Publications

Abstract

We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model.

Abstract

Print Friendly XML Gebre, Binyam Gebrekidan, Max Planck Institute for Psycholinguistics, The Netherlands, binyamgebrekidan.gebre [at] mpi.nl Wittenburg, Peter, Max Planck Institute for Psycholinguistics, The Netherlands, peter.wittenburg [at] mpi.nl Introduction Many gesture and sign language researchers manually annotate video recordings to systematically categorize, analyze and explain their observations. The number and kinds of annotations are so diverse and unpredictable that any attempt at developing non-adaptive automatic annotation systems is usually less effective. The trend in the literature has been to develop models that work for average users and for average scenarios. This approach has three main disadvantages. First, it is impossible to know beforehand all the patterns that could be of interest to all researchers. Second, it is practically impossible to find enough training examples for all patterns. Third, it is currently impossible to learn a model that is robustly applicable across all video quality-recording variations.

Abstract

Automatic annotation of gesture strokes is important for many gesture and sign language researchers. The unpredictable diversity of human gestures and video recording conditions require that we adopt a more adaptive case-by-case annotation model. In this paper, we present a work-in progress annotation model that allows a user to a) track hands/face b) extract features c) distinguish strokes from non-strokes. The hands/face tracking is done with color matching algorithms and is initialized by the user. The initialization process is supported with immediate visual feedback. Sliders are also provided to support a user-friendly adjustment of skin color ranges. After successful initialization, features related to positions, orientations and speeds of tracked hands/face are extracted using unique identifiable features (corners) from a window of frames and are used for training a learning algorithm. Our preliminary results for stroke detection under non-ideal video conditions are promising and show the potential applicability of our methodology.

Abstract

Recognizing the speech acts in our interlocutors’ utterances is a crucial prerequisite for conversation. However, it is not a trivial task given that the form and content of utterances is frequently underspecified for this level of meaning. In the present study we investigate participants’ competence in categorizing speech acts in such action-underspecific sentences and explore the time-course of speech act inferencing using a self-paced reading paradigm. The results demonstrate that participants are able to categorize the speech acts with very high accuracy, based on limited context and without any prosodic information. Furthermore, the results show that the exact same sentence is processed differently depending on the speech act it performs, with reading times starting to differ already at the first word. These results indicate that participants are very good at “getting” the speech acts, opening up a new arena for experimental research on action recognition in conversation.

Abstract

This paper explores the linguistic encoding of placement and removal events in Swedish. Drawing on elicited spoken data, it provides a unified approach to caused motion descriptions. The results show uniform syntactic behaviour of placement and removal descriptions and a consistent asymmetry between placement and removal in the semantic specificity of verbs. The results also reveal three further semantic patterns, pertaining to the nature of the relationship between Figure and Ground, that appear to account for how these event types are characterised, viz. whether the Ground is represented by a body part of the Agent; whether the Figure is contained within the Ground; or whether it is supported by the Ground.

Abstract

“The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” (Weiser 1991, S. 94). – Die Behauptung stammt aus einem vielzitierten Text von Mark Weiser, ehemals Chief Technology Officer am berühmten Xerox Palo Alto Research Center (PARC), wo nicht nur einige bedeutende computertechnische Innovationen ihren Ursprung hatten, sondern auch grundlegende anthropologische Einsichten zum Umgang mit technischen Artefakten gewonnen wurden.1 In einem populärwissenschaftlichen Artikel mit dem Titel „The Computer for the 21st Century” entwarf Weiser 1991 die Vision einer Zukunft, in der wir nicht mehr mit einem einzelnen PC an unserem Arbeitsplatz umgehen – vielmehr seien wir in jedem Raum umgeben von hunderten elektronischer Vorrichtungen, die untrennbar in Alltagsgegenstände eingebettet und daher in unserer materiellen Umwelt gleichsam „verschwunden“ sind. Dabei ging es Weiser nicht allein um das ubiquitäre Phänomen, das in der Medientheorie als „Transparenz der Medien“ bekannt ist2 oder in allgemeineren Theorien der Alltagserfahrung als eine selbstverständliche Verwobenheit des Menschen mit den Dingen, die uns in ihrem Sinn vertraut und praktisch „zuhanden“ sind.3 Darüber hinaus zielte Weisers Vision darauf, unsere bereits existierende Umwelt durch computerlesbare Daten zu erweitern und in die Operationen eines solchen allgegenwärtigen Netzwerks alltägliche Praktiken gleichsam lückenlos zu integrieren: In der Welt, die Weiser entwirft, öffnen sich Türen für denjenigen, der ein bestimmtes elektronisches Abzeichen trägt, begrüßen Räume Personen, die sie betreten, mit Namen, passen sich Computerterminals an die Präferenzen individueller Nutzer an usw. (Weiser 1991, S. 99).

Abstract

The standard for the analysis of distorted voices is perceptual rating of read-out texts or spontaneous speech. Automatic voice evaluation, however, is usually done on stable sections of sustained vowels. In this paper, text-based and established vowel-based analysis are compared with respect to their ability to measure hoarseness and its subclasses. 73 hoarse patients (48.3±16.8 years) uttered the vowel /e/ and read the German version of the text “The North Wind and the Sun”. Five speech therapists and physicians rated roughness, breathiness, and hoarseness according to the German RBH evaluation scheme. The best human-machine correlations were obtained for measures based on the Cepstral Peak Prominence (CPP; up to |r | = 0.73). Support Vector Regression (SVR) on CPP-based measures and prosodic features improved the results further to r ≈0.8 and confirmed that automatic voice evaluation should be performed on a text recording.

Abstract

We review data and hypotheses dealing with the mental representations for perceived and produced speech that infants build and use over the course of learning a language. In the early stages of speech perception and vocal production, before the emergence of a receptive or a productive lexicon, the dominant picture emerging from the literature suggests rather non-analytic representations based on units of the size of the syllable: Young children seem to parse speech into syllable-sized units in spite of their ability to detect sound equivalence based on shared phonetic features. Once a productive lexicon has emerged, word form representations are initially rather underspecified phonetically but gradually become more specified with lexical growth, up to the phoneme level. The situation is different for the receptive lexicon, in which phonetic specification for consonants and vowels seem to follow different developmental paths. Consonants in stressed syllables are somewhat well specified already at the first signs of a receptive lexicon, and become even better specified with lexical growth. Vowels seem to follow a different developmental path, with increasing flexibility throughout lexical development. Thus, children come to exhibit a consonant vowel asymmetry in lexical representations, which is clear in adult representations.

Abstract

Originally published in Diachronica 27:2 (2010) One attempt at explaining why some language families are large (while others are small) is the hypothesis that the families that are now large became large because their ancestral speakers had a technological advantage, most often agriculture. Variants of this idea are referred to as the Language Farming Dispersal Hypothesis. Previously, detailed language family studies have uncovered various supporting examples and counterexamples to this idea. In the present paper I weigh the evidence from ALL attested language families. For each family, I use the number of member languages as a measure of cardinal size, member language coordinates to measure geospatial size and ethnographic evidence to assess subsistence status. This data shows that, although agricultural families tend to be larger in cardinal size, their size is hardly due to the simple presence of farming. If farming were responsible for language family expansions, we would expect a greater east-west geospatial spread of large families than is actually observed. The data, however, is compatible with weaker versions of the farming dispersal hypothesis as well with models where large families acquire farming because of their size, rather than the other way around.

Abstract

This study investigated whether a shadowing task can provide insights in the nature of reduction processes that are typical of casual speech. We focused on the shortening and presence versus absence of schwa and /t/ in Dutch past participles. Results showed that the absence of these segments was affected by the same variables as their shortening, suggesting that absence mostly resulted from extreme gradient shortening. This contrasts with results based on recordings of spontaneous conversations. We hypothesize that this difference is due to non-casual fast speech elicited by a shadowing task.

Abstract

Conversation Analysis (CA) is an inductive, micro-analytic, and predominantly qualitative
method for studying human social interactions. This chapter describes and illustrates the basic
methods of CA. We first situate the method by describing its sociological foundations, key areas
of analysis, and particular approach in using naturally occurring data. The bulk of the chapter is
devoted to practical explanations of the typical conversation analytic process for collecting data
and producing an analysis. We analyze a candidate interactional practice – the assessmentimplicative
interrogative – using real data extracts as a demonstration of the method, explicitly
laying out the relevant questions and considerations for every stage of an analysis. The chapter
concludes with some discussion of quantitative approaches to conversational interaction, and
links between CA and psycholinguistic concerns

Abstract

Co-speech gestures are an integral part of human face-to-face communication, but little is known about how pragmatic factors influence our comprehension of those gestures. The present study investigates how different types of recipients process iconic gestures in a triadic communicative situation. Participants (N = 32) took on the role of one of two recipients in a triad and were presented with 160 video clips of an actor speaking, or speaking and gesturing. Crucially, the actor’s eye gaze was manipulated in that she alternated her gaze between the two recipients. Participants thus perceived some messages in the role of addressed recipient and some in the role of unaddressed recipient. In these roles, participants were asked to make judgements concerning the speaker’s messages. Their reaction times showed that unaddressed recipients did comprehend speaker’s gestures differently to addressees. The findings are discussed with respect to automatic and controlled processes involved in gesture comprehension.

Abstract

What makes some words harder to learn than others in a second language? Although some robust factors have been identified based on small scale experimental studies, many relevant factors are difficult to study in such experiments due to the amount of data necessary to test them. Here, we investigate what factors affect the ease of learning of a word in a second language using a large data set of users learning English as a second language through the Duolingo mobile app. In a regression analysis, we test and confirm the well-studied effect of cognate status on word learning accuracy. Furthermore, we find significant effects for both cross-linguistic semantic alignment and English semantic density, two novel predictors derived from large scale distributional models of lexical semantics. Finally, we provide data on several other psycholinguistically plausible word level predictors. We conclude with a discussion of the limits, benefits and future research potential of using big data for investigating second language learning.

Abstract

This paper examines how placement and removal events are lexicalised and conceptualised in Basque and Peninsular Spanish. After a brief description of the main linguistic devices employed for the coding of these types of events, the paper discusses how speakers of the two languages choose to talk about these events. Finally, the paper focuses on two aspects that seem to be crucial in the description of these events (1) the role of force dynamics: both languages distinguish between different degrees of force, causality, and intentionality, and (2) the influence of the verb-framed lexicalisation pattern. Data come from six Basque and ten Peninsular Spanish native speakers.

Abstract

This chapter deals with the question of whether there is one syntactic system that is shared by language production and comprehension or whether there are two separate systems. It first discusses arguments in favor of one or the other option and then presents the current evidence on the brain structures involved in sentence processing. The results of meta-analyses of numerous neuroimaging studies suggest that there is one system consisting of functionally distinct cortical regions: the dorsal part of Broca’s area subserving compositional syntactic processing; the ventral part of Broca’s area subserving compositional semantic processing; and the left posterior temporal cortex (Wernicke’s area) subserving the retrieval of lexical syntactic and semantic information. Sentence production, the comprehension of simple and complex sentences, and the parsing of sentences containing grammatical violations differ with respect to the recruitment of these functional components.

Abstract

Spanish can use dative as well as non-dative strategies to encode Possessors, Human Sources, Interestees (datives of interest) and Experiencers. In Dutch this optionality is virtually absent, restricting dative encoding mainly to the Recipient of a ditransitive. The present study examines whether this may lead to instability of the non-prototypical dative constructions in the Spanish of Dutch-Spanish bilinguals. Elicited data of 12 Chilean heritage informants from the Netherlands were analyzed. Whereas the evidence on the stability of dative Experiencers was not conclusive, the results indicate that the use of prototypical datives, dative External Possessors, dative Human Sources and datives of interest is fairly stable in bilinguals, except for those with limited childhood exposure to Spanish. It is argued that the consistent preference for non-dative strategies of this group was primarily attributable to instability of the dative clitic, which affected all constructions, even the encoding of prototypical indirect objects

Abstract

A common assumption in the cognitive sciences is that artificial and natural language learning rely on shared mechanisms. However, attempts to bridge the two have yielded ambiguous results. We suggest that an empirical disconnect between the computations employed during learning and the methods employed at test may explain these mixed results. Further, we propose statistically-based chunking as a potential computational link between artificial and natural language learning. We compare the acquisition of non-adjacent dependencies to that of natural language structure using two types of tasks: reflection-based 2AFC measures, and processing-based recall measures, the latter being more computationally analogous to the processes used during language acquisition. Our results demonstrate that task-type significantly influences the correlations observed between artificial and natural language acquisition, with reflection-based and processing-based measures correlating within – but not across – task-type. These findings have fundamental implications for artificial-to-natural language comparisons, both methodologically and theoretically.

Abstract

This study explores the expression of Source and Goal in describing placement and removal events in adult Japanese. Although placement and removal events a priori represent symmetry regarding the orientation of motion, their (c)overt expressions actually exhibit multiple asymmetries at various structural levels. The results show that the expression of the Source is less frequent than the expression of the Goal, but, if expressed, morphosyntactically more complex, suggesting that ‘taking’ events are more complex than ‘putting’ events in their construal. It is stressed that finer linguistic analysis is necessary before explaining linguistic asymmetries in terms of non-linguistic foundations of spatial language.

Abstract

Computer models of cultural evolution have shown language properties emerging on interacting agents with a brain that lacks dedicated, nativist language modules. Notably, models using Bayesian agents provide a precise specification of (extra-)liguististic factors (e.g., genetic) that shape language through iterated learning (biases on language), and demonstrate that weak biases get expressed more strongly over time (bias amplification). Other models attempt to lessen assumption on agents’ innate predispositions even more, and emphasize self-organization within agents, highlighting glossogenesis (the development of language from a nonlinguistic state). Ultimately however, one also has to recognize that biology and culture are strongly interacting, forming a coevolving system. As such, computer models show that agents might (biologically) evolve to a state predisposed to language adaptability, where (culturally) stable language features might get assimilated into the genome via Baldwinian niche construction. In summary, while many questions about language evolution remain unanswered, it is clear that it is not to be completely understood from a purely biological, cognitivist perspective. Language should be regarded as (partially) emerging on the social interactions between large populations of speakers. In this context, agent models provide a sound approach to investigate the complex dynamics of genetic biasing on language and speech

Abstract

It has been suggested that some individuals may benefit more from social robots than do others. Using second
language (L2) as an example, the present study examined how individual differences in attitudes toward robots and personality
traits may be related to learning outcomes. Preliminary results with 24 Turkish-speaking adults suggest that negative attitudes
toward robots, more specifically thoughts and anxiety about the negative social impact that robots may have on the society,
predicted how well adults learned L2 words from a social robot. The possible implications of the findings as well as future directions are also discussed

Abstract

Human speakers often produce sentences incrementally. They can start speaking having in mind only a fragmentary idea of what they want to say, and while saying this they refine the contents underlying subsequent parts of the utterance. This capability imposes a number of constraints on the design of a syntactic processor. This paper explores these constraints and evaluates some recent computational sentence generators from the perspective of incremental production.

Abstract

Language documentation involves linguistic analysis of the collected material, which is typically done manually. Automatic methods for language processing usually require large corpora. The method presented in this paper uses techniques from bioinformatics and contextual information to morphologically analyze raw text corpora. This paper presents initial results of the method when applied on a small Kilivila corpus.

Abstract

This chapter explores the expression of placement (or Goal-oriented) and removal (or Source-oriented) events by speakers of Polish (a West Slavic language). Its aim is to investigate the hypothesis known as ‘Source/Goal asymmetry’ according to which languages tend to favor the expression of Goals (e.g., into, onto) and to encode them more systematically and in a more fine-grained way than Sources (e.g., from, out of). The study provides both evidence and counter-evidence for Source/Goal asymmetry. On the one hand, it shows that Polish speakers use a greater variety of verbs to convey Manner and/or mode of manipulation in the expression of placement, encoding such events in a more fine-grained manner than removal events. The expression of placement is also characterized by a greater variety of verb prefixes conveying Path and prepositional phrases (including prepositions and case markers) conveying Ground. On the other hand, the study reveals that Polish speakers attend to Sources as often as to Goals, revealing no evidence for an attentional bias toward the endpoints of events.

Abstract

Citations play a crucial role in the scientific discourse, in information retrieval, and in bibliometrics. Many initiatives are currently promoting the idea of having free and open citation data. Creation of citation data, however, is not part of the cataloging workflow in libraries nowadays.
In this paper, we present our project Linked Open Citation Database, in which we design distributed processes and a system infrastructure based on linked data technology. The goal is to show that efficiently cataloging citations in libraries using a semi-automatic approach is possible. We specifically describe the current state of the workflow and its implementation. We show that we could significantly improve the automatic reference extraction that is crucial for the subsequent data curation. We further give insights on the curation and linking process and provide evaluation results that not only direct the further development of the project, but also allow us to discuss its overall feasibility.

Abstract

It is widely held that smells and flavors are impossible to put into words. In this paper we test this claim by seeking predictive patterns in wine reviews, which ostensibly aim to provide guides to perceptual content. Wine reviews have previously been critiqued as random and meaningless. We collected an English corpus of wine reviews with their structured metadata, and applied machine learning techniques to automatically predict the wine's color, grape variety, and country of origin. To train the three supervised classifiers, three different information sources were incorporated: lexical bag-of-words features, domain-specific terminology features, and semantic word embedding features. In addition, using regression analysis we investigated basic review properties, i.e., review length, average word length, and their relationship to the scalar values of price and review score. Our results show that wine experts do share a common vocabulary to describe wines and they use this in a consistent way, which makes it possible to automatically predict wine characteristics based on the review text alone. This means that odors and flavors may be more expressible in language than typically acknowledged.

Abstract

In different fields of the humanities annotations of multimodal resources are a necessary component of the research workflow. Examples include linguistics, psychology, anthropology, etc. However, creation of those annotations is a very laborious task, which can take 50 to 100 times the length of the annotated media, or more. This can be significantly improved by applying innovative audio and video processing algorithms, which analyze the recordings and provide automated annotations. This is the aim of the AVATecH project, which is a collaboration of the Max Planck Institute for Psycholinguistics (MPI) and the Fraunhofer institutes HHI and IAIS. In this paper we present a set of results of automated annotation together with an evaluation of their quality.

Abstract

Introduction Human spoken communication is multimodal, i.e. it encompasses both speech and gesture. Acoustic properties of voice, body movements, facial expression, etc. are an inherent and meaningful part of spoken interaction; they can provide attitudinal, grammatical and semantic information. In the recent years interest in audio-visual corpora has been rising rapidly as they enable investigation of different communicative modalities and provide more holistic view on communication (Kipp et al. 2009). Moreover, for some languages such corpora are the only available resource, as is the case for endangered languages for which no written resources exist.

Abstract

In this paper we describe audio and video processing algorithms that are developed in the scope of AVATecH project. The purpose of these algorithms is to shorten the time taken by manual annotation of audio and video recordings by extracting features from media files and creating semi-automated annotations. We show that the use of such supporting algorithms can shorten the annotation time to 30-50% of the time necessary to perform a fully manual annotation of the same kind.

Abstract

The topic of this paper is the way in which speakers order information in discourse. I will refer to this issue with the term "linearization", and will begin with two types of general remarks. The first one concerns the scope and relevance of the problem with reference to some existing literature. The second set of general remarks will be about the place of linearization in a theory of the speaker. The following, and main part of this paper, will be a summary report of research of linearization in a limited, but well-defined domain of discourse, namely the description of spatial networks.

Abstract

This paper describes the linguistic treatment of placement events in the Rossel Island (Papua New Guinea) language Yélî Dnye. Yélî Dnye is unusual in treating PUT and TAKE events symmetrically with a remarkable consistency. In what follows, we first provide a brief background for the language, then describe the six core PUT/TAKE verbs that were drawn upon by Yélî Dnye speakers to describe the great majority of the PUT/TAKE stimuli clips, along with some of their grammatical properties. In Section 5 we describe alternative verbs usable in particular circumstances and give an indication of the basis for variability in responses across speakers. Section 6 presents some reasons why the Yélî verb pattern for expressing PUT and TAKE events is of broad interest.

Abstract

We present the Narrative Brain Dataset, an fMRI dataset that was collected during spoken presentation of short excerpts of three
stories in Dutch. Together with the brain imaging data, the dataset contains the written versions of the stimulation texts. The texts are
accompanied with stochastic (perplexity and entropy) and semantic computational linguistic measures. The richness and unconstrained
nature of the data allows the study of language processing in the brain in a more naturalistic setting than is common for fMRI studies.
We hope that by making NBD available we serve the double purpose of providing useful neural data to researchers interested in natural
language processing in the brain and to further stimulate data sharing in the field of neuroscience of language.

Abstract

For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the abstract. Therefore, it is desirable to have good text mining and text classification algorithms that operate well already on the title of a publication. So far, the classification performance on titles is not competitive with the performance on the full-texts if the same number of training samples is used for training. However, it is much easier to obtain title data in large quantities and to use it for training than full-text data. In this paper, we investigate the question how models obtained from training on increasing amounts of title training data compare to models from training on a constant number of full-texts. We evaluate this question on a large-scale dataset from the medical domain (PubMed) and from economics (EconBiz). In these datasets, the titles and annotations of millions of publications are available, and they outnumber the available full-texts by a factor of 20 and 15, respectively. To exploit these large amounts of data to their full potential, we develop three strong deep learning classifiers and evaluate their performance on the two datasets. The results are promising. On the EconBiz dataset, all three classifiers outperform their full-text counterparts by a large margin. The best title-based classifier outperforms the best full-text method by 9.4%. On the PubMed dataset, the best title-based method almost reaches the performance of the best full-text classifier, with a difference of only 2.9%.

Abstract

What is the relationship between the language we speak and the way we think? Researchers working at the interface of language and cognition hope to understand the complex interplay between linguistic structures and the way the mind works. This is thorny territory in anthropology and its closely allied disciplines, such as linguistics and psychology.

Abstract

Scholars disagree about the extent to which language can tell us
about conceptualisation of the world. Some believe that language
is a direct window onto concepts: Having a word ‘‘bird’’, ‘‘table’’ or
‘‘sour’’ presupposes the corresponding underlying concept, BIRD,
TABLE, SOUR. Others disagree. Words are thought to be uninformative,
or worse, misleading about our underlying conceptual representations;
after all, our mental worlds are full of ideas that we
struggle to express in language. How could this be so, argue sceptics,
if language were a direct window on our inner life? In this presentation,
I consider what language can tell us about the
conceptualisation of taste. By considering linguistic data from
twenty unrelated cultures – varying in subsistence mode (huntergatherer
to industrial), ecological zone (rainforest jungle to desert),
dwelling type (rural and urban), and so forth – I argue any single language is, indeed, impoverished about what it can reveal about
taste. But recurrent lexicalisation patterns across languages can
provide valuable insights about human taste experience. Moreover,
language patterning is part of the data that a good theory of taste
perception has to be answerable for. Taste researchers, therefore,
cannot ignore the crosslinguistic facts.

Abstract

This Research Topic explores the question: what is the relationship between representations of time and space in cultures around the world? This question touches on the broader issue of how humans come to represent and reason about abstract entities – things we cannot see or touch. Time is a particularly opportune domain to investigate this topic. Across cultures, people use spatial representations for time, for example in graphs, time-lines, clocks, sundials, hourglasses, and calendars. In language, time is also heavily related to space, with spatial terms often used to describe the order and duration of events. In English, for example, we might move a meeting forward, push a deadline back, attend a long concert or go on a short break. People also make consistent spatial gestures when talking about time, and appear to spontaneously invoke spatial representations when processing temporal language. A large body of evidence suggests a close correspondence between temporal and spatial language and thought. However, the ways that people spatialize time can differ dramatically across languages and cultures. This research topic identifies and explores some of the sources of this variation, including patterns in spatial thinking, patterns in metaphor, gesture and other cultural systems. This Research Topic explores how speakers of different languages talk about time and space and how they think about these domains, outside of language. The Research Topic invites papers exploring the following issues: 1. Do the linguistic representations of space and time share the same lexical and morphosyntactic resources? 2. To what extent does the conceptualization of time follow the conceptualization of space?