Toward a New Scientific Visualization for the Language Sciences

Cognitive Science Progresses Toward Interactive Frameworks

Despite its many twists and turns, the arc of cognitive science generally bends toward progress, thanks to its interdisciplinary nature. By glancing at the last few decades of experimental and computational advances, it can be argued that-far from failing to converge on a shared set of conceptual assumptions-the field is indeed making steady consensual progress toward what can broadly be referred to as interactive frameworks. This inclination is apparent in the subfields of psycholinguistics, visual perception, embodied cognition, extended cognition, neural networks, dynamical systems theory, and more. This pictorial essay briefly documents this steady progress both from a bird's eye view and from the trenches. The conclusion is one of optimism that cognitive science is getting there, albeit slowly and arduously, like any good science should.

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

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

To catch a Snitch: Brain potentials reveal variability in the functional organization of (fictional) world knowledge during reading

We harnessed the temporal sensitivity of event-related brain potentials (ERPs) alongside individual differences in Harry Potter (HP) knowledge to investigate the extent to which the availability and timing of information relevant for real-time written word processing are influenced by variation in domain knowledge. We manipulated meaningful (category, event) relationships between sentence fragments about HP stories and their sentence final words. During word-by-word reading, N400 amplitudes to (a) linguistically supported and (b) unsupported but meaningfully related, but not to (c) unsupported, unrelated sentence endings varied with HP domain knowledge. Single-trial analyses revealed that only the N400s to linguistically supported (but not to either type of unsupported) sentence-final words varied as a function of whether individuals knew (or could remember) the correct (supported) ending for each HP "fact." We conclude that the quick availability of information relevant for word understanding in sentences is a function of individuals' knowledge of both specific facts and the domain to which the facts belong. During written sentence processing, as domain knowledge increases, it is clearly evident that individuals can make use of the relevant knowledge systematically organized around themes, events, and categories in that domain, to the extent they have it.

Interacting Timescales in Perspective-Taking

Through theoretical discussion, literature review, and a computational model, this paper poses a challenge to the notion that perspective-taking involves a fixed architecture in which particular processes have priority. For example, some research suggests that egocentric perspectives can arise more quickly, with other perspectives (such as of task partners) emerging only secondarily. This theoretical dichotomy-between fast egocentric and slow other-centric processes-is challenged here. We propose a general view of perspective-taking as an emergent phenomenon governed by the interplay among cognitive mechanisms that accumulate information at different timescales. We first describe the pervasive relevance of perspective-taking to cognitive science. A dynamical systems model is then introduced that explicitly formulates the timescale interaction proposed. This model illustrates that, rather than having a rigid time course, perspective-taking can be fast or slow depending on factors such as task context. Implications are discussed, with ideas for future empirical research.

Step by step: Harvesting the dynamics of delay discounting decisions

People show a tendency to devalue rewards when they are delayed in time. This so-called delay discounting often happens to an extent that seems irrational from an economical perspective. Research studying outcomes of delay discounting decisions has successfully derived descriptive models for such choice preferences. However, this outcome-based approach faces limitations in integrating the influence of contextual factors on the decision. Recently, this outcome-centred perspective on delay discounting has been complemented by a focus on the process dynamics leading to delay discounting decisions. Here, we embrace and add to this approach: We show how an attractor model can extend discounting descriptive discounting curves into the temporal dimension. From the model, we derive three predictions and study the predictions in a delay discounting experiment based on mouse tracking. We find differences in discounting depending on the order of option presentation and more direct movements to options presented first. Together with the analysis of specific temporal patterns of information integration, these results show that considering the continuous process dynamics of delay discounting decisions and harvesting them with continuous behavioural measures allow the integration of contextual factors into existing models of delay discounting, not as noise but as a signal on their own.

Process dynamics in delay discounting decisions: An attractor dynamics approach

How do people make decisions between an immediate but small reward and a delayed but large one? The outcome of such decisions indicates that people discount rewards by their delay and hence these outcomes are well described by discounting functions. However, to understand irregular decisions and dysfunctional behavior one needs models which describe how the process of making the decision unfolds dynamically over time: how do we reach a decision and how do sequential decisions influence one another? Here, we present an attractor model that integrates into and extends discounting functions through a description of the dynamics leading to a final choice outcome within a trial and across trials. To validate this model, we derive qualitative predictions for the intra-trial dynamics of single decisions and for the inter-trial dynamics of sequences of decisions that are unique to this type of model. We test these predictions in four experiments based on a dynamic delay discounting computer game where we study the intra-trial dynamics of single decisions via mouse tracking and the inter-trial dynamics of sequences of decisions via sequentially manipulated options. We discuss how integrating decision process dynamics within and across trials can increase our understanding of the processes underlying delay discounting decisions and, hence, complement our knowledge about decision outcomes.

The bottleneck may be the solution, not the problem

As a highly consequential biological trait, a memory "bottleneck" cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication.