This paper is about mechanisms and models, and how they interact. In part, it is a response to recent discussion in philosophy of biology regarding whether natural selection is a mechanism. We suggest that this debate is indicative of a more general problem that occurs when scientists produce mechanistic models of populations and their behaviour. We can make sense of claims that there are mechanisms that drive population-level phenomena such as macroeconomics, natural selection, ecology, and epidemiology. But talk of (...) mechanisms and mechanistic explanation evokes objects with well-defined and localisable parts which interact in discrete ways, while models of populations include parts and interactions that are neither local nor discrete in any actual populations. This apparent tension can be resolved by carefully distinguishing between the properties of a model and those of the system it represents. To this end, we provide an analysis that recognises the flexible relationship between a mechanistic model and its target system. In turn, this reveals a surprising feature of mechanistic representation and explanation: it can occur even when there is a mismatch between the mechanism of the model and that of its target. Our analysis reframes the debate, providing an alternative way to interpret scientists’ mechanism-talk , which initially motivated the issue. We suggest that the relevant question is not whether any population-level phenomenon such as natural selection is a mechanism, but whether it can be usefully modelled as though it were a particular type of mechanism. (shrink)

The paper argues that there is a fruitful analogy to be made between classic pre-analytic Euclidean geometry and a certain kind of mechanism models, called ideal mechanisms. Both supply necessary truths. Bunge is of the opinion that pure mathematics is about fictions, but that mathematics nonetheless is useful in science and technology because we can go “to reality through fictions.” Similarly, the paper claims that ideal mechanisms are useful because we can go to real mechanisms through the fictions of (...) ideal mechanisms. The view put forward takes it for granted that two important distinctions concerned with the classification of fictions can be made. One is between ideal and non-ideal fictions, and the other between social and non-social fictions. Pure numbers, purely geometric figures, and ideal mechanisms are claimed to be ideal and social fictions. (shrink)

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what (...) is required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)

Guiding core enterprises to participate in supply chain work safety governance is an innovative mode of work safety control, which has an important impact on improving the work safety level of small and medium-sized enterprises in the supply chain. Through in-depth interviews, the grounded theory is adopted to explore the driving factors of work safety management behaviors of core enterprise. It is found that the work safety management behavior of the core enterprise is driven by both internal and external factors. (...) External driving factors are the main category of institutional pressure composed of regulatory pressure, normative pressure, and cognitive pressure. Internal driving factors are divided into the main category of behavioral awareness and the behavioral capability of the core enterprises. The behavioral awareness is composed of responsibility sense, problem cognition and behavioral effect perception. The behavioral capability is composed of safety management strength and safety coordination capability. Institutional pressure, behavioral awareness, and behavioral capability all influence the work safety management behavior of core enterprise in supply chain significantly, but with different ways and paths. On this basis, the driving mechanism model of the above three main categories on the work safety management behavior in core enterprise supply chain is explored. The research can provide targeted policy ideas and implementation paths for the government to formulate effective guiding policies to promote the work safety management behavior of core enterprise. (shrink)

In this paper we argue that in recent literature on mechanistic explanations, authors tend to conflate two distinct features that mechanistic models can have or fail to have: plausibility and richness. By plausibility, we mean the probability that a model is correct in the assertions it makes regarding the parts and operations of the mechanism, i.e., that the model is correct as a description of the actual mechanism. By richness, we mean the amount of detail the (...) model gives about the actual mechanism. First, we argue that there is at least a conceptual reason to keep these two features distinct, since they can vary independently from each other: models can be highly plausible while providing almost no details, while they can also be highly detailed but plainly wrong. Next, focusing on Craver's continuum of ?how-possibly,? to ?how-plausibly,? to ?how-actually? models, we argue that the conflation of plausibility and richness is harmful to the discussion because it leads to the view that both are necessary for a model to have explanatory power, while in fact, richness is only so with respect to a mechanism's activities, not its entities. This point is illustrated with two examples of functional models. (shrink)

In the theory outlined in the target article, an animal forages continuously, making sequential decisions in a world where the amount of food and its uncertainty are fixed, but delays are variable. These assumptions contrast with the risk-sensitive foraging theory and create a problem for comparing the predictions of this model with many laboratory experiments that do not make these assumptions.

We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex, the less explanatory it will be. Since machine learning increases its performances when more components are (...) added, then it generates models which are not intelligible, and hence not explanatory. (shrink)

The rapid development of credit default swap market has changed the manner of credit risk management of banks to some extent and has had a new influence on the bank-enterprise credit model. In this study, the credit financing process of credit risk in small- and medium-sized enterprises gathers within a bank, which makes it difficult for SMEs to raise funds. On the basis of the perspective of CDS, we construct an incentive game model of bank-enterprise credit behavior and (...) analyze the influence mechanism of the credit financing of SMEs on CDS contract coupon rate, CDS payout ratio, bank-enterprise credit effort, and loan recovery rate when considering CDS. The result shows that the CDS contract leads to insufficient supervision after a bank loan, the moral hazard of the SMEs rises, and the probability of credit default events increases. In addition, in view of CDS, the SMEs can access more credit funds. (shrink)

Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this article, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ are new types of material manipulations and are important not because of their explanatory force, but because of the interventionist strategies they afford. This (...) is a distinction between aspects of the same model. In molecular biology, models may be treated either as ‘models of’ or as ‘models for’. By analysing the discovery and characterization of restriction–modification systems and their exploitation for DNA cloning and mapping, I identify the differences between treating a model as a ‘model of’ or as a ‘model for’. These lie in the cognitive disposition of the modeller towards the model: a modeller will look at a model as a ‘model of’ if interested in its explanatory force, or as a ‘model for’ if interested in the material manipulations it can possibly afford. (shrink)

To be successful, the research agenda for a novel control view of cognition should foresee more detailed, computationally specified process models of cognitive operations including higher cognition. These models should cover all domains of cognition, including those cognitive abilities that can be characterized as online interactive loops and detached forms of cognition that depend on internally generated neuronal processing.

Recent studies of Italian past definite and past participle forms show that human performance with regular and irregular inflections is not dissociated as Clahsen's model would predict. Some performance profiles, accounted for by dual-mechanism models in terms of an underlying symbol-manipulating combinatorial procedure, are generated in Italian by the higher learnability and generalizability of phonologically regular morphological processes.

Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a (...) uniform or desirable format. We describe a calibration strategy to address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy, this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature. (shrink)

Clahsen has added to the body of evidence that, on average, regular and irregular inflected words behave differently. However, the dual-mechanism account he supports predicts a crisp distinction; the empirical data instead suggest a fuzzy one, more in line with single-mechanism connectionist models.

The mechanistic approach in the cognitive and biological sciences emphasizes that scientific explanations succeed by analyzing the mechanisms underlying phenomena across multiple levels. In this paper, we propose a formal strategy to establish such multi-level mechanistic models, which are foundational to mechanistic explanations. Our objectives are twofold: First, we introduce the novel "mLCA" (multi-Level Coincidence Analysis) script, which transforms binary data tables from tests on mechanistic systems into mechanistic models consistent with those tables. Second, we provide several philosophical insights derived (...) from the outcomes generated by this script and its underlying algorithm. Using illustrative examples, we defend the following claims: 1. Inference methods for generating mechanistic models generally require information on how causal factors are assigned to different levels within data tables generated by multi-level structures. 2. The mLCA script successfully produces appropriate mechanistic models from binary data tables, demonstrating the practical application of the philosophical mechanistic approach in the sciences. 3. The number of solutions generated by mLCA increases significantly as the number of relevant factors grows, reflecting adaptations in causal inference methods to meet the demands of multi-level mechanistic modeling. 4. Any further reduction of solutions, if possible, involves pragmatic considerations, a point that carries profound implications for the broader ambitions of the mechanistic approach. By addressing these points, our paper contributes both to the development of practical algorithmic tools and to a deeper philosophical understanding of multi-level mechanistic modeling. (shrink)

Van Vugt, van der Velde, and collaborators show how cognitive architectures can implement verbal theories of psychiatric problems. They show how one theory of depressive rumination can be implemented in the ACT‐R cognitive architecture by changing the contents of its simulated memory. These manipulations of memory habits lead the model to show impairments in a sustained attention task‐‐a plausible impairment given that people who suffer from depression have concentration complaints.

Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematical models to design new experiments, discriminate between alternative mechanisms and explain the molecular basis of cellular properties. At the heart of this approach are mechanistic models of molecular networks. We focus on the articulation and development of mechanistic (...) models, identifying five constraints which guide the articulation of models in molecular systems biology. These constraints are not independent of one another, with the result that modeling becomes an iterative process. We illustrate the use of these constraints in the modeling of the mechanism for bistability in the lac operon. (shrink)

Over the past three decades, the discourse on the mechanistic approach to scientific modelling and explanation has notably sidestepped the topic of simplicity and fit within the process of model selection. This paper aims to rectify this disconnect by delving into the topic of simplicity and fit within the context of mechanistic explanations. More precisely, our primary objective is to address whether simplicity metrics hold any significance within mechanistic explanations. If they do, then our inquiry extends to the suitability (...) of the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and related criteria in determining the optimal balance of fit and simplicity of mechanistic models. As our main claims, we argue that mechanistic models inherently lend themselves to considerations of simplicity, and that the AIC and BIC and related criteria are applicable to some submodels of certain kinds of mechanistic models. However, these criteria and related criteria designed for curve fitting and causal modelling are of little help for a comparative assessment of full mechanistic models, and a fundamentally different approach is needed to make determinations of this kind. (shrink)

This computational study generates a hypothesis for the coagulation protein whose initial concentration greatly influences the course of coagulation. Many clinical malignancies of blood coagulation arise due to abnormal initial concentrations of coagulation factors. Sensitivity analysis of mechanistic models of blood coagulation is a convenient method to assess the effect of such abnormalities. Accordingly, the study presents sensitivity analysis, with respect to initial concentrations, of a recently developed mechanistic model of blood coagulation. Both the model and parameters to (...) which model sensitivity is being analyzed provide newer insights into blood coagulation: the model incorporates distinct equations for plasma-phase and platelet membrane-bound species, and sensitivity to initial concentrations is a new dimension in sensitivity analysis. The results show that model predictions are most uncertain with respect to changes in initial concentration of factor VIII, and this hypothesis is supported by results from other models developed independently. (shrink)

Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation (...) of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors. (shrink)

Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts themselves (...) must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences. (shrink)

In this paper we consider mechanistic explanations for biologic malfunctions. Drawing on Lipton’s work on difference making, we offer three reasons why one should distinguish i) mechanistic features that only make a difference to the malfunction one aims to explain, from ii) features that make a difference to both the malfunction and normal functioning. Recognition of the distinction is important for a) repair purposes, b) mechanism discovery, and c) understanding. This analysis extends current mechanistic thinking, which fails to appreciate (...) the distinction. We illustrate our contribution with a case on sleeping disorders as arising from disruptions of circadian rhythms. (shrink)

In 1949, Richard Feynman published the essentials of his solution to the recalcitrant problems that plagued quantum theories of electrodynamics of his days. The main problem was that the theory, that was considered to be correct and often led to correct observable consequences, also implied that some quantities should be infinite, while by common sense or empirical evidence they were finite. Feynman devised a method of solving the relevant theoretical equations in which particular combinations of elementary solutions yielded empirically adequate (...) results even when applied to complex problems. The combination of the basic solutions was guided by relatively simple graphical considerations... (shrink)

Model organisms became an indispensable part of experimental systems in molecular developmental and cell biology, constructed to investigate physiological and pathological processes. They are thought to play a crucial role for the elucidation of gene function, complementing the sequencing of the genomes of humans and other organisms. Accordingly, historians and philosophers paid considerable attention to various issues concerning this aspect of experimental biology. With respect to the representational features of model organisms, that is, their status as models, the (...) main focus was on generalization of phenomena investigated in such experimental systems. Model organisms have been said to be models for other organisms or a higher taxon. This, however, presupposes a representation of the phenomenon in question. I will argue that prior to generalization, model organisms allow researchers to built generative material models of phenomena – structures, processes or the mechanisms that explain them – through their integration in experimental set-ups that carve out the phenomena from the whole organism and thus represent them. I will use the history of zebrafish biology to show how model organism systems, from around 1970 on, were developed to construct material models of molecular mechanisms explaining developmental or physiological processes. (shrink)

Although clinicians and researchers working in the field of autism are generally not concerned with philosophical categories of kinds, a model for understanding the nature of autism is important for guiding research and clinical practice. Contemporary research in the field of autism is guided by the depiction of autism as a scientific object that can be identified with systematic neuroscientific investigation. This image of autism is compatible with a permissive account of natural kinds: the mechanistic property cluster (MPC) account (...) of natural kinds, recently proposed as the model for understanding psychiatric disorders. Despite the heterogeneity, multicausality and fuzzy boundaries that complicate autism research, a permissive account of natural kinds (MPC kinds) provides prescriptive guidance for the investigation of objective causal mechanisms that should inform nosologists in their attempt to carve autism’s boundaries at its natural joints. However, this essay will argue that a mechanistic model of autism is limited since it disregards the way in which autism relates to ideas about what kind of behavior is abnormal. As historical studies and definitions of autism show, normative issues concerning disability, impairment and societal needs have been and still are inextricably linked to how we recognize and understand autism. The current search for autism’s unity in neurobiological mechanisms ignores the values, social norms and various perspectives on mental pathology that play a significant role in 'the thing called autism’. Autism research needs to engage with these issues in order to achieve more success in the effort to become clinically valuable. (shrink)

In the Critique of Teleological Judgment, Kant endorses a distinct model of causal explanation. He claims that we explain natural wholes as the causal effect of their parts and the forces governing them, i. e., we explain mechanistically or following the analytic-synthetic method of modern science. According to McLaughlin’s influential interpretation, Kant endorses in this, without argument, the predominant scientific method of his time. The text suggests, however, that we explain mechanistically according to the constitution of our discursive understanding. (...) The paper attempts to reconstruct the argument establishing this claim. (shrink)

U poslednjih nekoliko dekada vise studija posvecenih percepciji novorodjencadi je ukazalo na to da cak i tek rodjena deca jesu osetljiva na nacin na koji se objekti pokrecu i na prirodu njihove interakcije. Da bi objasnili ranu pojavu ovakve osetljivosti na kauzalne odnose neki psiholozi zastupaju stanoviste da postoji urodjeno znanje vezano za objekte. Cilj ovog rada je da preispita ovakva nativisticka objasnjenja tako sto ce da preispita da li ova objasnjenja ispunjavaju uslove koji svaki mehanicisticki model mora da (...) ispuni da bi bio uspesan. Craver je razvio nekoliko takvih kriterijuma kao i distinkciju izmedju odgovarajuceg mehanicistickog modela i mehanicisticke skice. Moj cilj je da pokazem da nativisticki modeli razvoja ne ispunjavaju ove kriterijume. Prvo, ti modeli samo delimicno a ne u potpunosti opisuju fenomen. Drugo, nativisti ukljucuju u opis fenomena odredjenu teoriju percepcije koja zahteva dodatnu empirijsku evidenciju i ne moze biti bezrezervno prihvacena kao tacna. Ja cu takodje argumentovati da je urodjeno znanje dobar kandidat za ono sto Craver naziva?praznim terminom? i da ce najverovatnije biti napusten. Novija istrazivanja koja su ispitivala intersenzornu percepciju ukazuju na to da je mehanizam odgovoran za rano opazanje kauzalnih odnosa najverovatnije deo nase najosnovnije fiziologije i da kao takav ne ukljucuje sofisticirane kognitivne strukture niti urodjeno znanje. Takodje, ova istrazivanja ukazuju na to da je standardna debata izmedju nativista i empirista pogresno postavljena i da bi je trebalo napustiti. (shrink)

The transcription factor NF‐κB (p65/p50) plays a central role in the coordination of cellular responses by activating the transcription of numerous target genes. The precise role of the dynamics of NF‐κB signalling in regulating gene expression is still an open question. Here, we show that besides external stimulation intracellular parameters can influence the dynamics of NF‐κB. By applying mathematical modelling and bifurcation analyses, we show that NF‐κB is capable of exhibiting different types of dynamics in response to the same stimulus. (...) We identified the total NF‐κB concentration and the IκBα transcription rate constant as two critical parameters that modulate the dynamics and the fold change of NF‐κB. Both parameters might vary as a result of cell‐to‐cell variability. The regulation of the IκBα transcription rate constant, e.g. by co‐factors, provides the possibility of regulating the NF‐κB dynamics by crosstalk. (shrink)

I argue that informational models of consciousness, including those proposed by the Integrated Information Theory, don’t presuppose or entail any particular view about the physical or metaphysical nature of consciousness. Such models only tell us how certain properties of consciousness can be mathematically described, thus providing a quantitative characterization of the phenomenon of consciousness that may contribute to the development of new methods of assessment and guide the explanatory project by supplying additional constraints on theoretical proposals. While informational models are (...) in principle compatible with multiple physical interpretations, a preliminary argument in favour of the continuity between informational approaches and the mechanistic project in neuroscience can be made based on historical precedents. (shrink)

Patient RB has a peculiar memory impairment wherein he experiences his memories in rich contextual detail, but claims to not own them. His memories do not feel as if they happened to him. In this paper, I provide an explanatory model of RB’s phenomenology, the self-attentional model. I draw upon recent work in neuroscience on self-attentional processing and global workspace models of conscious recollection to show that RB has a self-attentional deficit that inhibits self-bias processes in broadcasting the (...) contents of episodic memories to the global workspace. Typically, self-related contents enjoy a higher salience level than other-related contents. Elimination of bias toward self-related contents diminishes the salience of those contents to the level of other-related contents. Because the typical high salience of self-related content is necessary for the feeling of ownership, RB lacks the feeling of ownership. I also discuss potential applications of the self-attentional model to other psychopathological cases. (shrink)

Rumination is a process of uncontrolled, narrowly focused negative thinking that is often self-referential, and that is a hallmark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we developed on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumination is caused by maladaptive habits of thought. (...) These habits of thought are modeled by adjusting the number of memory chunks and their associative structure, which changes the sequence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memories is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sampling study in healthy and depressed participants. On the basis of this empirically derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better understand the cognitive mechanisms underlying rumination and depression. (shrink)

Model organism databases are used extensively for knowledge retrieval and knowledge sharing among biologists. With the invention of genome sequencing and protein profiling technologies, large amount of molecular data provides practical insights into the molecular study of model organisms. The knowledge-intensive characteristic of model organism databases provides a reference point for the comparative study of other species. In this paper, I argue that algorithms could be used to facilitate cross-species research. I emphasize the epistemic significance of algorithms (...) in the integration of data for cross-species research. I examine (1) how algorithms guide data integration in model organism databases; and (2) the importance of algorithms for the use of model organism database in the cross-species research. I argue that an extrapolation from the stored data to other species is possible in virtue of the fact that algorithms can facilitate two modes of data integration—viz., inter-level and cross-species integration. Lastly, I examine the implication of the data integration role of an algorithm in light of mechanistic explanation. (shrink)

There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual models (...) do not provide mechanistic explanations by themselves. Instead, individual models are always used to complement a huge body of background information and pre-existing models about the target system. With this in mind, I argue that mechanistic explanations are distributed across sets of different, and sometimes contradictory, scientific models. Each of these models contributes limited, but essential, information to the same mechanistic explanation, but none can be considered a mechanistic explanation in isolation of the others. (shrink)

In this work a mechanistic explanation of the classical algae growth model built by M. R. Droop in the late sixties is proposed. We first recall the history of the construction of the “predictive” variable yield Droop model as well as the meaning of the introduced cell quota. We then introduce some theoretical hypotheses on the biological phenomena involved in nutrient storage by the algae that lead us to a “conceptual” model. Though more complex than Droop’s one, (...) our model remains accessible to a complete mathematical study: its confrontation to the Droop model shows both have the same asymptotic behavior. However, while Droop’s cell quota comes from experimental bio-chemical measurements not related to intra-cellular biological phenomena, its analogous in our model directly follows our theoretical hypotheses. This new model should then be looked at as a re-interpretation of Droop’s work from a theoretical biologist’s point of view. (shrink)

In the literature on dynamical models in cognitive science, two issues have recently caused controversy. First, what is the relation between dynamical and mechanistic models? I will argue that dynamical models can be upgraded to be mechanistic as well, and that there are mechanistic and non-mechanistic dynamical models. Second, there is the issue of explanatory power. Since it is uncontested the mechanistic models can explain, I will focus on the non-mechanistic variety of dynamical models. It is often claimed by proponents (...) of mechanistic explanations that such models do not really explain cognitive phenomena . I will argue against this view. Although I agree that the three arguments usually offered to vindicate the explanatory power of non-mechanistic dynamical models are not enough, I consider a fourth argument, namely that such models provide understanding. The Voss strong anticipation model is used to illustrate this. (shrink)

Carl Craver proposes a mechanistic model of explanation in science motivated by a desire to intervene, as exemplified by explanations in neuroscience which in his opinion are motivated by the desire to bring the central nervous system under control. In his discussion of causal relevancy conditions of mechanistic components Craver asserts that a cause need not make its effect probable in order to explain it . Although this is supported by some interpretations, Craver’s own is highlighted by his appeal (...) to an example in neuroscience of the apparently stochastic nature of neurotransmitter release events. I propose the view that this interpretation of such a causal relevancy condition is contradictory to Craver’s own proposed first norm of explanation : that a good explanation must fully account for the explanandum phenomenon including varied manifestations of the phenomenon. By defending Craver’s explanatory motivation of intervention I demonstrate that the attributed cause of a phenomenon must necessarily make the phenomenon probable in order to effectively explain it. (shrink)

Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective (...) and concentrates the most revolutionary elements of the cognitive neuroscience revolution (Boone & Piccinini, 2016). I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration. (shrink)

This paper discusses the relevance of models for cognitive science that integrate mechanistic and computational aspects. Its main hypothesis is that a model of a cognitive system is satisfactory and explanatory to the extent that it bridges phenomena at multiple mechanistic levels, such that at least several of these mechanistic levels are shown to implement computational processes. The relevant parts of the computation must be mapped onto distinguishable entities and activities of the mechanism. The ideal is contrasted with (...) two other accounts of modeling in cognitive science. The first has been presented by David Marr in combination with a distinction of “levels of computation”. The second builds on a hierarchy of “mechanistic levels” in the sense of Carl Craver. It is argued that neither of the two accounts secures satisfactory explanations of cognitive systems. The mechanistic-computational ideal can be thought of as resulting from a fusion of Marr’s and Craver’s ideals. It is defended as adequate and plausible in light of scientific practice, and certain metaphysical background assumptions are discussed. (shrink)

The regulation of chemicals is undergoing drastic changes with the use of computational models to predict environmental toxicity. This particular issue has not attracted much attention, despite its major impacts on the regulation of chemicals. This raises the problem of causality at the crossroads between data and regulatory sciences, particularly in the case models known as quantitative structure–activity relationship models. This paper shows that models establish correlations and not scientific facts, and it engages anew the way regulators deal with uncertainties. (...) It does so by exploring the tension and problems raised by the possibility of causal explanation afforded by quantitative structure–activity relationship models. It argues that the specificity of predictive modelling promotes rethinking of the regulation of chemicals. (shrink)

Hodgkin and Huxley’s model of the action potential is an apparent dream case of covering‐law explanation in biology. The model includes laws of physics and chemistry that, coupled with details about antecedent and background conditions, can be used to derive features of the action potential. Hodgkin and Huxley insist that their model is not an explanation. This suggests either that subsuming a phenomenon under physical laws is insufficient to explain it or that Hodgkin and Huxley were wrong. (...) I defend Hodgkin and Huxley against Weber’s heteronomy thesis and argue that explanations are descriptions of mechanisms. †To contact the author, please write to: Department of Philosophy, Philosophy‐Neuroscience‐Psychology Program, Washington University in St. Louis, One Brookings Drive, Wilson Hall, St. Louis, MO 63130; e‐mail: [email protected]. (shrink)

Mechanisms are usually viewed as inherently hierarchical, with lower levels of a mechanism influencing, and decomposing, its higher-level behaviour. In order to adequately draw quantitative predictions from a model of a mechanism, the model needs to capture this hierarchical aspect. The recursive Bayesian network formalism was put forward as a means to model mechanistic hierarchies by decomposing variables. The proposal was recently criticized by Gebharter and Gebharter and Kaiser, who instead propose to decompose arrows. In (...) this paper, I defend the RBN account from the criticism and argue that it offers a better representation of mechanistic hierarchies than the rival account. (shrink)

New mechanical philosophy posits that explanations in the life sciences involve the decomposition of a system into its entities and their respective activities and organization that are responsible for the explanandum phenomenon. This mechanistic account of explanation has proven problematic in its application to mathematical models, leading the mechanists to suggest different ways of aligning abstract models with the mechanist program. Initially, the discussion centered on whether the Hodgkin-Huxley model is explanatory. Network models provided another complication, as they apply (...) to a wide number of materially diverse systems. In this article, we examine the various attempts to integrate abstract models within the mechanist program, also presenting a further challenge: the Heimburg-Jackson model, which was introduced as an alternative to the Hodgkin-Huxley model. We argue that although the notion of abstraction as the omission of irrelevant mechanistic details appears to give a mechanistic solution for accommodating abstract models, this notion does not suit models whose epistemic strategy is not decompositional. As a result, the mechanist has to choose whether to dilute the mechanistic approach nearly beyond recognition or to claim that many, if not most, abstract theoretical models do not deliver mechanistic explanations, or qualify as explanatory at all. (shrink)

Context: The advent of a general artificial intelligence mechanism that learns like humans do would represent the realization of an old and major dream of science. It could be achieved by an artifact able to develop its own cognitive structures following constructivist principles. However, there is a large distance between the descriptions of the intelligence made by constructivist theories and the mechanisms that currently exist. Problem: The constructivist conception of intelligence is very powerful for explaining how cognitive development takes (...) place. However, until now, no computational model has successfully demonstrated the underlying mechanisms necessary to realize it. In other words, the artificial intelligence (AI) community has not been able to give rise to a system that convincingly implements the principles of intelligence as postulated by constructivism, and that is also capable of dealing with complex environments. Results: This paper presents the constructivist anticipatory learning mechanism (CALM), an agent learning mechanism based on the constructivist approach of AI. It is designed to deal dynamically and interactively with environments that are at the same time partially deterministic and partially observable. CALM can model the regularities experienced in the interaction with the environment, on the sensorimotor level as well, as by constructing abstract or high-level representational concepts. The created model provides the knowledge necessary to generate the agent behavior. The paper also presents the coupled agent environment system (CAES) meta-architecture, which defines a conception of an autonomous agent, situated in the environment, embodied and intrinsically motivated. Implications: The paper can be seen as a step towards a computational implementation of constructivist principles, on the one hand suggesting a further perspective of this refreshing movement on the AI field (which is still too steeped in a behaviorist influence and dominated by probabilistic models and narrow applied approaches), and on the other hand bringing some abstract descriptions of the cognitive process into a more concrete dimension, in the form of algorithms. Constructivist content: The connection of this paper with constructivism is the proposal of a computational and formally described mechanism that implements important aspects of the subjective process of knowledge construction based on key ideas proposed by constructivist theories. (shrink)

This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three- layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement (...) learning and fusion algorithms; the paper closes with concluding observations and a summary of the principal work and contributions of this paper. (shrink)