Chemical visualizations and models are special kinds of situated, inductive arguments. In this paper, I examine several historical case studies—an archive of images from museums, special collections, and popular magazines—as examples of emergent practices of physical modeling as theoretical play which became the basis for molecular biology and structural chemistry. Specifically, I trace a legacy of visualization tools that starts with Archibald Scott Cooper and Friedrich Kekulé in the late 1800s, crystallizes as material manipulatives in Kekulé’s student Jacobus Henricus (...) Van’t Hoff and his folded paper “toys,” is legitimized in the California lab of Linus Pauling, and is glorified in the popular imaginary with James Watson and Francis Crick’s model of DNA. My tracing then follows several threads into contemporary modeling practices. I ultimately argue that modeling play, originally outside of the boundary of deductive, positivist science, is now an accepted mode of inductive reasoning in these related chemical fields. (shrink)

This article analyses the fallacy of wrenching from context, using the dialectical notions of commitment and implicature as tools. The data, a set of key examples, is used to sharpen the conceptual borderlines around the related fallacies of straw man, accent, misquotation, and neglect of qualifications. According to the analysis, the main characteristics of wrenching from context are the manipulation of the meaning of the other’s statement through devices such as the use of misquotations, selective quotations, and quoting out of (...) context. The theoretical tools employed in the analysis are pragmatic theories of meaning and a dialectical model of commitment, used to explain how and why a standpoint is distorted. The analysis is based on a conception of fallacies as deceptive strategic moves in a game of dialogue. As a consequence, our focus is not only on misquotations as distortions of meaning, but on how they are used as dialectical tools to attack an opponent or win a dispute. Wrenching from context is described as a fallacy of unfairly attributing a commitment to another party that he never held. Its power as a deceptive argumentation tactic is based on complex mechanisms of implicit commitments and on their misemployment to improperly suggest an attribution of commitment. (shrink)

Both the irreducible complexity of biological phenomena and the aim of a universalized biology (life-as-it-could-be) have lead to a deep methodological shift in the study of life; represented by the appearance of ALife, with its claim that computational modelling is the main tool for studying the general principles of biological phenomenology. However this methodological shift implies important questions concerning the aesthetic, engineering and specially the epistemological status of computational models in scientific research: halfway between the well established categories of (...) theory and experiment. ALife models become powerful epistemic artefacts allowing the simulation of emergent phenomena, the interaction between different levels of organization and the integration of different causal factors in the very same manipulable object. The use of computational models in ALife can be classified in four main categories depending on their position between theoretical and empirical practices: generic, conceptual, functional and mechanistic. For each of these categories we analyse their epistemic value and select paradigmatic examples that illustrate how ALife models can be fruitfully inserted in the study of life. (shrink)

In this paper we introduce the view that realism about a social kind K entails that the grounding conditions of K are difficult (or impossible) to manipulate. In other words, we define social kind realism in terms of relative frame manipulability (RFM). In articulating our view, we utilize theoretical resources from Epstein’s (Epstein, The ant trap: Rebuilding the foundations of the Social Sciences. Oxford University Press, 2015) grounding/anchoring model and causal interventionism. After comparing our view with causal and principle-based (Tahko, (...) Synthese 200(2):1–23, 2022) proposals, we motivate RFM by showing that it accommodates important desiderata about the social landscape (such as recognizing the context-relativity of social properties and the emancipatory dimension of social practice). Finally, we consider three objections. First, we tackle frame-necessitarianism (FN), the view that social kind frames are metaphysically necessary (and thus unmanipulable). Secondly, we engage with what Epstein (Epstein, Philosophy and Phenomenological Research, 99(3):768–781 2019a) calls UNIVERSALITY (the view that social kinds can hold in the absence of anchors) and we argue that it should also be resisted. Finally, we tackle a recent objection from Mason’s (Mason, Philosophical Studies, 178(12):3975–3994) essentialism about social kinds. (shrink)

Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic resonance imaging, (...) a novel neuroimaging method whose significance for current debates on psychological models and mechanistic explanation has yet to be explored. (shrink)

In this paper, a two-link manipulator system stability performance is designed and analyzed using Optimal control technique. The manipulator system is highly nonlinear and unstable. The system is modelled using Lagrangian equation and linearized in upward unstable position. The closed loop system is designed using optimal sliding mode controller. The system is compared with a known PID controller with an impulse applied and disturbance torques and a promising results has been obtained.

Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aims to make the socio-organizational context of AI systems accessible to their users. In this work, we suggest extending the ST framework to address the risks of social misattributions in Large Language Models (LLMs), particularly in sensitive areas like mental health. In fact LLMs, which are remarkably capable of simulating roles and personas, may (...) lead to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors, cases of epistemic injustice, and unwarranted trust. To address these issues, we propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users. This addition aims to bridge the gap between LLM capabilities and user perceptions, promoting the ethically responsible development and use of LLM-based technology. (shrink)

Several quantitative studies (e.g. Kidd & Castano, 2013a; Djikic et al., 2013) have shown a positive correlation between literary reading and empathy. However, the literary nature of the stimuli used in these studies has not been defined at a more detailed, stylistic level. In order to explore the stylistic underpinnings of the hypothesized link between literariness and empathy, we conducted a qualitative experiment in which the degree of stylistic foregrounding was manipulated. Subjects (N = 37) read versions of Katherine Mansfield's (...) 'The Fly', a short story rich in foregrounding, while marking striking and evocative passages of their choosing. Afterwards, they were asked to select three markings and elaborate on their experiences in writing. One group read the original story, while the other read a 'non-literary' version, produced by an established author of suspense fiction for young adults, where stylistic foregrounding was reduced. We found that the non-literary version elicited significantly more (p < 0.05) explicitly empathic responses than the original story. This finding stands in contradiction to widely accepted assumptions in recent research, but can be assimilated in alternative models of literariness and affect in literary reading (e.g. Cupchik et al., 1998). We present an analysis of the data with a view to offering more than one interpretation of the observed effects of stylistic foregrounding. (shrink)

Causation has always been a philosophically controversial subject matter. While David Hume’s empiricist account of causation has been the dominant influence in analytic philosophy and science during modern times, a minority view has instead connected causation essentially to agency and manipulation. A related approach has for the first time gained widespread popularity in recent years, due to new powerful theories of causal inference in science that are based in a technical notion of intervention, and James Woodward’s closely connected interventionist theory (...) of causation in philosophy. This monograph assesses five manipulationist or interventionist theories of causation, viewed as theories that purport to tell us what causation is by providing us with the meaning of causal claims. It is shown that they cannot do this, as the conditions on causation that they impose are too weak, mainly due to ineliminable circularities in their definitions of causal terms. It is then argued that a subset of Woodward’s theory can nevertheless contribute crucially to an explanation of the unique role that manipulation has in our acquisition of causal knowledge. This explanation differs from the common regularist explanation of the epistemic utility of manipulation and experiment, and it is taken to confirm several important manipulationist intuitions. However, the success of the explanation depends on (this subset of) interventionism not itself being understood as a theory of causation, but as a theory of intervention. (shrink)

How do essentialist beliefs about categories arise? We hypothesize that such beliefs are transmitted via language. We subject large language models (LLMs) to vignettes from the literature on essentialist categorization and find that they align well with people when the studies manipulated teleological information -- information about what something is for. We examine whether in a classic test of essentialist categorization -- the transformation task -- LLMs prioritize teleological properties over information about what something looks like, or is made (...) of. Experiments 1 and 2 find that telos and what something is made of matter more than appearance. Experiment 3 manipulates all three factors and finds that what something is for matters more than what it's made of. Overall, these studies suggest that language alone may be sufficient to give rise to essentialist beliefs, and that information about what something is for matters more. (shrink)

There are many social psychological theories regarding the nature of prejudice, but only one major theory of prejudice reduction: under the right circumstances, prejudice between groups will be reduced with increased contact. On the one hand, the contact hypothesis has a range of empirical support and has been a major force in social change. On the other hand, there are practical and ethical obstacles to any large-scale controlled test of the hypothesis in which relevant variables can be manipulated. Here we (...) construct a spatialized model that tests the core hypothesis in a large array of game-theoretic agents. Robust results offer a new kind of support for the contact hypothesis: results in simulation do accord with a hypothesis of reduced prejudice with increased contact. The spatialized game-theoretic model also suggests a deeper explanation for at least some of the social psychological phenomena at issue. (shrink)

I investigate how theoretical assumptions, pertinent to different perspectives and operative during the modeling process, are central in determining how nature is actually taken to be. I explore two different models by Michael Turelli and Steve Frank of the evolution of parasite-mediated cytoplasmic incompatility, guided, respectively, by Fisherian and Wrightian perspectives. Since the two models can be shown to be commensurable both with respect to mathematics and data, I argue that the differences between them in the (1) mathematical (...) presentation of the models, (2) explanations, and (3) objectified ontologies stem neither from differences in mathematical method nor the employed data, but from differences in the theoretical assumptions, especially regarding ontology, already present in the respective perspectives. I use my "set up, mathematically manipulate, explain, and objectify" (SMEO) account of the modeling process to track the model-mediated imposition of theoretical assumptions. I conclude with a discussion of the general implications of my analysis of these models for the controversy between Fisherian and Wrightian perspectives. (shrink)

The depth-bounded approach seeks to provide realistic models of reasoners. Recognizing that most useful logics are idealizations in that they are either undecidable or likely to be intractable, the approach accounts for how they can be approximated in practice by resource-bounded agents. The approach has been applied to Classical Propositional Logic (CPL), yielding a hierarchy of tractable depth-bounded approximations to that logic, which in turn has been based on a KE/KI system. -/- This Thesis shows that the approach can (...) be naturally extended to useful nonclassical logics such as First-Degree Entailment (FDE), the Logic of Paradox (LP), Strong Kleene Logic (K3 ) and Intuitionistic Propositional Logic (IPL). To do this, we introduce a KE/KI-style system for each of those logics such that: is formulated via signed formulae, consist of linear operational rules and branching structural rule(s), can be used as a direct-proof and a refutation method, and is interesting independently of the approach in that it has an exponential speed-up on its tableau system counterpart. The latter given that we introduce a new class of examples which we prove to be hard for all tableau systems sharing the V/& rules with the classical one, but easy for their analogous KE-style systems. Then we focus on showing that each of our KE/KI-style systems naturally yields a hierarchy of tractable depth-bounded approximations to the respective logic, in terms of the maximum number of allowed nested applications of the branching rule(s). The rule(s) express(es) a generalized rule of bivalence, is (are) essentially cut rule(s) and govern(s) the manipulation of virtual information, which is information that an agent does not hold but she temporarily assumes as if she held it. Intuitively, the more virtual information needs to be invoked via the branching rule(s), the harder the inference is for the agent. So, the nested application the branching rule(s) provides a sensible measure of inferential depth. We also show that each hierarchy approximating FDE, LP, and K3 , admits of a 5-valued non-deterministic semantics; whereas, paving the way for a semantical characterization of the hierarchy approximating IPL, we provide a 3-valued non-deterministic semantics for the full logic that fixes the meaning of the connectives without appealing to “structural” conditions. -/- Moreover, we show a super-polynomial lower bound for the strongest possible version of clausal tableaux on the well-known class of “truly fat” expressions (which are easy for KE), settling a problem left open in the literature. Further, we investigate a hierarchy of tractable depth-bounded approximations to CPL based only on KE. Finally, we propose a refinement of the p-simulation relation which is adequate to establish positive results about the superiority of a system over another with respect to proof-search. (shrink)

Stable evolutionary strategy of Homo sapiens (SESH) is built in accordance with the modular and hierarchical principle and consists of the same type of self-replicating elements, i.e. is a system of systems. On the top level of the organization of SESH is the superposition of genetic, social, cultural and techno-rationalistic complexes. The components of this triad differ in the mechanism of cycles of generation - replication - transmission - fixing/elimination of adoptively relevant information. This mechanism is implemented either in accordance (...) with the Darwin-Weismann modus, or according to the Lamarck modus, the difference between them is clear from the title. The integral attribute of the system of systems including ESSH is the production of evolutionary risks. The sources of evolutionary risk for stable adaptive strategy of Homo sapiens are the imbalance of (1) the intra-genomic co-evolution (intragenomic conflicts); (2) the gene-cultural co- evolution; (3) the inter-cultural co-evolution; (4) techno-humanitarian balance; (5) inter- technological conflicts (technological traps). At least phenomenologically the components of the evolutionary risk are reversible, but in the aggregate they are in potentio irreversible destructive ones for bio-social, and cultural self-identity of Homo sapiens. When the actual evolution is the subject of a rationalist control and/or manipulation, the magnitude of the 4th and 5th components of the evolutionary risk reaches the level of existential significance. (shrink)

Recent philosophy of science has seen a number of attempts to understand scientific models by looking to theories of fiction. In previous work, I have offered an account of models that draws on Kendall Walton’s ‘make-believe’ theory of art. According to this account, models function as ‘props’ in games of make-believe, like children’s dolls or toy trucks. In this paper, I assess the make-believe view through an empirical study of molecular models. I suggest that the view (...) gains support when we look at the way that these models are used and the attitude that users take towards them. Users’ interaction with molecular models suggests that they do imagine the models to be molecules, in much the same way that children imagine a doll to be a baby. Furthermore, I argue, users of molecular models imagine themselves viewing and manipulating molecules, just as children playing with a doll might imagine themselves looking at a baby or feeding it. Recognising this ‘participation’ in modelling, I suggest, points towards a new account of how models are used to learn about the world, and helps us to understand the value that scientists sometimes place on three-dimensional, physical models over other forms of representation. (shrink)

The aim of this paper lies in characterizing the explanations and models used in the field of evolutionary developmental biology throughout its history. While manipulative experiments in controlled conditions have been useful to set the bases of the discipline and are still routinely performed, this approach supposes a tension between the reliability and the representativity of the conclusions. Given the recent changes in the understanding of evolutionary phenomena, different authors currently emphasize the need of avoiding excessive simplifications in (...) experimental approaches, incorporating the complexity of the analyzed systems as a relevant trait in the study of biological diversity. On the other hand, the fragile and contingent nature of evolutionary processes and the impossibility of intervening some of these phenomena underscore the importance of non-manipulative models that allow the elucidation of how-possibly mechanisms. We argue that a pluralism characterized by the integration of different kinds of explanations, models and metaphors used in evo-devo allows to exploit their respective advantages, favours interdisciplinarity and can contribute to form a representative description of evolutionary processes without relinquishing the local and detailed study of actual mechanisms. (shrink)

Abstract: There are epistemic manipulators in the world. These people are actively attempting to sacrifice epistemic goods for personal gain. In doing so, manipulators have led many competent epistemic agents into believing contrarian theories that go against well-established knowledge. In this paper, I explore one mechanism by which manipulators get epistemic agents to believe contrarian theories. I do so by looking at a prominent empirical model of trustworthiness. This model identifies three major factors that epistemic agents look for when trying (...) to determine who is trustworthy. These are (i) ability, (ii) benevolence, and (iii) moral integrity. I then show how manipulators can manufacture the illusion that they possess these factors. This leads epistemic agents to view manipulators as trustworthy sources of information. Additionally, I argue that fact-checking will be an ineffective – or even harmful – practice when correcting the beliefs of epistemic agents who have been tricked by this illusion of epistemic trustworthiness. I suggest that in such cases we should use an alternative correction, which I call trust undercutting. (shrink)

Recent epistemology of modality has seen a growing trend towards metaphysics-first approaches. Contrastingly, this paper offers a more philosophically modest account of justifying modal claims, focusing on the practices of scientific modal inferences. Two ways of making such inferences are identified and analyzed: actualist-manipulationist modality and relative modality. In AM, what is observed to be or not to be the case in actuality or under manipulations, allows us to make modal inferences. AM-based inferences are fallible, but the same holds for (...) practically all empirical inquiry. In RM, modal inferences are evaluated relative to what is kept fixed in a system, like a theory or a model. RM-based inferences are more certain but framework-dependent. While elements from both AM and RM can be found in some existing accounts of modality, it is worth highlighting them in their own right and isolating their features for closer scrutiny. This helps to establish their relevant epistemologies that are free from some strong philosophical assumptions often attached to them in the literature. We close by showing how combining these two routes amounts to a view that accounts for a rich variety of modal inferences in science. (shrink)

Manipulation of quotation, shown to be a common tactic of argumentation in this paper, is associated with fallacies like wrenching from context, hasty generalization, equivocation, accent, the straw man fallacy, and ad hominem arguments. Several examples are presented from everyday speech, legislative debates and trials. Analysis using dialog models explains the critical defects of argumentation illustrated in each of the examples. In the formal dialog system CB, a proponent and respondent take turns in making moves in an orderly goal-directed (...) sequence of argumentation in which the proponent tries to persuade the respondent to become committed to a conclusion by asking questions and offering arguments. Analyzing quotation by using the notion of commitment in dialog, it is shown (a) how an arguer’s previous assertions can be brought to light in the course of a dialog to deal with problems arising from misquotation, (b) how the profile of dialog model allows a critic to analyse the fundamental effects misquotation brings about in a dialog, and (c) how the critic can use such an analysis to correct the problem. (shrink)

In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a first (...) positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference. (shrink)

The recent developments in artificial intelligence (AI), particularly in light of the impressive capabilities of transformer-based Large Language Models (LLMs), have reignited the discussion in cognitive science regarding whether computational devices could possess semantic understanding or whether they are merely mimicking human intelligence. Recent research has highlighted limitations in LLMs’ reasoning, suggesting that the gap between mere symbol manipulation (syntax) and deeper understanding (semantics) remains wide open. While LLMs overcome certain aspects of the symbol grounding problem through human feedback, (...) they still lack true semantic understanding, struggling with common-sense reasoning and abstract thinking. This paper argues that while adding sensory inputs and embodying AI through sensorimotor integration with the environment might enhance its ability to connect symbols to real-world meaning, this alone would not close the gap between syntax and semantics. True meaning-making also requires a connection to subjective experience, which current AI lacks. The path to AGI must address the fundamental relationship between symbol manipulation, data processing, pattern matching, and probabilistic best guesses with true knowledge that requires conscious experience. A transition from AI to AGI can occur only if it possesses conscious experience, which is closely tied to semantic understanding. Recognition of this connection could furnish new philosophical insights into longstanding practical and philosophical questions for theories in biology and cognitive science and provide more meaningful tests of intelligence than the Turing test. (shrink)

Many philosophers and psychologists have attempted to elucidate the nature of mental representation by appealing to notions like isomorphism or abstract structural resemblance. The ‘structural representations’ that these theorists champion are said to count as representations by virtue of functioning as internal models of distal systems. In his 2007 book, Representation Reconsidered, William Ramsey endorses the structural conception of mental representation, but uses it to develop a novel argument against representationalism, the widespread view that cognition essentially involves the manipulation (...) of mental representations. Ramsey argues that although theories within the ‘classical’ tradition of cognitive science once posited structural representations, these theories are being superseded by newer theories, within the tradition of connectionism and cognitive neuroscience, which rarely if ever appeal to structural representations. Instead, these theories seem to be explaining cognition by invoking so-called ‘receptor representations’, which, Ramsey claims, aren’t genuine representations at all—despite being called representations, these mechanisms function more as triggers or causal relays than as genuine stand-ins for distal systems. I argue that when the notions of structural and receptor representation are properly explicated, there turns out to be no distinction between them. There only appears to be a distinction between receptor and structural representations because the latter are tacitly conflated with the ‘mental models’ ostensibly involved in offline cognitive processes such as episodic memory and mental imagery. While structural representations might count as genuine representations, they aren’t distinctively mental representations, for they can be found in all sorts of non-intentional systems such as plants. Thus to explain the kinds of offline cognitive capacities that have motivated talk of mental models, we must develop richer conceptions of mental representation than those provided by the notions of structural and receptor representation. (shrink)

Explainable AI (xAI) methods are important for establishing trust in using black-box models. However, recent criticism has mounted against current xAI methods that they disagree, are necessarily false, and can be manipulated, which has started to undermine the deployment of black-box models. Rudin (2019) goes so far as to say that we should stop using black-box models altogether in high-stakes cases because xAI explanations ‘must be wrong’. However, strict fidelity to the truth is historically not a desideratum (...) in science. Idealizations – the intentional distortions introduced to scientific theories and models – are commonplace in the natural sciences and are seen as a successful scientific tool. Thus, it is not falsehood qua falsehood that is the issue. In this paper, I outline the need for xAI research to engage in idealization evaluation. Drawing on the use of idealizations in the natural sciences and philosophy of science, I introduce a novel framework for evaluating whether xAI methods engage in successful idealizations or deceptive explanations (SIDEs). SIDEs evaluates whether the limitations of xAI methods, and the distortions that they introduce, can be part of a successful idealization or are indeed deceptive distortions as critics suggest. I discuss the role that existing research can play in idealization evaluation and where innovation is necessary. Through a qualitative analysis we find that leading feature importance methods and counterfactual explanations are subject to idealization failure and suggest remedies for ameliorating idealization failure. (shrink)

Marr’s seminal distinction between computational, algorithmic, and implementational levels of analysis has inspired research in cognitive science for more than 30 years. According to a widely-used paradigm, the modelling of cognitive processes should mainly operate on the computational level and be targeted at the idealised competence, rather than the actual performance of cognisers in a specific domain. In this paper, we explore how this paradigm can be adopted and revised to understand mathematical problem solving. The computational-level approach applies methods from (...) computational complexity theory and focuses on optimal strategies for completing cognitive tasks. However, human cognitive capacities in mathematical problem solving are essentially characterised by processes that are computationally sub-optimal, because they initially add to the computational complexity of the solutions. Yet, these solutions can be optimal for human cognisers given the acquisition and enactment of mathematical practices. Here we present diagrams and the spatial manipulation of symbols as two examples of problem solving strategies that can be computationally sub-optimal but humanly optimal. These aspects need to be taken into account when analysing competence in mathematical problem solving. Empirically informed considerations on enculturation can help identify, explore, and model the cognitive processes involved in problem solving tasks. The enculturation account of mathematical problem solving strongly suggests that computational-level analyses need to be complemented by considerations on the algorithmic and implementational levels. The emerging research strategy can help develop algorithms that model what we call enculturated cognitive optimality in an empirically plausible and ecologically valid way. (shrink)

We aimed to identify the critical insights from empirical peer-reviewed studies on online romance fraud published between 2000 and 2021 through a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The corpus of studies that met our inclusion criteria comprised twenty-six studies employing qualitative (n = 13), quantitative (n = 11), and mixed (n = 2) methods. Most studies focused on victims, with eight focusing on offenders and fewer investigating public perspectives. All the (...) victim-focused studies relied on data from the Global North, except for Malaysia. Five offender-focused studies used online data available in the public domain, and three derived their data from West Africa. Our review highlights offenders' techniques to deceive and manipulate victims, as revealed in these studies, and highlights some limitations of offender- and victim-focused studies. The dominant framework used across the studies was found to be the “Scammers Persuasive Techniques Model.” While this framework provides a helpful way of considering the stages of victim involvement, it also faces some limitations, which we highlight. Our study reviews the current state of empirical knowledge on romance fraud and identifies certain gaps and biases in the literature. We argue there is a need for further research into online romance fraud to enhance our understanding of it both from the perspective of the offender as well as the experience of the victim. We also highlight the need for a more inclusive and greater range of regional and global diverse range of data sources and perspectives. Given the scale and impact of online romance fraud, we conclude that its study would benefit from a richer empirical engagement that recognizes it as both a regional and global phenomenon. (shrink)

This paper discusses von Wright's theory of causation from Explanation and Understanding and Causality and Determinism in contemporary context. I argue that there are two important common points that von Wright's view shares with the version of manipulability currently supported by Woodward: the analysis of causal relations in a system modelled on controlled experiments, and the explanation of manipulability through counterfactuals - with focus on the counterfactual account of unmanipulable causes. These points also mark von Wright's departure from previous action-based (...) theories of causation. Owing to these two features, I argue that, upon classifying different versions of manipulability theories, von Wright's view should be placed closer to the interventionist approach than to the agency theory, where it currently stands. Furthermore, given its relevance in contemporary context, which this paper aims to establish, I claim that von Wright's theory can be employed to solve present problems connected to manipulability approaches to causation. (shrink)

The standard view of classical cognitive science stated that cognition consists in the manipulation of language-like structures according to formal rules. Since cognition is ‘linguistic’ in itself, according to this view language is just a complex communication system and does not influence cognitive processes in any substantial way. This view has been criticized from several perspectives and a new framework (Embodied Cognition) has emerged that considers cognitive processes as non-symbolic and heavily dependent on the dynamical interactions between the cognitive system (...) and its environment. But notwithstanding the successes of the embodied cognitive science in explaining low-level cognitive behaviors, it is still not clear whether and how it can scale up for explaining high-level cognition. In this paper we argue that this can be done by considering the role of language as a cognitive tool: i.e. how language transforms basic cognitive functions in the high-level functions that are characteristic of human cognition. In order to do that, we review some computational models that substantiate this view with respect to categorization and memory. Since these models are based on a very rudimentary form of non-syntactic ‘language’ we argue that the use of language as a cognitive tool might have been an early discovery in hominid evolution, and might have played a substantial role in the evolution of language itself. (shrink)

Recent work in cognitive modelling has found that most of the data that has been cited as evidence for the dual-process theory (DPT) of reasoning is best explained by non-linear, “monotonic” one-process models (Stephens et al., 2018, 2019). In this paper, I consider an important caveat of this research: it uses models that are committed to unrealistic assumptions about how effectively task conditions can isolate Type-1 and Type-2 reasoning. To avoid this caveat, I develop a coordinated theoretical, experimental, (...) and modelling strategy to better test DPT. First, I propose that Type-1 and Type-2 reasoning are defined as reasoning that precedes and follows metacognitive control, respectively. Second, I argue that reasoning that precedes and follows metacognitive control can be effectively isolated using debiasing paradigms that manipulate metacognitive heuristics (e.g., processing fluency) to prevent or trigger metacognitive control, respectively. Third, I argue that monotonic modelling can allow us to decisively test DPT only when we use them to analyse data from this particular kind of debiasing paradigm. (shrink)

The classical model of democracy that Schumpeter criticizes is manufactured out of a variety of earlier ideas, not those of any one thinker or even one school of thought. His critique of the central ideals by which he defines the model--those of the common will and the common good--remains persuasive. People's preferences are too messy and too manipulable to allow us to think that mass democracy can promote those ideals, as he defines them. Should we endorse his purely electoral model (...) of democracy then, and accept that people do not exercise any control over government? Not necessarily. We can expand democracy to include the constitutional and contestatory constraints that people impose on their rulers. We may hope that people can rely on such democratic controls to ensure that government operates by community standards. (shrink)

This short paper grew out of an observation—made in the course of a larger research project—of a surprising convergence between, on the one hand, certain themes in the work of Mary Hesse and Nelson Goodman in the 1950/60s and, on the other hand, recent work on the representational resources of science, in particular regarding model-based representation. The convergence between these more recent accounts of representation in science and the earlier proposals by Hesse and Goodman consists in the recognition that, in (...) order to secure successful representation in science, collective representational resources must be available. Such resources may take the form of (amongst others) mathematical formalisms, diagrammatic methods, notational rules, or—in the case of material models—conventions regarding the use and manipulation of the constituent parts. More often than not, an abstract characterization of such resources tells only half the story, as they are constituted equally by the pattern of (practical and theoretical) activities—such as instances of manipulation or inference—of the researchers who deploy them. In other words, representational resources need to be sustained by a social practice; this is what renders them collective representational resources in the first place. (shrink)

Many neurodegenerative and neuropsychiatric diseases and other brain disorders are accompanied by impairments in high-level cognitive functions including memory, attention, motivation, and decision-making. Despite several decades of extensive research, neuroscience is little closer to discovering new treatments. Key impediments include the absence of validated and robust cognitive assessment tools for facilitating translation from animal models to humans. In this review, we describe a state-of-the-art platform poised to overcome these impediments and improve the success of translational research, the Mouse Translational (...) Research Accelerator Platform (MouseTRAP), which is centered on the touchscreen cognitive testing system for rodents. It integrates touchscreen-based tests of high-level cognitive assessment with state-of-the art neurotechnology to record and manipulate molecular and circuit level activity in vivo in animal models during human-relevant cognitive performance. The platform also is integrated with two Open Science platforms designed to facilitate knowledge and data-sharing practices within the rodent touchscreen community, touchscreencognition and mousebytes. Touchscreencognition includes the Wall, showcasing touchscreen news and publications, the Forum, for community discussion, and Training, which includes courses, videos, SOPs, and symposia. To get started, interested researchers simply create user accounts. We describe the origins of the touchscreen testing system, the novel lines of research it has facilitated, and its increasingly widespread use in translational research, which is attributable in part to knowledge-sharing efforts over the past decade. We then identify the unique features of MouseTRAP that stand to potentially revolutionize translational research, and describe new initiatives to partner with similar platforms such as McGill’s M3 platform. (shrink)

Misguided use, manipulation, misappropriation, disruption and mismanagement of Information deeply affects the infosphere as well as the social and moral fabric of a society. Information ethics is an attempt to bring the creation, organization, dissemination, and use of information within the ambit of ethical standards and moral codes. The diverse and inherently pluralistic nature of societies however puts forth an additional demand on us - to come up with an intercultural information ethics. An intercultural ethics which is other-centric, context sensitive (...) and workable without being homogenizing, patronizing and colonizing. An endeavor in that direction has already been made by proponents of intercultural information ethics like: Charles M. Ess, Fay Sudweeks, Rafael Capurro, Pak-Hang Wong, Soraj Hongladarom et al. In our paper, we propose that the kind of ethical pluralism being sought in the domain of information ethics can be attained by having a reappraisal of the current methodological strategies, by casting a critical relook at the Eurocentric ethical model. This paper analyses the current framework of Intercultural Information Ethics. And in an endeavour to move towards an allencompassing, other-centric, workable, intercultural, harmonious and compassionate model of 'Pluralistic Information Ethics', it proposes the Indian / Asian philosophical method of 'Samvāda' to the current inventory which includes methods like: 'parrhesia/free speech' and 'interpretive phronēsis. (shrink)

While structural equations modeling is increasingly used in philosophical theorizing about causation, it remains unclear what it takes for a particular structural equations model to be correct. To the extent that this issue has been addressed, the consensus appears to be that it takes a certain family of causal counterfactuals being true. I argue that this account faces difficulties in securing the independent manipulability of the structural determination relations represented in a correct structural equations model. I then offer an alternate (...) understanding of structural determination, and I demonstrate that this theory guarantees that structural determination relations are independently manipulable. The account provides a straightforward way of understanding hypothetical interventions, as well as a criterion for distinguishing hypothetical changes in the values of variables which constitute interventions from those which do not. It additionally affords a semantics for causal counterfactual conditionals which is able to yield a clean solution to a problem case for the standard ‘closest possible world’ semantics. (shrink)

There is a commonly made distinction between two types of scientists: risk-taking, trailblazing mavericks and detail-oriented followers. A number of recent papers have discussed the question what a desirable mixture of mavericks and followers looks like. Answering this question is most useful if a scientific community can be steered toward such a desirable mixture. One attractive route is through credit incentives: manipulating rewards so that reward-seeking scientists are likely to form the desired mixture of their own accord. Here I argue (...) that this idea is less straightforward than it may seem. Interpreting mavericks as scientists who prioritize rewards over speed and risk, I show in a deliberatively simple model that there is a fixed mixture which is not particularly likely to be desirable and which credit incentives cannot alter. I consider a way around this result, but this has some major drawbacks. I conclude that credit incentives are not as promising a way to create a desirable mixture of mavericks and followers as one might have thought. (shrink)

We are in the information age. Nowadays, the information is a high power commodity. Your domain and handling have high economic, political, social value. However, we still know little about it. What is the information? How do we store it, retrieve it and manipulate it? Everyone have or should have equal access to information? What is the relationship between information and knowledge? How can both influence and be influenced by the action? Can they are modeled? The models can contribute (...) to your understanding? Information and knowledge can consolidate or destroy friendships? What are the most efficient means to produce, store, organize and retrieve knowledge and information? Such questions permeate the content of this book, bringing together Brazilian and foreign authors, seeking to contribute to the improvement of the understanding of each of the concepts involved and the relationships between them. This work is the result of EIICA IX, International Meeting information, knowledge and action, held between December, 2nd and 4th 2015, at the Faculty of Philosophy and Science of UNESP/ Marília. (shrink)

Background: Recent literature on addiction and judgments about the characteristics of agents has focused on the implications of adopting a ‘brain disease’ versus ‘moral weakness’ model of addiction. Typically, such judgments have to do with what capacities an agent has (e.g., the ability to abstain from substance use). Much less work, however, has been conducted on the relationship between addiction and judgments about an agent’s identity, including whether or to what extent an individual is seen as the same person after (...) becoming addicted. Methods: We conducted a series of vignette-based experiments (total N = 3,620) to assess lay attitudes concerning addiction and identity persistence, systematically manipulating key characteristics of agents and their drug of addiction. Conclusions: In Study 1, we found that US participants judged an agent who became addicted to drugs as being closer to ‘a completely different person’ than ‘completely the same person’ as the agent who existed prior to the addiction. In Studies 2-6, we investigated the intuitive basis for this result, finding that lay judgments of altered identity as a consequence of drug use and addiction are driven primarily by perceived negative changes in the moral character of drug users, who are seen as having deviated from their good true selves. (shrink)

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research (...) practice shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

I argue for an enactive account of musical experience — that is, the experience of listening ‘deeply’(i.e., sensitively and understandingly) to a piece of music. The guiding question is: what do we do when we listen ‘deeply’to music? I argue that these music listening episodes are, in fact, doings. They are instances of active perceiving, robust sensorimotor engagements with and manipulations of sonic structures within musical pieces. Music is thus experiential art, and in Nietzsche’s words, ‘we listen to music with (...) our muscles’. This paper attempts to explicate and defend this claim. First, I discuss enactive approaches to consciousness and cognition generally. Next, I apply an enactive model of perceptual consciousness to the experience of listening to music. To clarify what is at stake, I use Peter Kivy’s ‘enhanced formalism’ as a philosophical foil. I then look at how the animate body shapes musical experience. (shrink)

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic (...) opacity. The visualization is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity. (shrink)

What is the value of intellectual playfulness? Traditional characterizations of the ideal thinker often leave out playfulness; the ideal inquirer is supposed to be sober, careful, and conscientiousness. But elsewhere we find another ideal: the laughing sage, the playful thinker. These are models of intellectual playfulness. Intellectual playfulness, I suggest, is the disposition to try out alternate belief systems for fun – to try on radically different perspectives for the sheer pleasure of it. But what would the cog-nitive value (...) be of such playfulness? I suggest that intellectual playfulness function as, at the very least, a kind of intellectual insurance policy against epistemic traps. An epistemic trap is a belief system that re-directs good-faith inquiry to bad results. Am epistemic trap manipulates background beliefs to fend off contrary evidence. For example, a conspiracy theory might include a set of beliefs about how the mainstream media has been taken over by some vicious cabal. Normal epistemic attempts will be captured by a well-wrought epistemic trap, because normal attempts at inquiry are guided by these background beliefs -- which set what counts as a plausible path to explore, and what is implausible or beyond the pale. And a clever epistemic trap will manipulate those background beliefs for ill effect. Intellectual playfulness, on the other hand, isn’t motivated by an epistemic interest in the truth, but in the sheer pleasure of intellectual exploration. Since intellectual playfulness isn’t oriented towards the truth, it won’t be constrained by an agent’s background beliefs – it won’t, for example, prefer to investigate apparently more plausible pathways. Intellectual playfulness offers an opportunity to escape from epistemic traps. But intellectual playfulness has its own limitations. It will only drive us to explore belief systems when that exploration is fun. What we need is an array of differently-motivated exploratory tendencies – empathy, curiosity, playfulness – each of which will each cover for the others’ limitations. (shrink)

Recent breakthroughs in artificial intelligence have primarily resulted from training deep neural networks (DNNs) with vast numbers of adjustable parameters on enormous datasets. Due to their complex internal structure, DNNs are frequently characterized as inscrutable ``black boxes,'' making it challenging to interpret the mechanisms underlying their impressive performance. This opacity creates difficulties for explanation, safety assurance, trustworthiness, and comparisons to human cognition, leading to divergent perspectives on these systems. This chapter examines recent developments in interpretability methods for DNNs, with a (...) focus on interventionist approaches inspired by causal explanation in philosophy of science. We argue that these methods offer a promising avenue for understanding how DNNs process information compared to merely behavioral benchmarking and correlational probing. We review key interventionist methods and illustrate their application through practical case studies. These methods allow researchers to identify and manipulate specific computational components within DNNs, providing insights into their causal structure and internal representations. We situate these approaches within the broader framework of causal abstraction, which aims to align low-level neural computations with high-level interpretable models. While acknowledging current limitations, we contend that interventionist methods offer a path towards more rigorous and theoretically grounded interpretability research, potentially informing both AI development and computational cognitive neuroscience. (shrink)

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational (...) models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted. (shrink)

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, (...) which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine. (shrink)

Artificial Intelligence is becoming widespread and as we continue ask ‘can we implement this’ we neglect to ask ‘should we implement this’. There are various frameworks and conceptual journeys one should take to ensure a robust AI product; context is one of the vital parts of this. AI is now expected to make decisions, from deciding who gets a credit card to cancer diagnosis. These decisions affect most, if not all, of society. As developers if we do not understand or (...) use fundamental modelling principles then we can cause real harm to society. Recently more serious effects of AI have been observed. Dehumanisation is the human reaction to overused anthropomorphism and lack of social contact caused by excessive interaction with, or addiction to, technology. This can cause humans to devalue technology and to devalue other humans. This is a contradiction of the use of ‘social robots’ and ‘chatbots’, indicating that the negative effects of this technology would outweigh any perceived positive effects. Also, within cyberspace, anthropomorphism and similar techniques based on deep philosophical principles can, and are, being used to alter the behaviour of humans. These techniques are used to manipulate human behaviours at a basic level in the human mind. As these types of techniques are becoming more widespread, it is clear that we are entering unchartered territory that holds a vast array of consequences for society. (shrink)

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s prediction (...) are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance. -/- . (shrink)

The alignment problem in artificial intelligence (AI) is a critical challenge that extends beyond the need to align future superintelligent systems with human values. This paper argues that even "merely intelligent" AI systems, built on current-gen technologies, pose existential risks due to their competence-without-comprehension nature. Current AI models, despite their advanced capabilities, lack intrinsic moral reasoning and are prone to catastrophic misalignment when faced with ethical dilemmas, as illustrated by recent controversies. Solutions such as hard-coded censorship and rule-based restrictions (...) prove inadequate, as they fail to imbue AI with a true understanding of moral complexities, making these systems vulnerable to manipulation. To address these risks, the paper explores a paradigm shift from competence to comprehension, emphasizing the importance of developing AI systems with an intrinsic value framework capable of moral reasoning. By adopting intrinsic teleology, AI could self-reflect, simulate other minds, and develop empathy, which would enable it to navigate ethical dilemmas in ways that are currently impossible. Using theories from philosophy, ethics, and AI research, the paper proposes the development of AI systems that can reason through moral dilemmas autonomously, suggesting that such systems would be safer and more alignable than current generation competence-driven AI. (shrink)

Some cases show that counterfactual conditionals (‘counterfactuals’ for short) are inherently ambiguous, equivocating between forward-tracking and backtracking counterfactu- als. Elsewhere, I have proposed a causal modeling semantics, which takes this phenomenon to be generated by two kinds of causal manipulations. (Lee 2015; Lee 2016) In an important paper (Hiddleston 2005), Eric Hiddleston offers a different causal modeling semantics, which he claims to be able to explain away the inherent ambiguity of counterfactuals. In this paper, I discuss these two semantic treatments (...) and argue that my (bifurcated) semantics is theoretically more promising than Hiddleston’s (unified) semantics. (shrink)

Cognitive scientists used to deem reasoning either as a higher cognitive process based on the manipulation of abstract rules or as a higher cognitive process that is stochastic rather than involving abstract rules. I maintain that these different perspectives are closely intertwined with a theoretical and methodological endorsement of either cognitivism or connectionism. Cognitivism and connectionism represent two prevailing and opposed paradigms in cognitive science. I aim to extoll the virtues of connectionist models of enthymematic reasoning by following means: (...) via the phenomenon of creative enthymeme, viz. the inference where one cannot even articulate the missing premise, I introduce a connectionist mechanism of pattern recognition as underlying expertise; via Gestalt switch or Gestalt click, I demonstrate how differences in pattern recognition of an expert and a novice can be construed as qualitatively different, and not merely a matter of faster reasoning. (shrink)

Our rapidly increasing reliance on frictionless AI interactions may increase cognitive and emotional distance, thereby letting our adaptive resilience slacken and our ethical virtues atrophy from disuse. Many trends already well underway involve the offloading of cognitive, emotional, and ethical labor to AI software in myriad social, civil, personal, and professional contexts. Gradually, we may lose the inclination and capacity to engage in critically reflective thought, making us more cognitively and emotionally vulnerable and thus more anxious and prone to manipulation (...) from false news, deceptive advertising, and political rhetoric. In this article, I consider the overarching features of this problem and provide a framework to help AI designers tackle it through system enhancements in smartphones and other products and services in the burgeoning internet of things (IoT) marketplace. The framework is informed by two ideas: psychologist Daniel Kahneman’s cognitive dual process theory and moral self-awareness theory, a four-level model of moral identity that I developed with Benjamin M. Cole. (shrink)

L’analyse des juristes médiévaux nous montre comment la manipulation des contradictions déontiques prima facie est associée, dans l’argumentation interprétative, à la théorie de la légitimité de la hiérarchie normative, entendue non seulement comme instrument politique mais aussi et essentiellement comme un instrument de rationalité au sein d’une science juridique orientée vers une théologie politique. La notion de droit naturel telle qu’elle apparaît dans certains documents emblématiques dont le Decretum de Gratien du XIIe s., ne peut être réduite au modèle intellectualiste (...) aristotélo-thomiste du XIIIe s. Il y a plusieurs façons de penser le droit naturel dans le monde médiéval chrétien et latin ; elles se maintiennent sur plusieurs siècles et l’une d’elles au moins a des points de contacts et de désaccord avec les traditions juives et musulmanes. Ces courants de pensée qui s’écartent du modèle intellectualiste aristotélo-thomiste peuvent être regroupés au sein d’une famille (au sens wittgensteinien) de modèles volontaristes : les contradictions déontiques peuvent être plus fortes que des contradictions prima facie, elles peuvent être d’authentiques contradictions renvoyant à un nouvel acte de volonté comme la seule façon d’éliminer la contradiction déontique. La théorie de Duns Scot peut être utilisée comme illustrant le problème d’une approche ontologique volontariste extrême. | Une autre version de cette contribution a été publiée dans Parisoli (2008d : 124–141). (shrink)