Representing part–whole relations in conceptual spaces

In this paper, we propose a cognitive semantic approach to represent part–whole relations. We base our proposal on the theory of conceptual spaces, focusing on prototypical structures in part–whole relations. Prototypical structures are not accounted for in traditional mereological formalisms. In our account, parts and wholes are represented in distinct conceptual spaces; parts are joined to form wholes in a structure space. The structure space allows systematic similarity judgments between wholes, taking into consideration shared parts and their configurations. A point in the structure space denotes a particular part structure; regions in the space represent different general types of part structures. We argue that the structural space can represent prototype effects: structural types are formed around typical arrangements of parts. We also show how structure space captures the variations in part structure of a given concept across different domains. In addition, we discuss how some taxonomies of part–whole relations can be understood within our framework.     

Coinductive models and normal forms for modal logics (or how we learned to stop worrying and love coinduction)

We present a coinductive definition of models for modal logics and show that it provides a homogeneous framework in which it is possible to include different modal languages ranging from classical modalities to operators from hybrid and memory logics. Moreover, results that had to be proved separately for each different language—but whose proofs were known to be mere routine—now can be proved in a general way. We show, for example, that we can have a unique definition of bisimulation for all these languages, and prove a single invariance-under-bisimulation theorem.We then use the new framework to investigate normal forms for modal logics. The normal form we introduce may have a smaller modal depth than the original formula, and it is inspired by global modalities like the universal modality and the satisfiability operator from hybrid logics. These modalities can be extracted from under the scope of other operators. We provide a general definition of extractable modalities and show how to compute extracted normal forms. As it is the case with other classical normal forms—e.g., the conjunctive normal form of propositional logic—the extracted normal form of a formula can be exponentially bigger than the original formula, if we require the two formulas to be equivalent. If we only require equi-satisfiability, then every modal formula has an extracted normal form which is only polynomially bigger than the original formula, and it can be computed in polynomial time.     

Statistical Learning of Unfamiliar Sounds as Trajectories Through a Perceptual Similarity Space

In typical statistical learning studies, researchers define sequences in terms of the probability of the next item in the sequence given the current item (or items), and they show that high probability sequences are treated as more familiar than low probability sequences. Existing accounts of these phenomena all assume that participants represent statistical regularities more or less as they are defined by the experimenters—as sequential probabilities of symbols in a string. Here we offer an alternative, or possibly supplementary, hypothesis. Specifically, rather than identifying or labeling individual stimuli discretely in order to predict the next item in a sequence, we need only assume that the participant is able to represent the stimuli as evincing particular similarity relations to one another, with sequences represented as trajectories through this similarity space. We present experiments in which this hypothesis makes sharply different predictions from hypotheses based on the assumption that sequences are learned over discrete, labeled stimuli. We also present a series of simulation models that encode stimuli as positions in a continuous two‐dimensional space, and predict the next location from the current location. Although no model captures all of the data presented here, the results of three critical experiments are more consistent with the view that participants represent trajectories through similarity space rather than sequences of discrete labels under particular conditions.     

Causality, criticality, and reading words: distinct sources of fractal scaling in behavioral sequences

The finding of fractal scaling (FS) in behavioral sequences has raised a debate on whether FS is a pervasive property of the cognitive system or is the result of specific processes. Inferences about the origins of properties in time sequences are causal. That is, as opposed to correlational inferences reflecting instantaneous symmetrical relations, causal inferences concern asymmetric relations lagged in time. Here, I integrate Granger-causality with inferences about FS. Four simulations illustrate that causal analyses can isolate distinct FS sources, whereas correlational techniques cannot. I then analyze three simultaneous sequences of responses from a database of word-naming trials. I find that two, or perhaps three, distinct sources account for the presence of FS in these sequences, but FS is not a general property of the system. This suggests that FS arises due to the properties of a limited number of identifiable psychological and/or neural processes. Finally, I reanalyze a previously published dataset of acoustic frequency spectra using the new tools. The causality/criticality combination introduced here offers a new important perspective in the study of cognition.     

A hierarchical state space approach to affective dynamics

Linear dynamical system theory is a broad theoretical framework that has been applied in various research areas such as engineering, econometrics and recently in psychology. It quantifies the relations between observed inputs and outputs that are connected through a set of latent state variables. State space models are used to investigate the dynamical properties of these latent quantities. These models are especially of interest in the study of emotion dynamics, with the system representing the evolving emotion components of an individual. However, for simultaneous modeling of individual and population differences, a hierarchical extension of the basic state space model is necessary. Therefore, we introduce a Bayesian hierarchical model with random effects for the system parameters. Further, we apply our model to data that were collected using the Oregon adolescent interaction task: 66 normal and 67 depressed adolescents engaged in a conflict-oriented interaction with their parents and second-to-second physiological and behavioral measures were obtained. System parameters in normal and depressed adolescents were compared, which led to interesting discussions in the light of findings in recent literature on the links between cardiovascular processes, emotion dynamics and depression. We illustrate that our approach is flexible and general: The model can be applied to any time series for multiple systems (where a system can represent any entity) and moreover, one is free to focus on various components of this versatile model.     

Reasoning with prioritized information by iterative aggregation of distance functions

We introduce a general framework for reasoning with prioritized propositional data by aggregation of distance functions. Our formalism is based on a possible world semantics, where conclusions are drawn according to the most ‘plausible’ worlds (interpretations), namely: the worlds that are as ‘close’ as possible to the set of premises, and, at the same time, are as ‘faithful’ as possible to the more reliable (or important) information in this set. This implies that the consequence relations that are induced by our framework are derived from a pre-defined metric on the space of interpretations, and inferences are determined by a ranking function applied to the premises. We study the basic properties of the entailment relations that are obtained by this framework, and relate our approach to other methods of maintaining incomplete and inconsistent information, most specifically in the contexts of (iterated) belief revision, consistent query answering in database systems, and integration of prioritized data sources.     

Integrating personality and career adaptability into vocational interest space

The relations between interests, personality and career adaptability were explored in two separate studies. In the first study, the RIASEC measure Occupational Preference Scale was applied along with personality inventories HEXACO-PI-(R)-100 and IPIP-50 on a sample of 602 university students and young adults. In the second study, PGI-Short, HEXACO-60 and Career Adapt-Abilities Scale were applied on a sample of 981 high-school graduates. Results from both studies were discussed together, and general conclusions about overlapping of interests, personality and career adaptability domains were drawn on the basis of correlational analyses and property vector fitting. Both studies have shown weak to moderate relations between interests and personality. In the HEXACO framework, it was found that Openness to Experience was positively related to creative interests, Emotionality was positively related to social interests and negatively related to technical interests, Extraversion was positively related to social and managing interests, and Honesty–Humility was negatively related to interests for business and finance. In the Big Five framework, Agreeableness was related to Social and Artistic interests, and Intellect to Artistic interests. The HEXACO personality domains showed predictive advantage for explaining interests in comparison to Big Five dimensions. The relation between career adaptability and interests was weak, and almost negligible when personality was included in hierarchical regression analysis. Career adaptability was weakly related to highly prestigious interests. Adaptability facets Concern, Control and Confidence were oriented toward data pole of interest space. The general factor of interests was weakly correlated with Openness Extraversion, Career Adaptability, and adaptability facets Confidence and Curiosity. Observed findings are as expected and in line with previous research.     

Do People Prefer to Pass Along Good or Bad News? Valence and Relevance of News as Predictors of Transmission Propensity

Anecdotal evidence seems to indicate that exaggeratedly bad news may propagate in the marketplace of ideas. Three studies investigate whether people prefer to pass along pieces of bad news or good news that are equated for “surprisingness.” People typically prefer to pass along central rather than extreme information (i.e., news that is less surprising rather than more surprising). However, when confronted with extreme information, the results support a preference forcongruence,that is, people prefer to pass along news that is congruent with the emotional valence of the domain in question. This means that in emotionally negative domains, contrary to some theoretical predictions, people are willing to pass along bad news even when it is exaggeratedly bad. At the same time, however, people transmit exaggeratedly good news in emotionally positive domains. The general discussion indicates how these results may inform research on word of mouth for consumer products and social relations in organizations.     

Elementary Polyhedral Mereotopology

A region-based model of physical space is one in which the primitive spatial entities are regions, rather than points, and in which the primitive spatial relations take regions, rather than points, as their relata. Historically, the most intensively investigated region-based models are those whose primitive relations are topological in character; and the study of the topology of physical space from a region-based perspective has come to be called mereotopology. This paper concentrates on a mereotopological formalism originally introduced by Whitehead, which employs a single primitive binary relation C(x,y) (read: x is in contact with y). Thus, in this formalism, all topological facts supervene on facts about contact. Because of its potential application to theories of qualitative spatial reasoning, Whitehead's primitive has recently been the subject of scrutiny from within the Artificial Intelligence community. Various results regarding the mereotopology of the Euclidean plane have been obtained, settling such issues as expressive power, axiomatization and the existence of alternative models. The contribution of the present paper is to extend some of these results to the mereotopology of three-dimensional Euclidean space. Specifically, we show that, in a first-order setting where variables range over tame subsets of R ³, Whitehead's primitive is maximally expressive for topological relations; and we deduce a corollary constraining the possible region-based models of the space we inhabit.     

The Structural Metaphysics of Quantum Theory and General Relativity

The paper compares ontic structural realism in quantum physics with ontic structural realism about space–time. We contend that both quantum theory and general relativity theory support a common, contentful metaphysics of ontic structural realism. After recalling the main claim of ontic structural realism and its physical support, we point out that both in the domain of quantum theory and in the domain of general relativity theory, there are objects whose essential ways of being are certain relations so that these objects do not possess an intrinsic identity. Nonetheless, the qualitative, physical nature of these relations is in the quantum case (entanglement) fundamentally different from the classical, metrical relations treated in general relativity theory.     

Visual statistical learning in the newborn infant

Statistical learning – implicit learning of statistical regularities within sensory input – is a way of acquiring structure within continuous sensory environments. Statistics computation, initially shown to be involved in word segmentation, has been demonstrated to be a general mechanism that operates across domains, across time and space, and across species. Recently, statistical leaning has been reported to be present even at birth when newborns were tested with a speech stream. The aim of the present study was to extend this finding, by investigating whether newborns’ ability to extract statistics operates in multiple modalities, as found for older infants and adults. Using the habituation procedure, two experiments were carried out in which visual sequences were presented. Results demonstrate that statistical learning is a general mechanism that extracts statistics across domain since the onset of sensory experience. Intriguingly, present data reveal that newborn learner’s limited cognitive resources constrain the functioning of statistical learning, narrowing the range of what can be learned.     

The mental context for the description of odors: a semantic space

The experiments conducted in this research aimed to increase our understanding of olfactory properties and classify them hierarchically through entailment. The research objective was to build an odor classification based on internal knowledge (i.e. mental context). Twelve floral scents were described freely by 60 participants. First, three sensory judges undertook a semantic analysis of the 1145 olfactory terms gathered. Secondly, a study of the entailment between olfactory properties was conducted using 42 subjects working on four lists of terms describing the characteristics of floral perfumes. The results confirmed the hypothesis that olfactory properties show tree-like structure. This allowed us to propose a classification system composed of two superordinate categories: First, objects, being olfactory sources, constitute the first category and are considered as olfactory properties themselves (i.e. civilization, food and natural sources). Second non-objects properties are those applicable to a diversity of objects: (i.e. properties, such as objects' physical state: sensory, intensity, space, time) and those related to the subject (as personal feelings and value judgments). Based on these results, it appears that olfactory properties can be organized as a semantic network in line with the general theory of categorization.     

On the (numerical) ranking associated with any finite binary relation

We generalize the concept of a ‘ranking associated with a linear order’ from linear orders to arbitrary finite binary relations. Using the concept of differential of an object in a binary relation as theoretical primitive, we axiomatically introduce several measurement scales, some of which include the generalized ranking as a special case. We provide a computational formula for this generalized ranking, discuss its many elegant properties and offer some illustrating examples.     

Research Essays: An Introduction and Invitation

In this article, we offer an approach to conceiving of the relation between literacy practices and space–time. Literacy, embedded in other forms of activity, has a unique role in producing and organizing space–time relations, and such relations provide for different forms of cognition and learning. Closely examining how literacy practices produce and organize space–time helps researchers move beyond folk distinctions based on setting or context, such as "in school," versus "out of school," which necessarily associate different settings with different practices. Drawing on actor network theory, we propose a new way of reconceiving of literacy and its relations to space–time through the construct of the literacy network. We define and develop 5 space–time dimensions of literacy networks, including translation, the heterogeneity of space–time representations, movements and positions of texts in circulation, rhythms and speeds of circulation, and network continuity. Drawing on these dimensions, we analyze 3 different literacy networks that 1 youth, Brian, participates in, including 2 from school classrooms and 1 from his play of a massively multiplayer online game. We argue that the space–time dimensions of these networks have direct relevance to understanding Brian's engagement, agency, and identity.     

Arranging Objects in Space: Measuring Task‐Relevant Organizational Behaviors During Goal Pursuit

Human behavior unfolds primarily in built environments, where the arrangement of objects is a result of ongoing human decisions and actions, yet these organizational decisions have received limited experimental study. In two experiments, we introduce a novel paradigm designed to explore how individuals organize task‐relevant objects in space. Participants completed goals by locating and accessing sequences of objects in a computer‐based task, and they were free to rearrange the positions of objects at any time. We measure a variety of organization changes and evaluate how these measures relate to individual differences in performance. In Experiment 1, we show that with weak structure in task demands, changes in object positions that arise through performance of the task lead to improved order, characterized predominantly by a centralization of frequently used items and a peripheralization of infrequently used objects. In Experiment 2, with increased task structure, we observe more refined organizational tendencies, with selective contraction and clustering of interrelated task‐relevant objects. We further demonstrate that these more selective organization behaviors are reliably associated with individual differences in task performance. Collectively, these two studies reveal properties of space and of task demands that support and facilitate effective organization of the environment in support of ongoing behavior.     

Time–space synaesthesia – A cognitive advantage?

Is synaesthesia cognitively useful? Individuals with time–space synaesthesia experience time units (such as months of the year) as idiosyncratic spatial forms, and report that these forms aid them in mentally organising their time. In the present study, we hypothesised that time–space synaesthesia would facilitate performance on a time-related cognitive task. Synaesthetes were not specifically recruited for participation; instead, likelihood of time–space synaesthesia was assessed on a continuous scale based on participants’ responses during a semi-structured interview. Participants performed a month-manipulation task, which involved naming every second month or every third month in reverse-chronological order, beginning and ending with a target month. Using hierarchical multiple regression, we found that time–space synaesthesia corresponded with faster performance on both versions of the task. We propose that time–space synaesthesia may expedite the cognitive manipulation of time-based information. Our results also indicate that synaesthesia is far less unusual than widely believed.     

二次响应面回归方法及其在个体-环境匹配研究中的使用

二次响应面回归分析是指二次多项式回归与响应面分析相结合的一种研究方法, 该方法在工业与组织心理学领域起源于个体−环境(P-E)匹配研究。它使用对个体−环境进行间接匹配测量的策略, 考察二者间的最佳匹配以及与结果变量的复杂关系, 并在三维空间对其进行检验和解释。该文在分析以往P-E匹配研究方法(例如差异分数)缺陷性的基础上, 阐述了二次响应面回归方法的产生与发展, 逻辑思想, 统计分析方法, 并以实例演示其操作步骤与结果解释, 以及相对于其他方法的优势与不足。     

“Perceptual centers” in speech production and perception

Morton, Marcus, and Frankish (1976) report that listeners hear acoustically isochronous digit sequences as anisochronous. Moreover, given a chance to adjust intervals in the sequences until they are perceptually isochronous, the listeners introduce systematic deviations from isochrony. The present series of studies investigates these phenomena further. They indicate that when asked toproduce isochronous sequences, talkers generate precisely the acoustic anisochronies that listeners require in order tohear a sequence as isochronous. The acoustic anisochronies that talkers produce are expected if talkers initiate the articulation of successive items in the sequence at temporally equidistant intervals. Items whose initial consonants differ in respect to manner class will have acoustic consequences (other than silence) at different lags with respect to their articulatory onsets thereby generating the observed acoustic anisochronies. The findings suggest that listeners judge isochrony based on acoustic information aboutarticulatory timing rather than on some articulation-free acoustic basis.