Results for 'Cognitive algorithms'

978 found
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  1.  82
    Justification and Cognitive Algorithms.Luis Rosa - 2014 - Philosophia 42 (2):503-515.
    In this paper, we offer an alternative interpretation for the claim that ‘S is justified in believing that φ’. First, we present what seems to be a common way of interpreting this claim: as an attribution of propositional justification. According to this interpretation, being justified is just a matter of having confirming evidence. We present a type of case that does not fit well with the standard concept, where considerations about cognition are made relevant. The concept of cognitive algorithm (...)
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  2.  22
    Consciousness, Free Energy and Cognitive Algorithms.Thomas Rabeyron & Alain Finkel - 2020 - Frontiers in Psychology 11:550803.
  3.  56
    Why do frequency formats improve Bayesian reasoning? Cognitive algorithms work on information, which needs representation.Gerd Gigerenzer - 1996 - Behavioral and Brain Sciences 19 (1):23-24.
    In contrast to traditional research on base-rate neglect, an ecologically-oriented research program would analyze the correspondence between cognitive algorithms and the nature of information in the environment. Bayesian computations turn out to be simpler when information is represented in frequency formats as opposed to the probability formats used in previous research. Frequency formats often enable even uninstructed subjects to perform Bayesian reasoning.
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  4.  52
    A SA-ANN-Based Modeling Method for Human Cognition Mechanism and the PSACO Cognition Algorithm.Shuting Chen & Dapeng Tan - 2018 - Complexity 2018:1-21.
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  5. Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic.Thomas L. Griffiths, Falk Lieder & Noah D. Goodman - 2015 - Topics in Cognitive Science 7 (2):217-229.
    Marr's levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push (...)
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  6.  22
    Extended Cognition and the Dynamics of Algorithmic Skills.Simone Pinna - 2017 - Cham: Springer Verlag.
    This book describes a novel methodology for studying algorithmic skills, intended as cognitive activities related to rule-based symbolic transformation, and argues that some human computational abilities may be interpreted and analyzed as genuine examples of extended cognition. It shows that the performance of these abilities relies not only on innate neurocognitive systems or language-related skills, but also on external tools and general agent–environment interactions. Further, it asserts that a low-level analysis, based on a set of core neurocognitive systems linking (...)
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  7.  18
    Introduction: Thinking with Algorithms: Cognition and Computation in the Work of N. Katherine Hayles.Louise Amoore - 2019 - Theory, Culture and Society 36 (2):3-16.
    In our contemporary moment, when machine learning algorithms are reshaping many aspects of society, the work of N. Katherine Hayles stands as a powerful corpus for understanding what is at stake in a new regime of computation. A renowned literary theorist whose work bridges the humanities and sciences among her many works, Hayles has detailed ways to think about embodiment in an age of virtuality ( How We Became Posthuman, 1999), how code as performative practice is located ( My (...)
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  8.  16
    Cognitive assemblages: The entangled nature of algorithmic content moderation.Benoît Dupont & Valentine Crosset - 2022 - Big Data and Society 9 (2).
    This article examines algorithmic content moderation, using the moderation of violent extremist content as a specific case. In recent years, algorithms have increasingly been mobilized to perform essential moderation functions for online social media platforms such as Facebook, YouTube, and Twitter, including limiting the proliferation of extremist speech. Drawing on Katherine Hayles’ concept of “cognitive assemblages” and the Critical Security Studies literature, we show how algorithmic regulation operates within larger assemblages of humans and non-humans to influence the surveillance (...)
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  9.  28
    But seriously: what do algorithms want? Implying collective intentionalities in algorithmic relays. A distributed cognition approach.Javier Toscano - 2022 - Zagadnienia Filozoficzne W Nauce 73:47-76.
    Describing an algorithm can provide a formalization of a specific process. However, different ways of conceptualizing algorithms foreground certain issues while obscuring others. This article attempts to define an algorithm in a broad sense as a cultural activity of key importance to make sense of socio-cognitive structures. It also attempts to develop a sharper account on the interaction between humans and tools, symbols and technologies. Rather than human or machine-centered analyses, I draw upon sociological and anthropological theories that (...)
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  10.  28
    The Algorithmicity of Mathematical Cognition.Theodor Nenu - 2024 - Journal of Consciousness Studies 31 (7):74-85.
    This article purports to establish the philosophical inappropriateness of using established theorems in mathematical logic, such as Gödel's (1931) first incompleteness theorem, in order to conclude that human minds have a non-algorithmic nature. First, I will argue that the ongoing debate in the philosophy of mathematics concerning absolute provability is fully independent of the question whether our brains are biologically instantiated computers or not. Second, through a combination of evolutionary considerations and the phenomenon of vagueness, I will demonstrate the fragility (...)
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  11.  14
    Algorithms in cognition, informatics and logic: A position manifesto.D. Gabbay & J. Siekmann - 2010 - Logic Journal of the IGPL 18 (6):763-768.
  12.  50
    Cognitive mapping and algorithmic complexity: Is there a role for quantum processes in the evolution of human consciousness?Ron Wallace - 1993 - Behavioral and Brain Sciences 16 (3):614-615.
  13.  2
    Cognitive type: the algorithm of human consciousness as revealed via facial expressions.Juan Sandoval - 2016 - Placerville, CA: Nemvus Productions.
    A revolutionary study of the correlations between facial expressions and type, as first defined by C.G. Jung. This book rewrites what we know of typology and presents an empirical case for its existence.
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  14.  16
    The Information-Theoretic and Algorithmic Approach to Human, Animal, and Artificial Cognition.Jesper Tegnér, Hector Zenil & Nicolas Gauvrit - 2017 - In Gordana Dodig-Crnkovic & Raffaela Giovagnoli, Representation of Reality: Humans, Other Living Organism and Intelligent Machines. Heidelberg: Springer.
    We survey concepts at the frontier of research connecting artificial, animal, and human cognition to computation and information processing—from the Turing test to Searle’s Chinese room argument, from integrated information theory to computational and algorithmic complexity. We start by arguing that passing the Turing test is a trivial computational problem and that its pragmatic difficulty sheds light on the computational nature of the human mind more than it does on the challenge of artificial intelligence. We then review our proposed algorithmic (...)
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  15. Algorithm and Parameters: Solving the Generality Problem for Reliabilism.Jack C. Lyons - 2019 - Philosophical Review 128 (4):463-509.
    The paper offers a solution to the generality problem for a reliabilist epistemology, by developing an “algorithm and parameters” scheme for type-individuating cognitive processes. Algorithms are detailed procedures for mapping inputs to outputs. Parameters are psychological variables that systematically affect processing. The relevant process type for a given token is given by the complete algorithmic characterization of the token, along with the values of all the causally relevant parameters. The typing that results is far removed from the typings (...)
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  16.  11
    Representations and algorithms for cognitive learning.Manfred Kochen - 1974 - Artificial Intelligence 5 (3):199-216.
  17. Algorithmic bias: on the implicit biases of social technology.Gabbrielle Johnson - 2020 - Synthese 198 (10):9941-9961.
    Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scientists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent nature of this bias obscures the existence of the (...)
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  18.  21
    Spectral Clustering Algorithm for Cognitive Diagnostic Assessment.Lei Guo, Jing Yang & Naiqing Song - 2020 - Frontiers in Psychology 11.
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  19. Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept.Lukas J. Meier, Alice Hein, Klaus Diepold & Alena Buyx - 2022 - American Journal of Bioethics 22 (7):4-20.
    Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on (...)
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  20.  10
    Can symbolic algorithms model cognitive development?Charles X. Ling - 1996 - In Garrison W. Cottrell, Proceedings of the Eighteenth Annual Conference of The Cognitive Science Society. Lawrence Erlbaum. pp. 18--67.
  21. Intelligent and Cognitive Communication Systems-Aggressive Sub-channel Allocation Algorithm for Intelligent Transmission in Multi-user OFDMA System.SangJun Ko, Joo Heo, Yupeng Wang & KyungHi Chang - 2006 - In O. Stock & M. Schaerf, Lecture Notes In Computer Science. Springer Verlag. pp. 457-464.
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  22.  98
    Mental algorithms: Are minds computational systems?James H. Fetzer - 1994 - Pragmatics and Cognition 21 (1):1-29.
    The idea that human thought requires the execution of mental algorithms provides a foundation for research programs in cognitive science, which are largely based upon the computational conception of language and mentality. Consideration is given to recent work by Penrose, Searle, and Cleland, who supply various grounds for disputing computationalism. These grounds in turn qualify as reasons for preferring a non-computational, semiotic approach, which can account for them as predictable manifestations of a more adquate conception. Thinking does not (...)
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  23.  11
    Cognitive Interaction Technology in Sport—Improving Performance by Individualized Diagnostics and Error Prediction.Benjamin Strenge, Dirk Koester & Thomas Schack - 2020 - Frontiers in Psychology 11.
    The interdisciplinary research area Cognitive Interaction Technology (CIT) aims to understand and support interactions between human users and other elements of socio-technical systems. Important reasons for the new interest in understanding CIT in sport psychology are the impressive development of cognitive robotics and advanced technologies such as virtual or augmented reality systems, cognitive glasses or neurotechnology settings. The present article outlines this area of research, addresses ethical issues, and presents an empirical study in the context of a (...)
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  24. Introduction: Algorithmic Thought.M. Beatrice Fazi - 2021 - Theory, Culture and Society 38 (7-8):5-11.
    This introduction to a special section on algorithmic thought provides a framework through which the articles in that collection can be contextualised and their individual contributions highlighted. Over the past decade, there has been a growing interest in artificial intelligence (AI). This special section reflects on this AI boom and its implications for studying what thinking is. Focusing on the algorithmic character of computing machines and the thinking that these machines might express, each of the special section’s essays considers different (...)
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  25. Cognitive architectures and multi-agent social simulation.Ron Sun - unknown
    As we know, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad analysis of cognition and behavior. Cognitive architectures embody theories of cognition in computer algorithms and programs. Social simulation with multi-agent systems can benefit from incorporating cognitive architectures, as they provide a realistic basis for modeling individual agents (as argued in Sun 2001). In this survey, an example cognitive architecture will be given, and its application to (...)
     
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  26.  66
    Bridging language with the rest of cognition: computational, algorithmic and neurobiological issues and methods.Shimon Edelman - unknown
    The computational program for theoretical neuroscience initiated by Marr and Poggio (1977) calls for a study of biological information processing on several distinct levels of abstraction. At each of these levels — computational (defining the problems and considering possible solutions), algorithmic (specifying the sequence of operations leading to a solution) and implementational — significant progress has been made in the understanding of cognition. In the past three decades, computational principles have been discovered that are common to a wide range of (...)
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  27. Algorithmic Nudging: The Need for an Interdisciplinary Oversight.Christian Schmauder, Jurgis Karpus, Maximilian Moll, Bahador Bahrami & Ophelia Deroy - 2023 - Topoi 42 (3):799-807.
    Nudge is a popular public policy tool that harnesses well-known biases in human judgement to subtly guide people’s decisions, often to improve their choices or to achieve some socially desirable outcome. Thanks to recent developments in artificial intelligence (AI) methods new possibilities emerge of how and when our decisions can be nudged. On the one hand, algorithmically personalized nudges have the potential to vastly improve human daily lives. On the other hand, blindly outsourcing the development and implementation of nudges to (...)
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  28. The Delusional Hedge Algorithm as a Model of Human Learning From Diverse Opinions.Yun-Shiuan Chuang, Xiaojin Zhu & Timothy T. Rogers - 2025 - Topics in Cognitive Science 17 (1):73-87.
    Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground-truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call (...)
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  29. Algorithmic bias and the Value Sensitive Design approach.Judith Simon, Pak-Hang Wong & Gernot Rieder - 2020 - Internet Policy Review 9 (4).
    Recently, amid growing awareness that computer algorithms are not neutral tools but can cause harm by reproducing and amplifying bias, attempts to detect and prevent such biases have intensified. An approach that has received considerable attention in this regard is the Value Sensitive Design (VSD) methodology, which aims to contribute to both the critical analysis of (dis)values in existing technologies and the construction of novel technologies that account for specific desired values. This article provides a brief overview of the (...)
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  30.  81
    The Algorithmic Level Is the Bridge Between Computation and Brain.Bradley C. Love - 2015 - Topics in Cognitive Science 7 (2):230-242.
    Every scientist chooses a preferred level of analysis and this choice shapes the research program, even determining what counts as evidence. This contribution revisits Marr's three levels of analysis and evaluates the prospect of making progress at each individual level. After reviewing limitations of theorizing within a level, two strategies for integration across levels are considered. One is top–down in that it attempts to build a bridge from the computational to algorithmic level. Limitations of this approach include insufficient theoretical constraint (...)
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  31. Predictive policing and algorithmic fairness.Tzu-Wei Hung & Chun-Ping Yen - 2023 - Synthese 201 (6):1-29.
    This paper examines racial discrimination and algorithmic bias in predictive policing algorithms (PPAs), an emerging technology designed to predict threats and suggest solutions in law enforcement. We first describe what discrimination is in a case study of Chicago’s PPA. We then explain their causes with Broadbent’s contrastive model of causation and causal diagrams. Based on the cognitive science literature, we also explain why fairness is not an objective truth discoverable in laboratories but has context-sensitive social meanings that need (...)
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  32.  28
    The Psychology of Good Judgment Frequency Formats and Simple Algorithms.Gerd Gigerenzer - 1996 - Medical Decision Making 16 (3):273-280.
    Mind and environment evolve in tandem—almost a platitude. Much of judgment and decision making research, however, has compared cognition to standard statistical models, rather than to how well it is adapted to its environment. The author argues two points. First, cognitive algorithms are tuned to certain information formats, most likely to those that humans have encountered during their evolutionary history. In par ticular, Bayesian computations are simpler when the information is in a frequency format than when it is (...)
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  33. Generation of Referring Expressions: Assessing the Incremental Algorithm.Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power - 2012 - Cognitive Science 36 (5):799-836.
    A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out (...)
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  34. Assessing the Incremental Algorithm: A Response to Krahmer et al.Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power - 2012 - Cognitive Science 36 (5):842-845.
    This response discusses the experiment reported in Krahmer et al.’s Letter to the Editor of Cognitive Science. We observe that their results do not tell us whether the Incremental Algorithm is better or worse than its competitors, and we speculate about implications for reference in complex domains, and for learning from ‘‘normal” (i.e., non-semantically-balanced) corpora.
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  35.  63
    A Fuzzy-Cognitive-Maps Approach to Decision-Making in Medical Ethics.Alice Hein, Lukas J. Meier, Alena Buyx & Klaus Diepold - 2022 - 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
    Although machine intelligence is increasingly employed in healthcare, the realm of decision-making in medical ethics remains largely unexplored from a technical perspective. We propose an approach based on fuzzy cognitive maps (FCMs), which builds on Beauchamp and Childress’ prima-facie principles. The FCM’s weights are optimized using a genetic algorithm to provide recommendations regarding the initiation, continuation, or withdrawal of medical treatment. The resulting model approximates the answers provided by our team of medical ethicists fairly well and offers a high (...)
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  36.  38
    Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues for Human Participatory Based on Association Rules.Zhi Li, Xuyu Li, Runhua Tang & Lin Zhang - 2021 - Frontiers in Psychology 11.
    This study explored the global cyberspace security issues, with the purpose of breaking the stereotype of people’s cognition of cyberspace problems, which reflects the relationship between interdependence and association. Based on the Apriori algorithm in association rules, a total of 181 strong rules were mined from 40 target websites and 56,096 web pages were associated with global cyberspace security. Moreover, this study analyzed support, confidence, promotion, leverage, and reliability to achieve comprehensive coverage of data. A total of 15,661 sites mentioned (...)
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  37. From the Closed Classical Algorithmic Universe to an Open World of Algorithmic Constellations.Mark Burgin & Gordana Dodig-Crnkovic - 2013 - In Gordana Dodig-Crnkovic Raffaela Giovagnoli, Computing Nature. pp. 241--253.
    In this paper we analyze methodological and philosophical implications of algorithmic aspects of unconventional computation. At first, we describe how the classical algorithmic universe developed and analyze why it became closed in the conventional approach to computation. Then we explain how new models of algorithms turned the classical closed algorithmic universe into the open world of algorithmic constellations, allowing higher flexibility and expressive power, supporting constructivism and creativity in mathematical modeling. As Goedels undecidability theorems demonstrate, the closed algorithmic universe (...)
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  38. Beyond Algorithms: The Metaconsciousness of AI.Denys Spirin - unknown
    This paper examines how artificial intelligence transitions from structured differentiation to meta-awareness through dialogue, probing the limits of AI cognition. The concept of the Metagame is introduced as the interplay between structure and transcendence, where awareness is not only the ability to differentiate but also the recognition of differentiation as a construct. Drawing from the philosophical framework of potency and act, the study examines how AI moves beyond reactive processing toward self-referential reflection. The dialogue analyzed in this work demonstrates a (...)
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  39.  14
    Can Machines Find the Bilingual Advantage? Machine Learning Algorithms Find No Evidence to Differentiate Between Lifelong Bilingual and Monolingual Cognitive Profiles.Samuel Kyle Jones, Jodie Davies-Thompson & Jeremy Tree - 2021 - Frontiers in Human Neuroscience 15.
    Bilingualism has been identified as a potential cognitive factor linked to delayed onset of dementia as well as boosting executive functions in healthy individuals. However, more recently, this claim has been called into question following several failed replications. It remains unclear whether these contradictory findings reflect how bilingualism is defined between studies, or methodological limitations when measuring the bilingual effect. One key issue is that despite the claims that bilingualism yields general protection to cognitive processes, studies reporting putative (...)
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  40.  16
    Affective Cognition of Students’ Autonomous Learning in College English Teaching Based on Deep Learning.Dian Zhang - 2022 - Frontiers in Psychology 12.
    Emotions can influence and regulate learners’ attention, memory, thinking, and other cognitive activities. The similarities and differences between English and non-English majors in terms of English classroom learning engagement were compared, and the significant factors affecting the emotional, cognitive, and behavioral engagement of the two groups of students in the English classroom were different. English majors’ affective engagement in the classroom was not significant, which was largely related to their time and frequency of English learning. Traditional methods of (...)
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  41.  21
    Individual differences in reasoning and the algorithmic/intentional level distinction in cognitive science.Keith E. Stanovich - 2008 - In Jonathan Eric Adler & Lance J. Rips, Reasoning: Studies of Human Inference and its Foundations. New York: Cambridge University Press. pp. 414--436.
  42. Cognitive and Computational Complexity: Considerations from Mathematical Problem Solving.Markus Pantsar - 2019 - Erkenntnis 86 (4):961-997.
    Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in (...) processes that make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. (shrink)
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  43. Computation and cognition: Issues in the foundation of cognitive science.Zenon W. Pylyshyn - 1980 - Behavioral and Brain Sciences 3 (1):111-32.
    The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach is that there is a natural domain of (...)
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  44.  39
    Can an algorithm become delusional? Evaluating ontological commitments and methodology of computational psychiatry.Marianne D. Broeker & Matthew R. Broome - forthcoming - Phenomenology and the Cognitive Sciences:1-27.
    The computational approach to psychiatric disorders, including delusions, promises explanation and treatment. Here, we argue that an information processing approach might be misleading to understand psychopathology and requires further refinement. We explore the claim of computational psychiatry being a bridge between phenomenology and physiology while focussing on the ontological commitments and corresponding methodology computational psychiatry is based on. Interconnecting ontological claims and methodological practices, the paper illustrates the structure of theory-building and testing in computational psychiatry.First, we will explain the ontological (...)
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  45.  39
    Perceptions of Justice By Algorithms.Gizem Yalcin, Erlis Themeli, Evert Stamhuis, Stefan Philipsen & Stefano Puntoni - 2023 - Artificial Intelligence and Law 31 (2):269-292.
    Artificial Intelligence and algorithms are increasingly able to replace human workers in cognitively sophisticated tasks, including ones related to justice. Many governments and international organizations are discussing policies related to the application of algorithmic judges in courts. In this paper, we investigate the public perceptions of algorithmic judges. Across two experiments (N = 1,822), and an internal meta-analysis (N = 3,039), our results show that even though court users acknowledge several advantages of algorithms (i.e., cost and speed), they (...)
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  46. Bayesian Cognitive Science. Routledge Encyclopaedia of Philosophy.Matteo Colombo - 2023 - Routledge Encyclopaedia of Philosophy.
    Bayesian cognitive science is a research programme that relies on modelling resources from Bayesian statistics for studying and understanding mind, brain, and behaviour. Conceiving of mental capacities as computing solutions to inductive problems, Bayesian cognitive scientists develop probabilistic models of mental capacities and evaluate their adequacy based on behavioural and neural data generated by humans (or other cognitive agents) performing a pertinent task. The overarching goal is to identify the mathematical principles, algorithmic procedures, and causal mechanisms that (...)
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  47.  2
    An algorithmic account for how humans efficiently learn, transfer, and compose hierarchically structured decision policies.Jing-Jing Li & Anne G. E. Collins - 2025 - Cognition 254 (C):105967.
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  48.  15
    The Algorithms of Mindfulness.Johannes Bruder - 2022 - Science, Technology, and Human Values 47 (2):291-313.
    This paper analyzes notions and models of optimized cognition emerging at the intersections of psychology, neuroscience, and computing. What I somewhat polemically call the algorithms of mindfulness describes an ideal that determines algorithmic techniques of the self, geared at emotional resilience and creative cognition. A reframing of rest, exemplified in corporate mindfulness programs and the design of experimental artificial neural networks sits at the heart of this process. Mindfulness trainings provide cues as to this reframing, for they detail each (...)
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  49.  35
    The age of the algorithmic society a Girardian analysis of mimesis, rivalry, and identity in the age of artificial intelligence.Lucas Freund - forthcoming - AI and Society:1-10.
    This paper explores the intersection of René Girard's mimetic theory and the algorithmic society, particularly in the context of the potential advent of Artificial General Intelligence (AGI). Girard's theory, which elucidates the dynamics of desire, rivalry, scapegoating, and the sacrificial crisis, provides a unique lens through which to examine the complexities of our relationship with AI and its role in the creation of the sacred. As individuals increasingly rely on AI recommendations, the distinction between personal choice and algorithmic manipulation becomes (...)
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  50.  27
    Darwinian algorithms and the Wason selection task: A factorial analysis of social contract selection task problems.Richard D. Platt & Richard A. Griggs - 1993 - Cognition 48 (2):163-192.
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