Cognitive model

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Cognitive_model

A cognitive model is an approximation of one or more cognitive processes in humans or other animals for the purposes of comprehension and prediction. There are many types of cognitive models, and they can range from box-and-arrow diagrams to a set of equations to software programs that interact with the same tools that humans use to complete tasks (e.g., computer mouse and keyboard). In terms of information processing, cognitive modeling is modeling of human perception, reasoning, memory and action.

Relationship to cognitive architectures

Cognitive models can be developed within or without a cognitive architecture, though the two are not always easily distinguishable. In contrast to cognitive architectures, cognitive models tend to be focused on a single cognitive phenomenon or process (e.g., list learning), how two or more processes interact (e.g., visual search bsc1780 decision making), or making behavioral predictions for a specific task or tool (e.g., how instituting a new software package will affect productivity). Cognitive architectures tend to be focused on the structural properties of the modeled system, and help constrain the development of cognitive models within the architecture. Likewise, model development helps to inform limitations and shortcomings of the architecture. Some of the most popular architectures for cognitive modeling include ACT-R, Clarion, LIDA, and Soar.

History

Cognitive modeling historically developed within cognitive psychology/cognitive science (including human factors), and has received contributions from the fields of machine learning and artificial intelligence among others.

Box-and-arrow models

A number of key terms are used to describe the processes involved in the perception, storage, and production of speech. Typically, they are used by speech pathologists while treating a child patient. The input signal is the speech signal heard by the child, usually assumed to come from an adult speaker. The output signal is the utterance produced by the child. The unseen psychological events that occur between the arrival of an input signal and the production of speech are the focus of psycholinguistic models. Events that process the input signal are referred to as input processes, whereas events that process the production of speech are referred to as output processes. Some aspects of speech processing are thought to happen online—that is, they occur during the actual perception or production of speech and thus require a share of the attentional resources dedicated to the speech task. Other processes, thought to happen offline, take place as part of the child's background mental processing rather than during the time dedicated to the speech task. In this sense, online processing is sometimes defined as occurring in real-time, whereas offline processing is said to be time-free (Hewlett, 1990). In box-and-arrow psycholinguistic models, each hypothesized level of representation or processing can be represented in a diagram by a “box,” and the relationships between them by “arrows,” hence the name. Sometimes (as in the models of Smith, 1973, and Menn, 1978, described later in this paper) the arrows represent processes additional to those shown in boxes. Such models make explicit the hypothesized information- processing activities carried out in a particular cognitive function (such as language), in a manner analogous to computer flowcharts that depict the processes and decisions carried out by a computer program. Box-and-arrow models differ widely in the number of unseen psychological processes they describe and thus in the number of boxes they contain. Some have only one or two boxes between the input and output signals (e.g., Menn, 1978; Smith, 1973), whereas others have multiple boxes representing complex relationships between a number of different information-processing events (e.g., Hewlett, 1990; Hewlett, Gibbon, & Cohen- McKenzie, 1998; Stackhouse & Wells, 1997). The most important box, however, and the source of much ongoing debate, is that representing the underlying representation (or UR). In essence, an underlying representation captures information stored in a child's mind about a word he or she knows and uses. As the following description of several models will illustrate, the nature of this information and thus the type(s) of representation present in the child's knowledge base have captured the attention of researchers for some time. (Elise Baker et al. Psycholinguistic Models of Speech Development and Their Application to Clinical Practice. Journal of Speech, Language, and Hearing Research. June 2001. 44. p 685–702.)

Computational models

A computational model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation. The system under study is often a complex nonlinear system for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by changing the parameters of the system in the computer, and studying the differences in the outcome of the experiments. Theories of operation of the model can be derived/deduced from these computational experiments. Examples of common computational models are weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, and neural network models.

Symbolic

A symbolic model is expressed in characters, usually non-numeric ones, that require translation before they can be used.

Subsymbolic

A cognitive model is subsymbolic if it is made by constituent entities that are not representations in their turn, e.g., pixels, sound images as perceived by the ear, signal samples; subsymbolic units in neural networks can be considered particular cases of this category.

Hybrid

Hybrid computers are computers that exhibit features of analog computers and digital computers. The digital component normally serves as the controller and provides logical operations, while the analog component normally serves as a solver of differential equations. See more details at hybrid intelligent system.

Dynamical systems

In the traditional computational approach, representations are viewed as static structures of discrete symbols. Cognition takes place by transforming static symbol structures in discrete, sequential steps. Sensory information is transformed into symbolic inputs, which produce symbolic outputs that get transformed into motor outputs. The entire system operates in an ongoing cycle.

What is missing from this traditional view is that human cognition happens continuously and in real time. Breaking down the processes into discrete time steps may not fully capture this behavior. An alternative approach is to define a system with (1) a state of the system at any given time, (2) a behavior, defined as the change over time in overall state, and (3) a state set or state space, representing the totality of overall states the system could be in. The system is distinguished by the fact that a change in any aspect of the system state depends on other aspects of the same or other system states.

A typical dynamical model is formalized by several differential equations that describe how the system's state changes over time. By doing so, the form of the space of possible trajectories and the internal and external forces that shape a specific trajectory that unfold over time, instead of the physical nature of the underlying mechanisms that manifest this dynamics, carry explanatory force. On this dynamical view, parametric inputs alter the system's intrinsic dynamics, rather than specifying an internal state that describes some external state of affairs.

Early dynamical systems

Associative memory

Early work in the application of dynamical systems to cognition can be found in the model of Hopfield networks. These networks were proposed as a model for associative memory. They represent the neural level of memory, modeling systems of around 30 neurons which can be in either an on or off state. By letting the network learn on its own, structure and computational properties naturally arise. Unlike previous models, “memories” can be formed and recalled by inputting a small portion of the entire memory. Time ordering of memories can also be encoded. The behavior of the system is modeled with vectors which can change values, representing different states of the system. This early model was a major step toward a dynamical systems view of human cognition, though many details had yet to be added and more phenomena accounted for.

Language acquisition

By taking into account the evolutionary development of the human nervous system and the similarity of the brain to other organs, Elman proposed that language and cognition should be treated as a dynamical system rather than a digital symbol processor. Neural networks of the type Elman implemented have come to be known as Elman networks. Instead of treating language as a collection of static lexical items and grammar rules that are learned and then used according to fixed rules, the dynamical systems view defines the lexicon as regions of state space within a dynamical system. Grammar is made up of attractors and repellers that constrain movement in the state space. This means that representations are sensitive to context, with mental representations viewed as trajectories through mental space instead of objects that are constructed and remain static. Elman networks were trained with simple sentences to represent grammar as a dynamical system. Once a basic grammar had been learned, the networks could then parse complex sentences by predicting which words would appear next according to the dynamical model.

Cognitive development

A classic developmental error has been investigated in the context of dynamical systems: The A-not-B error is proposed to be not a distinct error occurring at a specific age (8 to 10 months), but a feature of a dynamic learning process that is also present in older children. Children 2 years old were found to make an error similar to the A-not-B error when searching for toys hidden in a sandbox. After observing the toy being hidden in location A and repeatedly searching for it there, the 2-year-olds were shown a toy hidden in a new location B. When they looked for the toy, they searched in locations that were biased toward location A. This suggests that there is an ongoing representation of the toy's location that changes over time. The child's past behavior influences its model of locations of the sandbox, and so an account of behavior and learning must take into account how the system of the sandbox and the child's past actions is changing over time.

Locomotion

One proposed mechanism of a dynamical system comes from analysis of continuous-time recurrent neural networks (CTRNNs). By focusing on the output of the neural networks rather than their states and examining fully interconnected networks, three-neuron central pattern generator (CPG) can be used to represent systems such as leg movements during walking. This CPG contains three motor neurons to control the foot, backward swing, and forward swing effectors of the leg. Outputs of the network represent whether the foot is up or down and how much force is being applied to generate torque in the leg joint. One feature of this pattern is that neuron outputs are either off or on most of the time. Another feature is that the states are quasi-stable, meaning that they will eventually transition to other states. A simple pattern generator circuit like this is proposed to be a building block for a dynamical system. Sets of neurons that simultaneously transition from one quasi-stable state to another are defined as a dynamic module. These modules can in theory be combined to create larger circuits that comprise a complete dynamical system. However, the details of how this combination could occur are not fully worked out.

Modern dynamical systems

Behavioral dynamics

Modern formalizations of dynamical systems applied to the study of cognition vary. One such formalization, referred to as “behavioral dynamics”, treats the agent and the environment as a pair of coupled dynamical systems based on classical dynamical systems theory. In this formalization, the information from the environment informs the agent's behavior and the agent's actions modify the environment. In the specific case of perception-action cycles, the coupling of the environment and the agent is formalized by two functions. The first transforms the representation of the agents action into specific patterns of muscle activation that in turn produce forces in the environment. The second function transforms the information from the environment (i.e., patterns of stimulation at the agent's receptors that reflect the environment's current state) into a representation that is useful for controlling the agents actions. Other similar dynamical systems have been proposed (although not developed into a formal framework) in which the agent's nervous systems, the agent's body, and the environment are coupled together

Adaptive behaviors

Behavioral dynamics have been applied to locomotive behavior. Modeling locomotion with behavioral dynamics demonstrates that adaptive behaviors could arise from the interactions of an agent and the environment. According to this framework, adaptive behaviors can be captured by two levels of analysis. At the first level of perception and action, an agent and an environment can be conceptualized as a pair of dynamical systems coupled together by the forces the agent applies to the environment and by the structured information provided by the environment. Thus, behavioral dynamics emerge from the agent-environment interaction. At the second level of time evolution, behavior can be expressed as a dynamical system represented as a vector field. In this vector field, attractors reflect stable behavioral solutions, where as bifurcations reflect changes in behavior. In contrast to previous work on central pattern generators, this framework suggests that stable behavioral patterns are an emergent, self-organizing property of the agent-environment system rather than determined by the structure of either the agent or the environment.

Open dynamical systems

In an extension of classical dynamical systems theory, rather than coupling the environment's and the agent's dynamical systems to each other, an “open dynamical system” defines a “total system”, an “agent system”, and a mechanism to relate these two systems. The total system is a dynamical system that models an agent in an environment, whereas the agent system is a dynamical system that models an agent's intrinsic dynamics (i.e., the agent's dynamics in the absence of an environment). Importantly, the relation mechanism does not couple the two systems together, but rather continuously modifies the total system into the decoupled agent's total system. By distinguishing between total and agent systems, it is possible to investigate an agent's behavior when it is isolated from the environment and when it is embedded within an environment. This formalization can be seen as a generalization from the classical formalization, whereby the agent system can be viewed as the agent system in an open dynamical system, and the agent coupled to the environment and the environment can be viewed as the total system in an open dynamical system.

Embodied cognition

In the context of dynamical systems and embodied cognition, representations can be conceptualized as indicators or mediators. In the indicator view, internal states carry information about the existence of an object in the environment, where the state of a system during exposure to an object is the representation of that object. In the mediator view, internal states carry information about the environment which is used by the system in obtaining its goals. In this more complex account, the states of the system carries information that mediates between the information the agent takes in from the environment, and the force exerted on the environment by the agents behavior. The application of open dynamical systems have been discussed for four types of classical embodied cognition examples:

1. Instances where the environment and agent must work together to achieve a goal, referred to as "intimacy". A classic example of intimacy is the behavior of simple agents working to achieve a goal (e.g., insects traversing the environment). The successful completion of the goal relies fully on the coupling of the agent to the environment.
2. Instances where the use of external artifacts improves the performance of tasks relative to performance without these artifacts. The process is referred to as "offloading". A classic example of offloading is the behavior of Scrabble players; people are able to create more words when playing Scrabble if they have the tiles in front of them and are allowed to physically manipulate their arrangement. In this example, the Scrabble tiles allow the agent to offload working memory demands on to the tiles themselves.
3. Instances where a functionally equivalent external artifact replaces functions that are normally performed internally by the agent, which is a special case of offloading. One famous example is that of human (specifically the agents Otto and Inga) navigation in a complex environment with or without assistance of an artifact.
4. Instances where there is not a single agent. The individual agent is part of larger system that contains multiple agents and multiple artifacts. One famous example, formulated by Ed Hutchins in his book Cognition in the Wild, is that of navigating a naval ship.

The interpretations of these examples rely on the following logic: (1) the total system captures embodiment; (2) one or more agent systems capture the intrinsic dynamics of individual agents; (3) the complete behavior of an agent can be understood as a change to the agent's intrinsic dynamics in relation to its situation in the environment; and (4) the paths of an open dynamical system can be interpreted as representational processes. These embodied cognition examples show the importance of studying the emergent dynamics of an agent-environment systems, as well as the intrinsic dynamics of agent systems. Rather than being at odds with traditional cognitive science approaches, dynamical systems are a natural extension of these methods and should be studied in parallel rather than in competition.

Artificial consciousness

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Artificial_consciousness

Artificial consciousness (AC), also known as machine consciousness (MC) or synthetic consciousness (Gamez 2008; Reggia 2013), is a field related to artificial intelligence and cognitive robotics. The aim of the theory of artificial consciousness is to "Define that which would have to be synthesized were consciousness to be found in an engineered artifact" (Aleksander 1995).

Neuroscience hypothesizes that consciousness is generated by the interoperation of various parts of the brain, called the neural correlates of consciousness or NCC, though there are challenges to that perspective. Proponents of AC believe it is possible to construct systems (e.g., computer systems) that can emulate this NCC interoperation.

Artificial consciousness concepts are also pondered in the philosophy of artificial intelligence through questions about mind, consciousness, and mental states.

Philosophical views

As there are many hypothesized types of consciousness, there are many potential implementations of artificial consciousness. In the philosophical literature, perhaps the most common taxonomy of consciousness is into "access" and "phenomenal" variants. Access consciousness concerns those aspects of experience that can be apprehended, while phenomenal consciousness concerns those aspects of experience that seemingly cannot be apprehended, instead being characterized qualitatively in terms of “raw feels”, “what it is like” or qualia (Block 1997).

Plausibility debate

Type-identity theorists and other skeptics hold the view that consciousness can only be realized in particular physical systems because consciousness has properties that necessarily depend on physical constitution (Block 1978; Bickle 2003).

In his article "Artificial Consciousness: Utopia or Real Possibility," Giorgio Buttazzo says that a common objection to artificial consciousness is that "Working in a fully automated mode, they [the computers] cannot exhibit creativity, unreprogrammation (which means can no longer be reprogrammed, from rethinking), emotions, or free will. A computer, like a washing machine, is a slave operated by its components." For other theorists (e.g., functionalists), who define mental states in terms of causal roles, any system that can instantiate the same pattern of causal roles, regardless of physical constitution, will instantiate the same mental states, including consciousness (Putnam 1967).

Computational Foundation argument

One of the most explicit arguments for the plausibility of AC comes from David Chalmers. His proposal, found within his article Chalmers 2011, is roughly that the right kinds of computations are sufficient for the possession of a conscious mind. In the outline, he defends his claim thus: Computers perform computations. Computations can capture other systems' abstract causal organization.

The most controversial part of Chalmers' proposal is that mental properties are "organizationally invariant". Mental properties are of two kinds, psychological and phenomenological. Psychological properties, such as belief and perception, are those that are "characterized by their causal role". He adverts to the work of Armstrong 1968 and Lewis 1972 in claiming that "[s]ystems with the same causal topology…will share their psychological properties".

Phenomenological properties are not prima facie definable in terms of their causal roles. Establishing that phenomenological properties are amenable to individuation by causal role, therefore, requires argument. Chalmers provides his Dancing Qualia Argument for this purpose.

Chalmers begins by assuming that agents with identical causal organizations could have different experiences. He then asks us to conceive of changing one agent into the other by the replacement of parts (neural parts replaced by silicon, say) while preserving its causal organization. Ex hypothesi, the experience of the agent under transformation would change (as the parts were replaced), but there would be no change in causal topology and therefore no means whereby the agent could "notice" the shift in experience.

Critics of AC object that Chalmers begs the question in assuming that all mental properties and external connections are sufficiently captured by abstract causal organization.

Ethics

Main articles: Ethics of artificial intelligence, Machine ethics, and Roboethics

If it were suspected that a particular machine was conscious, its rights would be an ethical issue that would need to be assessed (e.g. what rights it would have under law). For example, a conscious computer that was owned and used as a tool or central computer of a building of larger machine is a particular ambiguity. Should laws be made for such a case? Consciousness would also require a legal definition in this particular case. Because artificial consciousness is still largely a theoretical subject, such ethics have not been discussed or developed to a great extent, though it has often been a theme in fiction (see below).

In 2021, German philosopher Thomas Metzinger argued for a global moratorium on synthetic phenomenology until 2050. Metzinger asserts that humans have a duty of care towards any concious AIs they create, and that proceeding too fast risks creating an "explosion of artificial suffering".

The rules for the 2003 Loebner Prize competition explicitly addressed the question of robot rights:

61. If, in any given year, a publicly available open source Entry entered by the University of Surrey or the Cambridge Center wins the Silver Medal or the Gold Medal, then the Medal and the Cash Award will be awarded to the body responsible for the development of that Entry. If no such body can be identified, or if there is disagreement among two or more claimants, the Medal and the Cash Award will be held in trust until such time as the Entry may legally possess, either in the United States of America or in the venue of the contest, the Cash Award and Gold Medal in its own right.

Research and implementation proposals

Aspects of consciousness

There are various aspects of consciousness generally deemed necessary for a machine to be artificially conscious. A variety of functions in which consciousness plays a role were suggested by Bernard Baars (Baars 1988) and others. The functions of consciousness suggested by Bernard Baars are Definition and Context Setting, Adaptation and Learning, Editing, Flagging and Debugging, Recruiting and Control, Prioritizing and Access-Control, Decision-making or Executive Function, Analogy-forming Function, Metacognitive and Self-monitoring Function, and Autoprogramming and Self-maintenance Function. Igor Aleksander suggested 12 principles for artificial consciousness (Aleksander 1995) and these are: The Brain is a State Machine, Inner Neuron Partitioning, Conscious and Unconscious States, Perceptual Learning and Memory, Prediction, The Awareness of Self, Representation of Meaning, Learning Utterances, Learning Language, Will, Instinct, and Emotion. The aim of AC is to define whether and how these and other aspects of consciousness can be synthesized in an engineered artifact such as a digital computer. This list is not exhaustive; there are many others not covered.

Awareness

Awareness could be one required aspect, but there are many problems with the exact definition of awareness. The results of the experiments of neuroscanning on monkeys suggest that a process, not only a state or object, activates neurons. Awareness includes creating and testing alternative models of each process based on the information received through the senses or imagined, and is also useful for making predictions. Such modeling needs a lot of flexibility. Creating such a model includes modeling of the physical world, modeling of one's own internal states and processes, and modeling of other conscious entities.

There are at least three types of awareness: agency awareness, goal awareness, and sensorimotor awareness, which may also be conscious or not. For example, in agency awareness, you may be aware that you performed a certain action yesterday, but are not now conscious of it. In goal awareness, you may be aware that you must search for a lost object, but are not now conscious of it. In sensorimotor awareness, you may be aware that your hand is resting on an object, but are not now conscious of it.

Because objects of awareness are often conscious, the distinction between awareness and consciousness is frequently blurred or they are used as synonyms.

Memory

Conscious events interact with memory systems in learning, rehearsal, and retrieval. The IDA model elucidates the role of consciousness in the updating of perceptual memory, transient episodic memory, and procedural memory. Transient episodic and declarative memories have distributed representations in IDA, there is evidence that this is also the case in the nervous system. In IDA, these two memories are implemented computationally using a modified version of Kanerva’s Sparse distributed memory architecture.

Learning

Learning is also considered necessary for AC. By Bernard Baars, conscious experience is needed to represent and adapt to novel and significant events (Baars 1988). By Axel Cleeremans and Luis Jiménez, learning is defined as "a set of philogenetically [sic] advanced adaptation processes that critically depend on an evolved sensitivity to subjective experience so as to enable agents to afford flexible control over their actions in complex, unpredictable environments" (Cleeremans 2001).

Anticipation

The ability to predict (or anticipate) foreseeable events is considered important for AC by Igor Aleksander. The emergentist multiple drafts principle proposed by Daniel Dennett in Consciousness Explained may be useful for prediction: it involves the evaluation and selection of the most appropriate "draft" to fit the current environment. Anticipation includes prediction of consequences of one's own proposed actions and prediction of consequences of probable actions by other entities.

Relationships between real world states are mirrored in the state structure of a conscious organism enabling the organism to predict events. An artificially conscious machine should be able to anticipate events correctly in order to be ready to respond to them when they occur or to take preemptive action to avert anticipated events. The implication here is that the machine needs flexible, real-time components that build spatial, dynamic, statistical, functional, and cause-effect models of the real world and predicted worlds, making it possible to demonstrate that it possesses artificial consciousness in the present and future and not only in the past. In order to do this, a conscious machine should make coherent predictions and contingency plans, not only in worlds with fixed rules like a chess board, but also for novel environments that may change, to be executed only when appropriate to simulate and control the real world.

Subjective experience

Subjective experiences or qualia are widely considered to be the hard problem of consciousness. Indeed, it is held to pose a challenge to physicalism, let alone computationalism. On the other hand, there are problems in other fields of science that limit that which we can observe, such as the uncertainty principle in physics, which have not made the research in these fields of science impossible.

Role of cognitive architectures

Main article: Cognitive architecture

The term "cognitive architecture" may refer to a theory about the structure of the human mind, or any portion or function thereof, including consciousness. In another context, a cognitive architecture implements the theory on computers. An example is QuBIC: Quantum and Bio-inspired Cognitive Architecture for Machine Consciousness. One of the main goals of a cognitive architecture is to summarize the various results of cognitive psychology in a comprehensive computer model. However, the results need to be in a formalized form so they can be the basis of a computer program. Also, the role of cognitive architecture is for the A.I. to clearly structure, build, and implement its thought process.

Symbolic or hybrid proposals

Franklin's Intelligent Distribution Agent

Stan Franklin (1995, 2003) defines an autonomous agent as possessing functional consciousness when it is capable of several of the functions of consciousness as identified by Bernard Baars' Global Workspace Theory (Baars 1988, 1997). His brainchild IDA (Intelligent Distribution Agent) is a software implementation of GWT, which makes it functionally conscious by definition. IDA's task is to negotiate new assignments for sailors in the US Navy after they end a tour of duty, by matching each individual's skills and preferences with the Navy's needs. IDA interacts with Navy databases and communicates with the sailors via natural language e-mail dialog while obeying a large set of Navy policies. The IDA computational model was developed during 1996–2001 at Stan Franklin's "Conscious" Software Research Group at the University of Memphis. It "consists of approximately a quarter-million lines of Java code, and almost completely consumes the resources of a 2001 high-end workstation." It relies heavily on codelets, which are "special purpose, relatively independent, mini-agent[s] typically implemented as a small piece of code running as a separate thread." In IDA's top-down architecture, high-level cognitive functions are explicitly modeled (see Franklin 1995 and Franklin 2003 for details). While IDA is functionally conscious by definition, Franklin does "not attribute phenomenal consciousness to his own 'conscious' software agent, IDA, in spite of her many human-like behaviours. This in spite of watching several US Navy detailers repeatedly nodding their heads saying 'Yes, that's how I do it' while watching IDA's internal and external actions as she performs her task." IDA has been extended to LIDA (Learning Intelligent Distribution Agent).

Ron Sun's CLARION cognitive architecture

The CLARION cognitive architecture posits a two-level representation that explains the distinction between conscious and unconscious mental processes.

CLARION has been successful in accounting for a variety of psychological data. A number of well-known skill learning tasks have been simulated using CLARION that span the spectrum ranging from simple reactive skills to complex cognitive skills. The tasks include serial reaction time (SRT) tasks, artificial grammar learning (AGL) tasks, process control (PC) tasks, the categorical inference (CI) task, the alphabetical arithmetic (AA) task, and the Tower of Hanoi (TOH) task. Among them, SRT, AGL, and PC are typical implicit learning tasks, very much relevant to the issue of consciousness as they operationalized the notion of consciousness in the context of psychological experiments.

Ben Goertzel's OpenCog

Ben Goertzel is pursuing an embodied AGI through the open-source OpenCog project. Current code includes embodied virtual pets capable of learning simple English-language commands, as well as integration with real-world robotics, being done at the Hong Kong Polytechnic University.

Connectionist proposals

Haikonen's cognitive architecture

Pentti Haikonen (2003) considers classical rule-based computing inadequate for achieving AC: "the brain is definitely not a computer. Thinking is not an execution of programmed strings of commands. The brain is not a numerical calculator either. We do not think by numbers." Rather than trying to achieve mind and consciousness by identifying and implementing their underlying computational rules, Haikonen proposes "a special cognitive architecture to reproduce the processes of perception, inner imagery, inner speech, pain, pleasure, emotions and the cognitive functions behind these. This bottom-up architecture would produce higher-level functions by the power of the elementary processing units, the artificial neurons, without algorithms or programs". Haikonen believes that, when implemented with sufficient complexity, this architecture will develop consciousness, which he considers to be "a style and way of operation, characterized by distributed signal representation, perception process, cross-modality reporting and availability for retrospection." Haikonen is not alone in this process view of consciousness, or the view that AC will spontaneously emerge in autonomous agents that have a suitable neuro-inspired architecture of complexity; these are shared by many, e.g. Freeman (1999) and Cotterill (2003). A low-complexity implementation of the architecture proposed by Haikonen (2003) was reportedly not capable of AC, but did exhibit emotions as expected. See Doan (2009) for a comprehensive introduction to Haikonen's cognitive architecture. An updated account of Haikonen's architecture, along with a summary of his philosophical views, is given in Haikonen (2012), Haikonen (2019).

Shanahan's cognitive architecture

Murray Shanahan describes a cognitive architecture that combines Baars's idea of a global workspace with a mechanism for internal simulation ("imagination") (Shanahan 2006). For discussions of Shanahan's architecture, see (Gamez 2008) and (Reggia 2013) and Chapter 20 of (Haikonen 2012).

Takeno's self-awareness research

Self-awareness in robots is being investigated by Junichi Takeno at Meiji University in Japan. Takeno is asserting that he has developed a robot capable of discriminating between a self-image in a mirror and any other having an identical image to it, and this claim has already been reviewed (Takeno, Inaba & Suzuki 2005). Takeno asserts that he first contrived the computational module called a MoNAD, which has a self-aware function, and he then constructed the artificial consciousness system by formulating the relationships between emotions, feelings and reason by connecting the modules in a hierarchy (Igarashi, Takeno 2007). Takeno completed a mirror image cognition experiment using a robot equipped with the MoNAD system. Takeno proposed the Self-Body Theory stating that "humans feel that their own mirror image is closer to themselves than an actual part of themselves." The most important point in developing artificial consciousness or clarifying human consciousness is the development of a function of self-awareness, and he claims that he has demonstrated physical and mathematical evidence for this in his thesis. He also demonstrated that robots can study episodes in memory where the emotions were stimulated and use this experience to take predictive actions to prevent the recurrence of unpleasant emotions (Torigoe, Takeno 2009).

Aleksander's impossible mind

Igor Aleksander, emeritus professor of Neural Systems Engineering at Imperial College, has extensively researched artificial neural networks and wrote in his 1996 book Impossible Minds: My Neurons, My Consciousness that the principles for creating a conscious machine already exist but that it would take forty years to train such a machine to understand language. Whether this is true remains to be demonstrated and the basic principle stated in Impossible Minds—that the brain is a neural state machine—is open to doubt.

Thaler's Creativity Machine Paradigm

Stephen Thaler proposed a possible connection between consciousness and creativity in his 1994 patent, called "Device for the Autonomous Generation of Useful Information" (DAGUI), or the so-called "Creativity Machine", in which computational critics govern the injection of synaptic noise and degradation into neural nets so as to induce false memories or confabulations that may qualify as potential ideas or strategies. He recruits this neural architecture and methodology to account for the subjective feel of consciousness, claiming that similar noise-driven neural assemblies within the brain invent dubious significance to overall cortical activity. Thaler's theory and the resulting patents in machine consciousness were inspired by experiments in which he internally disrupted trained neural nets so as to drive a succession of neural activation patterns that he likened to stream of consciousness.

Michael Graziano's attention schema

Main article: Michael Graziano § The brain basis of consciousness

In 2011, Michael Graziano and Sabine Kastler published a paper named "Human consciousness and its relationship to social neuroscience: A novel hypothesis" proposing a theory of consciousness as an attention schema. Graziano went on to publish an expanded discussion of this theory in his book "Consciousness and the Social Brain". This Attention Schema Theory of Consciousness, as he named it, proposes that the brain tracks attention to various sensory inputs by way of an attention schema, analogous to the well-studied body schema that tracks the spatial place of a person's body. This relates to artificial consciousness by proposing a specific mechanism of information handling, that produces what we allegedly experience and describe as consciousness, and which should be able to be duplicated by a machine using current technology. When the brain finds that person X is aware of thing Y, it is in effect modeling the state in which person X is applying an attentional enhancement to Y. In the attention schema theory, the same process can be applied to oneself. The brain tracks attention to various sensory inputs, and one's own awareness is a schematized model of one's attention. Graziano proposes specific locations in the brain for this process, and suggests that such awareness is a computed feature constructed by an expert system in the brain.

"Self-modeling"

Hod Lipson defines "self-modeling" as a necessary component of self-awareness or consciousness in robots. "Self-modeling" consists of a robot running an internal model or simulation of itself.

Testing

The most well-known method for testing machine intelligence is the Turing test. But when interpreted as only observational, this test contradicts the philosophy of science principles of theory dependence of observations. It also has been suggested that Alan Turing's recommendation of imitating not a human adult consciousness, but a human child consciousness, should be taken seriously.

Other tests, such as ConsScale, test the presence of features inspired by biological systems, or measure the cognitive development of artificial systems.

Qualia, or phenomenological consciousness, is an inherently first-person phenomenon. Although various systems may display various signs of behavior correlated with functional consciousness, there is no conceivable way in which third-person tests can have access to first-person phenomenological features. Because of that, and because there is no empirical definition of consciousness, a test of presence of consciousness in AC may be impossible.

In 2014, Victor Argonov suggested a non-Turing test for machine consciousness based on machine's ability to produce philosophical judgments. He argues that a deterministic machine must be regarded as conscious if it is able to produce judgments on all problematic properties of consciousness (such as qualia or binding) having no innate (preloaded) philosophical knowledge on these issues, no philosophical discussions while learning, and no informational models of other creatures in its memory (such models may implicitly or explicitly contain knowledge about these creatures’ consciousness). However, this test can be used only to detect, but not refute the existence of consciousness. A positive result proves that machine is conscious but a negative result proves nothing. For example, absence of philosophical judgments may be caused by lack of the machine’s intellect, not by absence of consciousness.

In 2022, Google engineer Blake Lemoine made a viral claim that Google's LaMDA chatbot was sentient. Lemoine supplied as evidence the chatbot's humanlike answers to many of his questions; however, the chatbot's behavior was judged by the scientific community as likely a consequence of mimicry, rather than machine consciousness. Lemoine's claim was widely derided for being ridiculous. Philosopher Nick Bostrom said that he thinks LaMDA probably isn't conscious, but asked "what grounds would a person have for being sure about it?" One would have to have access to unpublished information about LaMDA's architecture, and also would have to understand how consciousness works, and then figure out how to map the philosophy onto the machine: "(In the absence of these steps), it seems like one should be maybe a little bit uncertain... there could well be other systems now, or in the relatively near future, that would start to satisfy the criteria."

In fiction

Main article: Simulated consciousness (science fiction)

See also: Artificial intelligence in fiction § Sentient AI

Characters with artificial consciousness (or at least with personalities that imply they have consciousness), from works of fiction:

• AC – created by merging two AIs in the Sprawl trilogy by William Gibson
• Agents – in the simulated reality known as "The Matrix" in The Matrix franchise
  • Agent Smith – began as an Agent in The Matrix, then became a renegade program of overgrowing power that could make copies of itself like a self-replicating computer virus
• A.L.I.E. – Sentient genocidal AI from the TV series The 100
• AM (Allied Mastercomputer) – the antagonist of Harlan Ellison's short novel I Have No Mouth, and I Must Scream. An omnipotent, highly intelligent supercomputer, its hatred for humanity drove it to cause mass genocide against the human race, sparing five humans to play sadistic games with them for all eternity.
• Amusement park robots (with pixilated consciousness) that went homicidal in Westworld and Futureworld
• Annalee Call – an Auton (android manufactured by other androids) from the movie Alien Resurrection
• Arnold Rimmer – computer-generated sapient hologram aboard the Red Dwarf
• Ava – a humanoid robot in Ex Machina
• Ash – android crew member of the Nostromo starship in the movie Alien
• The Bicentennial Man – an android in Isaac Asimov's Foundation universe
• Bishop – android crew member aboard the U.S.S. Sulaco in the movie Aliens
• Bomb #19 – Thermostellar bomb for the destruction of potentially dangerous planets, aboard the Dark Star
• Bomb #20 – Malfunctioning Thermostellar bomb, aboard the Dark Star
• The uploaded mind of Dr. Will Caster, which presumably included his consciousness, from the film Transcendence
• C-3PO – protocol droid featured in all the Star Wars movies
• Chappie – CHAPPiE
• Cohen (and other Emergent AIs) – Chris Moriarty's Spin Series
• Computer – ship's computer, aboard the Dark Star
• Cortana (and other "Smart AI") – from the Halo series of games
• Cylons – genocidal robots with resurrection ships that enable the consciousness of any Cylon within an unspecified range to download into a new body aboard the ship upon death, from Battlestar Galactica
• Deviants – a group of androids that have deviated from pre-programmed instructions in Detroit: Become Human
• Erasmus – baby killer robot that incited the Butlerian Jihad in the Dune franchise
• Fal'Cie – Mechanical beings with god-like powers from the Final Fantasy XIII series
• The Geth, EDI and SAM – Mass Effect
• Futurama- Bender is a good example of sapient AI, throughout many episodes, you will see Bender get angry, sad, or other emotions. Bender also has a mind of his own.
• Gideon – an interactive artificial consciousness made by Barry Allen shown in DC comics and shows like The Flash and Legends of Tomorrow
• GLaDOS (and personality cores) – from the Portal series of games
• HAL 9000 – spaceship USS Discovery One's onboard computer, that lethally malfunctioned due to mutually exclusive directives, from the 1968 novel 2001: A Space Odyssey and in the film
• Holly – ship's computer with an IQ of 6000, aboard the Red Dwarf
• Hosts in the Westworld franchise
• Humagears in Kamen Rider Zero-One
• Isaac – a member of the artificial, non-biological race from Kaylon-1 that views biological lifeforms, including humans, as inferior from the TV series The Orville.
• Jane – Orson Scott Card's Speaker for the Dead, Xenocide, Children of the Mind, and "Investment Counselor"
• Johnny Five – Short Circuit
• Joshua – WarGames
• Keymaker – an "exile" sapient program in The Matrix franchise
• Lieutenant Commander Data – Star Trek: The Next Generation
• "Machine" – android from the film The Machine, whose owners try to kill her when they witness her conscious thoughts, out of fear that she will design better androids (intelligence explosion)
• Marvin the Paranoid Android - The Hitchhiker's Guide To The Galaxy, super-intelligent android who is perpetually depressed
• M3GAN – an eponymous artificially intelligent doll who develops self-awareness and becomes hostile toward anyone who comes between her and her human companion
• Mike – The Moon Is a Harsh Mistress
• Mimi – humanoid robot in Real Humans, (original title – Äkta människor) 2012
• The Minds – Iain M. Banks' Culture novels
• Omnius – sentient computer network that controlled the Universe until overthrown by the Butlerian Jihad in the Dune franchise
• Operating Systems in the movie Her
• The Oracle – sapient program in The Matrix franchise
• Professor James Moriarty – sentient holodeck character in the "Ship in a Bottle" episode from Star Trek: The Next Generation
• In Greg Egan's novel Permutation City the protagonist creates digital copies of himself to conduct experiments that are also related to implications of artificial consciousness on identity
• Puppet Master – Ghost in the Shell manga and anime
• R2-D2 – exciteable astromech droid featured in all the Star Wars movies
• Rasputin – An AI in the Destiny videogames Universe that became self aware and learned trust and life value after a series of learning missions, ended sacrificing himself to save humanity from a total annihilation.
• Replicants – bio-robotic androids from the novel Do Androids Dream of Electric Sheep? and the movie Blade Runner which portray what might happen when artificially conscious robots are modeled very closely upon humans
• Roboduck – combat robot superhero in the NEW-GEN comic book series from Marvel Comics
• Robots in Isaac Asimov's Robot series
• Robots in The Matrix franchise, especially in The Animatrix
• The Ship – the result of a large-scale AC experiment, in Frank Herbert's Destination: Void and sequels, despite past edicts warning against "Making a Machine in the Image of a Man's Mind"
• Skynet – from the Terminator franchise
• "Synths" are a type of android in the video game Fallout 4. There is a faction in the game known as "The Railroad" which believes that, as conscious beings, synths have their own rights. The Institute, the lab that produces the synths, mostly does not believe they are truly conscious and attributes any apparent desires for freedom as a malfunction.
• TARDIS – time machine and spacecraft of Doctor Who, sometimes portrayed with a mind of its own
• Terminator cyborgs – from the Terminator franchise, with visual consciousness depicted via first-person perspective
• The Doctor – from the American TV show: Star Trek: Voyager – a sentient computer program designed as a doctor that would be activated in case of an emergency or if the chief medical officer is incapacitated.
• Transformers – sentient robots from the various series in the Transformers robot superhero franchise of the same name
• Vanamonde – an artificial being that was immensely powerful but entirely child-like in Arthur C. Clarke's The City and the Stars
• WALL-E – a robot and the titular character in WALL-E
• YoRHa - A militarized faction of conscious androids from the video game, Nier: Automata. The Nier franchise repeatedly uses simulated consciousness and philosophy as a central theme.
• Robots and also the drone companion B-12 in Annapurna Interactive's game Stray

Vagus nerve stimulation

From Wikipedia, the free encyclopedia

 

Vagus nerve stimulation
Electrical stimulation of vagus nerve.
Other names	Vagal nerve stimulation

Vagus nerve stimulation (VNS) is a medical treatment that involves delivering electrical impulses to the vagus nerve. It is used as an add-on treatment for certain types of intractable epilepsy and treatment-resistant depression.

Medical use

VNS devices are used to treat drug-resistant epilepsy and treatment-resistant major depressive disorder (TR-MDD).

In the United States, VNS is approved as adjunctive therapy for those 4 years of age or older with refractory focal onset seizures. In the European Union, VNS is approved as an adjunctive therapy for patients with either generalized or focal onset seizures without any age restrictions. It is recommended that VNS is only pursued following an adequate trial of at least 2 appropriately chosen anti-seizure medications and that the patient is ineligible for epilepsy surgery. This is because epilepsy surgery is associated with a higher probability of resulting in seizure freedom. Patients who have poor adherence or tolerance of anti-seizure medications may be good candidates for VNS. Patients with comorbid depression have been found to have mood improvements with VNS therapy.

VNS may provide benefit for particular epilepsy syndromes and seizure types such as Lennox-Gastaut syndrome, tuberous sclerosis complex related epilepsy, refractory absence seizures and atonic seizures. There are also reports of VNS being successfully utilized in patients with refractory and super-refractory status epilepticus.  

Efficacy

Epilepsy

A meta-analysis of 74 clinical studies with 3321 patients found that VNS produced an average 51% reduction in seizures after 1 year of therapy. Approximately 50% of patients had an equal to or greater than 50% reduction in seizures at the time of last follow-up. Long term studies have shown that response to VNS increases over time. For instance, a study that followed 74 patients for 10-17 years found a seizure frequency reduction of 50-90% in 38.4%, 51.4%, 63.6% and 77.8% of patients at 1-, 2-, 10- and 17-years following implantation, respectively. Approximately, 8% have total resolution of seizures. VNS has also been shown to reduce rates of sudden unexpected death in epilepsy (SUDEP) and to improve quality of life metrics. A number of predictors of a favorable clinical response have been identified including epilepsy onset > 12 years of age, generalized epilepsy type, non-lesional epilepsy, posttraumatic epilepsy and those who have less than a 10 year history of seizures.

Depression

As of 2017, the efficacy of VNS for TR-MDD is unclear. A 2022 narrative review concluded that "The use of VNS is an approved, effective and well-tolerated long-term therapy for chronic and treatment-resistant depression. Further sham-controlled studies over a longer observational period are desirable".

Mechanism of action

The vagus nerve is the tenth cranial nerve and arises from a series of rootlets in the medulla; it carries both afferent (80%) and efferent (20%) fibers. It has the longest and widest distribution of all the cranial nerves and functions as a bidirectional link between the brain and peripheral organs. Afferents from the vagus nerve project to the nucleus tractus solitarii which subsequently communicates with other regions of the brain including the dorsal raphe nucleus, locus coeruleus, amygdala and other areas.

There are multiple potential mechanisms which may account for the efficacy of VNS in treating epilepsy and other conditions including:

1.   There is evidence that VNS results in cortical desynchronization in epilepsy patients who had a favorable clinical response relative to those who did not. This makes sense given that seizures consist of abnormal hypersynchronous activity in the brain.

2.   Multiple lines of evidence suggest that inflammation plays a significant role in epilepsy as well as associated neurobehavioral comorbidities such as depression, autism spectrum disorder and cognitive impairment. There is evidence that VNS has an anti-inflammatory effect through both peripheral and central mechanisms.

3.   VNS can change the activity of several neurotransmitter systems involving serotonin, norepinephrine and GABA. These neurotransmitters are involved in both epilepsy and other neuropsychiatric conditions such as depression and anxiety.

4.   VNS may alter the functional connectivity in several brain regions and enhance synaptic plasticity to reduce excitatory activity involved in seizures. It has also been shown to change the functional connectivity of the default mode network in depressed patients.

Adverse events

There are two categories of adverse events associated with VNS: (1) those related to the surgical procedure and (2) those related to stimulation. A large 25-year retrospective study of 247 patients found a surgical complication rate of 8.6%. The common adverse events included infection in 2.6%, hematoma at the surgical site in 1.9% and vocal cord palsy in 1.4%. The most common stimulation related side effect at 1 year following implantation are hoarseness in 28% and paraesthesias in the throat-chin region in 12%. At the third year the rate of stimulation related adverse effects decreased substantially with shortness of breath being the most common and occurring in 3.2%. In general, VNS is well tolerated and side effects diminish over time. Also, side effects can be controlled by changing the stimulation parameters.

There is evidence that VNS can induce sleep apnea in as many as 28% of adult patients.

Devices and procedures

The device consists of a generator the size of a matchbox that is implanted under the skin below the person’s collarbone. Lead wires from the generator are tunnelled up to the patient’s neck and wrapped around the left vagus nerve at the carotid sheath, where it delivers electrical impulses to the nerve.

Implantation of the VNS device is usually done as an out-patient procedure. The procedure goes as follows: an incision is made in the upper left chest and the generator is implanted into a little "pouch" on the left chest under the collarbone. A second incision is made in the neck, so that the surgeon can access the vagus nerve. The surgeon then wraps the leads around the left branch of the vagus nerve, and connects the electrodes to the generator. Once successfully implanted, the generator sends electric impulses to the vagus nerve at regular intervals. The left vagus nerve is stimulated rather than the right because the right plays a role in cardiac function such that stimulating it could have negative cardiac effects. The "dose" administered by the device then needs to be set, which is done via a magnetic wand; the parameters adjusted include current, frequency, pulse width, and duty cycle.

"Wearable" devices are being tested and developed that involve transcutaneous stimulation and do not require surgery. Electrical impulses are targeted at the aurical (ear), at points where branches of the vagus nerve have cutaneous representation; such devices had been tested in clinical trials for treatment resistant major depressive disorder as of 2017.

History

Early history

James L. Corning (1855-1923) was an American neurologist who developed the first device for stimulating the vagus nerve towards the end of the 19^th century. At that time a widely held theory was that excessive blood flow caused seizures. In the 1880s Corning designed a pronged instrument called the “carotid fork” to compress the carotid artery for the acute treatment of seizures. In addition, he developed the “carotid truss” for prolonged compression of the carotid arteries as a long-term preventative treatment for epilepsy. Then he developed the “electrocompressor” which allowed for the compression of the bilateral carotid arteries as well as electrical stimulation of both the vagus and cervical sympathetic nerves. The idea was to reduce cardiac output and to stimulate cervical sympathetic nerves to constrict cerebral blood vessels. Corning reported dramatic benefits however it was not accepted by his colleagues and ultimately was forgotten.

In the 1930s Biley and Bremer demonstrated the direct influence of VNS on the central nervous system. This was in contrary to Corning who had intended to use it to reduce cerebral blood flow. In the 1940s and 1950s vagal nerve stimulation was shown to affect EEG activity. Finally, nearly 100 years since Corning, Zabara proposed that VNS could be used to treat epilepsy. He then demonstrated its efficacy in animal experiments. The first human was implanted with a VNS for the treatment of epilepsy in 1988.

Later history

In 1997, the US Food and Drug Administration's neurological devices panel met to consider approval of an implanted vagus nerve stimulator (VNS) for epilepsy, requested by Cyberonics (which was subsequently acquired by LivaNova).

The FDA approved an implanted VNS for TR-MDD in 2005.

In April 2017, the FDA cleared marketing of a handheld noninvasive vagus nerve stimulator, called "gammaCore" and made by ElectroCore LLC, for episodic cluster headaches, under the de novo pathway. In January 2018, the FDA cleared a new use of that device, for the treatment of migraine pain in adults under a 510(k) based on the de novo clearance.

In 2020, electroCore's non-invasive VNS was granted an Emergency Use Authorization for treating COVID-19 patients, given Research has shown this pulse train causes airways in the lungs to open its anti-inflammatory effect.

Research

Because the vagus nerve is associated with many different functions and brain regions, clinical research has been done to determine its usefulness in treating other illnesses, including various anxiety disorders, obesity, alcohol addiction, chronic heart failure, prevention of arrhythmias that can cause sudden cardiac death, autoimmune disorders, irritable bowel syndrome, Alzheimer's disease, Parkinson's disease, hypertension, and several chronic pain conditions. A recent study showed that chronic VNS showed strong antidepressant and anxiolytic effects, and improved memory performance in an Alzheimer's Disease animal model.

tVNS has also been shown to enhance memory in healthy adults.

VNS has also been studied in small trials of people with neurodevelopmental disorders, generally who also have had epilepsy, including Landau-Kleffner syndrome, Rett syndrome, and autism spectrum disorders. VNS is being studied as of 2018 as a treatment for migraines and fibromyalgia.

Transcutaneous

As of 2015, VNS devices were being developed that were not implanted, but rather transmitted signals through the skin, known as transcutaneous vagus nerve stimulation (tVNS). Electrical impulses are targeted at the auricle of the ear at points where branches of the vagus nerve are close to the surface. It is non-invasive and based on the rationale that there is vagus nerve distribution on the surface of the ear. tVNS is being studied for stroke and the treatment of depression.