Model theory of deduction: a unified computational approach

One of the most debated questions in psychology and cognitive science is the nature and the functioning of the mental processes involved in deductive reasoning. However, all existing theories refer to a specific deductive domain, like syllogistic, propositional or relational reasoning.
Our goal is to unify the main types of deductive reasoning into a single set of basic procedures. In particular, we bring together the microtheories developed from a mental models perspective in a single theory, for which we provide a formal foundation. We validate the theory through a computational model (UNICORE) which allows fine-grained predictions of subjects' performance in different reasoning domains.
The performance of the model is tested against the performance of experimental subjects—as reported in the relevant literature—in the three areas of syllogistic, relational and propositional reasoning. The computational model proves to be a satisfactory artificial subject, reproducing both correct and erroneous performance of the human subjects. Moreover, we introduce a developmental trend in the program, in order to simulate the performance of subjects of different ages, ranging from children (3–6) to adolescents (8–12) to adults (>21). The simulation model performs similarly to the subjects of different ages.
Our conclusion is that the validity of the mental model approach is confirmed for the deductive reasoning domain, and that it is possible to devise a unique mechanism able to deal with the specific subareas. The proposed computational model (UNICORE) represents such a unifying structure.     

EVOLUTION AND SUBJECTIVITY

Evolutionary theory is becoming an all‐encompassing form of explanation in many branches of philosophy. However, emergence theory uses the concept of self‐organization to support yet alter traditional evolutionary explanation. Biologist Stuart Kauffman suggests that the new science will need to tell stories, not simply as a heuristic device but as part of its fundamental task. This claim is reminiscent of C. S. Peirce's criticism of the doctrine of necessity. Peirce's suggestions reference Hegel, and this essay draws out this Hegelian background, addressing the question of subjectivity and issuing some Hegelian reminders so that such evolutionary and emergent theories will consider the implication of this research program on philosophy of mind. The primary focus is on two post‐Kantian, neo‐Hegelian thinkers in contemporary philosophy who deal with this problem: John McDowell and Robert Brandom.     

The intrinsic difficulty of recursive functions

This paper deals with a philosophical question that arises within the theory of computational complexity: how to understand the notion of INTRINSIC complexity or difficulty, as opposed to notions of difficulty that depend on the particular computational model used. The paper uses ideas from Blum's abstract approach to complexity theory to develop an extensional approach to this question. Among other things, it shows how such an approach gives detailed confirmation of the view that subrecursive hierarchies tend to rank functions in terms of their intrinsic, and not just their model-dependent, difficulty, and it shows how the approach allows us to model the idea that intrinsic difficulty is a fuzzy concept. Jan Zygmunt     

复杂性对倾斜汉字识别的影响

以字的复杂性探查汉字心理旋转方式与识别的加工方式,字的复杂性是以笔画数和部件可分性标志的。同时,它们构成两个独立的实验。每个实验中,字的倾斜角度变量,以４５°的间隔渐增,共有８个变式角度,即０°、４５°、９０°、１３５°、１８０°、－４５°、－９０°和－１３５°。实验的因变量为命名反应时。结果发现,字的复杂性标志,无论是笔画数或部件可分性都与倾斜角度变量有交互作用。这意味着,心旋与识别加工方式中的整字－成分的混合模型得到支持。     

Departure from normal assumptions: A promise for future psychometrics with substantive mathematical modeling

Normal assumptions have been used in many psychometric methods, to the extent that most researchers do not even question their adequacy. With the rapid advancement of computer technologies in recent years, psychometrics has extended its territory to include intensive cognitive diagnosis, etcetera, and substantive mathematical modeling ha become essential. As a natural consequence, it is time to consider departure from normal assumptions seriously. As examples of models which are not based on normality or its approximation, the logistic positive exponent family of models is discussed. These models include the item task complexity as the third parameter, which determines the single principle of ordering individuals on the ability scale.     

Modelling the human lane-change execution behaviour through Multilayer Perceptrons and Convolutional Neural Networks

Driving is a highly complex task that involves the execution of multiple cognitive tasks belonging to different levels of abstraction. Traffic emerges from the interaction of a big number of agents implementing those behaviours, but until recent years, modelling it by the interaction of these agents in the so called micro-simulators was a nearly impossible task as their number grows. However, with the growing computing power it is possible to model increasingly large quantities of individual vehicles according to their individual behaviours. These models are usually composed of two sub-models for two well-defined tasks: car-following and lane-change. In the case of lane-change the literature proposes many different models, but few of them use Computational Intelligence (CI) techniques, and much less use personalization for reaching individual granularity. This study explores one of the two aspects of the lane-change called lane-change acceptance, where the driver performs or not a lane-change given his intention and the vehicle environment. We demonstrate how the lane-change acceptance of a specific driver can be learned from his lane change intention and surrounding environment in an urban scenario using CI techniques such as feed-forward Artificial Neural Network (ANN). We work with Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN) architectures. How they perform one against the other and how the different topologies affect both to the generalization of the problem and the learning process are studied.     

Contrast effects in typicality judgements: A hierarchical Bayesian approach

We examine the influence of contrast categories on the internal graded membership structure of everyday concepts using computational models proposed in the artificial category learning tradition. In particular, the generalized context model (Nosofsky, 1986), which assumes that only members of a given category contribute to the typicality of a category member, is contrasted to the similarity–dissimilarity generalized context model (SD-GCM; Stewart & Brown, 2005), which assumes that members of other categories are also influential in determining typicality. The models are compared in a hierarchical Bayesian framework in their account of the typicality gradient of five animal categories and six artefact categories. For each target category, we consider all possible relevant contrast categories. Three separate issue are examined: (a) whether contrast effects can be found, (b) which categories are responsible for these effects, and (c) whether more than one category influences the typicality. Results indicate that the internal category structure is codetermined by dissimilarity towards potential contrast categories. In most cases, only a single contrast category contributed to the typicality. The present findings suggest that contrast effects might be more widespread than has previously been assumed. Further, they stress the importance of characteristics particular of everyday concepts, which require careful consideration when applying computational models of representation of the artificial category learning tradition to everyday concepts.     

Effects of referential ambiguity,time constraints and addressee orientation on the production of morphologically complex words

In five experiments, participants were asked to describe unambiguously a target picture in a picture–picture paradigm. In the same-category condition, target (e.g., water bucket) and distractor picture (e.g., ice bucket) had identical names when their preferred, morphologically simple, name was used (e.g., bucket). The ensuing lexical ambiguity could be resolved by compound use (e.g., water bucket). Simple names sufficed as means of specification in other conditions, with distractors identical to the target, completely unrelated, or geometric figures. With standard timing parameters, participants produced mainly ambiguous answers in Experiment 1. An increase in available processing time hardly improved unambiguous responding (Experiment 2). A referential communication instruction (Experiment 3) increased the number of compound responses considerably, but morphologically simple answers still prevailed. Unambiguous responses outweighed ambiguous ones in Experiment 4, when timing parameters were further relaxed. Finally, the requirement to name both objects resulted in a nearly perfect ambiguity resolution (Experiment 5). Together, the results showed that speakers overcome lexical ambiguity only when time permits, when an addressee perspective is given and, most importantly, when their own speech overtly signals the ambiguity.     

Predicting stress patterns in an unpredictable stress language: The use of non-lexical sources of evidence for stress assignment in Russian

The main goal of this research was to examine how readers of Russian assign stress to disyllabic words. In particular, we tested the claim that the process of stress assignment in Russian can only be accomplished lexically. Eleven potential non-lexical sources of evidence for stress in Russian were examined in regression and factorial studies. In Study 1, onset complexity, coda complexity, the orthography of the first syllable (CVC1), of the second syllable (CVC2), and of the ending of the second syllable (VC2) were found to be probabilistically associated with stress in Russian disyllables. In Studies 2 and 3, it was shown that Russian speakers do use 3 of these cues (CVC1, CVC2, and VC2) when making stress-assignment decisions. These results provide evidence against the idea that the nature of stress in the Russian language is so unpredictable that stress assignment can only be accomplished lexically. These results also suggest that any successful model of stress assignment in Russian needs to contain mechanisms allowing these 3 orthographic cues to play a role in the stress-assignment process.     

Schizophrenia Epigenesis?

I begin by examining how genetics drivesschizophrenia research, and raise both familiar andrelatively novel criticisms of the evidence putativelysupporting the genetic basis of schizophrenia. Inparticular, I call attention to a set of concernsabout the effects of placentation on concordance ratesof schizophrenia in monozygotic twins, which furtherweakens the case for schizophrenia's so-called stronggenetic component. I then underscore two criticalpoints. First, I emphasize the importance of takingseriously considerations about the complexity of bothontogenesis and the development of hereditarydiseases. The recognition of developmentalconstraints and supports is crucial, for attention todevelopment exposes the naivete of too many models ofgene action in the aetiology of disease. Secondly, Iattend to those schizophreniologists who ignoremethodological criticisms and thus presume a geneticbasis for schizophrenia, and then seek the schizophrenic genotype lacking an adequatephenotype. In response I attempt to demonstrate thenecessity of a sustained effort at characterizing thephenotype of schizophrenia as an enabling conditionfor the whole enterprise of psychiatric genetics – andfor psychiatry itself. Without the organism-levelphenotype, research at the level of genes will remainunproductive – assuming of course that research at thegenetic level is appropriate at all.