From movements to actions: two mechanisms for learning action sequences

When other individuals move, we interpret their movements as discrete, hierarchically-organized, goal-directed actions. However, the mechanisms that integrate visible movement features into actions are poorly understood. Here, we consider two sequence learning mechanisms – transitional probability-based (TP) and position-based encoding computations – that have been studied extensively in the domain of language learning, and investigate their potential for integrating movements into actions. If these learning mechanisms integrate movements into actions, then they should create memory units that contain (i) movement information, (ii) information about the order in which movements occurred, and (iii) information allowing actions to be recognized from different viewpoints. We show that both mechanisms retain movement information. However, only the position-based mechanism creates movement representations that are view-invariant and contain order information. The TP-based mechanism creates movement representations that are view-dependent and contain no order information. We therefore suggest that the TP-based mechanism is unlikely to play an important role for integrating movements into actions. In contrast, the position-based mechanism retains some of the types of information needed to represent goal-directed actions, which makes it an attractive target for further research to explore what, if any, role it plays in the perception of goal-directed actions.     

Self‐Directed Learning Favors Local,Rather Than Global,Uncertainty

Collecting (or “sampling”) information that one expects to be useful is a powerful way to facilitate learning. However, relatively little is known about how people decide which information is worth sampling over the course of learning. We describe several alternative models of how people might decide to collect a piece of information inspired by “active learning” research in machine learning. We additionally provide a theoretical analysis demonstrating the situations under which these models are empirically distinguishable, and we report a novel empirical study that exploits these insights. Our model‐based analysis of participants' information gathering decisions reveals that people prefer to select items which resolve uncertainty between two possibilities at a time rather than items that have high uncertainty across all relevant possibilities simultaneously. Rather than adhering to strictly normative or confirmatory conceptions of information search, people appear to prefer a “local” sampling strategy, which may reflect cognitive constraints on the process of information gathering.     

A Computational Model for the Item‐Based Induction of Construction Networks

According to usage‐based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form‐meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item‐based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous context. To determine meanings of constructions starting from ambiguous contexts, we rely on the principle of cross‐situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model, we show how cross‐situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences.     

Statistical models of syntax learning and use

This paper shows how to define probability distributions over linguistically realistic syntactic structures in a way that permits us to define language learning and language comprehension as statistical problems. We demonstrate our approach using lexical‐functional grammar (LFG), but our approach generalizes to virtually any linguistic theory. Our probabilistic models are maximum entropy models. In this paper we concentrate on statistical inference procedures for learning the parameters that define these probability distributions. We point out some of the practical problems that make straightforward ways of estimating these distributions infeasible, and develop a “pseudo‐likelihood” estimation procedure that overcomes some of these problems. This method raises interesting questions concerning the nature of the data available to a language learner and the modularity of language learning and processing.     

Interference resolution in face perception and name retrieval

Selective retrieval is a rather difficult task, and especially so when one attempts to retrieve personal representations such as faces or names. Retrieval of memories under strong competition conditions is pervasive in human memory and some have suggested that inhibitory control is used to overcome interference between competing stimuli. In the present study, we used the retrieval practice paradigm to investigate if competition among personal representations (such as facial features and names) is also resolved by inhibitory mechanisms. This question is theoretically relevant, since personal representations have been said to have a special status on cognition. Moreover, some models of face recognition assume that interference can arise between different representations, but that this interference would be automatically and rapidly solved, with no need for a controlled inhibitory mechanism to act. In two experiments we showed RIF for facial features and familiar names, but only when participants had to actively retrieve some information. This suggests that personal information is subject to mechanisms of inhibitory control, which could help explain everyday life difficulties in processes such as face feature recognition or name retrieval.     

Investigating the roles of phonological and semantic memory in sentence recall

The mechanisms underlying short-term sentence recall have been the subject of recent investigations. While both semantic and phonological information have been found to play a role in sentence recall, this has been established using different paradigms in previous research. As a result, it is not clear whether the contributions of semantic and phonological information are equivalent subject only to specific experimental conditions. The present study used a common paradigm with equally plausible lures to systematically compare the roles of semantic and phonological information in short-term sentence recall. The lure intrusion paradigm was used with three different lures that were all equally plausible in the context of the sentence: semantic, onset, and rhyme. Further, no contextual cues were provided in the sentence to bias the participant. The findings indicate that there were significantly more semantic and onset intrusions compared to rhyme intrusions. This is interpreted in light of models incorporating lexical information during sentence production.     

Preference‐based Learning of Ideal Solutions in TOPSIS‐like Decision Models

Combining established modelling techniques from multiple‐criteria decision aiding with recent algorithmic advances in the emerging field of preference learning, we propose a new method that can be seen as an adaptive version of TOPSIS, the technique for order preference by similarity to ideal solution decision model (or at least a simplified variant of this model). On the basis of exemplary preference information in the form of pairwise comparisons between alternatives, our method seeks to induce an ‘ideal solution’ that, in conjunction with a weight factor for each criterion, represents the preferences of the decision maker. To this end, we resort to probabilistic models of discrete choice and make use of maximum likelihood inference. First experimental results on suitable preference data suggest that our approach is not only intuitively appealing and interesting from an interpretation point of view but also competitive to state‐of‐the‐art preference learning methods in terms of prediction accuracy. Copyright © 2014 John Wiley & Sons, Ltd.     

Gender and leadership emergence: A meta‐analysis and explanatory model

Research has shown that men tend to emerge as leaders more frequently than women. However, societal role expectations for both women and leaders have changed in the decades since the last empirical review of the gender gap in leader emergence (Eagly & Karau, 1991). We leverage meta‐analytic evidence to demonstrate that the gender gap has decreased over time, but a contemporary gap remains. To understand why this gap in leader emergence occurs, we draw on social role theory to develop a Gender‐Agency/Communion‐Participation (GAP) Model—an integrative theoretical model that includes both trait and behavioral mechanisms. Specifically, we examine a sequence of effects: from gender to agentic and communal personality traits, from these traits to behavioral participation in group activities, and ultimately from participation to leader emergence. The model is tested using original meta‐analyses of the personality and behavioral mechanisms (coding 1,632 effect sizes total). Gender differences in leadership emergence are predominately explained by agentic traits (positive) and communal traits (negative), both directly and through the mechanism of participation in group discussions. In addition, several paths in the theoretical model are moderated by situational contingencies. Our study enhances knowledge of the mechanisms and boundary conditions underlying the gender gap in leader emergence.     

Models of Machines and Models of Phenomena

Experimental engineering models have been used both to model general phenomena, such as the onset of turbulence in fluid flow, and to predict the performance of machines of particular size and configuration in particular contexts. Various sorts of knowledge are involved in the method—logical consistency, general scientific principles, laws of specific sciences, and experience. I critically examine three different accounts of the foundations of the method of experimental engineering models (scale models), and examine how theory, practice, and experience are involved in employing the method to obtain practical results. Models of machines and mechanisms can be (and generally are) involved in establishing criteria for similar phenomena, which provide guidance in using events to model other events. Conversely, models of phenomena such as events that model other events can be (and generally are) involved in experimentation on models of machines. I conclude that often it is not more detailed models or the more precise equations they engender that leads to better understanding, but rather an insightful use of knowledge at hand to determine which similarity principles are appropriate in allowing us to infer what we do not know from what we are able to observe.     

Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task

Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can be directly compared with the mechanisms of non‐connectionist, rule‐based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule‐based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models.     

Sequential detection of learning in cognitive diagnosis

In order to look more closely at the many particular skills examinees utilize to answer items, cognitive diagnosis models have received much attention, and perhaps are preferable to item response models that ordinarily involve just one or a few broadly defined skills, when the objective is to hasten learning. If these fine‐grained skills can be identified, a sharpened focus on learning and remediation can be achieved. The focus here is on how to detect when learning has taken place for a particular attribute and efficiently guide a student through a sequence of items to ultimately attain mastery of all attributes while administering as few items as possible. This can be seen as a problem in sequential change‐point detection for which there is a long history and a well‐developed literature. Though some ad hoc rules for determining learning may be used, such as stopping after M consecutive items have been successfully answered, more efficient methods that are optimal under various conditions are available. The CUSUM, Shiryaev–Roberts and Shiryaev procedures can dramatically reduce the time required to detect learning while maintaining rigorous Type I error control, and they are studied in this context through simulation. Future directions for modelling and detection of learning are discussed.     

Sensitization and subjective health complaints

Why is it that some of us have more pain, more fatigue, and more gastrointestinal trouble than others? Is it possible that there are brain mechanisms and psychological mechanisms that make some people sensitized to specific complaints? In this concluding paper we review the historical and theoretical background, discuss the evidence and theoretical positions in the contributions, and draw some conclusions. Traditional psychosomatic models had less predictive value and less therapeutic importance than what was hoped for. The main problem with these models was the lack of a pathophysiological explanation for why psychological problems could be related to somatic disease. Sustained arousal or "allostatic load" offers more plausible and acceptable mechanisms for pathology, and, to some extent, for sensitization and illness. The combination of cognitive psychology and neurophysiology offers a model for somatic pathology, and, perhaps more important, also for the understanding of subjective complaints and illness.     

Emergent Processes in Group Behavior

ABSTRACT— Just as neurons interconnect in networks that create structured thoughts beyond the ken of any individual neuron, so people spontaneously organize themselves into groups to create emergent organizations that no individual may intend, comprehend, or even perceive. Recent technological advances have provided us with unprecedented opportunities for conducting controlled laboratory experiments on human collective behavior. We describe two experimental paradigms in which we attempt to build predictive bridges between the beliefs, goals, and cognitive capacities of individuals and patterns of behavior at the group level, showing how the members of a group dynamically allocate themselves to resources and how innovations diffuse through a social network. Agent-based computational models have provided useful explanatory and predictive accounts. Together, the models and experiments point to tradeoffs between exploration and exploitation—that is, compromises between individuals using their own innovations and using innovations obtained from their peers—and the emergence of group-level organizations such as population waves, bandwagon effects, and spontaneous specialization.     

Social Constraint and Self‐Doubt: Mechanisms of Social Network influence on Resistance to Persuasion

Those around us have a profound influence on our political attitudes and attitude strength, such that people whose social networks include a variety of perspectives have weaker, less deeply entrenched attitudes than those who are surrounded by like‐minded others. In particular, those embedded in attitudinally heterogeneous networks are more open to changing their views. The nature and mechanisms of this network influence on openness to attitude change remain unclear. A survey experiment examines two mechanisms proposed by prior literature: (1) social doubt triggered by network members' dissent and (2) social constraint to maintain similar attitudes. It also provides some data on the more commonly assumed mechanism, (3) information exchange. Results strongly support social constraint and are mixed on social doubt. This contrasts with the theoretical emphasis of much previous interdisciplinary social network research, which has focused primarily on information exchange, to the detriment of other mechanisms. Findings also indicate that like‐minded social network members solidify attitudes at least as much as dissent erodes them, suggesting that prior emphasis on the influence of heterogeneous rather than attitudinally congruent networks is overstated. Implications for political movements are discussed.     

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause‐effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre‐training (or even post‐training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue‐outcome co‐occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.     

Atypical shape bias and categorization in autism: Evidence from children and computational simulations

The shape bias, a preference for mapping new word labels onto the shape rather than the color or texture of referents, has been postulated as a word‐learning mechanism. Previous research has shown deficits in the shape bias in children with autism even though they acquire sizeable lexicons. While previous explanations have suggested the atypical use of color for label extension in individuals with autism, we hypothesize an atypical mapping of novel labels to novel objects, regardless of the physical properties of the objects. In Experiment 1, we demonstrate this phenomenon in some individuals with autism, but the novelty of objects only partially explains their lack of shape bias. In a second experiment, we present a computational model that provides a developmental account of the shape bias in typically developing children and in those with autism. This model is based on theories of neurological dysfunctions in autism, and it integrates theoretical and empirical findings in the literature of categorization, word learning, and the shape bias. The model replicates the pattern of results of our first experiment and shows how individuals with autism are more likely to categorize experimental objects together on the basis of their novelty. It also provides insights into possible mechanisms by which children with autism learn new words, and why their word referents may be idiosyncratic. Our model highlights a developmental approach to autism that emphasizes deficient representations of categories underlying an impaired shape bias.     

THE RELEVANCE OF EMERGENCE THEORY IN THE SCIENCE–RELIGION DIALOGUE

In this article, I call into question the relevance of emergence theories as presently used by thinkers in the science–religion discussion. Specifically, I discuss theories of emergence that have been used by both religious naturalists and proponents of panentheism. I argue for the following conclusions: (1) If we take the background theory to be metaphysical realism, then there seems to be no positive connection between the reality of emergent properties and the validity of providing reality with a religious interpretation, though one could perhaps construe an argument for the positive ontological status of emergence as a negative case for a religious worldview. (2) To be considered more plausible, religious naturalism should interpret religious discourse from the perspective of pragmatic realism. (3) Panentheistic models of divine causality are unable to avoid ontological dualism. (4) It is not obvious that emergent phenomena and/or properties are nonreducible in the ontological sense of the terms; indeed, the tension between weak and strong emergence makes it difficult for the emergentist to make ontological judgments. My general conclusion is that the concept of emergence has little metaphysical significance in the dialogue between science and theology.     

Noise,Economy, and the Emergence of Information Structure in a Laboratory Language

The acceptability of sentences in natural language is constrained not only grammaticality, but also by the relationship between what is being conveyed and such factors as context and the beliefs of interlocutors. In many languages the critical element in a sentence (its focus) must be given grammatical prominence. There are different accounts of the nature of focus marking. Some researchers treat it as the grammatical realization of a potentially arbitrary feature of universal grammar and do not provide an explicit account of its origins; others have argued, however, that focus marking is a (grammaticalized) functional solution to the problem of efficiently transmitting information via a noisy channel. By adding redundancy to highlight critical elements in particular, focus protects key parts of the message from noise. If this information‐theoretic account is true, then we should expect focus‐like behavior to emerge even in non‐linguistic communication systems given sufficient noise and pressures for efficiency. We tested this in an experiment in which participants played a simple communication game in which they had to click cells on a grid to communicate one of two line figures drawn across the grid. We manipulated the noise, available time, and required effort, and measured patterns of redundancy. Because the lines in many cases overlapped, meaning that only some parts of each line could be used to distinguish it from the other, we were able to compare the extent to which effort was expended on adding redundancy to critical (non‐overlapping) and non‐critical (overlapping) parts of the message. The results supported the information‐theoretic account of focus and shed light on the emergence of information structure in language.     

Social attention in keas, dogs, and human children

Understanding animals’ abilities to cooperate with and learn from each other has been an active field of research in recent years. One important basis for all types of social interactions is the disposition of animals to pay attention to each other—a factor often neglected in discussions and experiments. Since attention differs between species as well as between individuals, it is likely to influence the amount and type of information different species and/or observers may extract from conspecifics in any given situation. Here, we carried out a standardized comparative study on attention towards a model demonstrating food-related behavior in keas, dogs and children. In a series of experimental sessions, individuals watched different conspecific models while searching, manipulating and feeding. Visual access to the demonstration was provided by two observation holes, which allowed us to determine exactly how often and for how long observers watched the model. We found profound differences in the factors that influence attention within as well as between the tested species. This study suggests that attention should be incorporated as an important variable when testing species in social situations.     

Knowledge of Counterfactual Interventions through Cognitive Models of Mechanisms

Here I consider the relative merits of two recent models of explanation, James Woodward’s interventionist‐counterfactual model and the model model. According to the former, explanations are largely constituted by information about the consequences of counterfactual interventions. Problems arise for this approach because countless relevant interventions are possible in most cases and because it overlooks other kinds of equally relevant information. According the model model, explanations are largely constituted by cognitive models of actual mechanisms. On this approach, explanations tend not to represent any of the aforementioned information explicitly but can instead be used to produce it on demand. The model model thus offers the more plausible account of the information of which we are aware when we have an explanation and of the ratiocinative process through which we derive many kinds of information that are relevant to the evaluation of explanations.