Consciousness in Artificial Intelligence: Insights from the Science of Consciousness — part 4/14

Cleaned-text code points [46027, 61523). Bibliography omitted here; source.md includes it.

We do not aim to adjudicate between the theories which we consider in this section, although we do indicate some of their strengths and weaknesses. We do not adopt any one theory, claim that any particular condition is definitively necessary for consciousness, or claim that any combination of conditions is jointly sufficient. This is why we describe the list we offer in section 2.5 as a list of indicators of consciousness, rather than a list of conditions. The features in the list are there because theories or theorists claim that they are necessary or sufficient, but our claim is merely that it is credible that they are necessary or (in combination) sufficient because this is implied by credible theories. Their presence in a system makes it more probable that the system is conscious. We claim that assessing whether a system has these features is the best way to judge whether it is likely to be conscious given the current state of scientific knowledge of the subject.

### 2.1 Recurrent Processing Theory

#### 2.1.1 Introduction to recurrent processing theory

The recurrent processing theory (RPT; Lamme 2006, 2010, 2020) is a prominent member of a group of neuroscientific theories of consciousness that focus on processing in perceptual areas in the brain (for others, see Zeki & Bartels 1998, Malach 2021). These are sometimes referred to as “local” (as opposed to “global”) theories of consciousness because they claim that activity of the right form in relatively circumscribed brain regions is sufficient for consciousness, perhaps provided that certain background conditions are met. RPT is primarily a theory of visual consciousness: it seeks to explain what distinguishes states in which stimuli are consciously seen from those in which they are merely unconsciously represented by visual system activity. The theory claims that unconscious vs. conscious states correspond to distinct stages in visual processing. An initial feedforward sweep of activity through the hierarchy of visual areas is sufficient for some visual operations like extracting features from the scene, but not sufficient for conscious experience. When the stimulus is sufficiently strong or salient, however, recurrent processing occurs, in which signals are sent back from higher areas in the visual hierarchy to lower ones. This recurrent processing generates a conscious representation of an organised scene, which is influenced by perceptual inference—processing in which some features of the scene or percept are inferred from other features. On this view, conscious visual experience does not require the involvement of non-visual areas like the prefrontal cortex, or attention—in contrast with “global” theories like global workspace theory and higher-order theories, which we will consider shortly.

#### 2.1.2 Evidence for recurrent processing theory

The evidence for RPT is of two kinds: the first is evidence that recurrent processing is necessary for conscious vision, and the second is evidence against rival theories which claim that additional processing for functions beyond perceptual organisation is required.

Evidence of the first kind comes from experiments involving backward masking and transcranial magnetic stimulation, which indicate that feedforward activity in the primary visual cortex (the first stage of processing mentioned above) is not sufficient for consciousness (Lamme 2006). Lamme also argues that, although feedforward processing is sufficient for basic visual functions like categorising features, important functions like feature grouping and binding and figure-ground segregation require recurrence. He, therefore, claims that recurrent processing is necessary for the generation of an organised, integrated visual scene—the kind of scene that we seem to encounter in conscious vision (Lamme 2010, 2020).

Evidence against more demanding rival theories includes results from lesion and brain stimulation studies suggesting that additional processing in the prefrontal cortex is not necessary for conscious visual perception. This counts against non-“local” views insofar as they claim that functions in the prefrontal cortex are necessary for consciousness (Malach 2022; for a countervailing analysis see Michel 2022). Proponents of RPT also argue that the evidence used to support rival views is confounded by experimental requirements for downstream cognitive processes associated with making reports. The idea is that when participants produce the reports (and other behavioural responses) that are used to indicate conscious perception, this requires cognitive processes that are not themselves necessary for consciousness. So where rival theories claim that downstream processes are necessary for consciousness, advocates of RPT and similar theories respond that the relevant evidence is explained by confounding factors (see the methodological issues discussed in section 1.2.2).

#### 2.1.3 Indicators from recurrent processing theory

There are various possible interpretations of RPT that have different implications for AI consciousness. For our purposes, a crucial issue is that the claim that recurrent processing is necessary for

consciousness can be interpreted in two different ways. In the brain, it is common for individual neurons to receive inputs that are influenced by their own earlier outputs, as a result of feedback loops from connected regions. However, a form of recurrence can be achieved without this structure: any finite sequence of operations by a network with feedback loops can be mimicked by a suitable feedforward network with enough layers. To achieve this, the feedforward network would have multiple layers with shared weights, so that the same operations would be performed repeatedly—thus mimicking the effect of repeated processing of information by a single set of neurons, which would be produced by a network with feedback loops (Savage 1972, LeCun et al. 2015). In current AI, recurrent neural networks are implemented indistinguishably from deep feedforward networks in which layers share weights, with different groups of input nodes for successive inputs feeding into the network at successive layers.

We will say that networks with feedback loops such as those in the brain, which allow individual physically-realised neurons to process information repeatedly, display implementational recurrence . However, deep feedforward networks with weight-sharing display only algorithmic recurrence —they are algorithmically similar to implementationally recurrent networks but have a different underlying structure. So there are two possible interpretations of RPT available here: it could be interpreted either as claiming that consciousness requires implementational recurrence, or as making only the weaker claim that algorithmic recurrence is required. Doerig et al. (2019) interpret RPT as claiming that implementational recurrence is required for consciousness and criticise it for this claim. However, in personal communication, Lamme has suggested to us that RPT can also be given the weaker algorithmic interpretation.

Implementational and algorithmic recurrence are both possible indicators of consciousness in AI, but we focus on algorithmic recurrence. It is possible to build an artificial system that displays implementational recurrence, but this would involve ensuring that individual neurons were physically realised by specific components in the hardware. This would be a very different approach from standard methods in current AI, in which neural networks are simulated without using specific hardware components to realise each component of the network. An implementational recurrence indicator would therefore be less relevant to our project, so we do not adopt this indicator.

Using algorithmic recurrence, in contrast, is a weak condition that many AI systems already meet. However, it is non-trivial, and we argue below that there are other reasons, besides the evidence for RPT, to believe that algorithmic recurrence is necessary for consciousness. So we adopt this as our first indicator:

RPT-1: Input modules using algorithmic recurrence

This is an important indicator because systems that lack this feature are significantly worse candidates for consciousness.

RPT also suggests a second indicator, because it may be interpreted as claiming that it is sufficient for consciousness that algorithmic recurrence is used to generate integrated perceptual representations of organised, coherent scenes, with figure-ground segregation and the representation of objects in spatial relations. This second indicator is:

RPT-2: Input modules generating organised, integrated perceptual representations

An important contrast for RPT is between the functions of feature extraction and perceptual organisation. Features in visual scenes can be extracted in unconscious processing in humans, but operations of perceptual organisation such as figure-ground segregation may require conscious vision; this is why RPT-2 stresses organised, integrated perceptual representations.

There are also two further possible interpretations of RPT, which we set aside for different reasons. First, according to the biological interpretation of RPT, recurrent processing in the brain is necessary and sufficient for consciousness because it is associated with certain specific biological phenomena, such as recruiting particular kinds of neurotransmitters and receptors which facilitate synaptic plasticity. This biological interpretation is suggested by some of Lamme’s arguments (and was suggested to us by Lamme in personal communication): Lamme (2010) argues that there could be a “fundamental neural difference” between feedforward and recurrent processing in the brain and that we should expect consciousness to be associated with a “basic neural mechanism”. We set this interpretation aside because if some particular, biologically-characterised neural mechanism is necessary for consciousness, artificial systems cannot be conscious.

Second, RPT may be understood as a theory only of visual consciousness, which makes no commitments about what is necessary or sufficient for consciousness more generally. On this interpretation, RPT would leave open both: (i) whether non-visual conscious experiences require similar processes to visual ones, and (ii) whether some further background conditions, typically met in humans but not specified by the theory, must be met even for visual consciousness. This interpretation of the theory is reasonable given that the theory has not been extended beyond vision and that it is doubtful whether activity in visual brain areas sustained in vitro would be sufficient for consciousness (Block 2005). But on this interpretation, RPT would have very limited implications for AI.

### 2.2 Global Workspace Theory

#### 2.2.1 Introduction to global workspace theory

The global workspace theory of consciousness (GWT) is founded on the idea that humans and other animals use many specialised systems, often called modules, to perform cognitive tasks of particular kinds. These specialised systems can perform tasks efficiently, independently and in parallel. However, they are also integrated to form a single system by features of the mind which allow them to share information. This integration makes it possible for modules to operate together in co-ordinated and flexible ways, enhancing the capabilities of the system as a whole. GWT claims that one way in which modules are integrated is by their common access to a “global workspace”—a further “space” in the system where information can be represented. Information represented in the global workspace can influence activity in any of the modules. The workspace has a limited capacity, so an ongoing process of competition and selection is needed to determine what is represented there.

GWT claims that what it is for a state to be conscious is for it to be a representation in the global workspace. Another way to express this claim is that states are conscious when they are “globally broadcast” to many modules, through the workspace. GWT was introduced by Baars (1988) and has been elaborated and defended by Dehaene and colleagues, who have developed a neural version of the theory (Dehaene et al. 1998, 2003, Dehaene & Naccache 2001, Dehaene & Changeux 2011, Mashour et al. 2020). Proponents of GWT argue that the global workspace explains why some privileged subset of perceptual (and other) representations are available at any given time for functions such as reasoning, decision-making and storage in episodic memory. Perceptual representations get stronger due to the strength of the stimulus or are amplified by attention because they are relevant to ongoing tasks; as a result, these representations “win the contest” for entry to the global workspace. This allows them to influence processing in modules other than those that produced them.

The neural version of GWT claims there is a widely distributed network of “workspace neurons”, originating in frontoparietal areas, with activity in this network, which is sustained by recurrent processing, constituting conscious representations. When perceptual representations become sufficiently strong, a process called “ignition” takes place in which activity in the workspace neurons comes to code for their content. Ignition is a step-function, so whether a given representation is broadcast, and, therefore, conscious, is not a matter of degree.

GWT is typically presented as a theory of access consciousness—that is, of the phenomenon that some information represented in the brain, but not all, is available for rational decision-making. However, it can also be interpreted as a theory of phenomenal consciousness, motivated by the thought that access consciousness and phenomenal consciousness may coincide, or even be the same property, despite being conceptually distinct (Carruthers 2019). Since our topic is phenomenal consciousness, we interpret the theory in this way. It is notable that although GWT does not explicitly require agency, it can only explain access consciousness if the system is a rational agent since access consciousness is defined as availability for rational control of action (we discuss agency in section 2.4.5)

#### 2.2.2 Evidence for global workspace theory

There is extensive evidence for global workspace theory, drawn from many studies, of which we can mention only a few representative examples (see Dehaene 2014 and Mashour et al. 2020 for reviews). These studies generally employ the method of contrastive analysis, in which brain activity is measured and a comparison is made between conscious and unconscious conditions, with efforts made to control for other differences. Various stimuli and tasks are used to generate the conscious and unconscious conditions, and activity is measured using fMRI, MEG, EEG, or single-cell recordings. According to GWT advocates, these studies show that conscious perception is associated with reverberant activity in widespread networks which include the prefrontal cortex (PFC)—this claim contrasts with the “local” character of RPT discussed above—whereas unconscious states involve more limited activity confined to particular areas. This widespread activity seems to arise late in perceptual processing, around 250-300ms after stimulus onset, supporting the claim that global broadcast requires sustained perceptual representations (Mashour et al. 2020).