We identify a number of principles with respect to prediction that, we argue, underpin adult language comprehension: (a) comprehension consists in realizing a mapping between the unfolding sentence and the event representation corresponding to the real‐world event being described; (b) the realization of this mapping manifests as the ability to predict both how the language will unfold, and how the real‐world event would unfold if it were being experienced directly; (c) concurrent linguistic and nonlinguistic inputs, and the prior internal states (...) of the system, each drive the predictive process; (d) the representation of prior internal states across a representational substrate common to the linguistic and nonlinguistic domains enables the predictive process to operate over variable time frames and variable levels of representational abstraction. We review empirical data exemplifying the operation of these principles and discuss the relationship between prediction, event structure, thematic role assignment, and incrementality. (shrink)

As linguistic competence so clearly illustrates, processing sequences of events is a fundamental aspect of human cognition. For this reason perhaps, sequence learning behavior currently attracts considerable attention in both cognitive psychology and computational theory. In typical sequence learning situations, participants are asked to react to each element of sequentially structured visual sequences of events. An important issue in this context is to determine whether essentially associative processes are sufficient to understand human performance, or whether more powerful learning mechanisms are (...) necessary. To address this issue, we explore how well human participants and connectionist models are capable of learning sequential material that involves complex, disjoint, longdistance contingencies. We show that the popular Simple Recurrent Network model (Elman, 1990), which has otherwise been shown to account for a variety of empirical findings (Cleeremans, 1993), fails to account for human performance in several experimental situations meant to test the model’s specific predictions. In previous research (Cleeremans, 1993) briefly described in this paper, the structure of center-embedded sequential structures was manipulated to be strictly identical or probabilistically different as a function of the elements surrounding the embedding. While the SRN could only learn in the second case, human subjects were found to be insensitive to the manipulation. In the new experiment described in this paper, we tested the idea that performance benefits from “starting small effects” (Elman, 1993) by contrasting two conditions in which the training regimen was either incremental or not. Again, while the SRN is only capable of learning in the first case, human subjects were able to learn in both. We suggest an alternative model based on Maskara & Noetzel’s (1991) Auto-Associative Recurrent Network as a way to overcome the SRN model’s failure to account for the empirical findings.. (shrink)

The presupposition triggered by an expression E is generally satisfied by information that comes before rather than after E in the sentence or discourse. In Heim’s classic theory (1983), this left-right asymmetry is encoded in the lexical semantics of dynamic connectives and operators. But several recent analyses offer a more nuanced approach, in which presupposition satisfaction has two separate components: a general principle (which varies from theory to theory) specifies under what conditions a presupposition triggered by an expression E is (...) satisfied; and an ‘incremental’ component specifies that the principle must be checked on the basis of information that comes before E. Several researchers take this incremental component to be a processing bias, which can be overcome at some cost. If so, it should be possible, though costly, to satisfy presuppositions ‘symmetrically’, i.e. by taking into account linguistic material that comes both before and after the presupposition trigger. We test this claim with experimental means. Using inferential (and to some extent acceptability) tasks involving the anaphoric trigger aussi (‘too’) in French, we argue that symmetric readings are indeed possible (albeit degraded) in environments involving the connectives if, or, and unless. (shrink)

A new system of dynamic logic is introduced and motivated, witha novel approach to variable binding for incremental interpretation. Thesystem is shown to be equivalent to first order logic and complete.The new logic combines the dynamic binding idea from DynamicPredicate Logic with De Bruijn style variable free indexing. Quantifiersbind the next available variable register; the indexing mechanismguarantees that active registers are never overwritten by newquantifiers actions. Apart from its interest in its own right, theresulting system has certain advantages over (...) Dynamic Predicate Logic orDiscourse Representation Theory. It comes with a more well behaved(i.e., transitive) consequence relation, it gives a more explicitaccount of how anaphoric context grows as text gets processed, and ityields new insight into the dynamics of anaphoric linking in reasoning.Incremental dynamics also points to a new way of handling contextdynamically in Montague grammar. (shrink)

Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions to accommodate multiple outcomes and subject dropout. We give an identifying expression for incremental intervention effects when dropout is conditionally ignorable and derive the nonparametric efficiency bound for estimating such effects. Then we present efficient nonparametric estimators, showing that they converge (...) at fast parametric rates and yield uniform inferential guarantees, even when nuisance functions are estimated flexibly at slower rates. We also study the variance ratio of incremental intervention effects relative to more conventional deterministic effects in a novel infinite time horizon setting, where the number of timepoints can grow with sample size and show that incremental intervention effects yield near-exponential gains in statistical precision in this setup. Finally, we conclude with simulations and apply our methods in a study of the effect of low-dose aspirin on pregnancy outcomes. (shrink)

As a class of familiar nonlinear systems, nonaffine systems are frequently encountered in practical applications. Currently, in the context of learning control, there is a lack of research results about such general class of nonlinear systems, especially for the case of performing infinite interval tasks. This article focuses on the incremental adaptive control for nonlinear systems in nonaffine form, without requiring periodicity or repeatability. Instead of using the integral adaptation, incremental adaptive mechanisms are developed and the corresponding control (...) schemes are presented, by which the numerical integration for implementation can be avoided. With the proposed incremental adaptation, the implicit function theorem is introduced to solve the intractability problem of the nonaffine structure. The robustness of the tracking error is characterized, with the aid of a proposed key lemma, while the boundedness of all the variables is examined. Numerical results are presented to verify the effectiveness of the proposed control design. (shrink)

While a large body of work in sentence comprehension has explored how different types of linguistic information are used to guide syntactic parsing, less is known about the effect of discourse structure. This study investigates this question, focusing on the main and subordinate discourse contrast manifested in the distinction between restrictive relative clauses (RRCs) and appositive relative clauses (ARCs) in American English. In three self-paced reading experiments, we examined whether both RRCs and ARCs interfere with the matrix clause content and (...) give rise to the agreement attraction effect. While the standard attraction effect was consistently observed in the baseline RRC structures, the effect varied in the ARC structures. These results collectively suggest that discourse structure indeed constrains syntactic dependency resolution. Most importantly, we argue that what is at stake is not the static discourse structure properties at the global sentence level. Instead, attention should be given to the incremental update of the discourse structure in terms of which discourse questions are active at any given moment of a discourse. The current findings have implications for understanding the way discourse structure, specifically the active state of discourse questions, constrains memory retrieval. (shrink)

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words. We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: the acquisition of features that discriminate among categories, and the grouping of concepts into categories based on those features. (...) Our model learns categories incrementally using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (shrink)

This paper presents a theory of the syntactic aspects of human sentence production. An important characteristic of unprepared speech is that overt pronunciation of a sentence can be initiated before the speaker has completely worked out the meaning content he or she is going to express in that sentence. Apparently, the speaker is able to build up a syntactically coherent utterance out of a series of syntactic fragments each rendering a new part of the meaning content. This incremental, left‐to‐right (...) mode of sentence production is the central capability of the proposed Incremental Procedural Grammar (IPG). Certain other properties of spontaneous speech, as derivable from speech errors, hesitations, self‐repairs, and language pathology, are accounted for as well.The psychological plausibility thus gained by the grammar appears compatible with a satisfactory level of linguistic plausibility in that sentences receive structural descriptions which are in line with current theories of grammar. More importantly, an explanation for the existence of configurational conditions on transformations and other linguistics rules is proposed.The basic design feature of IPG which gives rise to these psychologically and linguistically desirable properties, is the “Procedures + Stack” concept. Sentences are built not by a central constructing agency which overlooks the whole process but by a team of syntactic procedures (modules) which work—in parallel—on small parts of the sentence, have only a limited overview, and whose sole communication channel is a stack.IPG covers object complement constructions, interrogatives, and word order in main and subordinate clauses. It handles unbounded dependencies, crossserial dependencies and coordination phenomena such as gapping and conjunction reduction. It is also capable of generating self‐repairs and elliptical answers to questions. IPG has been implemented as an incremental Dutch sentence generator written in LISP. (shrink)

Structured argumentation systems, and their implementation, represent an important research subject in the area of Knowledge Representation and Reasoning. Structured argumentation advances over abstract argumentation frameworks by providing the internal construction of the arguments that are usually defined by a set of (strict and defeasible) rules. By considering the structure of arguments, it becomes possible to analyze reasons for and against a conclusion, and the warrant status of such a claim in the context of a knowledge base represents the main (...) output of a dialectical process. Computing such statuses is a costly process, and any update to the knowledge base could potentially have a huge impact if done naively. In this work, we investigate the case of updates consisting of both additions and removals of pieces of knowledge in the Defeasible Logic Programming (DeLP) framework, first analyzing the complexity of the problem and then identifying conditions under which we can avoid unnecessary computations—central to this is the development of structures (e.g. graphs) to keep track of which results can potentially be affected by a given update. We introduce a technique for the incremental computation of the warrant statuses of conclusions in DeLP knowledge bases that evolve due to the application of (sets of) updates. We present the results of a thorough experimental evaluation showing that our incremental approach yields significantly faster running times in practice, as well as overall fewer recomputations, even in the case of sets of updates performed simultaneously. (shrink)

For a robot to cohabit with people, it should be able to learn people’s nonverbal social behavior from experience. In this paper, we propose a novel machine learning method for recognizing gestures used in interaction and communication. Our method enables robots to learn gestures incrementally during human–robot interaction in an unsupervised manner. It allows the user to leave the number and types of gestures undefined prior to the learning. The proposed method (HB-SOINN) is based on a self-organizing incremental neural (...) network and the hidden Markov model. We have added an interactive learning mechanism to HB-SOINN to prevent a single cluster from running into a failure as a result of polysemy of being assigned more than one meaning. For example, a sentence: “Keep on going left slowly” has three meanings such as, “Keep on (1)”, “going left (2)”, “slowly (3)”. We experimentally tested the clustering performance of the proposed method against data obtained from measuring gestures using a motion capture device. The results show that the classification performance of HB-SOINN exceeds that of conventional clustering approaches. In addition, we have found that the interactive learning function improves the learning performance of HB-SOINN. (shrink)

Over the last decades, various predictors have proven relevant for job performance [e.g., general mental ability, broad personality traits, such as the Big Five]. However, prediction of job performance is far from perfect, and further potentially relevant predictors need to be investigated. Narrower personality traits, such as individuals' character strengths, have emerged as meaningfully related to different aspects of job performance. However, it is still unclear whether character strengths can explain additional variance in job performance over and above already known (...) powerful predictors. Consequently, the present study aimed at examining the incremental validity of character strengths as predictors of job performance beyond GMA and/or the Big Five traits and identifying the most important predictors of job performance out of the 24 character strengths, GMA, and the Big Five. Job performance was operationalized with multidimensional measures of both productive and counterproductive work behavior. A sample of 169 employees from different occupations completed web-based self-assessments on character strengths, GMA, and the Big Five. Additionally, the employees' supervisors provided web-based ratings of their job performance. Results showed that character strengths incrementally predicted job performance beyond GMA, the Big Five, or GMA plus the Big Five; explained variance increased up to 54.8, 43.1, and 38.4%, respectively, depending on the dimension of job performance. Exploratory relative weight analyses revealed that for each of the dimensions of job performance, at least one character strength explained a numerically higher amount of variance than GMA and the Big Five, except for individual task proactivity, where GMA exhibited the numerically highest amount of explained variance. The present study shows that character strengths are relevant predictors of job performance in addition to GMA and other conceptualizations of personality. This also highlights the role of socio-emotional skills, such as character strengths, for the understanding of performance outcomes above and beyond cognitive ability. (shrink)

Empirical evidence from both utility and psychophysical experiments suggests that people respond quite differently—perhaps discontinuously—to stimulus pairs when one consequence or signal is set to `zero.' Such stimuli are called unitary. The author's earlier theories assumed otherwise. In particular, the key property of segregation relating gambles and joint receipts (or presentations) involves unitary stimuli. Also, the representation of unitary stimuli was assumed to be separable (i.e., multiplicative). The theories developed here do not invoke separability. Four general cases based on two (...) distinctions are explored. The first distinction is between commutative joint receipts, which are relevant to utility, and the non-commutative ones, which are relevant to psychophysics. The second distinction concerns how stimuli of the form (x, C; y) and the operation of joint receipt are linked: by segregation, which mixes stimuli and unitary ones, and by distributivity, which does not involve any unitary stimuli. A class of representations more general than rank-dependent utility (RDU) is found in which monotonic functions of increments U(x)-U(y), where U is an order preseving representation of gambles, and joint receipt play a role. This form and its natural generalization to gambles with n > 2 consequences, which is also axiomatized, appear to encompass models of configural weights and decision affect. When joint receipts are not commutative, somewhat similar representations of stimuli arise, and joint receipts are shown to have a conjoint additive representation and in some cases a constant bias independent of signal intensity is predicted. (shrink)

Despite the incremental nature of Dynamic Syntax, the semantic grounding of it remains that of predicate logic, itself grounded in set theory, so is poorly suited to expressing the rampantly context-relative nature of word meaning, and related phenomena such as incremental judgements of similarity needed for the modelling of disambiguation. Here, we show how DS can be assigned a compositional distributional semantics which enables such judgements and makes it possible to incrementally disambiguate language constructs using vector space semantics. (...) Building on a proposal in our previous work, we implement and evaluate our model on real data, showing that it outperforms a commonly used additive baseline. In conclusion, we argue that these results set the ground for an account of the non-determinism of lexical content, in which the nature of word meaning is its dependence on surrounding context for its construal. (shrink)

Causal inference plays a central role in behavioral science. Historically, behavioral science methodologies have typically sought to infer a single causal relation. Each of the major approaches to causal inference in the behavioral sciences follows this pattern. Nonetheless, such approaches sometimes differ in the causal relation that they infer. Incremental causal inference offers an alternative to this conceptualization of causal inference that divides the inference into a series of incremental steps. Different steps infer different causal relations. Incremental (...) causal inference is consistent with both causal pluralism and anti-pluralism. However, anti-pluralism places greater constraints the possible topology of sequential inferences. Arguments against causal inference include questioning consistency with causation as an explanatory principle, charging undue complexity, and questioning the need for it. Arguments in favor of incremental inference include better explanation of diverse causal inferences in behavioral science, tailored causal inference, and more detailed and explicit description of causal inference. Incremental causal inference offers a viable and potentially fruitful alternative to approaches limited to a single causal relation. (shrink)

Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental data over time. In this study, we propose an incremental maximal frequent itemset mining algorithms that integrate subjective interestingness criterion during the process of mining. The proposed framework is designed to (...) deal with incremental data, which usually come at different times. It extends FP-Max algorithm, which is based on FP-Growth method by pushing interesting measures during maximal frequent itemset mining, and performs dynamic and early pruning to leave uninteresting frequent itemsets in order to avoid uninteresting rule generation. The framework was implemented and tested on public databases, and the results found are promising. (shrink)

An incrementalist view of the R&Dprocess is developed, according to which R&D consists of informed trial and error. One way of avoiding expensive mistakes is to avoid choices within the R&D program that are highly sensitive to a particular scientific claim, because a great deal of time and money may have been spent to no avail should the claim turn out to befalse. The incremental view of R&D therefore entails that no choice within any R&D program is sensitive to (...) any scientific claim made at the time. Two tests are presented, antiseptic surgery and the first atomic pile, both of which confirm the incrementalist thesis. (shrink)

Japanese exhibits the Multiple Nominative Construction (MNC), where more than one nominative-marked NP appear within a single clause. Though the MNC has been extensively investigated in the syntax literature, its relation to rightward-displacement constructions has hardly been discussed. In the present article, we provide new sets of MNC data relating to the three types of right-displacement constructions: relatives, clefts, and postposing. The generalisation is that for the linearly ordered nominative-marked NPs in an MNC string, only the leftmost NP may be (...) right-displaced (that is, relativised, clefted, postposed). This pattern is shown to follow as an outcome of modelling incremental parsing, without stipulations specifically made for the observed data. We propose an account of MNCs in the light of an incremental grammar, formalising it within Dynamic Syntax. In this account, an MNC sentence is parsed on a word-by-word basis, and its interpretation is gradually built up, modelled as the growth of a semantic structure. In cases where a right-displacement is permitted, the right-displaced NP is successfully parsed against the structure built by the preceding part of the sentence. In cases where a right-displacement is disallowed, the right-displaced NP cannot be properly processed because the preceding part of the sentence has created no structural position into which the content of the right-displaced NP is reflected. As theoretical implications of the proposed account, we suggest the concept of time-linear grammaticality. (shrink)

Real engines of the artificial intelligence revolution, machine learning models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In (...) this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs. (shrink)

The WEAVER ++ model discussed by Levelt et al. assumes incremental encoding and articulation following complete encoding. However, many of the response latency results can also be accounted for by assuming incremental articulation. Another temporal variable, initial segment duration, can distinguish WEAVER ++'s incremental encoding account from the incremental articulation account.

Applications of willingness to pay have shown the difficultly to discriminate between various options. This reflects the problem of embedding in both its specific sense, of options being nested within one another, and its more general sense, whereby respondents cannot discriminate between close substitutes or between more-disparate rivals for the same budget. Furthermore, high proportions of reversals between WTP-value and simple preference based rankings of options are often highlighted. Although an incremental WTP approach was devised to encourage more differentiated (...) answers and a higher degree of consistency among respondents, a theoretical basis for this approach has not been elucidated, and there is little evidence to show that this approach might indeed achieve greater consistency between explicit and implicit rankings inferred from WTP values. We address both these issues. Following our theoretical exposition, standard and incremental approaches were compared with explicit ranking in a study assessing preferences for different French emergency care services. 280 persons, representative of the French adult population, were interviewed. Half received the incremental version, the other half the standard version. Results suggest that the incremental approach provides a ranking of options fully in line with explicit ranking. The standard approach was reasonably consistent with explicit ranking but proved unable to differentiate between the five most preferred providers, as predicted by theory. Our findings suggest that the incremental approach provides results which can be used in priority-setting contexts. (shrink)

Approaches to programming ethical behavior for computer systems face challenges that are both technical and philosophical in nature. In response, an incrementalist account of machine ethics is developed: a successive adaptation of programmed constraints to new, morally relevant abilities in computers. This approach allows progress under conditions of limited knowledge in both ethics and computer systems engineering and suggests reasons that we can circumvent broader philosophical questions about computer intelligence and autonomy.

Research investigating the brain basis of language comprehension has associated the left anterior temporal lobe with sentence-level combinatorics. Using magnetoencephalography, we test the parsing strategy implemented in this brain region. The number of incremental parse steps from a predictive left-corner parsing strategy that is supported by psycholinguistic research is compared with those from a less-predictive strategy. We test for a correlation between parse steps and source-localized MEG activity recorded while participants read a story. Left-corner parse steps correlated with activity (...) in the left ATL around 350–500 ms after word onset. No other correlations specific to sentence comprehension were observed. These data indicate that the left ATL engages in combinatoric processing that is well characterized by a predictive left-corner parsing strategy. (shrink)

A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. It turns out (...) that the success of the IA depends substantially on the ‘‘preference order’’ (PO) employed by the IA, particularly in complex domains. While some POs cause the IA to produce referring expressions that are very similar to expressions produced by human subjects, others cause the IA to perform worse than its main competitors; moreover, it turns out to be difficult to predict the success of a PO on the basis of existing psycholinguistic findings or frequencies in corpora. We also examine the computational complexity of the algorithms in question and argue that there are no compelling reasons for preferring the IA over some of its main competitors on these grounds. We conclude that future research on the generation of referring expressions should explore alternatives to the IA, focusing on algorithms, inspired by the Greedy Algorithm, which do not work with a fixed PO. (shrink)

Case-based reasoning has long been used to facilitate instructional effectiveness. Although much remains to be known concerning the most beneficial way to present case material, recent literature suggests that simplifying case material is favorable. Accordingly, the current study manipulated two instructional techniques, incremental case presentation and forecasting outcomes, in a training environment in an attempt to better understand the utility of simplified versus complicated case presentation for learning. Findings suggest that pairing these two cognitively demanding techniques reduces satisfaction and (...) detracts from the effectiveness of the learning approach. Implications regarding the use of instructional techniques in training programs are discussed. (shrink)

The central claim of this paper is that surface-faithful word-by-word update is feasible and desirable, even in languages where word order is supposedly free. As a first step, in sections 1 and 2, I review an argument from Bittner 2001a that semantic composition is not a static process, as in PTQ, but rather a species of anaphoric bridging. But in that case the context-setting role of word order should extend from cross-sentential discourse anaphora to sentence-internal anaphoric composition. This can be (...) spelled out as a two-part hypothesis. First, in all languages anaphoric composition derives incremental updates based on the topological order rather than the syntactic hierarchy. And secondly, rigid vs. free word order is simply rigid vs. free mapping from syntax to topology. To formalize this hypothesis, I first present, in section 3, Sevensorted Logic of Change with Centering. This makes it possible, in section 4, to articulate a system of constraints on basic meanings in Kalaallisut — a polysynthetic language with free word order, ideally suited to test the hypothesis of incremental update. The key assumptions about topology as the input to anaphoric composition are spelled out in section 5, which concludes the development of a general formal framework. This formal framework then serves, in sections 6 through 8, t o explicate topologically based incremental updates for increasingly more complex samples of an actual Kalaallisut text. This reveals ubiquitous patterns of prominence-guided anaphora, in all semantic domains, t o increasingly more complex types of discourse referents. These anaphoric patterns show that the context-setting role of word order indeed does extend from discourse to word-to-word anaphora. And this, in turn, strongly supports the hypothesis of topologically based anaphoric composition. Finally, in section 9 I adduce evidence from English that this hypothesis also holds for languages with rigid word order, albeit the fixed mapping keeps the topology close to the syntax. I conclude that both free and rigid word orders receive a natural account if semantic composition is viewed as topologically based anaphoric bridging.. (shrink)

Confirmation of a hypothesis by evidence can be measured by one of the so far known incremental measures of confirmation. As we show, incremental measures can be formally defined as the measures of confirmation satisfying a certain small set of basic conditions. Moreover, several kinds of incremental measure may be characterized on the basis of appropriate structural properties. In particular, we focus on the so-called Matthew properties: we introduce a family of six Matthew properties including the reverse (...) Matthew effect; we further prove that incremental measures endowed with reverse Matthew effect are possible; finally, we shortly consider the problem of the plausibility of Matthew properties. (shrink)