David Reitter
Carnegie Mellon University
Contact: David Reitter, Carnegie Mellon University.
Among the most fascinating abilities of human beings is their propensity to verbalize, communicate and adopt ideas within a vast network of social contacts. Human cognitive capabilities are uniquely suited to communication, and they are crucial to the intelligence emerging from human communities. The cognitive and psycholinguistic mechanisms underlying language comprehension and production are still poorly understood. While recent studies paint a picture of how memory and contextualization help humans comprehend a dialogue partner's ideas and individual language, we do not understand whether human memory has evolved to support team-work and social cognition.
Cognitive modeling and network simulation techniques have recently sparked interest in the interaction of cognitive mechanisms with the social environment. Individuals adapt their linguistic expressions quickly to their conversation partners, and new communicative conventions may soon spread through a network of connected agents. Cognitive modeling frameworks, validated and refined through careful experimentation, as well as computational tools can now simulate the co-dependency of individual cognition and emergent phenomena in human societies. Networked experimentation platforms facilitate large-scale data-collection. Language resources (corpora) provide data collected in real-life situations that let us test cognitive and psycholinguistic models. Once validated, they will make better predictions and cover broad ranges of human behavior. This combination of broad coverage and large-scale simulation requires new computational tools, new methodologies, new datasets and new experimental designs. We expect that these will lead to advances in the quest for a standard architecture of the human language faculty.
Recent work covers three areas.
1. Psycholinguistics of dialogue. Using corpus data, statistical analysis and machine learning, we show how speakers align and increase their task success based on mutual adaptation; cognitive architectures show how the basic underlying process (syntactic priming) can be framed as a memory retrieval effect (with JD Moore and F Keller, e.g., 2006, 2007, 2008, 2011 J.CogSci). More about my work on adaptation in dialogue.
2. Communication of small teams and larger communities. Adaptation between speakers or players of a naming game can be shown to lead to emerging, wide-spread communication systems. Multi-agent cognitive models (using the ACT-R architecture) explain empirical results and make predictions for large groups. In ongoing work, convergence and the effect of communication policy is observed empirically using a multi-player game platform (with C Lebiere and K Sycara, et al., e.g., 2009, 2010, 2011, 2011 J.CSR). More about my work on social cognition.
3. Scalability of cognitive modeling. Cognitive models written in
architectures such as ACT-R and SOAR tend to be overly specific
compared to the evaluation they receive using reaction times, learning
effects and, more recently, neurophysiological data. In a
reformulation of ACT-R theory, ACT-UP proposes to underspecify
portions of a subject's task strategy. At the same time, the ACT-UP library allows modelers
to scale up simulations to thousands of interacting models. ACT-UP is
well-validated, well-documented and available for . (2010 ACT-R, 2010 JAGI)
See Publications.
