Instantaneous conventions : the emergence of flexible communicative signals

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 towards a common goal. We observed participants creating new communicative conventions using the most minimal possible signals. These conventions, furthermore, changed trial-by-trial in response to shared environmental and task constraints. Strikingly, as a result, signals of the same form were able to successfully convey contradictory messages from trial to trial. Such behavior implicates what we term "joint inference," in which social interactants are inferring, in the moment, the most sensible communicative convention in light of their common ground. 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

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

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

How should autonomous vehicles overtake other drivers?

Previous research examining trust of autonomous vehicles has largely focused on holistic trust, with little work on evaluation of specific behaviours and interactions with human-controlled vehicles. Six experiments examined the influence of pull-in distance, vehicle perspective (overtaking/being overtaken), following distance and immersion on self-reported evaluations of, and physiological responses to, autonomous motorway overtakes. We found that: (i) overtake manoeuvres were viewed more positively as pull-in distance increased before reaching a plateau at approximately 28 m, (ii) physiological-based orienting responses occurred for the smallest pull-in distances, (iii) participants being overtaken were more forgiving of a sharper pull-in if the overtaking vehicle was followed closely by another vehicle, and (iv) for two of three cross-experiment comparisons participants were more forgiving of smaller pull-in distances with lower immersion levels. Overall, the results suggest that the acceptability of an overtake manoeuvre increases linearly with pull-in distance up to a set point for both overtaking and being overtaken manoeuvres, with some influence of traffic context and levels of immersion. We discuss the findings in terms of implications for the development of assisted and fully autonomous vehicle systems that perform in a way that will be acceptable to both the vehicle occupants and other road users

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

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

Extending Statistical Learning Farther and Further: Long-Distance Dependencies, and Individual Differences in Statistical Learning and Language

While statistical learning (SL) and language acquisition have been perceived as intertwined, such a view must contend with theoretical and empirical challenges. Against the backdrop of criticism leveled at early associationist efforts to account for language, a key concern for current SL approaches is whether it may suffice to enable the detection of long-distance relationships akin to those ubiquitously abounding in natural language. In Experiment 1, we extend results from previous work on the learning of nonadjacent dependencies to the learning of long-distance relations spanning three intervening elements; such learning is shown to obtain under two separate contexts. In Experiment 2, we additionally test the strength of SL and language's proposed relatedness by documenting the nature of correlations in individual differences between the two. Both experiments support the thesis that SL may overlap with mechanisms for language, while raising questions as to the singularity or duality of such underlying mechanism(s)