Foundations Of Cognitive Science Research Articles

Comparing cognition across major transitions using the hierarchy of formal automata.

The evolution of cognition can be understood in terms of a few major transitions-changes in the computational architecture of nervous systems that changed what cognitive capacities could be evolved by downstream lineages. We demonstrate how the idea of a major cognitive transition can be modeled in terms of where a system's effective computational architecture falls on the well-studied hierarchy of formal automata (HFA). We then use recent work connecting artificial neural networks to the HFA, which provides a way to make the structure-architecture link in natural systems. We conclude with reflections on the power and the challenges of traditional thinking when applied to neural architectures. This article is categorized under: Cognitive Biology > Evolutionary Roots of Cognition Psychology > Comparative Philosophy > Foundations of Cognitive Science.

Computation for cognitive science: Analog versus digital.

Cognitive science was founded on the idea that the mind/brain can be understood in computational terms. While computational modeling in science is ubiquitous, cognitive science takes the stronger stance that the mind/brain literally performs computations. Moreover, performing computations is crucial to explaining what the mind/brain does, qua mind/brain. Unfortunately, most scientists fail to consider analog computation as a legitimate and theoretically useful type of computation in addition to digital computation; to the extent that analog computation is acknowledged, it is mostly based on a simplistic and incomplete understanding. Taking computation to consist of only one type (i.e., digital) while ignoring another, interestingly distinct type (i.e., analog) leads to an impoverished understanding of what it could mean for minds/brains to compute. A full appreciation and understanding of analog computation-particularly in relation to digital computation-allows researchers to develop computational frameworks and hypotheses in new and exciting ways. Thus, somewhat counterintuitively, looking to the once-dominant computing paradigm of yesteryear can provide novel computational ways of thinking about the mind and brain. This article is categorized under: Philosophy > Foundations of Cognitive Science.

The 21st century engram.

The search for the engram-the neural mechanism of memory-has been a guiding research project for neuroscience since its emergence as a distinct scientific field. Recent developments in the tools and techniques available for investigating the mechanisms of memory have allowed researchers to proclaimed the search is over. While there is ongoing debate about the justification for that claim, renewed interest in the engram is clear. This attention highlights the impoverished status of the engram concept. As research accelerates, the simple characterization of the engram as an enduring physical change is stretched thin. Now that the engram commitment has been made more explicit, it must also be made more precise. If the project of 20th century neurobiology was finding the engram, the project of the 21st must be supplying a richer account of what's been found. This paper sketches a history of the engram, and a way forward. This article is categorized under: Philosophy > Foundations of Cognitive Science.

Embodiment and language.

The findings of cognitive linguistics demonstrate the thoroughly embodied grounding of linguistic constructions and linguistic meaning ranging from abstract thought to interactive communication. A historical survey and updated summary of work in this area illustrates the many layers of bodily meaning that we rely on when thinking and communicating as human beings. Key distinctions, definitions, and clarifications, plus an overview of key works on embodied cognition in cognitive linguistics provide necessary context for understanding specific aspects of linguistic embodiment, including schemas and iconicity, mapping and metaphor, categories and projections, embodied grammar and abstract thought, intersubjectivity, and textual meaning. Realigning philosophical presuppositions with the findings of cognitive linguistics has important consequences: Body and mind can be reunited in lived experience. Both imaginative thought and rational thought can be understood as reliant on the same movement and memory structures. Even the most habituated form-content relationships in language can be understood as growing out of vital networks of real-world experiential relations, from the personal to the interpersonal. And instead of being understood as the narrow purview of semantics and pragmatics, the study of meaning can be embraced as the purpose and function of linguistics. These consequences have potential for revolutionizing scientific inquiry and theory building across a wide array of disciplines. This article is categorized under: Linguistics > Cognitive Linguistics Psychology > Language Philosophy > Foundations of Cognitive Science.

Causal inference in cognitive neuroscience.

Causal inference is a key step in many research endeavors in cognitive science and neuroscience, and particularly cognitive neuroscience. Statistical knowledge is sufficient for prediction and diagnosis, but causal knowledge is required for action and intervention. Most statistics courses and textbooks emphasize the difficulty of causal inference, focusing on the maxim that "correlation does not mean causation": there can be multiple causal possibilities, often many of them, consistent with given observed statistics. This paper focuses instead on the conceptual issues and assumptions that confront causal and other kinds of inference, primarily focusing on cognitive neuroscience. We connect inference methods with goals and challenges, and provide concrete guidance about how to select appropriate tools for the scientific task. This article is categorized under: Psychology > Theory and Methods Philosophy > Foundations of Cognitive Science.

What attention is. The priority structure account.

'Everyone knows what attention is' according to William James. Much work on attention in psychology and neuroscience cites this famous phrase only to quickly dismiss it. But James is right about this: 'attention' was not introduced into psychology and neuroscience as a theoretical concept. I argue that we should therefore study attention with broadly the same methodology that David Marr has applied to the study of perception. By focusing more on Marr's Computational Level of analysis, we arrive at a unified answer to the question of what attention is, what role it plays in the mind, and why organisms like us have that capacity. I propose a methodology for studying attention at Marr's Computational Level that optimizes in a three-dimensional space: it should capture core aspects of our first-person experience of attention, be explanatorily powerful in psychology and neuroscience, and fertile in an interdisciplinary context. I show how this methodology leads to what I call the priority structure account of attention. Attention is what organizes current information to make it more useful for the organism. We can identify it by four features. Attention, in this way, helps a cognitive system to integrate its informational state with its current motivational state. I describe how this account improves on alternatives and shows why attention is a useful concept in many disciplines and for connecting them. This article is categorized under: Philosophy > Psychological Capacities Psychology > Attention Philosophy > Foundations of Cognitive Science.

A Critique of the Philosophical Presuppositions of Cognitive Psychology

In the historical interaction of science and philosophy, empirical science has used cumulative research to shed new light on how the science of philosophy understands fundamental scientific issues. As an effort of self-renewal, the initial impetus for the birth of cognitive science was dialogue among disciplines, with a view to making it possible to unlock the mysteries of cognition. In other words, cognitive science was to pave the way for fundamentally updating the philosophical premises of the science of mind or even making possible a new scientific worldview. As a branch of cognitive science, psychology was to have contributed to the renovation of the ontological, methodological and scientific worldview of cognitive science through its unique interpretation of human nature. However, in the course of the development of cognitive science and psychology, psychology has failed to employ its disciplinary endeavors to renew the ontology, methodology and values of the philosophical foundations of cognitive science, and has also failed to contribute to the promotion of cognitive science. On the contrary, cognitive science’s mechanistic worldview and positivist methodology are the sole determinants of the background of cognitive science that shapes psychology’s model of development—that is, cognitive psychology (if one sees cognitive psychology as a concrete manifestation of the interaction of cognitive science and psychology). This study examines cognitive psychology’s ontological, methodological and value presuppositions and scientific worldview and reflects on the blind and passive nature of the discipline’s development. Moreover, from a history of science perspective, it considers how the development of this discipline can contribute to the renewal of scientific concepts in the philosophy of science through the interaction of the mature natural sciences and philosophy and thence provide possibilities for handling the relationship between psychology and philosophy in psychology’s future development.

What is attention? Adverbialist theories.

This article presents theories of attention that attempt to derive their answer to the question of what attention is from their answers to the question of what it is for some activity to be done attentively. Such theories provide a distinctive account of the difficulties that are faced by the attempt to locate processes in the brain by which the phenomena of attention can be explained. Their account does not share the pessimism of theories suggesting that the concept of attention is defective. Instead it reconstrues the explanatory relationship between attention and the processes that constitute it, in a way that is illustrated here by considering the relationship between attention and the processes that are identified by the biased competition theory. After considering some of the ways in which an adverbialist approach might be developed, the article concludes by suggesting some possible solutions to a problem concerning distraction, by which prominent adverbialist theories of attention have been dogged. This article is categorized under: Psychology > Attention Philosophy > Metaphysics Philosophy > Foundations of Cognitive Science.

Fundamental Physics and Computation: The Computer-Theoretic Framework

The central goal of this manuscript is to survey the relationships between fundamental physics and computer science. We begin by providing a short historical review of how different concepts of computer science have entered the field of fundamental physics, highlighting the claim that the universe is a computer. Following the review, we explain why computational concepts have been embraced to interpret and describe physical phenomena. We then discuss seven arguments against the claim that the universe is a computational system and show that those arguments are wrong because of a misunderstanding of the extension of the concept of computation. Afterwards, we address a proposal to solve Hempel’s dilemma using the computability theory but conclude that it is incorrect. After that, we discuss the relationship between the proposals that the universe is a computational system and that our minds are a simulation. Analysing these issues leads us to proposing a new physical principle, called the principle of computability, which claims that the universe is a computational system (not restricted to digital computers) and that computational power and the computational complexity hierarchy are two fundamental physical constants. On the basis of this new principle, a scientific paradigm emerges to develop fundamental theories of physics: the computer-theoretic framework (CTF). The CTF brings to light different ideas already implicit in the work of several researchers and provides a new view on the universe based on computer theoretic concepts that expands the current view. We address different issues regarding the development of fundamental theories of physics in the new paradigm. Additionally, we discuss how the CTF brings new perspectives to different issues, such as the unreasonable effectiveness of mathematics and the foundations of cognitive science.

COVID-19: Nurses have responded, now it is time to support them as we move forward.

The inspiration for The American Nurse Project, Dr. Rhonda Collins, DNP, RN, FAAN, is Chief Nursing Officer for Vocera Communications. Every year around Nurses Week, Dr. Collins publishes a report examining important issues that impact the nursing profession worldwide. Her 2020 CNO report looks at many of the challenges impacting nurses before, during, and after COVID-19—highlighting the mental anguish and physical burdens that COVID-19 has placed on nurses and other frontline healthcare workers as they put themselves in harm’s way to protect others. Dr. Collins examines the foundation of cognitive science and outlines a three-point strategy to guide hospital and nurse leaders moving forward as they strive to support staff members: lightening clinicians’ cognitive load by addressing the difference between documentation and communication, relieving the burden of adapting to multiple systems by giving clinicians control over how they communicate, and providing clinicians with clear, contextual, just-in-time information—using software to enhance workflow, not distract from it. During these unprecedented times, health leaders can honour nurses by providing them with the tools to help strengthen resiliency and healing from this crisis.

Bayesian modeling of the mind: From norms to neurons.

Bayesian decision theory is a mathematical framework that models reasoning and decision-making under uncertain conditions. The past few decades have witnessed an explosion of Bayesian modeling within cognitive science. Bayesian models are explanatorily successful for an array of psychological domains. This article gives an opinionated survey of foundational issues raised by Bayesian cognitive science, focusing primarily on Bayesian modeling of perception and motor control. Issues discussed include the normative basis of Bayesian decision theory; explanatory achievements of Bayesian cognitive science; intractability of Bayesian computation; realist versus instrumentalist interpretation of Bayesian models; and neural implementation of Bayesian inference. This article is categorized under: Philosophy > Foundations of Cognitive Science.

The science of belief: A progress report.

The empirical study of belief is emerging at a rapid clip, uniting work from all corners of cognitive science. Reliance on belief in understanding and predicting behavior is widespread. Examples can be found, inter alia, in the placebo, attribution theory, theory of mind, and comparative psychological literatures. Research on belief also provides evidence for robust generalizations, including about how we fix, store, and change our beliefs. Evidence supports the existence of a Spinozan system of belief fixation: one that is automatic and independent of belief rejection. Independent research supports the existence of a system of fragmented belief storage: one that relies on large numbers of causally isolated, context-sensitive stores of belief in memory. Finally, empirical and observational data support at least two systems of belief change. One system adheres, mostly, to epistemological norms of updating; the other, the psychological immune system, functions to guard our most centrally held beliefs from potential inconsistency with newly formed beliefs. Refining our understanding of these systems can shed light on pressing real-world issues, such as how fake news, propaganda, and brainwashing exploit our psychology of belief, and how best to construct our modern informational world. This article is categorized under: Psychology > Reasoning and Decision Making Philosophy > Knowledge and Belief Philosophy > Foundations of Cognitive Science.

Cognitive Modeling Approach for Dealing with Challenges in Cyber-Physical Systems

In this paper, inspired by our previous works, we propose an architecture for the design and realization of cyber-physical systems (CPS) that considers the spatio-temporal context of events, promotes anomaly detection, facilitates efficient human-computer interaction and is capable of discovering novel human and/or machine knowledge. We view deep neural networks as smart sensors and sensory data from the environment represents the semantic and episodic input to a consistency seeking component of the cyber-space. Starting from a knowledge base infused with a deterministic world assumption, this module can detect anomalies and correct estimation errors by combining the outputs of multiple sensors. We also exploit an episodic description of ongoing situations by integrating temporal segmentation with kernel and low-dimensional embedding based methods. We demonstrate parts of the architecture through illustrative examples on our self-collected driving dataset. Our framework can be related to cognitive science foundations and may facilitate reliable functioning of CPS through integrating traditional AI and deep learning methods with deterministic models and reasoning tools. We expect that such knowledge base and cognition driven approaches of joining deep neural networks will be adopted in complex CPS. This looks like a scalable, and beneficial match between human knowledge and the exploding deep learning technologies.

Peirce y la ciencia cognitiva

In this paper I try to establish the relevance of Peirce's Semeiotics to the conceptual foundations of Cognitive Science. Given Cognitive Science's commitment to the Representational Theory of Mind, I try to clarify the nature of mental representation from the standpoint of Peirce's general theory of signs. As it turns out, mental representations, because of their special role as interpretants of non-mental signs, present especial problems for their interpretation, whose solution Peirce anticipated in a way very close to that developed in Cognitive Science.

The mind-body problem.

The mind-body problem is the problem of explaining how the happenings of our mental lives are related to physical states, events and processes. Proposed solutions to the problem vary by whether and how they endorse physicalism, the claim that mental states are ultimately "nothing over and above" physical states, and by how they understand the interactions between mental and physical states. Physicalist solutions to the mind-body problem have been dominant in the last century, with the variety of physicalism endorsed (reductive or nonreductive) depending upon both the outcome of philosophical arguments and methodological developments in the cognitive and neural sciences. After outlining the dominant contemporary approach to the mind-body problem, I examine the prospects for a solution in light of developments in the cognitive sciences, especially the scientific study of consciousness. This article is categorized under: Philosophy > Consciousness Philosophy > Metaphysics Philosophy > Foundations of Cognitive Science.

Introduction to Special Issue: Foundations of Cognitive Science for the Design of Human–Computer Interactive Systems

The primary goal of this special issue is to provide an overview of select topics in cognitive science and their applications to the design of interactive systems. Authors were requested to show ho...

Are insects sentient?

I comment on the methodology used by Klein & Barron for dealing with the question of insect sentience and I briefly make a proposal of my own. Once it is granted that insects are sentient, a further question arises: which insects are subject to which states of sentience? Do insects feel pain, for example? If so, which ones? On the further question, I note, Klein & Barron have nothing to say.

Basic mathematical cognition.

Mathematics is a powerful tool for describing and developing our knowledge of the physical world. It informs our understanding of subjects as diverse as music, games, science, economics, communications protocols, and visual arts. Mathematical thinking has its roots in the adaptive behavior of living creatures: animals must employ judgments about quantities and magnitudes in the assessment of both threats (how many foes) and opportunities (how much food) in order to make effective decisions, and use geometric information in the environment for recognizing landmarks and navigating environments. Correspondingly, cognitive systems that are dedicated to the processing of distinctly mathematical information have developed. In particular, there is evidence that certain core systems for understanding different aspects of arithmetic as well as geometry are employed by humans and many other animals. They become active early in life and, particularly in the case of humans, develop through maturation. Although these core systems individually appear to be quite limited in application, in combination they allow for the recognition of mathematical properties and the formation of appropriate inferences based upon those properties. In this overview, the core systems, their roles, their limitations, and their interaction with external representations are discussed, as well as possibilities for how they can be employed together to allow us to reason about more complex mathematical domains.

Indexing the World? Visual Tracking, Modularity, and the Perception–Cognition Interface

Research in vision science, developmental psychology, and the foundations of cognitive science has led some theorists to posit referential mechanisms similar to indices. This hypothesis has been framed within a Fodorian conception of the early vision module. The article shows that this conception is mistaken, for it cannot handle the ‘interface problem’—roughly, how indexing mechanisms relate to higher cognition and conceptual thought. As a result, I reject the inaccessibility of early vision to higher cognition and make some constructive remarks on the perception–cognition interface. 1 The Case for Visual Indices 1.1 Preliminary assumptions 1.2 Transcendental arguments 1.3 Evidence from vision science2 Visual Indices, Object Files, and Fodorian Modularity3 The Interface Problem 3.1 Top-down attention and modularity 3.2 Selective attention and information4 Revising the Indexing Hypothesis 4.1 Revising the perception–cognition interface 4.2 Revising the modularity of early vision5 Concluding Remarks

Box-and-arrow explanations need not be more abstract than neuroscientific mechanism descriptions.

The nature of the relationship between box-and-arrow (BA) explanations and neuroscientific mechanism descriptions (NMDs) is a key foundational issue for cognitive science. In this article we attempt to identify the nature of the constraints imposed by BA explanations on the formulation of NMDs. On the basis of a case study about motor control, we argue that BA explanations and NMDs both identify regularities that hold in the system, and that these regularities place constraints on the formulation of NMDs from BA analyses, and vice versa. The regularities identified in the two kinds of explanation play a crucial role in reasoning about the relationship between them, and in justifying the use of neuroscientific experimental techniques for the empirical testing of BA analyses of behavior. In addition, we make claims concerning the similarities and differences between BA analyses and NMDs. First, we argue that both types of explanation describe mechanisms. Second, we propose that they differ in terms of the theoretical vocabulary used to denote the entities and properties involved in the mechanism and engaging in regular, mutual interactions. On the contrary, the notion of abstractness, defined as omission of detail, does not help to distinguish BA analyses from NMDs: there is a sense in which BA analyses are more detailed than NMDs. In relation to this, we also focus on the nature of the extra detail included in NMDs and missing from BA analyses, arguing that such detail does not always concern how the system works. Finally, we propose reasons for doubting that BA analyses, unlike NMDs, may be considered “mechanism sketches.” We have developed these views by critically analyzing recent claims in the philosophical literature regarding the foundations of cognitive science.