Current Events

TBA

Communication is inherently social and requires an efficient exchange of complex cues between speakers and listeners. However, language processing is typically studied using individual listeners and simplistic stimuli. What are the interpersonal mechanisms that allow us to connect with and learn from others across the lifespan? My lab studies everyday interactions using behavioral, computational, and dual-brain neuroimaging techniques in real-life environments. To understand the real-time dynamics of communication at the biological level, I have used brain-to-brain coupling (child-caregiver, adult-adult) as a measure of interpersonal alignment to predict communicative success and learning outcomes. In one fNIRS study, we found that activation in the infant prefrontal cortex preceded and drove similar activation in the adult brain, a result that advances our understanding of children’s influence over the accommodative behaviors of caregivers. In ongoing work using dual-brain EEG during adult dialogue, we are exploring the causal relationship between representations of fine-grained linguistic features, speaker-listener coupling, and overall communication quality. Across several studies, we have developed new methods for quantifying the acoustic and semantic structure of naturalistic speech in different communicative modes (e.g., infant-directed speech, podcasting to diverse audiences) and measuring how this structure relates to overall impressions of a speaker (e.g., creativity, personality). This collection of findings provides a new understanding of how our brains and behaviors both shape and reflect different audiences during everyday communication.

People regularly encounter a series of positive numbers in everyday life, such as price lists, expense streams, amounts saved, calories consumed, or time estimates for work tasks or household chores. Often, people must integrate this information in real-time to make judgments and decisions. Such integration can take several forms, including forming an impression of a count, an average, or a sum. We refer to people’s perception of running totals as intuitive sums, and we show that people’s intuitive summation is systematically lower than the actual value—a phenomenon we call Undersum bias. The phenomenon does not occur for intuitive estimates of averages. Undersum bias is robust to whether participants have the numbers before them when estimating the sum, suggesting a limited role of errors due to memory retrieval. The bias is absent for sequences generated with numbers in the subitizing range, suggesting a plausible role of initial encoding. Consistent with this, the accuracy of memory recalls is significantly correlated with estimates of intuitive summation both in magnitude and direction. Undersum bias has both practical and theoretical implications. We find that it is a novel cognitive antecedent of overconsumption behavior and highlight how individuals’ representations of their consumption are an understudied and theoretically relevant determinant of self-control failure.

My "Fidelity" model of human move-choice at chess resembles utility-based predictive models that gauge risk and forecast consumer behavior. The model puts a probability on every possible move in any chess position based on utility values for those moves given by strong chess programs and parameters denoting the skill of a player or players P. The parameters have a strong many-one correspondence to the Elo rating system for measuring skill at chess. Those probabilities generate both projections and internal confidence intervals for aggregate statistics such as the projected rate at which P will play the same move as the chess program and the total utility loss from inferior moves. The outputs are z-scores quantifying the unlikelihood of P's deviations from the projections and the Intrinsic Performance Rating (IPR) of P's moves in the games. They are used by the International Chess Federation (FIDE) and various national federations to help arbitrate allegations of players cheating with strong computer programs in human-only matches.

The model also promotes general research on human decision making, in real competitive settings whose evaluation metrics are robust and long established. When and why does a player decide to stop thinking and make a move? How does reduced thinking time affect quality? One surprising new result is that moves on which P thought for a long time have vastly lower IPR than moves made almost instantly. Can broader psychological tendencies be mapped into chess and thereby be validated in a data-rich environment? How can we tell between results indicating human psychological phenomena and artifacts of the model's construction and graininess? The talk will more broadly address modes of cross-validation, the "replication crisis", and general conduct of science as employed in forecasting. This will open to general Q&A.

Too much data has been collected, in too many different fields, using too many different methods, to possibly sort through manually. If our collection efforts are to ever be justified, we need a way to interoperate this data,  and make it comprehensible with minimal cognitive effort. The field I work in - Applied Ontology - includes these tasks among its aims. Applied Ontology intersects metaphysics, epistemology, logic, artificial intelligence, and data science. Simplifying a bit, we use philosophical insights to help subject-matter experts - in domains ranging across physics, virology, immigration law, and psychology among others - organize datasets to promote interoperability, identify novel predictions, and extract implicit information. In this talk, I will introduce you to the field of Applied Ontology, its history, its many present employment opportunities, and its future. Throughout, I will provide examples of interesting applications one encounters when working with experts in other fields and highlight reasons why the applied ontology toolkit is particularly well-suited to this work.

TBA