DOCSLIB.ORG

Cognitive Machine Theory of Mind

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Cognitive Machine Theory of Mind Cognitive Machine Theory of Mind Thuy Ngoc Nguyen ([email protected]) Dynamic Decision Making Laboratory, Carnegie Mellon University 5000 Forbes Ave., Pittsburgh PA 15213 USA Cleotilde Gonzalez ([email protected]) Dynamic Decision Making Laboratory, Carnegie Mellon University 5000 Forbes Ave., Pittsburgh PA 15213 USA Abstract (Baker, Saxe, & Tenenbaum, 2011; Baker, Jara-Ettinger, A major challenge for research in Artificial Intelligence (AI) Saxe, & Tenenbaum, 2017). In this work, researchers devel- is to develop systems that can infer humans’ goals and beliefs oped a Bayesian ToM (BToM) model that is able to predict when observing their behavior alone (i.e., systems that have and attribute beliefs and desires of other agents, given the ob- Theory of Mind, ToM). In this research we use a theoretically- grounded, pre-existent cognitive model to demonstrate the de- servation of their actions. The BToM uses Bayes’ probabil- velopment of ToM from observation of other agents’ behavior. ities and an assumption of utility maximization (i.e., human The cognitive model relies on Instance-Based Learning The- rationality) to determine the posterior probability of “mental ory (IBLT) of experiential decision making, that distinguishes it from previous models that are hand-crafted for particular set- states”. Another recent example is the work of Rabinowitz tings, complex, or unable to explain a cognitive development et al. (2018), who developed a Machine ToM (MToM) archi- of ToM. An IBL model was designed to be an observer of tecture involving three modules: a character net that parses agents’ navigation in gridworld environments and was queried afterwards to predict the actions of new agents in new (not agents’ past trajectories of navigation in gridworlds; a mental experienced before) gridworlds. The IBL observer can infer state net, which parses agents’ trajectories in recent episodes, and predict potential behaviors from just a few samples of that are then used by the prediction net which is queried re- agents’ past behavior of random and goal-directed reinforce- ment learning agents. Furthermore the IBL observer is able to garding future behaviors of new agents. These authors offer infer the agent’s false belief and pass a classic ToM test com- a set of tests of the observer’s predictions regarding various monly used in humans. We discuss the advantages of using types of agents, and a test of recognition of false beliefs, the IBLT to develop models of ToM, and the potential to predict human ToM. Sally-Anne test (Wimmer & Perner, 1983). Keywords: cognitive model; machine theory of mind; Our research builds on these efforts, making the follow- instance-based learning theory. ing contributions. First, we present a Cognitive Machine Theory of Mind (CogToM) framework that relies on a gen- Introduction eral cognitive theory of decisions from experience, Instance- Theory of mind (ToM) refers to the ability of humans to in- Based Learning Theory (IBLT) (Gonzalez, Lerch, & Lebiere, fer and understand the beliefs, desires, and intentions of oth- 2003). Our approach is different from the standard compu- ers (Premack & Woodruff, 1978). ToM is known to develop tational models of ToM, summarized above, in that it uses very early in life (Wimmer & Perner, 1983; Keysar, Lin, & the IBL process and the formulations of the ACT-R archi- Barr, 2003) and it is one of the most important social skills tecture (Anderson & Lebiere, 2014) for memory-based in- used to predict others’ behavior and intentions, and to theo- ference to demonstrate how ToM develops from observation rize about others’ beliefs and desires in future situations. of other agents’ actions. Second, we demonstrate that an Since its origins, Artificial Intelligence (AI) attempted to IBL model of an observer (i.e, IBL observer) is able to ex- “replicate” various human behaviors in computational form, plain the inferences made by three types of acting agents in aiming at passing an imitation game (i.e., Turing Test) (Lake, gridworlds: Random, Reinforcement Learning (RL), and IBL Ullman, Tenenbaum, & Gershman, 2017; Turing, 1950): agents. Third, we find that the IBL observer predicts beliefs where a machine behavior would be indistinguishable from and actions of IBL acting agents more accurately than it pre- that of a human. AI work on ToM has investigated how hu- dicts the beliefs and actions of RL or Random agents. mans “mentalize” robots (machines more generally) and how human ToM develops when interacting with machines rather Instance-Based Learning Theory than other humans (Banks, 2019). While this work is ex- IBLT is a theory of decisions from experience, developed tremely relevant for developing machine representations of to explain human learning in dynamic decision environ- ToM, it does not address the major problem of how to build ments (Gonzalez et al., 2003). IBLT provides a decision mak- an algorithm that can develop ToM from the limited observa- ing algorithm and a set of cognitive mechanisms used to im- tion of other agents’ actions; a capability that humans excel plement computational models. The algorithm involves the at (Lake et al., 2017; Botvinick et al., 2017). recognition and retrieval of past experiences (i.e., instances) Recently, researchers built computational architectures of according to their similarity to a current decision situation. ToM. A notable example is the work of Baker and colleagues An “instance” in IBLT is a memory unit, that results from 2560 ©2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY). the potential alternatives evaluated. These are memory rep- gridworld. The observer should be able to accomplish ToM resentations consisting of three elements: a situation (S) (set given full or partial observation of the agent’s action traces of attributes that give a context to the decision, or state s); a in past gridworlds. The observer model in CogToM is built decision (D) (the action taken corresponding to an alternative according to IBLT (Gonzalez et al., 2003). in state s, or action a); and a utility (U) (expected utility u or experienced outcome x of the action taken in a state). Gridworld IBLT relies on sub-symbolic mechanisms that have been A gridworld is a sequential decision making problem wherein discussed extensively (e.g. (Gonzalez et al., 2003; Gonzalez agents move through a N × N grid to search for targets and & Dutt, 2011; Gonzalez, 2013)), but we summarize here for avoid obstacles. We use gridworlds of 11 × 11 size following completeness. Each instance i in memory has a value of Acti- (Rabinowitz et al., 2018) (see Fig 1). A gridworld contains vation, which represents how readily available that informa- randomly-located obstacles (black bars) and the number of tion is in memory (Anderson & Lebiere, 2014). A simplified obstacles varies from zero (no obstacles) to six with the size version of the Activation equation captures how recently and of 1 ×1. In each grid, there are four goals of different values, frequently the considered instances are activated: represented as four colored objects (blue, green, orange, and purple), which are put at random locations that do not over- ! 1 − g lap the obstacles. Starting at a random position (i.e., (x;y)), A = ln (t −t0)−d + ln i ; (1) i ∑ s the agent (black dot) makes sequential decisions about the t02f1::t−1g gi actions to take (i.e., up, down, left, right) to reach one of the where d and s are respectively the decay and noise parame- four objects. A sequence of moves from the initial location to ters; t0 refers to the previous timestamp in which the outcome the end location forms a trajectory (dotted red line) which is of instance i was observed resulting from choosing an action produced by the strategy (the sequence of decisions) that the a at state s. The rightmost term represents the Gaussian noise agent takes. for capturing individual variation in activation, and gi is a ran- Generally, a gridworld task can be formulated as a Par- dom number drawn from a uniform distribution U(0;1). tially Observable Markov Decision Process (POMDP) as Activation of an instance i is used to determine its memory in (Rabinowitz et al., 2018). Each POMDP M j has a state retrieval probability: space S j, and each square in the grid is called a state s 2 S j. At each state s, an agent Ak is required to take an action a Ai=t e from an action space A j. Each agent follows their policy (i.e. pi = ; (2) Al=t ∑l e strategy), to decide how to move around the grid. By execut- p ing its policy pk in the gridworld M j, the agent Ak creates a T where t = s 2 representing the variability in recalling in- trajectory denoted by Tk j = f(st ;at )gt=0. If the agent has a stances from memory, and l refers to the index of all stored full observation of the grid, POMDP is referred to as MDP. instances to normalize pi. The expected utility of taking action a at state s is calcu- Models of Acting Agents in the Gridworld lated through a mechanism in IBLT called Blending: We consider three different types of acting agents that play in the gridworlds: Random, Reinforcement Learning (RL), and n Instance-based Learning (IBL) agents. V(s;a) = ∑ pixi: (3) i=1 A random agent Ak selects an action a in state s based on the probability pk(ajs). Precisely, the policy of Ak is drawn Essentially, the blended value is the sum of all the out- from a Dirichlet distribution pk ∼ Dir(a) with concentration comes weighted by their probability of retrieval, where xi is parameter a, so that ∑a2A pk(ajs) = 1 and pk(ajs) > 0.
Load more

Recommended publications
  • KNOWLEDGE ACCORDING to IDEALISM Idealism As a Philosophy
    KNOWLEDGE ACCORDING TO IDEALISM Idealism as a philosophy had its greatest impact during the nineteenth century. It is a philosophical approach that has as its central tenet that ideas are the only true reality, the only thing worth knowing. In a search for truth, beauty, and justice that is enduring and everlasting; the focus is on conscious reasoning in the mind. The main tenant of idealism is that ideas and knowledge are the truest reality. Many things in the world change, but ideas and knowledge are enduring. Idealism was often referred to as “idea-ism”. Idealists believe that ideas can change lives. The most important part of a person is the mind. It is to be nourished and developed. Etymologically Its origin is: from Greek idea “form, shape” from weid- also the origin of the “his” in his-tor “wise, learned” underlying English “history.” In Latin this root became videre “to see” and related words. It is the same root in Sanskrit veda “knowledge as in the Rig-Veda. The stem entered Germanic as witan “know,” seen in Modern German wissen “to know” and in English “wisdom” and “twit,” a shortened form of Middle English atwite derived from æt “at” +witen “reproach.” In short Idealism is a philosophical position which adheres to the view that nothing exists except as it is an idea in the mind of man or the mind of God. The idealist believes that the universe has intelligence and a will; that all material things are explainable in terms of a mind standing behind them. PHILOSOPHICAL RATIONALE OF IDEALISM a) The Universe (Ontology or Metaphysics) To the idealist, the nature of the universe is mind; it is an idea.
    [Show full text]
  • A Distributed Computational Cognitive Model for Object Recognition
    SCIENCE CHINA xx xxxx Vol. xx No. x: 1{15 doi: A Distributed Computational Cognitive Model for Object Recognition Yong-Jin Liu1∗, Qiu-Fang Fu2, Ye Liu2 & Xiaolan Fu2 1Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China, 2State Key Lab of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China Received Month Day, Year; accepted Month Day, Year Abstract Based on cognitive functionalities in human vision processing, we propose a computational cognitive model for object recognition with detailed algorithmic descriptions. The contribution of this paper is of two folds. Firstly, we present a systematic review on psychological and neurophysiological studies, which provide collective evidence for a distributed representation of 3D objects in the human brain. Secondly, we present a computational model which simulates the distributed mechanism of object vision pathway. Experimental results show that the presented computational cognitive model outperforms five representative 3D object recognition algorithms in computer science research. Keywords distributed cognition, computational model, object recognition, human vision system 1 Introduction Object recognition is one of fundamental tasks in computer vision. Many recognition algorithms have been proposed in computer science area (e.g., [1, 2, 3, 4, 5]). While the CPU processing speed can now be reached at 109 Hz, the human brain has a limited speed of 100 Hz at which the neurons process their input. However, compared to state-of-the-art computational algorithms for object recognition, human brain has a distinct advantage in object recognition, i.e., human being can accurately recognize one from unlimited variety of objects within a fraction of a second, even if the object is partially occluded or contaminated by noises.
    [Show full text]
  • Is AI Intelligent, Really? Bruce D
    Seattle aP cific nivU ersity Digital Commons @ SPU SPU Works Summer August 23rd, 2019 Is AI intelligent, really? Bruce D. Baker Seattle Pacific nU iversity Follow this and additional works at: https://digitalcommons.spu.edu/works Part of the Artificial Intelligence and Robotics Commons, Comparative Methodologies and Theories Commons, Epistemology Commons, Philosophy of Science Commons, and the Practical Theology Commons Recommended Citation Baker, Bruce D., "Is AI intelligent, really?" (2019). SPU Works. 140. https://digitalcommons.spu.edu/works/140 This Article is brought to you for free and open access by Digital Commons @ SPU. It has been accepted for inclusion in SPU Works by an authorized administrator of Digital Commons @ SPU. Bruce Baker August 23, 2019 Is AI intelligent, really? Good question. On the surface, it seems simple enough. Assign any standard you like as a demonstration of intelligence, and then ask whether you could (theoretically) set up an AI to perform it. Sure, it seems common sense that given sufficiently advanced technology you could set up a computer or a robot to do just about anything that you could define as being doable. But what does this prove? Have you proven the AI is really intelligent? Or have you merely shown that there exists a solution to your pre- determined puzzle? Hmmm. This is why AI futurist Max Tegmark emphasizes the difference between narrow (machine-like) and broad (human-like) intelligence.1 And so the question remains: Can the AI be intelligent, really, in the same broad way its creator is? Why is this question so intractable? Because intelligence is not a monolithic property.
    [Show full text]
  • The Place of Modeling in Cognitive Science
    The place of modeling in cognitive science James L. McClelland Department of Psychology and Center for Mind, Brain, and Computation Stanford University James L. McClelland Department of Psychology Stanford University Stanford, CA 94305 650-736-4278 (v) / 650-725-5699 (f) [email protected] Running Head: Modeling in cognitive science Keywords: Modeling frameworks, computer simulation, connectionist models, Bayesian approaches, dynamical systems, symbolic models of cognition, hybrid models, cognitive architectures Abstract I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. As explorations, simplification is essential – it is only through simplification that we can fully understand the implications of the ideas. This is not to say that simplification has no downsides; it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000-fold increase? 1. Introduction With the inauguration of a new journal for cognitive science, thirty years after the first meeting of the Cognitive Science Society, it seems essential to consider the role of computational modeling in our discipline.
    [Show full text]
  • A Bayesian Computational Cognitive Model ∗
    A Bayesian Computational Cognitive Model ∗ Zhiwei Shi Hong Hu Zhongzhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology, CAS, China Kexueyuan Nanlu #6, Beijing, China 100080 {shizw, huhong, shizz}@ics.ict.ac.cn Abstract problem solving capability and provide meaningful insights into mechanisms of human intelligence. Computational cognitive modeling has recently Besides the two categories of models, recently, there emerged as one of the hottest issues in the AI area. emerges a novel approach, hybrid neural-symbolic system, Both symbolic approaches and connectionist ap- which concerns the use of problem-specific symbolic proaches present their merits and demerits. Al- knowledge within the neurocomputing paradigm [d'Avila though Bayesian method is suggested to incorporate Garcez et al., 2002]. This new approach shows that both advantages of the two kinds of approaches above, modal logics and temporal logics can be effectively repre- there is no feasible Bayesian computational model sented in artificial neural networks [d'Avila Garcez and concerning the entire cognitive process by now. In Lamb, 2004]. this paper, we propose a variation of traditional Different from the hybrid approach above, we adopt Bayesian network, namely Globally Connected and Bayesian network [Pearl, 1988] to integrate the merits of Locally Autonomic Bayesian Network (GCLABN), rule-based systems and neural networks, due to its virtues as to formally describe a plausible cognitive model. follows. On the one hand, by encoding concepts
    [Show full text]
  • Artificial Intelligence and National Security
    Artificial Intelligence and National Security Artificial intelligence will have immense implications for national and international security, and AI’s potential applications for defense and intelligence have been identified by the federal government as a major priority. There are, however, significant bureaucratic and technical challenges to the adoption and scaling of AI across U.S. defense and intelligence organizations. Moreover, other nations—particularly China and Russia—are also investing in military AI applications. As the strategic competition intensifies, the pressure to deploy untested and poorly understood systems to gain competitive advantage could lead to accidents, failures, and unintended escalation. The Bipartisan Policy Center and Georgetown University’s Center for Security and Emerging Technology (CSET), in consultation with Reps. Robin Kelly (D-IL) and Will Hurd (R-TX), have worked with government officials, industry representatives, civil society advocates, and academics to better understand the major AI-related national and economic security issues the country faces. This paper hopes to shed more clarity on these challenges and provide actionable policy recommendations, to help guide a U.S. national strategy for AI. BPC’s effort is primarily designed to complement the work done by the Obama and Trump administrations, including President Barack Obama’s 2016 The National Artificial Intelligence Research and Development Strategic Plan,i President Donald Trump’s Executive Order 13859, announcing the American AI Initiative,ii and the Office of Management and Budget’s subsequentGuidance for Regulation of Artificial Intelligence Applications.iii The effort is also designed to further June 2020 advance work done by Kelly and Hurd in their 2018 Committee on Oversight 1 and Government Reform (Information Technology Subcommittee) white paper Rise of the Machines: Artificial Intelligence and its Growing Impact on U.S.
    [Show full text]
  • Theory of Mind As Inverse Reinforcement Learning. Current
    Available online at www.sciencedirect.com ScienceDirect Theory of mind as inverse reinforcement learning 1,2 Julian Jara-Ettinger We review the idea that Theory of Mind—our ability to reason think or want based on how they behave. Research in about other people’s mental states—can be formalized as cognitive science suggests that we infer mental states by inverse reinforcement learning. Under this framework, thinking of other people as utility maximizers: constantly expectations about how mental states produce behavior are acting to maximize the rewards they obtain while mini- captured in a reinforcement learning (RL) model. Predicting mizing the costs that they incur [3–5]. Using this assump- other people’s actions is achieved by simulating a RL model tion, even children can infer other people’s preferences with the hypothesized beliefs and desires, while mental-state [6,7], knowledge [8,9], and moral standing [10–12]. inference is achieved by inverting this model. Although many advances in inverse reinforcement learning (IRL) did not have Theory of mind as inverse reinforcement human Theory of Mind in mind, here we focus on what they learning reveal when conceptualized as cognitive theories. We discuss Computationally, our intuitions about how other minds landmark successes of IRL, and key challenges in building work can be formalized using frameworks developed in a human-like Theory of Mind. classical area of AI: model-based reinforcement learning 1 Addresses (hereafter reinforcement learning or RL) . RL problems 1 Department of Psychology, Yale University, New Haven, CT, United focus on how to combine a world model with a reward States function to produce a sequence of actions, called a policy, 2 Department of Computer Science, Yale University, New Haven, CT, that maximizes agents’ rewards while minimizing their United States costs.
    [Show full text]
  • Artificial Intelligence in the Context of Human Consciousness
    Running head: AI IN THE CONTEXT OF HUMAN CONSCIOUSNESS 1 Artificial Intelligence in the Context of Human Consciousness Hannah DeFries A Senior Thesis submitted in partial fulfillment of the requirements for graduation in the Honors Program Liberty University Spring 2019 AI IN THE CONTEXT OF HUMAN CONSCIOUSNESS 2 Acceptance of Senior Honors Thesis This Senior Honors Thesis is accepted in partial fulfillment of the requirements for graduation from the Honors Program of Liberty University. ______________________________ Kyung K. Bae, Ph.D. Thesis Chair ______________________________ Jung-Uk Lim, Ph.D. Committee Member ______________________________ Mark Harris, Ph.D. Committee Member ______________________________ James H. Nutter, D.A. Honors Director ______________________________ Date AI IN THE CONTEXT OF HUMAN CONSCIOUSNESS 3 Abstract Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence
    [Show full text]
  • Defense Primer: National and Defense Intelligence
    Updated December 30, 2020 Defense Primer: National and Defense Intelligence The Intelligence Community (IC) is charged with providing Intelligence Program (NIP) budget appropriations, which insight into actual or potential threats to the U.S. homeland, are a consolidation of appropriations for the ODNI; CIA; the American people, and national interests at home and general defense; and national cryptologic, reconnaissance, abroad. It does so through the production of timely and geospatial, and other specialized intelligence programs. The apolitical products and services. Intelligence products and NIP, therefore, provides funding for not only the ODNI, services result from the collection, processing, analysis, and CIA and IC elements of the Departments of Homeland evaluation of information for its significance to national Security, Energy, the Treasury, Justice and State, but also, security at the strategic, operational, and tactical levels. substantially, for the programs and activities of the Consumers of intelligence include the President, National intelligence agencies within the DOD, to include the NSA, Security Council (NSC), designated personnel in executive NGA, DIA, and NRO. branch departments and agencies, the military, Congress, and the law enforcement community. Defense intelligence comprises the intelligence organizations and capabilities of the Joint Staff, the DIA, The IC comprises 17 elements, two of which are combatant command joint intelligence centers, and the independent, and 15 of which are component organizations military services that address strategic, operational or of six separate departments of the federal government. tactical requirements supporting military strategy, planning, Many IC elements and most intelligence funding reside and operations. Defense intelligence provides products and within the Department of Defense (DOD).
    [Show full text]
  • The Role of Language in Theory of Mind Development
    Top Lang Disorders Vol. 34, No. 4, pp. 313–328 Copyright c 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins The Role of Language in Theory of Mind Development Jill G. de Villiers and Peter A. de Villiers Various arguments are reviewed about the claim that language development is critically connected to the development of theory of mind. The different theories of how language could help in this process of development are explored. A brief account is provided of the controversy over the capacities of infants to read others’ false beliefs. Then the empirical literature on the steps in theory of mind development is summarized, considering studies on both typically developing and various language-delayed children. Suggestions are made for intervention by speech language pathologists to enhance the child’s access to understanding the minds of others. Key words: conversation, false beliefs, false complements, language, narrative, theory of mind N THIS ARTICLE, we review arguments on interactions between language and ToM I and evidence for the claim that children’s in both typically developing and language- language acquisition is crucially connected to delayed children. We end with suggestions their development of a theory of mind (ToM). for using language interventions to facilitate Theory of mind refers to the child’s ability to children’s understanding of their own mental understand that other people have minds, and states as well as the minds of others. those minds contain beliefs, knowledge, de- sires, and emotions that may be different from THEORIES OF THE RELATIONSHIP those of the child. If the child can figure out BETWEEN LANGUAGE AND ToM those contents, then other people’s behavior will begin to make sense and, therefore, be Many writers have made a convincing case predictable and explainable.
    [Show full text]
  • Connectionist Models of Language Processing
    Cognitive Studies Preprint 2003, Vol. 10, No. 1, 10–28 Connectionist Models of Language Processing Douglas L. T. Rohde David C. Plaut Massachusetts Institute of Technology Carnegie Mellon University Traditional approaches to language processing have been based on explicit, discrete represen- tations which are difficult to learn from a reasonable linguistic environment—hence, it has come to be accepted that much of our linguistic representations and knowledge is innate. With its focus on learning based upon graded, malleable, distributed representations, connectionist modeling has reopened the question of what could be learned from the environment in the ab- sence of detailed innate knowledge. This paper provides an overview of connectionist models of language processing, at both the lexical and sentence levels. Although connectionist models have been applied to the full and processes of actual language users: Language is as lan- range of perceptual, cognitive, and motor domains (see Mc- guage does. In this regard, errors in performance (e.g., “slips Clelland, Rumelhart, & PDP Research Group, 1986; Quin- of the tongue”; Dell, Schwartz, Martin, Saffran, & Gagnon, lan, 1991; McLeod, Plunkett, & Rolls, 1998), it is in their 1997) are no less valid than skilled language use as a measure application to language that they have evoked the most in- of the underlying nature of language processing. The goal is terest and controversy (e.g., Pinker & Mehler, 1988). This not to abstract away from performance but to articulate com- is perhaps not surprising in light of the special role that lan- putational principles that account for it. guage plays in human cognition and culture.
    [Show full text]
  • Consciousness and Theory of Mind: a Common Theory?
    Consciousness and Theory of Mind: a Common Theory? Keywords: Consciousness, Higher-Order Theories, Theory of Mind, Mind-reading, Metacognition. 1 Introduction Many of our mental states are phenomenally conscious. It feels a certain way, or to borrow Nagel's expression, there is something it is like to be in these states. Examples of phenomenally conscious states are those one undergoes while looking at the ocean or at a red apple, drinking a glass of scotch or a tomato juice, smelling coee or the perfume of a lover, listening to the radio or a symphonic concert, or feeling pain or hunger. A theory of consciousness has to explain the distinctive properties that phe- nomenally conscious states have and other kind of states lack. Higher-Order Representational (HOR) theories1 attempt to provide such an explanation. Ac- coding to these theories, phenomenally conscious states are those that are the objects of some kind of higher-order process or representation. There is some- thing higher-order, a meta-state, in the case of phenomenal conscious mental states, which is lacking in the case of other kind of states. According to these theories, consciousness depends on our Theory of Mind. A Theory of Mind, henceforth ToM, is the ability of humans to identify their own mental states and attribute mental states dierent from their owns to others. Such an ability can, at least conceptually, be decomposed into another two: mindreading and metacognition. Human beings are able to entertain representations of other people men- tal states thanks to the mindreading ability. We attribute beliefs, perceptions, feelings or desires to other people and predict and explain their behaviour ac- cordingly.
    [Show full text]