It has been a commonplace from the beginnings of philosophical thought that what distinguishes humans from other species is the ability to reason; reason- ing is held to be an essential characteristic of the species and one that is unique to it. The essence condition requires that all humans possess at least the capacity for reasoning and that it be exercised in many of the ordinary cases of acquiring beliefs. And uniqueness entails that non-humans cannot reason, no matter how much (...) their behavior resembles that of humans. I think that a certain specific model of reasoning has been presupposed by proponents of these claims. And there are grounds to believe that this model is inaccurate in essential respects: if reasoning does correspond to it, then few human beliefs are acquired through reason. However, given a more accurate description of what goes on in normal human reasoning, it is no longer plausible to think that only humans reason. So either the essence or the uniqueness claim must be given up. In this paper I argue for the second option. (shrink)

In "The Comparative Method" Ragin (1987) has outlined a procedure of Boolean causal reasoning operating on pure coincidence data that has meanwhile become widely known as QCA (Qualitative Comparative Analysis) among social scientists. QCA -- also in its recent form as presented in Ragin (2000) -- is designed to analyze causal structures featuring one effect and a possibly complex configuration of mutually independent direct causes of that effect. The paper at hand presents a procedure of causal reasoning that operates on (...) the same type of empirical data as QCA and that implements Boolean techniques related to the ones resorted to by QCA, yet, in contrast to QCA, the procedure introduced here successfully identifies causal structures involving both mutually dependent causes, i.e. causal chains, and multiple effects, i.e. epiphenomena. In this sense, the paper at hand generalizes QCA. (shrink)

It is traditionally thought that metaphorical utterances constitute a special— nonliteral—kind of departure from lexical constraints on meaning. Dan Sperber and Deirdre Wilson have been forcefully arguing against this: according to them, relevance theory’s comprehension/interpretation procedure for metaphorical utterances does not require details specifi c to metaphor (or nonliteral discourse); instead, the same type of comprehension procedure as that in place for literal utterances covers metaphors as well. One of Sperber and Wilson’s central reasons for holding this is that metaphorical (...) utterances occupy one end of a continuum that includes literal, loose and hyperbolic utterances with no sharp boundaries in between them. Call this the continuum argument about interpreting metaphors. My aim is to show that this continuum argument doesn’t work. For if it were to work, it would have an unwanted consequence: it could be converted into a continuum argument about interpreting linguistic errors, including slips of the tongue, of which malaprops are a special case. In particular, based on the premise that the literal–loose–metaphorical continuum extends to malaprops also, we could conclude that the relevance-theoretic comprehension procedure for malaprops does not require details specifi c to linguistic errors, that is, details beyond those already in place for interpreting literal utterances. Given that we have good reason to reject this conclusion, we also have good reason to rethink the conclusion of the continuum argument about interpreting metaphors and consider what additional (metaphor-specifi c) details—about the role of constraints due to what is lexically encoded by the words used—might be added to relevance-theoretic comprehension procedures. (shrink)

This paper deals with the problem of reconstruction of the intergenic interaction graph from the raw data of genetic co-expression coming with new technologies of bio-arrays (DMA-arrays, protein-arrays, etc.). These new imaging devices in general only give information about the asymptotical part (fixed configurations of co-expression or limit cycles of such configurations) of the dynamical evolution of the regulatory networks (genetic and/or proteic) underlying the functioning of living systems. Extracting the casual structure and interaction coefficients of a gene interaction network (...) from the observed configurations is a complex problem. But if all the fixed configurations are supposedly observed and if they are factorizable into two or more subsets of values, then the interaction graph possesses as many connected components as the number of factors and the solution is obtained in polynomial time. This new result allows us for example to partly solve the topology of the genetic regulatory network ruling the flowering in Arabidopsis thaliana. (shrink)

Cognitive Science, Volume 46, Issue 3, March 2022.

Science continually contributes new models and rethinks old ones. The way inferences are made is constantly being re-evaluated. The practice and achievements of science are both shaped by this process, so it is important to understand how models and inferences are made. But, despite the relevance of models and inference in scientific practice, these concepts still remain contro-versial in many respects. The attempt to understand the ways models and infer-ences are made basically opens two roads. The first one is to (...) produce an analy-sis of the role that models and inferences play in science. The second one is to produce an analysis of the way models and inferences are constructed, especial-ly in the light of what science tells us about our cognitive abilities. The papers collected in this volume go both ways. (shrink)

How much of everyday language use takes place on autopilot, how much are speakers aware of, and how do their attitudes relate to this? In particular, how do these factors together account for variation between speakers? Limburgian, a regional language within the Netherlands, is under pressure from Dutch in an intensive language contact situation. The use of a non-feminine subject pronoun for women is a Limburgian feature which is not shared with Dutch. Limburgian speakers show a large range of variation (...) regarding this feature, both when it comes to its use, and how it is perceived. By studying speakers’ self-reports of three concepts – automaticity, attitudes, and metalinguistic awareness – as well as how these together relate to self-reported language use (N = 405), this paper investigates to what extent speakers have control over their own language use. Our findings suggest that self-reported automaticity is the driving force in the use of the non-feminine pronoun, but also that this autopilot may be curbed by metalinguistic awareness and attitudes. Importantly, speakers vary considerably on all three concepts, highlighting once more that language users are not a monolith, and that individual speakers may react differently in a language contact situation. (shrink)

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike-time (...) dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla–Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions. (shrink)

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in (...) reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein–protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment. (shrink)

The incidence and moral implications of cheating depend on how it is defined and measured. Research that defines and operationalizes cheating as an inventory of acts, that is, “cheating in any form,” has often fueled concern that cheating is reaching “epidemic proportions.” Such inventory measures appear, however, to conflate moral and administrative conceptions of the problem. Inasmuch as the immorality of behavior is a function of moral judgment, academic misconduct is immoral only when it is intentional, and the greatest moral (...) weight should be ascribed to violations that students judge to be the most “serious.”. (shrink)

Research on metaphors has shown that individuals form associations between the verticality, brightness, and distance of stimuli and their valence. Building on the literature on conceptual metaphor...

Molecular methods have revolutionised virtually every area of biology, and metazoan phylogenetics is no exception: molecular phylogenies, molecular clocks, comparative phylogenomics, and developmental genetics have generated a plethora of molecular data spanning numerous taxa and collectively transformed our understanding of the evolutionary history of animals, often corroborating but at times opposing results of more traditional approaches. Moreover, the diversity of methods and models within molecular phylogenetics has resulted in significant disagreement among molecular phylogenies as well as between these and earlier (...) phylogenies. How should this broad and multifaceted problem be tackled? I argue that the answer lies in integrating evidence to infer the best evolutionary scenario. I begin with an overview of recent development in early metazoan phylogenetics, followed by a discussion of key conceptual issues in phylogenetics revolving around phylogenetic evidence, theory, methodology, and interrelations thereof. I then argue that the integration of different kinds of evidence is necessary for arriving at the best evolutionary scenario rather than merely the best-fitting cladogram. Finally, I discuss the prospects of this view in stimulating interdisciplinary cross-talk in early metazoan research and beyond, and challenges that need to be overcome. (shrink)

A symposium on Michael Strevens' book "Tychomancy", concerning the psychological roots and historical significance of physical intuition about probability in physics, biology, and elsewhere.

In this paper the authors argue that many of the ethical problems raised by Ambient Intelligence stems from presupposing a behaviourist conception of the relation between human desires and behaviour. Insofar as Ambient Intelligence systems take overt, natural behaviour as input, they are likely to suffer from many of the same problems that have fuelled the widespread criticism of behaviourist explanations of human behaviour. If these limitations of the technology are not sufficiently recognized, the technology is likely to be insufficiently (...) successful in supporting the needs and desires of human users. We will focus on four distinct challenges that result from this behaviourist presupposition, all of which ought to be taken into consideration at the design stage: reciprocal adaptation, bias towards isolated use, culture-specific behaviour, and inability to manually configure the system. By considering these issues, our purpose is to raise awareness of the ethical problems that can arise because of intelligent user interfaces that rely on natural, overt behaviour. (shrink)

This monograph provides a new account of justified inference as a cognitive process. In contrast to the prevailing tradition in epistemology, the focus is on low-level inferences, i.e., those inferences that we are usually not consciously aware of and that we share with the cat nearby which infers that the bird which she sees picking grains from the dirt, is able to fly. Presumably, such inferences are not generated by explicit logical reasoning, but logical methods can be used to describe (...) and analyze such inferences. Part 1 gives a purely system-theoretic explication of belief and inference. Part 2 adds a reliabilist theory of justification for inference, with a qualitative notion of reliability being employed. Part 3 recalls and extends various systems of deductive and nonmonotonic logic and thereby explains the semantics of absolute and high reliability. In Part 4 it is proven that qualitative neural networks are able to draw justified deductive and nonmonotonic inferences on the basis of distributed representations. This is derived from a soundness/completeness theorem with regard to cognitive semantics of nonmonotonic reasoning. The appendix extends the theory both logically and ontologically, and relates it to A. Goldman's reliability account of justified belief. This text will be of interest to epistemologists and logicians, to all computer scientists who work on nonmonotonic reasoning and neural networks, and to cognitive scientists. (shrink)

Causal inference in the empiricalsciences is based on counterfactuals. The mostcommon approach utilizes a statistical model ofpotential outcomes to estimate causal effectsof treatments. On the other hand, one leadingapproach to the study of causation inphilosophical logic has been the analysis ofcausation in terms of counterfactualconditionals. This paper discusses and connectsboth approaches to counterfactual causationfrom philosophy and statistics. Specifically, Ipresent the counterfactual account of causationin terms of Lewis's possible-world semantics,and reformulate the statistical potentialoutcome framework using counterfactualconditionals. This procedure highlights variousproperties and (...) mechanisms of the statisticalmodel. (shrink)