Instantaneous systems of communicative conventions through virtual bargaining.

People can instantaneously create novel conventions that link individual communicative signals to meanings, both in experiments and everyday communication. Yet a basic principle of natural communication is that the meaning of a signal typically contrasts with the meanings of alternative signals that were available but not chosen. That is, communicative conventions typically form a system, rather than consisting of isolated signal-meaning pairs. Accordingly, creating a novel convention linking a specific signal and meaning seems to require creating a system of conventions linking possible signals to possible meanings, of which the signal-meaning pair to be communicated is merely a sub-case. If so, people will not link signals and meanings in isolation; signal-meaning pairings will depend on alternative signals and meanings. We outline and address theoretical challenges concerning how instantaneous conventions can be formed, building on prior work on "virtual bargaining," in which people simulate the results of a process of negotiation concerning which convention, or system of conventions, to choose. Moreover, we demonstrate empirically that instantaneous systems of conventions can flexibly be created in a 'minimal' experimental paradigm. Experimental evidence from 158 people playing a novel signaling game shows that modifying both the set of signals, and the set of meanings, can indeed systematically modify the signal-meaning mappings that people may instantaneously construct. While consistent with the virtual bargaining account, accounting for these results may be challenging for some accounts of pragmatic inference.

Comparing statistical learning across perceptual modalities in infancy: An investigation of underlying learning mechanism(s).

Statistical learning (SL), sensitivity to probabilistic regularities in sensory input, has been widely implicated in cognitive and perceptual development. Little is known, however, about the underlying mechanisms of SL and whether they undergo developmental change. One way to approach these questions is to compare SL across perceptual modalities. While a decade of research has compared auditory and visual SL in adults, we present the first direct comparison of visual and auditory SL in infants (8-10 months). Learning was evidenced in both perceptual modalities but with opposite directions of preference: Infants in the auditory condition displayed a novelty preference, while infants in the visual condition showed a familiarity preference. Interpreting these results within the Hunter and Ames model (1988), where familiarity preferences reflect a weaker stage of encoding than novelty preferences, we conclude that there is weaker learning in the visual modality than the auditory modality for this age. In addition, we found evidence of different developmental trajectories across modalities: Auditory SL increased while visual SL did not change for this age range. The results suggest that SL is not an abstract, amodal ability; for the types of stimuli and statistics tested, we find that auditory SL precedes the development of visual SL and is consistent with recent work comparing SL across modalities in older children.

Negotiating the Traffic: Can Cognitive Science Help Make Autonomous Vehicles a Reality?

To drive safely among human drivers, cyclists and pedestrians, autonomous vehicles will need to mimic, or ideally improve upon, humanlike driving. Yet, driving presents us with difficult problems of joint action: 'negotiating' with other users over shared road space. We argue that autonomous driving provides a test case for computational theories of social interaction, with fundamental implications for the development of autonomous vehicles.

Reading span task performance, linguistic experience, and the processing of unexpected syntactic events.

Accounts of individual differences in online language processing ability often focus on the explanatory utility of verbal working memory, as measured by reading span tasks. Although variability in reading span task performance likely reflects individual differences in multiple underlying traits, skills, and processes, accumulating evidence suggests that reading span scores also reflect variability in the linguistic experiences of an individual. Here, through an individual differences approach, we first demonstrate that reading span scores correlate significantly with measures of the amount of experience an individual has had with written language (gauged by measures that provide "proxy estimates" of print exposure). We then explore the relationship between reading span scores and online language processing ability. Individuals with higher reading spans demonstrated greater sensitivity to violations of statistical regularities found in natural language-as evinced by higher reading times (RTs) on the disambiguating region of garden-path sentences-relative to their lower span counterparts. This result held after statistically controlling for individual differences in a non-linguistic operation span task. Taken together, these results suggest that accounts of individual differences in sentence processing can benefit from a stronger focus on experiential factors, especially when considered in relation to variability in perceptual and learning abilities that influence the amount of benefit gleaned from such experience.

Instantaneous Conventions.

Humans can communicate even with few existing conventions in common (e.g., when they lack a shared language). We explored what makes this phenomenon possible with a nonlinguistic experimental task requiring participants to coordinate toward a common goal. We observed participants creating new communicative conventions using the most minimal possible signals. These conventions, furthermore, changed on a trial-by-trial basis in response to shared environmental and task constraints. Strikingly, as a result, signals of the same form successfully conveyed contradictory messages from trial to trial. Such behavior is evidence for the involvement of what we term joint inference, in which social interactants spontaneously infer the most sensible communicative convention in light of the common ground between them. Joint inference may help to elucidate how communicative conventions emerge instantaneously and how they are modified and reshaped into the elaborate systems of conventions involved in human communication, including natural languages.

Virtual bargaining: a theory of social decision-making.

An essential element of goal-directed decision-making in social contexts is that agents' actions may be mutually interdependent. However, the most well-developed approaches to such strategic interactions, based on the Nash equilibrium concept in game theory, are sometimes too broad and at other times 'overlook' good solutions to fundamental social dilemmas and coordination problems. The authors propose a new theory of social decision-making-virtual bargaining-in which individuals decide among a set of moves on the basis of what they would agree to do if they could openly bargain. The core principles of a formal account are outlined (vis-à-vis the notions of 'feasible agreement' and explicit negotiation) and further illustrated with the introduction of a new game, dubbed the 'Boobytrap game' (a modification on the canonical Prisoner's Dilemma paradigm). In the first empirical data of how individuals play the Boobytrap game, participants' experimental choices accord well with a virtual bargaining perspective, but do not match predictions from a standard Nash account. Alternative frameworks are discussed, with specific empirical tests between these and virtual bargaining identified as future research directions. Lastly, it is proposed that virtual bargaining underpins a vast range of human activities, from social decision-making to joint action and communication.

Unwritten rules: virtual bargaining underpins social interaction, culture, and society.

Many social interactions require humans to coordinate their behavior across a range of scales. However, aspects of intentional coordination remain puzzling from within several approaches in cognitive science. Sketching a new perspective, we propose that the complex behavioral patterns - or 'unwritten rules' - governing such coordination emerge from an ongoing process of 'virtual bargaining'. Social participants behave on the basis of what they would agree to do if they were explicitly to bargain, provided the agreement that would arise from such discussion is commonly known. Although intuitively simple, this interpretation has implications for understanding a broad spectrum of social, economic, and cultural phenomena (including joint action, team reasoning, communication, and language) that, we argue, depend fundamentally on the virtual bargains themselves.

Phonological typicality influences sentence processing in predictive contexts: reply to Staub, Grant, Clifton, and Rayner (2009).

In 2 separate self-paced reading experiments, Farmer, Christiansen, and Monaghan (2006) found that the degree to which a word's phonology is typical of other words in its lexical category influences online processing of nouns and verbs in predictive contexts. Staub, Grant, Clifton, and Rayner (2009) failed to find an effect of phonological typicality when they combined stimuli from the separate experiments into a single experiment. We replicated Staub et al.'s experiment and found that the combination of stimulus sets affects the predictiveness of the syntactic context; this reduces the phonological typicality effect as the experiment proceeds, although the phonological typicality effect was still evident early in the experiment. Although an ambiguous context may diminish sensitivity to the probabilistic relationship between the sound of a word and its lexical category, phonological typicality does influence online sentence processing during normal reading when the syntactic context is predictive of the lexical category of upcoming words.

Sequential expectations: the role of prediction-based learning in language.

Prediction-based processes appear to play an important role in language. Few studies, however, have sought to test the relationship within individuals between prediction learning and natural language processing. This paper builds upon existing statistical learning work using a novel paradigm for studying the on-line learning of predictive dependencies. Within this paradigm, a new "prediction task" is introduced that provides a sensitive index of individual differences for developing probabilistic sequential expectations. Across three interrelated experiments, the prediction task and results thereof are used to bridge knowledge of the empirical relation between statistical learning and language within the context of nonadjacency processing. We first chart the trajectory for learning nonadjacencies, documenting individual differences in prediction learning. Subsequent simple recurrent network simulations then closely capture human performance patterns in the new paradigm. Finally, individual differences in prediction performances are shown to strongly correlate with participants' sentence processing of complex, long-distance dependencies in natural language.

On-line individual differences in statistical learning predict language processing.

Considerable individual differences in language ability exist among normally developing children and adults. Whereas past research have attributed such differences to variations in verbal working memory or experience with language, we test the hypothesis that individual differences in statistical learning may be associated with differential language performance. We employ a novel paradigm for studying statistical learning on-line, combining a serial-reaction time task with artificial grammar learning. This task offers insights into both the timecourse of and individual differences in statistical learning. Experiment 1 charts the micro-level trajectory for statistical learning of nonadjacent dependencies and provides an on-line index of individual differences therein. In Experiment 2, these differences are then shown to predict variations in participants' on-line processing of long-distance dependencies involving center-embedded relative clauses. The findings suggest that individual differences in the ability to learn from experience through statistical learning may contribute to variations in linguistic performance.