Explanation in Cognitive Science

It has been claimed that cognitive therapists endorse sets of uplifting beliefs BECAUSE the client feels better believing them: not because they lead towards greater verisimilitude, a purported cognitivists’ hallmark of rational choice. Since standard cognitive therapists sometimes ask us to choose sets of beliefs that interpret evidence on the basis of greater individual happiness (all other things being equal), this suggests that the basis of choice goes beyond rationality. I contend that the case against the rationality of cognitive therapy (...) is not made if one allows a broadening of what to count as rational cognitive therapy. The rationality of therapy consists in how well it achieves its goal. My claim is that at least one goal is, or ought to be, greater information value of the client’ dialogues. Among other things, information values encode affect. Understanding reason in this way effectively transforms our understanding of rationality in a way that may be incommensurable with the standard view. If these radically divergent approaches to cognitive therapy are incommensurable, there is no way to discover that we are still talking about the same thing. So, a challenge for this competing view is to say on what basis the term cognitive therapy may be projectable. I identify some constraints on this project and sketch a possible solution. (shrink)

We depend upon explanation in order to “make sense” out of our world. And, making sense is all the more important when dealing with change. But, what happens if our explanations are wrong? This question is examined with respect to two types of explanatory model. Models based on labels and categories we shall refer to as “representations.” More complex models involving stories, multiple algorithms, rules of thumb, questions, ambiguity we shall refer to as “compressions.” Both compressions and representations are reductions. (...) But representations are far more reductive than compressions. Representations can be treated as a set of defined meanings – coherence with regard to a representation is the degree of fidelity between the item in question and the definition of the representation, of the label. By contrast, compressions contain enough degrees of freedom and ambiguity to allow us to make internal predictions so that we may determine our potential actions in the possibility space. Compressions are explanatory via mechanism. Representations are explanatory via category. Managers are often confusing their evocation of a representation as the creation of a context of compression . When this type of explanatory error occurs, more errors follow. In the drive for efficiency such substitutions are all too often proclaimed – at the manager’s peril. (shrink)

Over the last fifteen years, an ambitious explanatory framework has been proposed to unify explanations across biology and cognitive science. Active inference, whose most famous tenet is the free energy principle, has inspired excitement and confusion in equal measure. Here, we lay the ground for proper critical analysis of active inference, in three ways. First, we give simplified versions of its core mathematical models. Second, we outline the historical development of active inference and its relationship to other theoretical approaches. Third, (...) we describe three different kinds of claim -- labelled mathematical, empirical and general -- routinely made by proponents of the framework, and suggest dialectical links between them. Overall, we aim to increase philosophical understanding of active inference so that it may be more readily evaluated. -/- This is a manuscript draft of the Introduction to the Topical Collection "The Free Energy Principle: From Biology to Cognition", forthcoming in Biology & Philosophy. (shrink)

Many ideas from Georg Nortoff’s works (published one paper in 2010, mainly his book in 2011, other papers in 2012, 2103, 2014, especially those related to Kant’s philosophy and the notion of the “observer”, the mind-brain problem, default mode network, the self, the mental states and their “correspondence” to the brain) are surprisingly very similar to my ideas published in my article from 2002, 2005 and my book from 2008. In two papers from 2002 (also my paper from 2005 and (...) my book 2008), following Kant’s philosophy, I introduced the notion of the “observer” for the mind-brain problem. After 2010 (mainly his book 2011 and other papers after this book), Nortoff also uses Kant’s philosophy (even if his knowledge about Kant’s philosophy is very superficial!) and the notion of the “observer” for the mind-brain problem in a methodology very similar to my methodology. Moreover, instead of EDWs, Nortoff uses a kind of “transdisciplinary” view, quite close to parallelism – the closest approach to my EDWs! In his works until 2014, Northoff’s conclusion within the unicorn world was different than my conclusion. However, in his book 2014 (two volumes) using notions like “correlations” and even “correspondences” many times, his conclusion is very closed to my EDWs! This dramatic change of framework in 3 years is quite unbelievable!!! Incredible many ideas from this book are very similar to my ideas from 2005 and 2008! (shrink)

Description Logics (DLs) are a family of formal knowledge representation formalisms and the most well-known formalisms in semantics-based systems. The central focus of this research is on logical-terminological characterisation/analysis of possibilistic and probabilistic descriptions of events in DLs. Based on a logical characterisation of the concept of `being', this paper conceptualises events within DLs world descriptions. Accordingly, it deals with the concepts of `possibility of events' and `probability of events'. The main goal of this research is to investigate how possible (...) and probable notations can, terminologically and logically, be interpreted and analysed within DLs world descriptions. In other words, the research logically-terminologically investigates how possibilistic and probabilistic descriptions of events can be expressed in DLs. (shrink)

The explosion of data generated during human interactions online presents an opportunity for psychologists to evaluate cognitive models outside the confines of the laboratory. Moreover, the size of these online data sets can allow researchers to construct far richer models than would be feasible with smaller in-lab behavioral data. In the current article, we illustrate this potential by evaluating 3 popular psychological models of generalization on 2 web-scale online data sets typically used to build automated recommendation systems. We show that (...) each psychological model can be efficiently implemented at scale and in certain cases can capture trends in human judgments that standard recommendation systems from machine learning miss. We use these results to illustrate the opportunity Internet-scale data sets offer to psychologists and to underscore the importance of using insights from cognitive modeling to supplement the standard predictive-analytic approach taken by many existing machine learning approaches. (shrink)

Clark (2018) worries that predictive processing accounts of perception introduce a puzzling disconnect between the content of personal-level perceptual states and their underlying subpersonal representations. According to PP, in perception, the brain encodes information about the environment in conditional probability density distributions over causes of sensory input. But it seems perceptual experience only presents us with one way the world is at a time. If perception is at bottom probabilistic, shouldn’t this aspect of subpersonally represented content show up in consciousness? (...) To address this worry, Clark argues that representations underlying personal-level content are constrained by the need to provide a single action-guiding take on the environment. However, this proposal rests a conception of the nature of agency, famously articulated by Davidson (1980a,b), that is inconsistent with a view of the mind as embodied-extended. Since Clark and other enactivist PP theorists present the extended mind as an important consequence of the predictive framework, the proposal is in tension with his complete view. I claim that this inconsistency could be resolved either by retaining the Davidsonian view of action and abandoning the extended-embodied approach, or by adopting a more processual, world-involving account of agency and perceptual experience than Clark currently endorses. To solve the puzzle he raises, Clark must become a radical enactivist or a consistent internalist. (shrink)

What is the relationship between the concepts of the predictive processing theory of brain functioning and the everyday concepts with which people conduct and explain their mental lives? To answer this question, we focus on predictive processing explanations of mental disorder that appeal to false inference. After distinguishing two concepts of false inference, we survey four ways of understanding the relationship between explanations of mental phenomena at the personal and sub-personal level. We then argue that if predictive processing accurately accounts (...) for psychological and socio-cultural dynamics, then personal and sub-personal concepts must display a looping relationship that can impact the nature of at least some mental phenomena. In particular, explaining the phenomenon of delusion in terms of the sub-personal concept of false inference should change the nature of delusional experiences themselves. (shrink)

For some, the states and processes involved in the realisation of phenomenal consciousness are not confined to within the organismic boundaries of the experiencing subject. Instead, the sub-personal basis of perceptual experience can, and does, extend beyond the brain and body to implicate environmental elements through one’s interaction with the world. These claims are met by proponents of predictive processing, who propose that perception and imagination should be understood as a product of the same internal mechanisms. On this view, as (...) visually imagining is not considered to be world-involving, it is assumed that world-involvement must not be essential for perception, and thus internalism about the sub-personal basis is true. However, the argument for internalism from the unity of perception and imagination relies for its strength on a questionable conception of the relationship between the two experiential states. I argue that proponents of the predictive approach are guilty of harbouring an implicit commitment to the common kind assumption which does not follow trivially from their framework. That is, the assumption that perception and imagination are of the same fundamental kind of mental event. I will argue that there are plausible alternative ways of conceiving of this relationship without drawing internalist metaphysical conclusions from their psychological theory. Thus, the internalist owes the debate clarification of this relationship and further argumentation to secure their position. (shrink)

At its core this book is concerned with logic and computation with respect to the mathematical characterization of sentient biophysical structure and its behavior. -/- Three related theories are presented: The first of these provides an explanation of how sentient individuals come to be in the world. The second describes how these individuals operate. And the third proposes a method for reasoning about the behavior of individuals in groups. -/- These theories are based upon a new explanation of experience in (...) nature, the construction of senses, and motile behavior. This new approach is developed from first principles to enable a rigorous and systematic explanation of the variety of associated intelligent behaviors. -/- Alongside this development is a further account that focuses upon the nature of our work. It discusses the existential aspects of scientific inquiry, its epistemology and logic. It seeks to clarify the nature of the mathematical characterization and computation of natural behaviors, dealing with questions in the foundations of logic. It explores methodological issues related to reduction and the refinement of ideas from intuition to formal logical structure. -/- In support of this inquiry we work toward the development of a calculus for biophysical construction and its dynamics. If successful this mechanics mathematically characterizes sensory and motile behavior. -/- Upon this foundation we propose a model of apprehension and explore how its products are processed by the organism. Finally, we develop a probabilistic theory that enables us to reason about inaccessible factors in group behavior. -/- The mechanics we propose suggests the design and physical realization of a new model of computation; one in which structure and the concurrency of action are a first-order consideration. -/- We identify opportunities for experimental verification of the theory and we suggest a proof of our results in practice by the identification of this mechanism, allowing the construction of machines that experience. (shrink)

After introducing the new field of cultural evolution, we review a growing body of empirical evidence suggesting that culture shapes what people attend to, perceive and remember as well as how they think, feel and reason. Focusing on perception, spatial navigation, mentalizing, thinking styles, reasoning (epistemic norms) and language, we discuss not only important variation in these domains, but emphasize that most researchers (including philosophers) and research participants are psychologically peculiar within a global and historical context. This rising tide of (...) evidence recommends caution in relying on one’s intuitions or even in generalizing from reliable psychological findings to the species, Homo sapiens. Our evolutionary approach suggests that humans have evolved a suite of reliably developing cognitive abilities that adapt our minds, information-processing abilities and emotions ontogenetically to the diverse culturally-constructed worlds we confront. (shrink)

In this article, we develop a new approach to integrating philosophical phenomenology with qualitative research. The approach uses phenomenology’s concepts, namely existentials, rather than methods such as the epoché or reductions. We here introduce the approach to both philosophers and qualitative researchers, as we believe that these studies are best conducted through interdisciplinary collaboration. In section 1, we review the debate over phenomenology’s role in qualitative research and argue that qualitative theorists have not taken full advantage of what philosophical phenomenology (...) has to offer, thus motivating the need for new approaches. In section 2, we introduce our alternative approach, which we call Phenomenologically Grounded Qualitative Research (PGQR). Drawing parallels with phenomenology’s applications in the cognitive sciences, we explain how phenomenological grounding can be used to conceptually front-load a qualitative study, establishing an explicit focus on one or more structures of human existence, or of our being in the world. In section 3, we illustrate this approach with an example of a qualitative study carried out by one of the authors: a study of the existential impact of early parental bereavement. In section 4, we clarify the kind of knowledge that phenomenologically grounded studies generate and how it may be integrated with existing approaches. (shrink)

Whether a person is pretending, or not, is a function of their beliefs and intentions. This poses a challenge to 4E accounts of pretense, which typically seek to exclude such cognitive states from their explanations of psychological phenomena. Resulting tensions are explored within three recent accounts of imagination and pretense offered by theorists working in the 4E tradition. A path forward is then charted, through considering ways in which explanations can invoke beliefs and intentions while remaining true to 4E precepts. (...) To make real progress in explaining pretense, 4E theorists will need to grow comfortable with the idea that two agents whose outward behaviors and environments are, in the short term, the same, may be guided by quite different beliefs and intentions, in virtue of which only one is pretending. In this way, the scientific project of explaining pretense remains inseparable from the more general project of determining which beliefs and intentions are appropriate to ascribe to which kinds of entities, given which kinds of behaviors. (shrink)

Recent developments in the literature suggest cognitive representation can be conceived of as a kind of mechanism that meets the functional profile set out by the S-representation account. However, this approach is threatened by worries that the S-representation account cannot tell a satisfactory story about content determination at the subpersonal level. One solution is to complement the S-representation account with a traditional etiological theory of content determination. This paper argues such a move is unwarranted and threatens the broader project of (...) understanding representation in mechanism-friendly terms. Once properly situated within a mechanistic framework, the S-representation account can address the issue of content determination without supplementing it with an etiological theory. In turn, this strengthens the notion that combing a mechanistic framework with the S-representation account provides a robust and comprehensive defense of representation’s positive role in cognitive science. (shrink)

This chapter will be a brief survey of the concepts from general philosophy of science for those interested in cognitive science. It covers several major topics in the philosophy of science: scientific explanation and underdetermination, reductionism and levels of nature, and scientific realism. We will discuss the goals of science, the methods of science, and the most plausible interpretations of science. To demonstrate the importance of these topics, the chapter includes cases in which confusion over these issues has led scientists (...) astray. These cases include instances in which cognitive neuroscience has failed to discover adequate explanations for phenomena, when previously established research did not withstand further scrutiny, and the increasingly complex and bewildering interrelationship between the study of the mind and the study of the brain and its parts. These issues are common to many areas of science, but they can be particularly fraught in a field like cognitive neuroscience, as researchers from a wide variety of disciplinary backgrounds and research foci come together to develop a systematic understanding of the mind. (shrink)

Objective: To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application. Study Design and Setting: This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC. Results: We find that concerns regarding explainability are (...) not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating unexplainable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine. Conclusion: Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems. (shrink)

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

Philosophers of psychology debate, among other things, which psychological models, if any, are (or provide) mechanistic explanations. This should seem a little strange given that there is rough consensus on the following two claims: 1) a mechanism is an organized collection of entities and activities that produces, underlies, or maintains a phenomenon, and 2) a mechanistic explanation describes, represents, or provides information about the mechanism producing, underlying, or maintaining the phenomenon to be explained (i.e. the explanandum phenomenon) (Bechtel and Abrahamsen (...) 2005; Craver 2007). If there is a rough consensus on what mechanisms are and that mechanistic explanations describe, represent, or provide information about them, then how is there no consensus on which psychological models are (or provide) mechanistic explanations? Surely the psychological models that are mechanistic explanations are the models that describe, represent, or provide information about mechanisms. That is true, of course; the trouble arises when determining what exactly that involves. Philosophical disagreement over which psychological models are mechanistic explanations is often disagreement about what it means to describe, represent, or provide information about a mechanism, among other things (Hochstein 2016; Levy 2013). In addition, one's position in this debate depends on a host of other seemingly arcane metaphysical issues, such as the nature of mechanisms, computational and functional properties (Piccinini 2016), and realization (Piccinini and Maley 2014), as well as the relation between models, methodologies, and explanations (Craver 2014; Levy 2013; Zednik 2015). Although I inevitably advocate a position, my primary aim in this chapter is to spell out all these relationships and canvas the positions that have been taken (or could be taken) with respect to mechanistic explanation in psychology, using dynamical systems models and cognitive models (or functional analyses) as examples. (shrink)

In this paper, I suggest that some tales developed in the literature of embodied and radical embodied cognitive science can contribute to the solution of two longstanding issues in the cognitive neuroscience of perception and action. The two issues are the fundamental problem of perception, or how to bridge the gap between sensations and the environment, and the fundamental problem of motor control, or how to better characterize the relationship between brain activity and behavior. In both cases, I am going (...) to propose that cognitive neuroscience could incorporate embodied insights—coming from the sensorimotor approach to perception and action, and from ecological psychology—to advance the solution for each issue without the need for abandoning or undergoing a substantial revision of its core assumptions. Namely, cognitive neuroscience could incorporate the forgotten tales of embodiment without undergoing through a complete revolution. In this sense, I am proposing not a call but a farewell to arms. (shrink)

One challenge faced by aesthetics is the development of an account able to trace out the continuities and discontinuities between general experience and aesthetic experiences. Regarding this issue, in this paper, I present an enactive model of some raw cognitive dynamics that might drive the progressive emergence of aesthetic experiences from the stream of general experience. The framework is based on specific aspects of John Dewey’s pragmatist philosophy and embodied aesthetic theories, while also taking into account research in ecological psychology, (...) cognitive sciences, and dynamic systems theory. The model focuses on dynamically relevant nodes at the pre-reflective and the reflective side of experience that would work as nested rhythmic constraints at different cognitive timescales with the potential to shunt experiences toward the aesthetic in everyday situations. My proposal constitutes a way to explore aesthetic experiences from an enactive perspective that regards them as transformative events in which cognitive processes entrain and are entrained by changes taking place in the environment, the brain, and the body. (shrink)

In this paper, we explore a rationalistic orientation in Western society. We suggest that this orientation is one of the predominant ways in which Western society tends to frame, understand and deal with a majority of problems and questions – namely in terms of mathematical analysis, calculation and quantification, relying on logic, numbers, and statistics. Our main goal in this paper is to uncover the affective structure of this rationalistic orientation. In doing so, we illustrate how this orientation structures the (...) way not only individuals but society as a whole frames and solves problems. We firstly point towards some exemplary instances of the rationalistic orientation, specifically regarding science, society, and lifeworld practice. Crucially, we argue that the rationalistic orientation is not merely based on a set of beliefs we could easily correct; but rather, that it is an affective condition tacitly shaping our engagement with the world in an encompassing way. Relating to the work of Martin Heidegger, we argue that what we have called an orientation in the beginning is in fact a rationalistic attunement. This attunement fundamentally shapes the pre-reflective level of how individuals approach the world. We elaborate this claim by showing how the rationalistic attunement concretely manifests in tangible socio-material affect dynamics. In the end, we motivate a critical stance towards this attunement, providing the ability to reflect upon and question instances where this way of framing and solving problems is counterproductive. (shrink)

Many debates in philosophy focus on whether folk or scientific psychological notions pick out cognitive natural kinds. Examples include memory, emotions and concepts. A potentially interesting type of kind is: kinds of mental representations (as opposed, for example, to kinds of psychological faculties). In this chapter we outline a proposal for a theory of representational kinds in cognitive science. We argue that the explanatory role of representational kinds in scientific theories, in conjunction with a mainstream approach to explanation in cognitive (...) science, suggest that representational kinds are multi-level. This is to say that representational kinds’ properties cluster at different levels of explanation and allow for intra- and inter-level projections. (shrink)

In the present article, I introduce Husserl’s analyses of ‘natural causality’ and ‘volitional causality’, which are collected in the volume ‘Wille und Handlung’ of the Husserliana edition Studien zur Struktur des Bewußtseins. My aim is to show that Husserl’s insight into these phenomena enables us to understand more clearly both the specificity of, and the relation between, the motivational nexus belonging to the sphere of the will in contrast with the causal laws of nature. In light of this understanding, in (...) the last part of the article I reflect on whether and to what extent there is, in fact, an ontological and epistemological compatibility between volitional causality and natural causality. (shrink)

Events—the experiences we think we are having and recall having had—are constructed; they are not what actually occurs. What occurs is ongoing dynamic, multidimensional, sensory flow, which is somehow transformed via psychological processes into structured, describable, memorable units of experience. But what is the nature of the redescription processes that fluently render dynamic sensory streams as event representations? How do such processes cope with the ubiquitous novelty and variability that characterize sensory experience? How are event‐rendering skills acquired and how do (...) event representations change with development? This review considers emerging answers to these questions, beginning with evidence that an implicit tendency to monitor predictability structure via statistical learning is key to event rendering. That is, one way that the experience of bounded events (e.g., actions within behavior, words within speech) arises is with the detection of “troughs” in sensory predictability. Interestingly, such troughs in predictability are often predictable; these regions of predictable‐unpredictability provide articulation points to demarcate one event from another in representations derived from the actual streaming information. In our information‐optimization account, a fluent event‐processor predicts such troughs and selectively attends to them—while suppressing attention to other regions—as sensory streams unfold. In this way, usage of attentional resources is optimized for efficient sampling of the most relevant, information‐rich portions of the unfolding flow of sensation. Such findings point to the development of event‐processing fluency—whether in action, language, or other domains—depending crucially on rapid and continual cognitive reorganization. As knowledge of predictability grows, attention is adaptively redeployed. Accordingly, event experiences undergo continuous alteration. (shrink)

‘Possibilist Explanation’ is a promising account of scientific explanation which avoids the familiar problems of “how-possibly explanations”. It explains an event by showing how-actually it was epistemically possible, instead of why it was epistemically necessary. Its explanandum is the epistemic possibility of an actual event previously considered epistemically impossible. To define PE, two new concepts are introduced: ‘permissive condition’ and ‘possibilist law’. A permissive condition for an event is something that does not entail the event itself, but a necessary condition (...) for it. A ‘Possibilist Law’ is a kind of scientific law that predicts that in the absence of a necessary condition N, the event E is not possible. Both PE and PL are legitimate and neglected parts of scientific knowledge and are especially suitable for human sciences. (shrink)

Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use (...) a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient of the speech signal is used as the input of the improved long and short-term memory network. To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed. (shrink)

Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach (...) is overall better supported by the empirical evidence. So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted. Naturalistic philosophers of mind should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true. For continuing on with an exclusive focus and praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of scientific practice in computational cognitive science as well as of the architecture of the mind. (shrink)

Over the last 30 years, representationalist and dynamicist positions in the philosophy of cognitive science have argued over whether neurocognitive processes should be viewed as representational or not. Major scientific and technological developments over the years have furnished both parties with ever more sophisticated conceptual weaponry. In recent years, an enactive generalization of predictive processing – known as active inference – has been proposed as a unifying theory of brain functions. Since then, active inference has fueled both representationalist and dynamicist (...) campaigns. However, we believe that when diving into the formal details of active inference, one should be able to find a solution to the war; if not a peace treaty, surely an armistice of a sort. Based on an analysis of these formal details, this paper shows how both representationalist and dynamicist sensibilities can peacefully coexist within the new territory of active inference. (shrink)

Most infant cognitive studies use visual fixation time as the measure of interest. There are, however, some serious methodological and theoretical concerns regarding what these studies reveal about infant cognition and how their results ought to be interpreted. We propose a Bayesian modeling framework which helps address these concerns. This framework allows us to more precisely formulate hypotheses about infants’ cognitive representations, formalize “linking hypotheses” that relate infants’ visual fixation behavior with stimulus complexity, and better determine what questions a given (...) experiment can and cannot answer. (shrink)

Hohwy et al.’s (2008) model of binocular rivalry (BR) is taken as a classic illustration of predictive coding’s explanatory power. I revisit the account and show that it cannot explain the role of reward in BR. I then consider a more recent version of Bayesian model averaging, which recasts the role of reward in (BR) in terms of optimism bias. If we accept this account, however, then we must reconsider our conception of perception. On this latter view, I argue, organisms (...) engage in what amounts to policy-driven, motivated perception. (shrink)

Events and event prediction are pivotal concepts across much of cognitive science, as demonstrated by the papers in this special issue. We first discuss how the study of events and the predictive processing framework may fruitfully inform each other. We then briefly point to some links to broader philosophical questions about events.

This paper addresses a challenge proposed against non-mindreading explanations of infant spontaneous-response task data. The challenge is a foundational assumption of mindreading explanations best summed up by Carruthers : 141-172, 2013, Consciousness and Cognition, 36: 498-507, 2015) claim that only by appealing to a theory of mind is it possible to explain infant responses in spontaneous-response false-belief tasks when there are no one-to-one correspondences between observable behavior and mental states. Heyes, 131–143, 2014a, Developmental Science, 17, 647–659. b) responds to this (...) challenge arguing discrete stimuli is informationally wealthy, and interprets the spontaneous-response results in terms of low-level perceptual novelty. However, Scott and Baillargeon : 660-664, 2014) challenge Heyes’ explanation on the grounds that her account of spontaneous-response false-belief tasks cannot also explain spontaneous-response ignorance tasks. In response to this, an enactive, ecological goal-tracking explanation of spontaneous-response tasks is presented, and argued to be superior to both mindreading explanations and Heyes’ account. This is done by recasting perception and anticipation and appealing to the central role social normativity plays in constraining social cognition. Finally, it is argued the cognitivist framework that both Heyes and mindreading proponents appeal to, begs the question in favor of the indispensability of mental representations for explaining perception and anticipation. (shrink)

An emerging perspective on human cognition and performance sees it as a kind of self-organizing phenomenon involving dynamic coordination across the body, brain and environment. Measuring this coordination faces a major challenge. Time series obtained from such cognitive, behavioral, and physiological coordination are often complicated in terms of non-stationarity and non-linearity, and in terms of continuous vs. categorical scales. Researchers have proposed several analytical tools and frameworks. One method designed to overcome these complexities is recurrence quantification analysis, developed in the (...) study of non-linear dynamics. It has been applied in various domains, including linguistic data or motion data. However, most previous studies have applied recurrence methods individually to categorical or continuous data. To understand how complex coordination works, an integration of these types of behavior is needed. We aimed to integrate these methods to investigate the relationship between language and motion directly. To do so, we added temporal information to categorical data, and applied joint recurrence analysis methods to visualize and quantify speech-motion coordination coupling during a rap performance. We illustrate how new dynamic methods may capture this coordination in a small case-study design on this expert rap performance. We describe a case study suggesting this kind of dynamic analysis holds promise, and end by discussing the theoretical implications of studying complex performances of this kind as a dynamic, coordinated phenomenon. (shrink)

Likert scales are useful for collecting data on attitudes and perceptions from large samples of people. In particular, they have become a well-established tool in soundscape studies for conducting in situ surveys to determine how people experience urban public spaces. However, it is still unclear whether the metrics of the scales are consistently interpreted during a typical assessment task. The current work aims at identifying some general trends in the interpretation of Likert scale metrics and introducing a procedure for the (...) derivation of metric corrections by analyzing a case study dataset of 984 soundscape assessments across 11 urban locations in London. According to ISO/TS 12913-2:2018, soundscapes can be assessed through the scaling of 8 dimensions: pleasant, annoying, vibrant, monotonous, eventful, uneventful, calm, and chaotic. The hypothesis underlying this study is that a link exists between correlations across the percentage of assessments falling in each Likert scale category and a dilation/compression factor affecting the interpretation of the scales metric. The outcome of this metric correction value derivation is introduced for soundscape, and a new projection of the London soundscapes according to the corrected circumplex space is compared with the initial ISO circumplex space. The overall results show a general non-equidistant interpretation of the scales, particularly on the vibrant-monotonous direction. The implications of this correction have been demonstrated through a Linear Ridge Classifier task for predicting the London soundscape responses using objective acoustic parameters, which shows significant improvement when applied to the corrected data. The results suggest that the corrected values account for the non-equidistant interpretation of the Likert metrics, thereby allowing mathematical operations to be viable when applied to the data. (shrink)

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 cognitive 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)

Can we think of a task in a distinctively practical way? Can there be practical concepts? In recent years, epistemologists, philosophers of mind, as well as philosophers of psychology have appealed to practical concepts in characterizing the content of know-how or in explaining certain features of skilled action. However, reasons for positing practical concepts are rarely discussed in a systematic fashion. This paper advances a novel argument for the psychological reality of practical concepts that relies on evidence for a distinctively (...) productive kind of reasoning. (shrink)

Many philosophers maintain that causation is to be explicated in terms of a kind of dependence between cause and effect. These “dependence” theories are opposed by “production” accounts which hold that there is some more fundamental causal “oomph”. A wide range of experimental research on everyday causal judgments seems to indicate that ordinary people operate primarily with a dependence-based notion of causation. For example, people tend to say that absences and double preventers are causes. We argue that the impression that (...) commonsense causal discourse is largely dependence-based is the result of focusing on a very narrow class of causal verbs. Almost all of the vignette-based experimental work on causal judgment has been prosecuted using the word “cause”. But much ordinary causal discourse involves special causal verbs, such as “burn” and “crack”. We find that these verbs display a quite different pattern from the verb “cause”. For instance, for absences and double preventers (Studies 1-3), we find that while people are inclined to say that X caused Y to burn, turn, crack or start, they are less inclined to think that X burned, turned, cracked or started Y. In Study 4, we find that for chains involving a distal and proximal event, people are inclined to say that the distal event is not a special cause of the outcome, though it is a “cause” of the outcome. Together, we find a surprising double dissociation between “cause” and a stock of special causal verbs. We conclude by suggesting that much commonsense causal judgment, which heavily trades in special causal verbs, might be better captured by production-based accounts of causation. (shrink)

Causal knowledge is not static; it is constantly modified based on new evidence. The present set of seven experiments explores 1 important case of causal belief revision that has been neglected in research so far: causal interpolations. A simple prototypic case of an interpolation is a situation in which we initially have knowledge about a causal relation or a positive covariation between 2 variables but later become interested in the mechanism linking these 2 variables. Our key finding is that the (...) interpolation of mechanism variables tends to be misrepresented, which leads to the paradox of knowing more: The more people know about a mechanism, the weaker they tend to find the probabilistic relation between the 2 variables (i.e., weakening effect). Indeed, in all our experiments we found that, despite identical learning data about 2 variables, the probability linking the 2 variables was judged higher when follow-up research showed that the 2 variables were assumed to be directly causally linked (i.e., C→E) than when participants were instructed that the causal relation is in fact mediated by a variable representing a component of the mechanism (M; i.e., C→M→E). Our explanation of the weakening effect is that people often confuse discoveries of preexisting but unknown mechanisms with situations in which new variables are being added to a previously simpler causal model, thus violating causal stability assumptions in natural kind domains. The experiments test several implications of this hypothesis. (shrink)

This book evaluates whether or not we can decide on the best theory of concepts by appealing to the explanatory results of cognitive science. It undertakes an in-depth analysis of different theories of concepts and of the explanations formulated in cognitive science. As a result, two reasons are provided for thinking that an appeal to cognitive science cannot help to decide on the best theory of concepts.

Investigates the hypothesis that spatial processes are involved in judgments on membership in a category. It is argued that membership versus non-membership of an object or a concept, in a category, is spatially simulated in mental space by a minimal continuum with 2 levels, left for membership and right for non-membership. In analogy to other embodied dimensions (e.g., time line or number line), the orientation of membership levels on the mental dimension is assumed to follow the acquired reading/writing schema, with (...) procedural primacy implying dominance, hence leftward positioning of dominant elements. This rationale is tested in 7 experiments. A recognition memory paradigm (modified 2AFC paradigm, Experiment 1) revealed that participants were faster indicating the location of an old word on the screen when displayed left within a pair of words, indicating a spatial representation of category membership (“member” = left, “nonmember” = right). For category discrimination (Experiment 2) we found faster and more accurate performance when a target word is presented left as compared with right. This pattern is replicated in Experiments 4a and 4b, with different response alternatives. Discriminating categories in a stimulus-response compatibility paradigm (Experiment 3), participants were faster making correct responses with their left hand than with their right hand in target category trials. In contrast, no differences were found for distractor trials. Experiments 5a and 5b address the spatial bias in spontaneous sorting situations. Overall, this pattern of results across the 7 experiments provides evidence in support of a spatial simulation of category assignment. (shrink)

Due to the increasingly heterogeneous trajectories of aging, gerontology requires theoretical models and empirical methods that can meaningfully, reliably, and precisely describe, explain, and predict causes and effects within the aging process, considering particular contexts and situations. Human behavior occurs in contexts; nevertheless, situational changes are often neglected in context-based behavior research. This article follows the tradition of environmental gerontology research based on Lawton’s Person-Environment-Interaction model and the theoretical developments of recent years. The authors discuss that, despite an explicit time (...) component, current P-E models could be strengthened by focusing on detecting P-E interactions in various everyday situations. Enhancing Lawton’s original formula via a situationally based component not only changes the theoretical perspectives on the interplay between person and environment but also demands new data collection approaches in empirical environmental research. Those approaches are discussed through the example of collecting mobile data with smartphones. Future research should include the situational dimension to investigate the complex nature of person environment interactions. (shrink)

Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...) philosophy of science, this framework is modeled after accounts of explanation in cognitive science. The framework distinguishes between the explanation-seeking questions that are likely to be asked by different stakeholders, and specifies the general ways in which these questions should be answered so as to allow these stakeholders to perform their roles in the Machine Learning ecosystem. By applying the normative framework to recently developed techniques such as input heatmapping, feature-detector visualization, and diagnostic classification, it is possible to determine whether and to what extent techniques from Explainable Artificial Intelligence can be used to render opaque computing systems transparent and, thus, whether they can be used to solve the Black Box Problem. (shrink)

People often answer why-questions with what we call experiential explanations: narratives or stories with temporal structure and concrete details. In contrast, on most theories of the epistemic function of explanation, explanations should be abstractive: structured by general relationships and lacking extraneous details. We suggest that abstractive and experiential explanations differ not only in level of abstraction, but also in structure, and that each form of explanation contributes to the epistemic goals of individual learners and of science. In particular, experiential explanations (...) support mental simulation and survive transitions across background theories; as a result, they support learning and help us translate between competing frameworks. Experiential explanations play an irreducible role in human cognition – and perhaps in science. (shrink)

When constrained by limited resources, how do we choose axioms of rationality? The target article relies on Bayesian reasoning that encounter serioustractabilityproblems. We propose another axiomatic foundation: quantum probability theory, which provides for less complex and more comprehensive descriptions. More generally, defining rationality in terms of axiomatic systems misses a key issue: rationality must be defined by humans facing vague information.

Optogenetics and DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) are important research tools in recent neurobiology. These tools allow unprecedented control over activity in specifically targeted neurons in behaving animals. Two approaches in philosophy of neuroscience, mechanism and ruthless reductionism, provide explicit accounts of experiments and results using tools like these, but each offers a different picture about how levels of mechanisms relate. I argue here that the ruthless reductionist’s direct mind‐to‐cellular/molecular activities linkages “in a single bound” better fits (...) with both the experimental designs using these tools and some of the scientists’ own judgments about their results than does the mechanist’s “nested hierarchies of mechanisms‐within‐mechanisms.” So at least some important work in current neuroscience appears to be ruthlessly reductive. Mechanism may not correctly characterize all current work in neuroscience, despite its recent popularity. (shrink)