Dissociating Artificial Intelligence from Artificial Consciousness

Abstract

Key Aspects

• Central Research Question and Context: The abstract introduces the central research question: whether a computer that is functionally equivalent to a human would also possess consciousness. This question is framed within the context of rapid advancements in artificial intelligence (AI), particularly the development of artificial general intelligence (AGI). The abstract highlights the practical significance of this question, noting its implications for how we interact with and assign rights to AI systems.
• Theoretical Framework: Integrated Information Theory (IIT): Integrated Information Theory (IIT) is presented as the theoretical foundation for addressing the research question. IIT is described as a theory that provides tools to determine consciousness based on a system's intrinsic causal properties. This contrasts with approaches that focus on neural correlates or cognitive functions. The abstract positions IIT as a principled framework for assessing consciousness in both evolved and engineered systems.
• Methodological Approach: Comparing Functionally Equivalent Systems: The abstract outlines the core methodological approach: comparing pairs of systems composed of Boolean units. One system is a basic stored-program computer that simulates the other with full functional equivalence. This setup allows for a direct comparison of systems that perform the same computations but differ in their physical organization. The application of IIT's principles to these systems is intended to reveal whether functional equivalence necessarily implies phenomenal equivalence.
• Main Findings: Dissociation of Functional and Phenomenal Equivalence: The abstract presents the main findings of the study: (i) two systems can be functionally equivalent without being phenomenally equivalent, and (ii) this conclusion is independent of the simulated system's function. These findings are based on the application of IIT's principles to the systems under consideration. The abstract also states that, according to IIT, a computer can simulate human behavior without replicating human experience.
• Contrast with Computational Functionalism: The abstract explicitly contrasts the study's findings with computational functionalism. Computational functionalism is defined as the thesis that performing computations of the right kind is necessary and sufficient for consciousness. The abstract states that the study's results challenge this thesis, highlighting a key point of contention between IIT and a prevalent view in the philosophy of mind and AI.

Strengths

• Clear and Concise Summary

  The abstract clearly states the central question, the theoretical framework (IIT), the approach (comparing functionally equivalent systems), the main findings, and the contrast with computational functionalism.

  "We consider pairs of systems constituted of simple Boolean units, one of which—a basic stored-program computer—simulates the other with full functional equivalence. By applying the principles of IIT, we demonstrate that (i) two systems can be functionally equivalent without being phenomenally equivalent, and (ii) that this conclusion is not dependent on the simulated system’s function." (Page 1)
• Introduction of Theoretical Framework

  The abstract effectively introduces Integrated Information Theory (IIT) as the theoretical basis for the study, which is crucial for understanding the subsequent analysis.

  "Here we employ Integrated Information Theory (IIT), which provides principled tools to determine whether a system is conscious, to what degree, and the content of its experience." (Page 1)
• Relevance to Current AI Developments

  The abstract succinctly highlights the growing importance of understanding artificial consciousness in the context of advancing AI.

  "Because of the torrential growth of the size and power of AI systems, these questions are shifting from purely hypothetical to practically significant: how we interact with AI, and what rights we accord to them, will hinge on whether we consider them as conscious entities..." (Page 1)

Suggestions for Improvement

• Explicitly State Implications

  This high-impact improvement would enhance the abstract's completeness and provide a more precise understanding of the study's implications. The abstract section sets the stage for the entire paper, and including a brief mention of the specific implications strengthens its impact.

  "We further demonstrate that, according to IIT, it is possible for a digital computer to simulate our behavior, possibly even by simulating the neurons in our brain, without replicating our experience." (Page 1)

  Implementation: Add a sentence at the end of the abstract summarizing the key implication. For example: "These findings suggest that achieving artificial general intelligence does not guarantee the emergence of artificial consciousness, highlighting the need for further research into the physical substrates of consciousness."

• Clarify IIT's Distinctive Approach

  This medium-impact improvement would provide additional context and clarity for readers unfamiliar with the core concepts of IIT. While the abstract introduces IIT, briefly mentioning its core distinction from other approaches would strengthen the reader's understanding of the theoretical underpinnings.

  "Here we employ Integrated Information Theory (IIT), which provides principled tools to determine whether a system is conscious, to what degree, and the content of its experience." (Page 1)

  Implementation: Add a phrase or clause clarifying IIT's focus on intrinsic causal properties. For example, modify the sentence introducing IIT to: "Here we employ Integrated Information Theory (IIT), which, unlike approaches based on neural correlates or cognitive functions, provides principled tools based on a system's intrinsic causal properties to determine whether it is conscious, to what degree, and the content of its experience."

• Refine Terminology for Functional Equivalence

  This low-impact change would enhance the abstract's precision and clarity. Using more specific language to describe the type of functional equivalence would benefit readers familiar with the nuances of computational theory.

  "if a computer were to be functionally equivalent to a human, being able to do all we do, would it experience sights, sounds, and thoughts, as we do when we are conscious?" (Page 1)

  Implementation: Replace "functionally equivalent" with "computationally equivalent" or "behaviorally equivalent," depending on the intended meaning. If the equivalence refers to the ability to perform the same computations, use "computationally equivalent." If it refers to producing the same observable behavior, use "behaviorally equivalent."

Introduction

Key Aspects

• Framing the Central Question: The introduction establishes the central question of whether a functionally equivalent computer would possess consciousness, framing it within the context of advancing AI and its practical implications. This builds upon the abstract's initial presentation of the problem, adding further depth and emphasizing the growing importance of understanding artificial consciousness.
• Introducing Computational Functionalism: The introduction positions computational functionalism as a dominant view, defining it as the thesis that performing the right kind of computations is necessary and sufficient for consciousness. This sets up a clear contrast with IIT and highlights the core theoretical debate addressed in the paper. The introduction expands on the abstract's brief mention of computational functionalism, providing a more detailed explanation of its core tenets.
• Presenting Integrated Information Theory (IIT): Integrated Information Theory (IIT) is presented as a general theory of consciousness that provides a principled framework for addressing the central question. The introduction emphasizes IIT's focus on the essential properties of experience and its mathematical framework for assessing consciousness. This builds upon the abstract's introduction of IIT, elaborating on its distinctive approach and contrasting it with other theories.
• Outlining IIT's Axioms and Postulates: The introduction outlines IIT's five axioms of phenomenal existence (intrinsic, specific, unitary, definite, and structured) and their corresponding causal postulates (intrinsicality, information, integration, exclusion, and composition). This provides essential background for understanding IIT's approach and its application in the subsequent analysis. This section provides a deeper dive into the theoretical underpinnings of IIT, expanding on the abstract's concise summary.
• Mentioning Supporting Evidence for IIT: The introduction mentions supporting evidence for IIT, including its account of neurological findings and experimental predictions. This strengthens the credibility of IIT as a viable theoretical framework for the study. This aspect connects the theoretical framework to empirical observations, enhancing the overall argument.
• Describing the Methodological Approach: The introduction describes the core methodological approach: applying IIT's mathematical framework to a simple target system and a computer that simulates it. This sets the stage for the subsequent results section and provides a clear link between the theoretical framework and the experimental design. This section connects to the abstract's description of the methodology, providing more detail about the systems being compared.

Strengths

• Builds Upon Abstract

  The introduction effectively builds upon the abstract by expanding on the central question of artificial consciousness, the limitations of computational functionalism, and the theoretical framework of Integrated Information Theory (IIT).

  "Developments in machine learning and computing power suggest that artificial general intelligence is within reach. This raises the question of artificial consciousness: if a computer were to be functionally equivalent to a human, being able to do all we do, would it experience sights, sounds, and thoughts, as we do when we are conscious?" (Page 1)
• Clearly Defines Core Problem

  The introduction clearly defines the core problem, which is whether functional equivalence implies phenomenal equivalence, and sets the stage for the theoretical and methodological approach.

  "But is functional equivalence really sufficient for phenomenal equivalence? To address this question, it is critical to rely on a general theory of consciousness, one that states the necessary and sufficient conditions for any system, evolved or engineered, to support subjective experience [17]." (Page 1)
• Contrasts IIT with Other Approaches

  The introduction effectively contrasts IIT with other approaches to consciousness, emphasizing its focus on the essential properties of experience itself rather than neural correlates or cognitive functions.

  "Crucially, IIT does not start from neural correlates of consciousness, such as neural activity patterns [21–23], nor from cognitive functions often associated with consciousness... Instead, IIT starts with the essential properties of consciousness itself—those that are irrefutably true of every conceivable experience." (Page 1)
• Overview of IIT's Axioms and Postulates

  The introduction provides a concise overview of IIT's axioms and postulates, laying the groundwork for the subsequent theoretical analysis.

  "These are IIT’s five axioms of phenomenal existence: every experience is intrinsic (for itself), specific (this one), unitary (a whole, irreducible to separate experiences), definite (this whole, containing all it contains, neither less nor more), and structured (composed of distinctions bound by relations that make it feel the way it feels)." (Page 2)
• Mentions Supporting Evidence for IIT

  The introduction mentions supporting evidence for IIT, enhancing its credibility and grounding it in empirical findings.

  "IIT is supported by growing scientific evidence about our own consciousness. For example, IIT provides a self-consistent account of why the cerebral cortex is important for consciousness but the cerebellum is not, despite having four times more neurons; why consciousness fades in deep slow-wave sleep, even though cortical neurons remain active; and why consciousness might have evolved [19]." (Page 2)

Suggestions for Improvement

• Preview Main Results and Implications

  This medium-impact improvement would enhance the introduction's clarity and provide a more complete picture of the study's scope. The Introduction section's role is to set the stage for the entire paper, and a preview of the results strengthens its connection to subsequent sections.

  "To demonstrate that functional equivalence does not imply equivalence of cause–effect structure, and therefore does not imply equivalence of phenomenology, we first apply IIT’s mathematical framework to a simple target system (the simulandum) and to a computer that simulates it (the simulans)." (Page 2)

  Implementation: Add a brief paragraph at the end of the introduction summarizing the main results and their implications. For example: "By applying IIT's mathematical framework to a simple target system and a computer that simulates it, we demonstrate that functional equivalence does not imply phenomenal equivalence. This finding challenges the core assumption of computational functionalism and suggests that achieving artificial consciousness may require more than simply replicating the computational functions of the brain."

• Define Key Terminology (Complexes)

  This low-impact improvement would make the introduction more accessible to readers unfamiliar with the specific terminology of IIT. The Introduction section should be understandable to a broad scientific audience, and clarifying key terms enhances its readability.

  "The analysis identifies systems that can support consciousness, called complexes." (Page 2)

  Implementation: Provide a brief, parenthetical definition of "complexes" when first introduced. For example: "The analysis identifies systems that can support consciousness, called complexes (systems with a maximum of integrated information)."

• Provide a Roadmap for the Paper

  This low-impact improvement would enhance the introduction's flow and provide a smoother transition to the "Theory" section. The Introduction section should seamlessly lead into the subsequent sections, and a brief roadmap helps guide the reader.

  "TheoryThis section provides a high-level overview of IIT and highlights aspects of the theory that are relevant to the results presented here." (Page 2)

  Implementation: Add a sentence at the end of the introduction briefly outlining the structure of the paper. For example: "The following section provides a more detailed overview of IIT and its mathematical framework. We then present our results, demonstrating the dissociation of functional and phenomenal equivalence in a simple computational system. Finally, we discuss the implications of these findings for the broader debate on artificial consciousness."

Theory

Key Aspects

• Overview of Integrated Information Theory (IIT): The section provides a high-level overview of Integrated Information Theory (IIT), emphasizing its relevance to the results presented in the paper. This overview builds upon the introduction of IIT in the previous sections, providing more detail about its core principles and how they relate to the study's objectives. The section highlights the key aspects of IIT that are necessary for understanding the subsequent analysis of the target system and the computer.
• Definition of Causal Models: The section defines causal models as the starting point of IIT's analysis. A causal model is described as a representation of a physical system that captures all the interactions within it, consisting of micro units with internal states, inputs, and outputs. The section specifies that the causal model is fully characterized by its transition probability matrix (TPM). This definition provides the necessary technical background for understanding how IIT analyzes systems.
• Identifying Complexes and the Exclusion Postulate: The section explains the process of identifying complexes, which are substrates that can support consciousness according to IIT. This involves evaluating the system integrated information (φs), which quantifies the extent to which a system's intrinsic information is affected by its minimum partition into causally independent parts. The section also describes the exclusion postulate, which requires that a complex specify a maximum of integrated information compared to all competing candidate systems.
• Unfolding the Cause-Effect Structure: The section describes how the cause-effect structure of a complex is unfolded. This involves assessing how a complex's causal power is structured, identifying distinctions (causal powers of subsets of units) and relations (how their powers overlap). The cause-effect structure is claimed to fully account for the quality of an experience, with its structure integrated information (Φ) measuring the quantity of consciousness.
• Macroing and Grain Size: The section introduces the concept of "macroing," which involves evaluating a system at multiple grains by grouping subsets of micro units into macro units. This is done because a system's intrinsic causal powers (φs) may be higher at a coarser grain than at a finer grain. The section emphasizes that macro units, like complexes, must satisfy IIT's postulates, including integration and exclusion.

Strengths

• Clear Definition of Core Concepts

  The section clearly defines the core concepts and terminology used in IIT, including causal models, complexes, and cause-effect structures. This provides the necessary theoretical foundation for the subsequent analysis.

  "This section provides a high-level overview of IIT and highlights aspects of the theory that are relevant to the results presented here. For a more detailed and complete exposition of IIT and its justification, see [18, 20]." (Page 2)
• Explanation of Complex Identification

  The section explains the process of identifying complexes by evaluating system integrated information (φs) and applying the exclusion postulate. This provides a methodological basis for the subsequent analysis of the target system and the computer.

  "To determine whether a system of units is a complex, one evaluates its system integrated information (φs)—the extent to which the system’s intrinsic information (capturing the postulates of intrinsicality and information) [37] is affected by its minimum partition into causally independent parts (capturing the integration postulate)." (Page 2)
• Description of Cause-Effect Structure Unfolding

  The section describes the process of unfolding the cause-effect structure of a complex, including distinctions, relations, and structure integrated information (Φ). This clarifies how IIT accounts for the quality and quantity of consciousness.

  "According to IIT, the cause–effect structure of a complex in a state accounts for the quality of its experience in full, with no additional ingredients needed (“quality is structure,” [18, 20, 40, 41]). The quantity of consciousness associated with a cause–effect structure is measured by its structure integrated information (Φ)—the total irreducibility of its distinctions and relations (P φd + P φr)." (Page 3)
• Introduction of Macroing

  The section introduces the concept of "macroing," which is important for determining a system's intrinsic causal powers at different grains. This is relevant to the later analysis of the computer at different levels of granularity.

  "Moreover, every system is evaluated at multiple grains, by exhaustively grouping subsets of its micro units into macro units, and mapping states of the constituent micro units to states of the resulting macro units. This procedure, called macroing, is done because a system’s intrinsic causal powers (φs) may be higher at a coarser than at a finer grain, depending on its internal organization [34, 38, 39]." (Page 2)

Suggestions for Improvement

• Include Key Mathematical Formulations

  This medium-impact improvement would enhance the section's clarity and provide a more complete picture of IIT's mathematical framework. While the section mentions that IIT can be formulated mathematically, it doesn't provide any specific equations or formulas. Including a few key equations would strengthen the reader's understanding of how IIT is operationalized. This is important in a Theory section, as it forms the basis for the analytical tools used.

  "These five postulates—intrinsicality, information, integration, exclusion, and composition—can be formulated mathematically and assessed algorithmically for any substrate, given the system’s state and complete causal model (see Causal models below) [18, 20]." (Page 2)

  Implementation: Include a brief subsection or paragraph summarizing the key mathematical formulations of IIT. For example, include the equation for system integrated information (φs) and briefly explain its components. Refer to the relevant publications ([18, 20]) for the full mathematical details.

• Define Key Terminology (Cause-Effect Structure)

  This low-impact improvement would make the section more accessible to readers unfamiliar with the specific terminology of IIT. The Theory section should be understandable to a broad scientific audience, and clarifying key terms enhances its readability. It also builds upon the previous sections by providing more in-depth definitions.

  "The causal powers of a complex are then fully unfolded, yielding a cause–effect structure [18, 20]." (Page 2)

  Implementation: Provide a brief, parenthetical definition of "cause-effect structure" when first introduced. For example: "The causal powers of a complex are then fully unfolded, yielding a cause–effect structure (the complete set of a system's causal relationships)."

• Provide a Roadmap for the Application of Theory

  This low-impact improvement would enhance the section's flow and provide a smoother transition to the "Results" section. The Theory section should seamlessly lead into the subsequent sections, and a brief roadmap helps guide the reader. It also provides a connection to the previously analyzed sections.

  "ResultsBelow, we apply IIT’s causal powers analysis to a target system constituted of four micro units (PQRS) and to a functionally-equivalent four-bit computer, constituted of 117 micro units, able to simulate PQRS indefinitely." (Page 3)

  Implementation: Add a sentence or two at the end of the section briefly outlining how the theoretical concepts will be applied in the subsequent analysis. For example: "Having outlined the core principles and mathematical framework of IIT, we now apply this analysis to a simple target system and a computer that simulates it to demonstrate the dissociation of functional and phenomenal equivalence."

Non-Text Elements

Figure 10: Constraints on intrinsic units.

Figure/Table Image (Page 39)

First Reference in Text

Not explicitly referenced in main text

Description

• Explanation of Constraints: Non-Overlapping and Non-Mediation: Figure 10 explains some fundamental rules that define 'intrinsic units' within Integrated Information Theory (IIT). Intrinsic units are the building blocks of a system that has cause-effect power, a key concept in IIT related to consciousness. Panel A illustrates the constraint of non-overlapping units. It shows two units, α and β. These units cannot share any underlying components. This is because if they did, we would be counting the same cause-effect power multiple times. To assess their cause-effect power, we need to be able to manipulate each unit independently and observe the results. Panel B illustrates that a unit within a complex cannot act as a mediator, or go-between, for other units in the same complex. In other words, if unit 'a' influences unit 'c', and unit 'b' is in between, then 'b' cannot be considered an intrinsic unit of the same complex as 'a' and 'c'. All of a's influence on c must be accounted for by b's cause-effect power.
• Explanation of key terms: The figure uses the terms 'causal marginalization,' 'observation,' and 'manipulation.' 'Manipulation' refers to setting the state of a unit directly. 'Observation' refers to simply noting the state of a unit. 'Causal marginalization' is a technical term in IIT that refers to averaging over all possible states of a unit, weighted by their probabilities. It's a way of removing a unit's influence to see what remains.

Scientific Validity

• Fundamental postulates of IIT: The constraints illustrated in the figure are fundamental postulates of IIT. They are essential for ensuring that cause-effect power is properly quantified and that the identified intrinsic units are indeed irreducible and non-overlapping.
• Accurate visual representation: The figure accurately represents these constraints in a simplified, visual manner, making them easier to understand.

Communication

• Clear and effective visual representation: The figure uses simple diagrams and clear labels to illustrate the constraints on intrinsic units within the framework of Integrated Information Theory (IIT). The use of distinct panels (A and B) to represent different constraints is effective. The distinction between 'observation', 'manipulation' and 'causal marginalization' is well presented.
• Caption could be more descriptive; lack of explicit reference in main text: The caption is concise, but could be slightly more descriptive. The figure is not explicitly referenced in the main text, which might make it difficult for readers to fully appreciate its significance without carefully reading the supplementary material.

Figure 11: A four-cell elementary cellular automaton implementing Rule 110.

Figure/Table Image (Page 39)

First Reference in Text

Not explicitly referenced in main text

Description

• System Description and Rule 110: Figure 11 introduces a different four-unit system called WXYZ, which, unlike the previous PQRS system, follows a specific rule known as "Rule 110" from the field of elementary cellular automata. Panel A shows the four units (W, X, Y, Z) and their connections. Each unit's next state depends on its own current state and the states of its immediate neighbors. Panel B presents this rule in two ways: a truth table and a transition probability matrix. Both show all possible combinations of the four units' states and their corresponding next states. It is called an elementary cellular automaton because each cell (or unit) only has two possible states and updates based on simple, local rules. Panel C and D show the application of IIT analysis to the system, resulting in cause-effect structures. Panel C shows that the system can be broken down into two complexes: WZ and y. Panel D shows the unfolded cause-effect structures.
• Significance of Rule 110: Rule 110 is significant because it has been proven to be Turing-complete. This means that, in principle, it can perform any computation that any other computer can, given enough time and space.

Scientific Validity

• Accurate representation of Rule 110: The representation of WXYZ as an elementary cellular automaton implementing Rule 110 is accurate and consistent with established definitions in the field. The truth table and transition probability matrix correctly reflect the rule's dynamics.
• Validity of IIT analysis: The application of IIT analysis to WXYZ, as shown in panels C and D, is methodologically sound within the context of the paper's theoretical framework.

Communication

• Comprehensive but could improve clarity in panel A: The figure presents a four-cell elementary cellular automaton (WXYZ) and its dynamics. The use of multiple panels (A, B, C, D, E) to show different aspects (system diagram, truth table, transition probability matrix, and cause-effect structures) is effective for a comprehensive presentation. However, panel A could benefit from explicitly labeling the inputs to each cell, as this is crucial for understanding how Rule 110 is applied.
• Clear caption: The caption clearly states what the figure represents. The use of 'Rule 110' might require prior knowledge, but the figure itself provides the necessary information to understand the rule.

Figure 12: The four-bit computer with labeled units.

Figure/Table Image (Page 40)

First Reference in Text

Not explicitly referenced in main text

Description

• Detailed Labeled Diagram of the Computer: Figure 12 presents the same four-bit computer as shown in Figure 2, but this time with all of its 117 individual components, or units, clearly labeled. These labels (like P₁, P₂, R₁, etc.) allow for precise identification of each component within the computer's architecture. The computer is organized into distinct functional modules: the clock and frequency dividers (which control the timing of operations), the program and instruction register (which store the instructions for the simulation), the data registers and buffer (which store the current state of the simulated system), and the multiplexer (which selects data from different sources). Each unit is a logic gate that performs a basic boolean function (COPY, AND, OR, XOR).
• Labels for cross-referencing: The labels are used to cross-reference with the supplementary code and proofs. Each label indicates the module and the position of the logic gate within the module.

Scientific Validity

• Complete and accurate schematic: The figure provides a complete and accurate schematic of the four-bit computer, which is crucial for the reproducibility of the results and for verifying the claims made in the paper. The labeling allows for a precise mapping between the theoretical model and its implementation.
• Valid computer design: The design of the computer, as represented in the figure, is a valid implementation of a digital computer capable of simulating the target system.

Communication

• Detailed but complex diagram: The figure provides a detailed, labeled diagram of the four-bit computer. The use of labels for each unit (e.g., P₁, P₂, R₁, R₂, Co, X₁, A₁, etc.) is helpful for referencing specific components in the supplementary materials. However, like previous figures showing the computer, the sheer complexity of the diagram can be overwhelming for a reader. It is difficult to follow the connections and understand the overall flow of information without a very close examination.
• Clear caption: The caption is clear and concise, indicating that the units are labeled.
• Not referenced in the main text: The figure is not referenced in the main text, relegating it to supplementary material. While this is understandable given its complexity, it might be beneficial to reference it at least once in the main text to guide readers to this detailed schematic.

Figure 13: Update 0: Initialization.

Figure/Table Image (Page 41)

First Reference in Text

Not explicitly referenced in main text

Description

• Initial State of the Computer: Figure 13 depicts the four-bit computer (previously shown in Figures 2 and 12) at the very beginning of a simulation, a stage called 'Update 0: Initialization.' This is the starting point, where the computer is set up to mimic the initial state and transition rules of the target system, PQRS. The figure shows the state of every single logic gate (all 117 of them) within the computer. The black units are 'ON' (representing a binary value of 1), and the white units are 'OFF' (representing a binary value of 0). Specific parts of the computer are initialized: the data registers (which hold the current state of the simulated system) are set to match the initial state of PQRS (0101), and the program memory is loaded with the transition rules of PQRS. The clock is also set to its initial state.

Scientific Validity

• Crucial for accurate simulation: The initialization process is crucial for ensuring that the computer accurately simulates the target system. Setting the data registers to the initial state of PQRS and loading the program memory with the correct transition rules are necessary steps for a valid simulation.
• Complete and verifiable description: The figure, in conjunction with the supplementary text, provides a complete and verifiable description of the initialization process, allowing for replication of the simulation.

Communication

• Clear visual representation of the initial state, but inherently complex: The figure shows the four-bit computer in its initial state, ready to begin simulating the PQRS system. The use of color-coding (black for ON/1, white for OFF/0) is consistent and helps to visually represent the state of each unit. However, the figure is inherently complex, and understanding the initialization process requires careful attention to the details and the accompanying supplementary text.
• Clear caption: The caption is concise and clearly indicates the figure's purpose.

Figure 14: Update 1: The instruction register loads P's truth table, and...

Full Caption

Figure 14: Update 1: The instruction register loads P's truth table, and current state selects a multiplexer input.

Figure/Table Image (Page 42)

First Reference in Text

Not explicitly referenced in main text

Description

• Fetching Information for P's Next State: Figure 14 shows the four-bit computer during the first step ('Update 1') of simulating a single transition of the PQRS system. At this stage, the computer begins to determine what the next state of unit P in the PQRS system would be, given its current state. It does this in two main steps, as indicated in the caption. First, the 'instruction register' loads P's 'truth table.' The truth table, loaded into the program in the initialization, contains the information about how P should behave based on all possible inputs. Think of it like looking up the rules for P in a rulebook. Second, the current state of the PQRS system (stored in the data registers) 'selects a multiplexer input.' The multiplexer acts like a selector switch. Based on the current state of PQRS (0101), a specific input to the multiplexer is chosen. This selected input corresponds to the relevant part of P's truth table for the current situation.

Scientific Validity

• Valid implementation of simulation: The depicted process of loading the truth table information and selecting a multiplexer input based on the current state is a valid and standard way to implement a computational simulation of a system with defined state transitions.
• Verifiable simulation process: The figure, together with the supplementary text describing the computer's operation, provides a verifiable account of the simulation process at this specific update step.

Communication

• Clear visual representation, but requires background knowledge: The figure depicts the state of the four-bit computer at 'Update 1' of the simulation, focusing on the process of fetching information needed to compute the next state of unit P. The use of color-coding (black/white for ON/OFF) is consistent and helps visualize the state. However, understanding the specific operations and their sequence requires a good understanding of the computer's architecture and the accompanying supplementary text. The caption could be improved by explicitly mentioning that this update is part of the simulation of PQRS.
• Caption conciseness: The caption is informative, but very concise.

Figure 15: Update 2: The next state of P is computed.

Figure/Table Image (Page 43)

First Reference in Text

Not explicitly referenced in main text

Description

• Computation of P's Next State: Figure 15 shows the four-bit computer during the second step ('Update 2') of simulating a single transition of the PQRS system. At this point, the computer computes what the next state of unit P will be. The previous step (Figure 14) involved fetching the relevant information (P's truth table and the current state of PQRS). Now, that information is used. Specifically, the multiplexer, acting like a selector switch, uses the current state of PQRS (0101) to choose the correct output from the program memory. This output represents what P should do according to its transition rules, given the current state. The instruction register now loads Q's truth table, preparing for the next step.
• P's next state: The figure shows, by the states of the logic gates, that the next state of P will be 1 (ON).

Scientific Validity

• Valid computational procedure: The depicted process of using the current state and the truth table information to compute the next state of a unit is a valid and standard computational procedure.
• Verifiable computation process: The figure, combined with the supplementary text, provides a verifiable account of the computation performed at this update step.

Communication

• Clear visual representation, but relies on prior knowledge: The figure depicts the state of the four-bit computer at 'Update 2' of the simulation, specifically showing the computation of the next state of unit P. The consistent use of color-coding (black/white for ON/OFF) aids in visualizing the state. However, understanding the process requires familiarity with the computer's architecture and the sequence of operations. The caption could benefit by explicitly mentioning the ongoing simulation of the PQRS system.
• Concise caption: The caption concisely states the key operation occurring at this update step.

Figure 16: Update 3: The next state of Q is computed.

Figure/Table Image (Page 44)

First Reference in Text

Not explicitly referenced in main text

Description

• Computation of Q's Next State and Propagation of P's next state: Figure 16 shows the four-bit computer during the third step ('Update 3') of simulating a single transition of the PQRS system. At this stage, the computer computes the next state of unit Q, mirroring the process used for unit P in the previous step (Figure 15). The output of the multiplexer (MO), which represents the next state of P (which was calculated in the previous step), is now propagated one step forward in the circuit. The computer is now using the current state of PQRS and the pre-loaded truth table information to determine what the next state of Q should be, based on the rules of the PQRS system. The instruction register now loads R's truth table.

Scientific Validity

• Valid computational procedure: The depicted process—computing the next state of a unit based on the current state and pre-loaded truth table information—is a valid and standard computational procedure for simulating system dynamics.
• Verifiable computation process: The figure, in conjunction with the supplementary text, provides a verifiable account of the computation performed at this update step.

Communication

• Clear visual representation, but relies on prior knowledge: The figure depicts the state of the four-bit computer at 'Update 3' of the simulation, specifically showing the computation of the next state of unit Q. The consistent use of color-coding (black/white for ON/OFF) aids in visualizing the state. However, understanding the process requires familiarity with the computer's architecture and the sequence of operations, as well as the previous steps. The caption could benefit by explicitly mentioning the ongoing simulation of the PQRS system.
• Concise caption: The caption concisely indicates the operation at this step.

Figure 17: Update 4: The next state of R is computed.

Figure/Table Image (Page 45)

First Reference in Text

Not explicitly referenced in main text

Description

• Computation of R's Next State and Propagation of P and Q's next states: Figure 17 shows the four-bit computer during the fourth step ('Update 4') of simulating a single transition of the PQRS system. At this stage, the computer computes the next state of unit R, following the same process used for units P and Q in the previous steps. The next state of P, which was computed in step 2 and propagated forward in step 3, is now represented by the state of a buffer unit (B3). The next state of Q is represented by the output of the multiplexer (MO). The computer is now using the current state of PQRS and the pre-loaded truth table information to determine what the next state of R should be. The instruction register now loads S's truth table.

Scientific Validity

• Valid computational procedure: The depicted process—computing the next state of a unit based on the current state and pre-loaded truth table information, and propagating previous results—is a valid and standard computational procedure for simulating system dynamics.
• Verifiable computation process: The figure, combined with the supplementary text, provides a verifiable account of the computation performed at this update step.

Communication

• Clear visual representation, but relies on prior knowledge: The figure shows the four-bit computer at 'Update 4' of the simulation, focusing on the computation of the next state of unit R. The color-coding (black/white for ON/OFF) consistently represents the state of each unit. However, understanding the specific operations and their sequence necessitates familiarity with the computer's architecture, as well as the previous steps in the simulation. The caption could be improved by explicitly mentioning the ongoing simulation of the PQRS system.
• Concise caption: The caption concisely describes the main action occurring at this update step.

Figure 18: Update 5: The next state of S is computed.

Figure/Table Image (Page 46)

First Reference in Text

Not explicitly referenced in main text

Description

• Computation of S's next state and propagation of prior results: Figure 18 shows the four-bit computer during the fifth step ('Update 5') of simulating a single transition of the PQRS system. At this stage, the computer computes the next state of unit S. The next states of P, Q, and R, which were computed in earlier steps, are now propagating through the buffer units (B1, B2, and B3). The computer is now using the current state of PQRS and the truth table to calculate the next state of S.

Scientific Validity

• Valid computational procedure: The process shown—computing the next state of a unit based on current state and truth table information—is a standard and valid computational procedure.
• Verifiable computation process: The figure, along with the supplementary text, gives a verifiable account of the calculations performed at this step.

Communication

• Clear visual representation, but requires prior knowledge: The figure shows the four-bit computer at 'Update 5' of the simulation, focusing on the computation of the next state of unit S. The color-coding (black/white for ON/OFF) consistently represents the state. However, to fully grasp the operations and their sequence, prior knowledge of the computer's architecture and the preceding simulation steps is required. The caption would be more informative if it explicitly stated that this update is part of the ongoing PQRS simulation.
• Concise caption: The caption concisely describes the main action taking place at this update step.

Figure 19: Update 6: Each simulated unit's next state arrives at its respective...

Full Caption

Figure 19: Update 6: Each simulated unit's next state arrives at its respective data register.

Figure/Table Image (Page 47)

First Reference in Text

Not explicitly referenced in main text

Description

• Arrival of Next States at Data Registers: Figure 19 shows the four-bit computer during the sixth step ('Update 6') of simulating a single transition of the PQRS system. At this stage, the next states of all four units of the PQRS system (P, Q, R, and S) —which were computed in the previous steps—have propagated through the circuit and are now present at the inputs of their respective data registers. Specifically, these next states are present at the inputs of each data register's RXOR unit (i.e., B₁ to RXOR, B₂ to RXOR, B₃ to RXOR, and MO to RXOR).

Scientific Validity

• Necessary step in the simulation: The depicted process—the arrival of the computed next states at the data registers—is a necessary and valid step in the simulation process. It sets the stage for updating the stored state of the simulated system.
• Verifiable state of the computer: The figure, along with the supplementary text, provides a verifiable account of the state of the computer at this update step.

Communication

• Clear visual representation, but requires prior knowledge: The figure depicts the four-bit computer at 'Update 6' of the simulation. The color-coding (black/white for ON/OFF) consistently represents the state of each unit. However, understanding the illustrated process requires familiarity with the computer's architecture and the previous simulation steps. The caption would be more informative if it explicitly stated that this update is a step within the PQRS system simulation.
• Concise caption: The caption concisely describes the key event occurring at this update step.

Figure 20: Update 7: The clock enables each register.

Figure/Table Image (Page 48)

First Reference in Text

Not explicitly referenced in main text

Description

• Clock Enabling Registers and XOR Comparison: Figure 20 shows the four-bit computer during the seventh step ('Update 7') of simulating a single transition of the PQRS system. At this stage, a specific part of the clock circuit (unit A₂) turns ON. This signal 'enables' each of the data registers. Enabling a register means preparing it to update its stored value based on the new inputs it has received. Each register's lower XOR unit receives two inputs: one from its upper XOR, which holds the current state of the simulated unit (P, Q, R, or S), and one from another unit that holds the computed next state of that unit. Each XOR gate performs a comparison of these two inputs, to determine if the register needs to change its state or not.

Scientific Validity

• Fundamental operation in digital circuits: The depicted process—using a clock signal to enable registers and prepare them for updating—is a fundamental and valid operation in digital circuits and computer architecture.
• Verifiable computer state: The figure, along with the supplementary text, provides a verifiable account of the computer's state at this update step.

Communication

• Clear visual representation, but relies on prior knowledge: The figure depicts the four-bit computer at 'Update 7' of the simulation, focusing on the clock signal enabling the data registers. The color-coding (black/white for ON/OFF) consistently represents the state. However, understanding the process requires familiarity with the computer's architecture and prior simulation steps. The caption would be more informative if it explicitly mentioned the ongoing simulation of the PQRS system and the purpose of enabling the registers.
• Concise caption: The caption concisely describes the key action at this update step.

Figure 21: Update 8: Each register's toggle signal arrives at its output.

Figure/Table Image (Page 49)

First Reference in Text

Not explicitly referenced in main text

Description

• Propagation of Toggle Signals: Figure 21 shows the four-bit computer during the eighth step ('Update 8') of simulating a single transition of the PQRS system. During the previous step (Figure 20), the clock signal enabled each of the data registers, preparing them to update their stored values. Now, at Update 8, each register's 'toggle signal' is allowed to influence its stored value. The 'toggle signal' is the output of the AND gate within each register (RAND, RAND, RAND, RAND). This signal indicates whether the register's stored value needs to change to reflect the newly computed next state of the corresponding PQRS unit (P, Q, R, or S). If the toggle signal is ON (1), it means the current state and the next state are different, and the stored value should be flipped. If the toggle signal is OFF (0), it means the current state and the next state are the same, and the stored value should remain unchanged.

Scientific Validity

• Necessary step in the simulation: The depicted process—allowing the 'toggle signal' to influence the stored value in the registers—is a necessary and valid step in implementing the simulation. It ensures that the registers update correctly based on the computed next states.
• Verifiable computer state and operation: The figure, along with the supplementary text, provides a verifiable account of the computer's state and operation at this update step.

Communication

• Clear visual representation, but requires prior knowledge: The figure shows the four-bit computer at 'Update 8' of the simulation. The color-coding (black/white for ON/OFF) consistently represents the states of each unit. However, fully understanding the illustrated process and its significance requires familiarity with the computer's architecture and the preceding steps in the simulation. The caption accurately describes what happens in this step, but could mention that this step is part of the PQRS simulation.
• Concise caption: The caption concisely describes the key action taking place at this update step.

Figure 22: Update 9: The registers adopt the next state of PQRS, and the cycle...

Full Caption

Figure 22: Update 9: The registers adopt the next state of PQRS, and the cycle repeats.

Figure/Table Image (Page 50)

First Reference in Text

Not explicitly referenced in main text

Description

• Registers Update to the Next State of PQRS: Figure 22 depicts the four-bit computer during the ninth step ('Update 9') of simulating a single transition of the PQRS system. This is the final step of a single simulation iteration. At this point, the data registers, which store the current state of the simulated PQRS system, update their values to reflect the newly computed next state. The units RSIM, RSIM, RSIM, and RSIM simultaneously adopt the values that were determined by the previous computations. After this update, the computer is ready to begin the next cycle, simulating the next state transition of PQRS, starting again from 'Update 1'. This completes one full cycle of the simulation.
• Specific state change: The figure shows the states of RSIM, RSIM, RSIM, and RSIM changing to 1110. This corresponds to the next state of PQRS, given the example initial state of 0101.

Scientific Validity

• Crucial final step in simulation: The updating of the registers with the newly computed next state is the final, crucial step in correctly simulating the dynamics of the PQRS system. This ensures that the computer's internal representation of PQRS is updated according to the defined transition rules.
• Verifiable account of the simulation cycle: The figure, together with the supplementary text, provides a verifiable account of the computer's state and operation at this update step, completing the description of a full simulation cycle.

Communication

• Clear visual representation, but relies on prior knowledge: The figure shows the four-bit computer at 'Update 9' of the simulation, which completes one full cycle of simulating a single state transition of the PQRS system. The color-coding (black/white for ON/OFF) is consistent, allowing for visual tracking of the state changes. However, understanding this final step requires familiarity with all the preceding steps and the overall architecture of the computer. The caption clearly states the two key events: the update of the registers and the beginning of a new cycle.
• Indication of cyclical process: The figure and caption indicate that the simulation process is cyclical and can continue indefinitely.

Results

Key Aspects

• Functional Equivalence Does Not Imply Cause-Effect Structure Equivalence: The section presents the core finding that functional equivalence between the target system (PQRS) and the simulating computer does not imply equivalence of their cause-effect structures at the level of micro-units. This establishes the initial dissociation between function and phenomenology, a central theme of the paper. This finding directly addresses the core research question and sets the stage for further analysis.
• Description of the Target System (PQRS) and its Cause-Effect Structure: The section introduces the target system, PQRS, a simple system of four binary units with defined dynamics. It describes PQRS's state transitions, transition probability matrix (TPM), and its status as a complex according to Integrated Information Theory (IIT), with a calculated system integrated information (φs) of 1.51 ibits. The cause-effect structure of PQRS in a specific state (0101) is detailed, consisting of 13 distinctions and 8184 relations, with a structure integrated information (Φ) of 391.25 ibits. This provides a concrete example for comparison with the simulating computer.
• Description of the Simulating Computer: The section describes a basic four-bit computer designed to simulate PQRS indefinitely. The computer's architecture, including its clock, program memory, data registers, and multiplexer, is explained. The initialization procedure for simulating PQRS is detailed, demonstrating the functional equivalence between the computer and the target system (modulo eight updates). This establishes the computational setup for comparing the two systems.
• Fragmentation of the Computer into Multiple Complexes: Applying IIT's causal powers analysis, the section demonstrates that the computer, unlike PQRS, has a system integrated information (φs) of 0 ibits, indicating that it is not a single integrated complex. Instead, the computer fragments into 24 smaller, disjoint complexes, each with a significantly simpler cause-effect structure (Φ ≤ 6 ibits) compared to PQRS. This highlights the fundamental difference in their intrinsic causal organization despite functional equivalence.
• Failure of Macroing to Replicate the Target System's Cause-Effect Structure: The section addresses the possibility of replicating PQRS's cause-effect structure by treating the computer's units at macro grains (macroing). It explains that, according to IIT, macro units must also satisfy the postulates of physical existence, including integration and exclusion. The section concludes that no function-relevant macroing of the computer, consistent with IIT's postulates, can replicate the cause-effect structure of PQRS. This reinforces the dissociation between function and phenomenology, even at coarser levels of analysis.
• Insensitivity to Feedback Connections: The section demonstrates that the computer's lack of integration, and thus its failure to replicate PQRS's cause-effect structure, is not simply due to a lack of feedback connections. Even with added feedback, the computer remains reducible and fragments into the same small complexes. This highlights that the computer's architecture, with its sparse connectivity and bottlenecks, fundamentally limits its intrinsic causal integration.
• Dissociation of Computer's Cause-Effect Structures from Simulated Systems: The section shows how the cause-effect structures of the computer are insensitive to which system is being simulated. The computer fragments into the same small complexes with trivial cause-effect structures, regardless of whether it simulates PQRS or another four-unit system (WXYZ, implementing Wolfram's Rule 110). The cause-effect structures specified by the computer are therefore dissociated from the cause-effect structures of the systems it simulates.
• Inductive Extension to Larger Computers: The section describes how the four-bit computer can be extended to simulate larger systems, and by induction, it can be shown that the cause-effect structures specified by subsystems within the computer will not change qualitatively at the micro grain. In contrast, the cause-effect structures of the simulated target systems may grow double-exponentially in size and richness, resulting in an increasing phenomenal dissociation between the computer and the systems it simulates.

Strengths

• Clear Presentation of Main Findings

  The section clearly presents the main findings: functional equivalence does not imply equivalence of cause-effect structures at the micro-unit level, and no function-relevant macroing of the computer replicates the target system's cause-effect structure.

  "We first demonstrate that functional equivalence does not imply equivalence of cause–effect structures at the grain of micro units. We then demonstrate that there is no function-relevant macroing of the computer compliant with IIT’s postulates that replicates the cause–effect structure of the target system." (Page 3)
• Introduction of Target System (PQRS)

  The section introduces a concrete target system (PQRS) and describes its cause-effect structure, providing a specific example for comparison with the computer.

  "A target system (PQRS) and the cause–effect structure it specifies. Fig. 1A shows PQRS, the target system to be simulated, comprising a set of four binary units whose dynamics are defined by a truth table or, more generally, a transition probability matrix (Fig. 1B–C)." (Page 3)
• Description of the Simulating Computer

  The section describes a computer capable of simulating PQRS and explains its architecture and initialization procedure. This provides a clear contrast to the target system.

  "A computer constituted of 117 units that can simulate PQRS indefinitely. Fig. 2A shows a basic four-bit computer capable of simulating PQRS." (Page 3)
• Application of IIT's Analysis to the Computer

  The section applies IIT's causal powers analysis to the computer and demonstrates that it fragments into multiple small complexes, none of which replicate the target system's cause-effect structure.

  "The computer fragments into multiple complexes, none of which specifies a cause–effect structure identical to that of PQRS." (Page 3)
• Addressing Macroing

  The section addresses the issue of macroing and demonstrates that no function-relevant macroing of the computer can replicate the target system's cause-effect structure.

  "The computer’s failure to replicate the cause–effect structure of PQRS cannot be rescued by treating its units or states at macro grains." (Page 3)
• Effective Use of Figures

  The section effectively uses figures to illustrate the target system, the computer, and their respective cause-effect structures. These figures aid in understanding the complex concepts and comparisons.

  "Figure 1: A target system for simulation." (Page 4)

Suggestions for Improvement

• Summarize Significance of Findings

  This medium-impact improvement would strengthen the Results section by providing a clearer and more direct connection to the broader implications of the study. While the section presents the findings, explicitly stating their significance for the overall argument would enhance the reader's understanding of their importance. The Results section's role is to present the findings, and connecting these to the broader context strengthens its impact.

  "We then demonstrate that there is no function-relevant macroing of the computer compliant with IIT’s postulates that replicates the cause–effect structure of the target system." (Page 3)

  Implementation: Add a concluding paragraph summarizing the significance of the findings. For example: "These results demonstrate a fundamental dissociation between functional and phenomenal equivalence in a simple computational system. The fact that the computer, despite perfectly simulating the target system's behavior, fails to replicate its cause-effect structure at both the micro and macro levels has significant implications for the debate on artificial consciousness. It suggests that achieving artificial consciousness may require more than simply replicating the computational functions of the brain."

• Preview the Extension to Turing-Completeness

  This low-impact improvement would enhance the Results section's clarity and provide a smoother transition to the subsequent discussion of Turing-completeness. The Results section should flow logically, and a brief preview of the next step helps guide the reader.

  "Finally, we extend the four-bit computer to be Turing-complete and demonstrate that the previous results do not depend on the complexity of the function being implemented." (Page 3)

  Implementation: Add a sentence or two at the end of the section briefly outlining the next step in the analysis. For example: "Having demonstrated the dissociation of functional and phenomenal equivalence in this simple system, we now extend these results to a Turing-complete version of the computer to show that this conclusion is independent of the complexity of the simulated system's function."

• Define Key Terminology (Cause-Effect Structure)

  This low-impact improvement would enhance the Results section's clarity and accessibility for readers unfamiliar with the specific terminology of IIT. While the section uses technical terms, providing brief, parenthetical definitions would enhance its readability. The Results section should be understandable to a broad scientific audience, and clarifying key terms helps achieve this.

  "The computer fragments into multiple complexes, none of which specifies a cause–effect structure identical to that of PQRS." (Page 3)

  Implementation: Provide a brief, parenthetical definition of "cause-effect structure" when first introduced. For example: "The computer fragments into multiple complexes, none of which specifies a cause–effect structure (the complete set of a system's causal relationships) identical to that of PQRS."

Non-Text Elements

Figure 1. A target system for simulation.

Figure/Table Image (Page 4)

First Reference in Text

Fig. 1A shows PQRS, the target system to be sim- ulated, comprising a set of four binary units whose dynamics are defined by a truth table or, more generally, a transition probability matrix (Fig. 1B-C).

Description

• System Composition and Dynamics: Figure 1 presents a system named PQRS, designed for simulation. This system is made up of four interconnected units, and each of these units can be in one of two states, represented as binary values (0 or 1). Think of each unit like a light switch that can be either OFF (0) or ON (1). The way these units interact and change their states over time is defined by a truth table, or more generally, a transition probability matrix. A truth table is a simple way to show all possible combinations of inputs (the states of the four units) and their corresponding outputs (the next states of the units). A transition probability matrix, on the other hand, is a table that contains the probabilities of transitioning from one state to another. This matrix is helpful when the change of state is not definite but probabilistic. The figure consists of multiple parts (A, B, C) to fully describe the system.

Scientific Validity

• Methodological approach: The introduction of the target system (PQRS) is methodologically sound. The use of binary units and representation via a truth table/transition probability matrix are standard and valid approaches for modeling system dynamics in computational neuroscience and related fields.
• Foundation for simulation: The figure provides a clear foundation for subsequent simulations. It establishes the basic parameters and rules governing the system, which is critical for the reproducibility and validity of the simulation results.

Communication

• Integration of figure panels: The figure effectively introduces the target system (PQRS) and its components, providing a clear visual representation and explanation of its dynamics. However, splitting the figure into multiple panels (1A, 1B, 1C) makes it slightly harder to follow as a single cohesive unit. Combining these or better integrating the information could improve the flow.
• Clarity of system dynamics: The use of a truth table and transition probability matrix is well-explained and appropriate for the intended audience, enhancing the overall clarity of how the system's dynamics are defined.