain functional
organization, furthermore, this functional organization characterizes the
system in terms of its informational processes in computational terms.

While computational functionalism is often seen as the most plausible
alternative for a materialist, the exact meaning of "computational" is unclear
and leaves open several clarificatory issues as well as some apparent
problems. What it means for a physical process to implement the right
computational process, or computational representation, or algorithm, or
computation, is not well agreed upon and has been subject to debate and
confusion [30, 31, 32]. While a serious consideration is out of scope of the
current report, let me mention three relevant sources of disagreement. First,
while the Church-Turing thesis establishes an equivalence between different
computational paradigms regarding the set of computable functions that can be
instantiated; the way in which those functions are implemented can vary, and
different architectures (think of parallel vs serial as an example) might have
different implications on what counts as being implemented or not,
establishing the correct level of abstraction is not trivial. Second, the
notion of representation is important in both machine learning research
(suffice it to say that one of the most important conferences is called
International Conference on Learning Representations) and cognitive science,
as well as in consciousness research (the potential importance of a self-
representation for example); and yet the notion of representation has been
subject to rich debate in philosophy [33, 34], and it is far from clear what
being a representation means [35, 36]. Third, there can be several unintuitive
implications of characterizing consciousness as computational which are
related to multiple realizability. Including, among others, triviality
problems [37, 38] which highlight a sense in which any computational run is
implemented by any sufficiently complex system such as a wall or a leaf
falling, and slicing problems [39, 40] which suggest that a computation could
be divided into several computations yielding several conscious experiences.
Many theories deviate from computationalism [41], or emphasize the importance
of non-computational factors such as time [42], biological complexity [43],
and the analog-discrete distinction [44, 45].

Clarifying the computational functionalist thesis requires further research
and conceptual thought, left for future work. Nevertheless, as argued by
Butlin et al. [2], its plausibility makes worthwhile its analysis.

### Digital Sentience

While only a few years ago, the posibility of Artificial Intelligence (AI)
systems having conscious experience seemed almost esoteric, striking progress
increasingly brings the question of AI consciousness and Moral Patienthood
closer to home. Especially so if what makes a system be conscious is indeed
the right algorithmic or computational structure, in that case it is not
implausible that current or near-future AI algorithms might have some forms of
conscious experience.

### Motivation for Researching Conscious Valenced Experience

There are at least three important reasons which motivate this research
project:

  * •

Risks of Moral Patienthood: Phenomenal consciousness and valenced experience
are taken to be primary criteria for moral patienthood [46, 47] and the
question of whether AI models are subject to suffering has direct implications
on the risk of vastly exceeding all suffering that has existed so far.

  * •

Risks of AI misalignment: The experience of pain and pleasure is a primary
motivator in humans, and seems directly relevant to whether AI models might
behave according to human values.

  * •

Scientific understanding: Phenomenal consciousness is commonly taken to be one
of the most perplexing scientific questions, and is core to the nature of our
minds and to the meaning of life.

## Functional Indicators

I enlist functional conditions which have been posed to be constituent of
conscious valenced experience. While the degree of explicitness as to whether
they are necessary, or sufficient, or just specific examples, or even just
important properties, varies; following [2] I call all of them indicators,
underscoring the possibility that they could potentially be used to evaluate
whether a system has phenomenological valenced experiences. The compiled
mechanisms are not often posed in the same vocabulary, level of abstraction,
or level of detail, and some could even better be conceived as theories
instead of concrete indicators; nevertheless, in one form of another they do
establish functional properties which characterize the condition.222We note
that the experience of pain goes beyond nociception [48], which is the
signalling that the body might be in damage (whether mechanical, chemical,
thermal, or other). Most of the considered research takes nociceptive signals
for granted and explores the mechanisms that interface with those signals to
generate conscious experience. This is worth noting and might require further
work but we consider it less pressing, as a functional/computational
characterization of nociception seems less perplexing and more feasible to
achieve. These indicators are not comprehensive and vary in the degree of
detail required to be implemented or to afford evaluation of existing AI
algorithms (this would be interesting future work).

Indicators are presented as an Appendix. I categorize the first 9 indicators
according to whether they emphasize a functional role in decision making, in
learning, or in internal processing. Besides summarizing the proposed
functional mechanisms, their ontological status, and the main additional
computational properties, whenever made explicit I note the implied
phenomenological qualities, the stated implications for the moral patienthood
of different animals, and the relationship to other indicators or theories.
The last 4 indicators come from a broader literature and are only generally
described.

  1. Emphasis on motivational role in decision making

  2. 1.

Anticipatory Behavioral Autonomy
Decision and action selection are based on the anticipated desirability of
future states through a hedonic metric.

  3. 2.

Hedonic-Cognitive Interface
Pleasure and displeasure representations are the arbitrer between perception
and goal-directed decision making.

  4. Emphasis on Reinforcement Learning role

  5. 3.

Seymour’s RL Model
Pain is the internal reinforcement signal used for learning at the cognitive
level.

  6. 4.

Actor-Critic (Liking vs Wanting)
A ’liking’ critic determines valenced experience and value, while a ’wanting’
actor updates the policy.

  7. Emphasis on structure of internal processing

  8. 5.

Hierarchical Forward Model
A nested pair of forward predictive models generate conscious awareness of
noxious stimulus.

  9. 6.

Conscious Turing Machines (CTM)
Within a CTM architecture, pain and pleasure take hold of short term memory
through a high intensity scalar.

  10. 7.

Multiple Actors - Cost of Bidding
Pain is the paying of an intra-individual cost imposed on a signaling bidding
process to ensure honesty.

  11. 8.

Hierarchical Theories of Self
Within a hierarchical representation of self, emotions evaluate interoceptive
signals constituting "affective me".

  12. 9.

Imperativism about Pain
Valenced signals have an imperative profile, carrying more information about
action than about the world.

  13. General indicators

  14. 10.

Threshold Theory
When nociceptive signals cross an intensity threshold, pain is experienced.

  15. 11.

Appraisal Theories
Emotional processes evaluate the relevance of events for the organism.

  16. 12.

Predictive Processing
Emotions are determined by predictions and errors of interoceptive signals in
hierarchical Bayesian Models.

  17. 13.

Animal Assessment Criteria
Pain requires nociception, integration, motivational trade-offs, and
associative learning.

## Limitations and Concluding Comment

As with any taxonomy, the list is not exhaustive and has several limitations.
Indicators do not all come in the same flavors, are often not established at
the same level of abstraction, and are often posed with different dialectical
purposes and ontological statuses. What they have in common is that they all
come from research which suggested some functional criteria constituent of
valenced experience. Further work is needed to investigate both broad
questions such as the relationship of consciousness to valenced experience;
and specific questions to each indicator such as the characteristics in virtue
of which valenced experiencie has the phenomenological qualities that it has.
These and other questions will modulate the plausibility of the different
indicators.

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## 1\. Anticipatory Behavioral Autonomy (Farnsworth and Elwood, 2023 [3])

Functional Mechanism: A system has valenced experience when it has the
following characteristics:

  * •

The processes of perception and action selection are isolated. Valenced states
are the arbiter among them.

  * •

The mechanism for decision among a range of options for action is based on the
evaluation of the desirability of future states Figure 1(a). This evaluation
is a form of predictive panning, a forward model outputs a hedonic metric to
serve as a currency to compare actions. The maximization of this metric leads
to the decision.

  * •

The anticipatory predictive model has an internal model of the self which
takes input signals such as memory, nociception, and homeostatic signals; and
compares a goal state with the desirability of the outcome to produce the
hedonic valuation Figure 1(b).

  * •

Pain is the state in the self-model representing the gulf between a current
state and the goal state.

(a) Forward models predict the desirability of actions and output a hedonic
metric used to decide which action to take.

(b) Example of an anticipatory behavioural autonomy system, S are sensory
inputs, G are goals, their difference informs a self-model about the current
state, which outputs the signal that modulates the drive to perform actions.

Figure 1: Anticipatory Behavioral Autonomy

Nature of Indicator: Full algorithmic characterization.

#### Other computational properties:

  * •

The maximization of the metric can happen through different mechanisms such as
down-regulating or up regulating thresholds for different actions.

  * •

Pain is a dispositional state (being in pain is being in a particular self-
model state), which uses feelings as the arbitrer of choice among possible
actions.

#### Phenomenological Qualities:

Pain, rather than information about bodily damage, is a command or motivation
to take action. And being in a particular self-model state is equivalent to
being in pain, it feels bad because it is far from the desired state. The
model can be in as many qualia as it has states.

Patienthood: Authors argue drosophila flies, cephalopods, corvids, mollusks
and arthropods have shown anticipatory action selection which reflects the
previous mechanism.

#### Relationships to other indicators:

Reinforcement Learning (RL) - to learn the forward model. Imperativism - as
pain is a command rather than information about damage. Active Inference -
inference can be done through Bayesian updating. Hierarchical Forward Model -
similar but with an emphasis on action selection.

## 2\. Hedonic-Cognitive Interface (Butlin 2023 [24] and Dickinson and
Balleine 2009 [49])

#### Functional Mechanism:

  * •

Pleasure and displeasure are the interface between motivation and cognition.
Valenced states are evaluative representations which are available for use in
goal-directed selection.

  * •

Goal-directed action selection implies two types of representation:
representations of values of different outcomes, and representations of
contingencies between actions and outcomes.

  * •

Those representations are combined to calculate the expected reward value of
possible actions.

Nature of Indicator: Full Characterization at some level of abstraction.
Butlin says "conscious experiences of pleasure and displeasure are necessary
for information concerning a range of bodily states to influence goal-directed
action selection.” and ""acting as an interface of this kind is the function
of consciousness more generally, and hence that the capacity for goal-directed
control distinguishes conscious creatures from mere ‘beast machines’."

Other computational properties:

  * •

In humans, conscious representation is supported by a perceptual state,
because perceptual imagination plays a role in evaluating potential goals.

  * •

After repetition and increase familiarity, control is handed to unconscious
habitual control.

Phenomenological Qualities: NA

Patienthood: Apart from rats Butlin argues there is little conclusive evidence
for other animal branches.

Relationships to other indicators: Model-based RL - it requires to learn both
outcome values and contingencies between action and outcome.

## 3\. Seymour’s RL Model (Seymour 2019 [50])

#### Functional Mechanism:

  * •

Pain is the internal reinforcement signal used for learning at the cognitive
level.

  * •

Basic representations of pain receive ascending nociceptive input and are used
to generate the internal reinforcement signal used for control, which is
distinct from the nociceptive signal itself.

  * •

A nested hierarchical architecture includes Pavlovian, Instrumental, and
Cognitive systems; to balance speed response with processing sophistication,
the different models span rapid reflexes to slower internal models. All of
them interacting through endogenous control. Figure 2

  * •

The higher cognitive controller is associated with the conscious processing

Figure 2: Seymour’s RL Model

Nature of Indicator: Necessary condition that conscious pain most act as a
control signal to cognitive learning systems.

Other computational properties:

  * •

Pain is inherently predictive, predictions and expectations are central to the
function of its circuits.

  * •

Innate responses are relatively hard-wired early in the ascending pain
pathway.

Phenomenological Qualities: NA

Patienthood: NA

Relationships to other indicators: As a type of RL it is related to several
theories and indicators.

## 4\. Actor-Critic - Liking vs Wanting (Holton and Berridge 2013 [51])

Functional Mechanism: Two independent subsystems compose the desire formation
system

  * •

The ’Liking’ subsystem is equivalent to valenced processing and determines the
goodness of an object/action. Acting as the critic of the architecture which
estimates the value function.

  * •

The ’Wanting’ subsystem regulates consumption accordingly, being the actor
which updates the policy distribution.

Nature of Indicator: One way of instantiation

Other computational properties:

  * •

Liking and hence valence, are often myopically tied to immediate sensory
experience, while wanting becomes associated with long-run consequences of
choices.

  * •

Peter Dayan [52] proposed an alternative but related account in which,
"liking" provides a preliminary quick version of the long-run worth of a good
or state, through its role as what is known as potential-based shaping in RL.
He shows how in some settings this can help with the temporal credit
assignment problem.

Phenomenological Qualities:

  * •

Consider appetite, the functional division explains how it can learn about the
desirability of a nutrient without acting upon it. Information about a food
can impact the dispositional desire for it even if it doesn’t want it now.

  * •

Addiction is often a malfunction of the wanting system

Patienthood: There is evidence that rats employ an actor-critic model. The
brain circuitry that mediates the psychological process of ’wanting’ is
dissociable from the circuity that mediates the degree to which it is ’liked’.

Relationships to other indicators: Hedonic Cognitive Interface is related to
liking mechanisms, however wanting can operate without reference to current
liking values.

## 5\. Hierarchical Forward Model (Key and Brown 2018, 2021 and 2022 [53, 54,
55])

#### Functional Mechanism:

A system has conscious experience when it has the following characteristics:

  * •

A nested pair of forward predictive models which are separate from the sensory
processing module. The forward model predicts the response to the stimulus,
while the second forward model predicts the error or difference between the
first model’s prediction and the realised response (Figure 3).

  * •

This third-order model enables the system to learn the consequences of its
internal processing and generate an integrated awareness.

  * •

The feeling of pain requires this nested hierarchical predictive modeling of a
sensory noxious stimulus. Only this second internal model becomes aware that
one is in a certain state of pain.

Figure 3: Hierarchical Forward Model. Two different depictions of the same
model with three tiers. The first level is the sensory processing. A second
monitoring circuit, forms second order awareness ($IM_{1}$ on the right), and
receives a copy of the input to predict the output of the sensory module, the
prediction is compared to the actual output to update the model. A second
internal model $IM_{2}$ is responsible for conscious awareness and uses the
input and the Global Input from other processors to predict the second-order
prediction. The prediction of $IM_{2}$ is broadcasted globally and the error
is used to update the second model.

Nature of Indicator: Necessary condition but not sufficient.

#### Other computational properties:

  * •

By feeding prediction back into processing, the internal model can bias
processing, this way awareness can gain the functions of noise reduction.

  * •

Feedback is essential to training the internal models.

  * •

The second model can integrate information from other subsystems, this
hierarchical arrangement generates faster and more accurate predictions for
both local and global control.

  * •

The feedback connections of the second model satisfy a form of global
broadcasting of information.

  * •

The second model explains how it is possible to experience subjectively in the
absence of sensory stimulus.

Phenomenological Qualities: They make it explicit that their model fails to
account why awareness should feel like something, rather than nothing.

Patienthood: To assess patienthood, one should identify independent
computations which are isolated, appropriately interconnected and with
functional organization that is temporally appropriate. Insects, cephalopods,
and other mollusks do not seem to have the necessary circuits and
consciousness is not required to explain their complex behaviors.

Relationships to other indicators: Predictive Processing - predictive
processing on its own can predict causes of sensory stimuli but does not
explain the awareness of the content that is experienced.

## 6\. Conscious Turing Machine (Blum and Blum 2021 [56, 57])

#### Functional Mechanism:

The Conscious Turing Machine (CTM) is posed to be conscious because of its
architecture, its basic processors, its expressive inner language, and its
dynamics (Figure 4):

  * •

Conscious experience is whatever the Short-Term-Memory (STM) module broadcasts
to all the Long-Term-Memory (LTM) modules.

  * •

The modules produce and communicate through chunks = <address, t, gist,
weight, intensity, mood>

  * •

The current mood of the CTM is a scalar present in the content of the Short
Term Memory module.

  * •

The Model-of-the-World processor tags its self-representation "CTM" of the CTM
with "conscious".

  * •

Pain is experienced when a chunk of information that describes pain in
brainish takes hold of STM. Its great intensity makes it hard for other chunks
to compete successfully for STM.

  * •

Equivalently, the CTM experiences pleasure when a processor expresses
"pleasure" and takes hold of the STM through a high intensity.

Figure 4: Conscious Turing Machine

Nature of Indicator: Sufficient condition. The CTM is a sufficient mechanism
to explain consciousness.

#### Other computational properties:

  * •

All modules compete for conscious access

  * •

The Inner-Speech, Inner-Vision, and Inner-Sensation processors are special
purpose decoders that extract speech, vision, and sensation from the multi-
modal gists that STM broadcasts.

  * •

Direct links between LTM modules that emerge in the life of the CTM enable
conscious processing to become unconscious.

#### Phenomenological Qualities:

  * •

The illusion of qualia is a consequence of the highly suggestive information
and expressivity contained in the inner language.

  * •

Additionally, the chunk broadcasted by the STM is heard by the "inner ear",
seen by the "inner eye", and so on in ways that closely match what is actually
heard, seen, and felt by the same modules which are responsible for sensing.

  * •

The time-ordered chunks that are broadcasted from STM to LTM form a stream of
consciousness. This stream, contributes the subjective feeling of
consciousness.

Patienthood: NA

Relationships to other indicators: This architecture is very much inspired by
the Global Workspace Theory of Consciousness [11] which in turn inspires many
of the indicators.

## 7\. Multiple Actors, Cost of Bidding (Kolodny 2021 [58])

#### Functional Mechanism:

  * •

Pain is the paying of an intra-individual cost imposed on a signaling process
to ensure its honesty, which facilitates a reliable course of action.

  * •

Multiple actors engage in a bidding process, where the pain signalling cost is
used to manifest an actor’s confidence in the plan it embodies. Actors gain
access to output control by paying the up-front cost. Global cost is
experienced as pain.

Nature of Indicator: One way of instantiation.

#### Other computational properties:

  * •

Confidence is depleted by experienced pain (and replenished by outcome-driven
positive affect)

  * •

Objectives of the different actors are not perfectly aligned.

  * •

This honesty-ensuring evolutionary function of pain is an independent basis
for action selection, orthogonal to outcome-based reward.

Phenomenological Qualities: NA

Patienthood: NA

Relationships to other indicators: Predictive Processing - compatible at a
different level of analysis.

## 8\. Hierarchical Theories of Self ( Carruthers 2020 [14], Deane 2021 [59])

#### Functional Mechanism:

  * •

The representation of Self is formed in a hierarchy of levels (Bodily ->
Interoceptive -> Affective -> Narrative/Cognitive). Emotions are parts of this
hierarchy of active inference.

  * •

While the interoceptive level integrates information from the bodily state;
emotional processes in turn, evaluate, metarepresent, and interpret
interoceptive signals against expectations about goal satisfaction in context.

  * •

The mechanism that models the fluctuation of the affective states attributes
them to an entity equivalent to the affective me.

  * •

The still higher levels of narrative and conceptual modeling interpret and
predict the states of affective me.

Nature of Indicator: Additional property of Valenced States.

#### Phenomenological Qualities:

  * •

Klein [15], and then Gerrans [14] argue that Pain Asymbolia arises from the
failure to assign the pain experience to oneself at the affective self-model
level. The bodily self-model is functioning optimally by signalling damage to
the body, and the narrative self-model is also intact saying that pain should
be producing a negative affect. The patient can sense bodily damage, and can
know, intellectually that she has bodily damage but without feeling affective
distress.

  * •

Affective states are intimately felt as states of self. Relatedly, the sense
of being a self arises from inferring oneself to be the endogenous cause of
sensation [59].

Patienthood: NA

Relationships to other indicators: Predictive Processing - some versions have
been explicitly endorsed by Hierarchical Theories of Self proponents [23, 14]

## 9\. Imperativisim about Pain (Martinez and Klein [60], Martinez and
Barlassina [61])

#### Functional Mechanism:

Valenced have imperative rather than evaluative content in an information-
theoretical sense:

  * •

they carry more information about behavior than about the world

  * •

they occupy a stage in the information-processing chain that is closer to
behavior production than to the uptake of sensory information.

  * •

In valenced signals, the mutual information between signal and action is
higher than that between signal and the state of the world (see Figure 5 for
an example game theoretical setting)

(a) Pain Game

(b) Sender’s Rule

(c) Receiver’s Rule

Figure 5: Imperativism about pain. Signalling game from Martinez and Klein
2016 [60]. The game is played in the presence of inflammation. The environment
can be in states S1, S2, S3 of a nail receiving severe, mild, or null
mechanical stimulation, respectively. The sender (a nail nociceptor perhaps)
has four available messages; the receiver (the motor cortex) can protect the
nail with high, medium, low, or null priority (A1,A2,A3,A4 respectively). (a)
Reflects the shared payoffs of the game where in the absence of inflammation,
the best action to a mild mechanical stimulation is low priority protection
and to a severe stimulation it is high priority protection. The Nash
equilibrium is given by receiver and sender responses (b) and (c). If all
states are equiprobable, the mutual information between action and message is
higher than that between state and message ( $\mathbf{I(A;M)>I(S;M)}$), hence
the messages are predominantly imperative. These messages can be reasonably
interpreted as the imperatives: "protect with very high, high, low, or null
priority" (M1, M2, M3, M4 respectively).

Nature of Indicator: Necessary Condition. All valenced states have an
imperative informational profile.

Phenomenological Qualities: Valenced states come with an intrinsic
motivational force as the signal fully explains the motivation to get rid of
it.

Patienthood: NA

Relationships to other indicators: This indicator seems orthogonal to most
other indicators.

## 10\. Threshold Theory (Baliki and Apkarian 2015 [62])

Related to Melzack and Wall’s Gate Control Theory [63], this indicator poses a
threshold process that gates the transformation of nociceptive activity into
conscious pain (Figure 6). Pain perception can be delayed for hours or days,
as in the case of soldiers surviving a war without reporting pain.

Figure 6: A threshold mechanism marks the boundary between conscious painful
events (P) and nociception (N)

## 11\. Appraisal Theories of Emotion

Under appraisal theories, emotional processes evaluate (appraise) the
relevance of events for the organism. The emotional state represents the
significance of those states for the well being of the body. According to
Damasio, to be pleased or displeased is to feel certain bodily, often
interoceptive and homeostatic, states[64]. Different versions of appraisal
differ in how they see the mechanism which takes care of evaluation, for
example dimensional versions specify a collection of appraisal criteria (goal-
relevance, control, agency,…) which influence the emotional state and the
consequent action tendency [65, 66, 67].

## 12\. Predictive Processing

There are many different versions of Predictive Processing (PP) which is often
presented as a unifying theory of human cognition, perhaps most famously
advocated by Karl Friston [68, 69] and further developed by several prominent
researchers including Seth, Clark, and Hohwy[23, 70, 71, 72], see [73] for a
recent review in the context of affective experience. The main idea behind the
framework is that the brain is a dynamical, hierarchical, Bayesian,
hypothesis-testing mechanism. The brain is always trying to optimize precision
through error minimization over time. As argued by Hohwy, conscious perception
is determined by the prediction with the highest overall posterior
probability[72].333In AI, predictive coding has been implemented in different
algorithms with different degrees of association to PP[74, 75, 76]. While
mechanistic accounts come in many flavors, I follow [73] and consider three
different families of theories from which a functional indicator can be posed.

#### 12.1 Interoceptive Inference Theories.

Any representation that generates predictions about interoceptive signal comes
with emotion or affective content [77]. As such, feelings are determined by
interoceptive predictions and are a generalization of appraisal theories.

#### 12.2 Error Dynamics Theories.

Feelings are determined by error dynamics where valance is equivalent to a
positive/negative rate of error reduction (Van de Cruys 2017 [78]). Valence is
used to maintain policies that minimize error over time.

#### 12.3 Affective Inference Theory.

Valence corresponds to the expected rate of prediction error reduction [73].
While feelings co-vary with error dynamics, what we experience cannot come
directly from them, but from their prediction.

## 13\. Animal Assessment Criteria (Crump et al. 2022 [79])

Crump et al. suggest a list of indicators which can be used to asses pain
experience in animals. While several of their indicators are behavioral and
neurophysiological, the functional ones are:

  * •

The animal has receptors that fire to noxious stimuli.

  * •

There is sensory integration from different sources.

  * •

The receptors are connected to the integrative modules

  * •

Motivational trade-offs lead to flexible decision making

  * •

There is associative learning beyond senzitisation and habituation