Ellen Peters
The Ohio State University, Psychology Department
Title: Numeracy and decision making
Numerical information must frequently be considered when making decisions (consider stock prices, earthquake risks, calorie counts). Although many decisions rely on basic mathematical understanding, little research has examined theoretical mechanisms of the influence of number skills on risk perceptions and decisions. In my talk, I examine judgment and decision effects, previously considered universal, that depend on numeric ability. Numeracy goes beyond number comprehension, influencing the processing and use of numerical and non numerical information.
DeLiang Wang
OSU Center for Cognitive Science and Department of Computer Science & Engineering
Title: A Classification Approach to the Cocktail Party Problem
The cocktail party problem, also known as the speech segregation problem, has evaded a solution for decades in speech and audio processing. Motivated by recent advances in psychoacoustics and computational auditory scene analysis, I will advocate a new formulation to this old problem: instead of aiming at extracting the target speech, it classifies time-frequency units into two classes: those dominated by the target speech and the rest. This new formulation shifts the emphasis from signal estimation to signal classification, with an important implication that the cocktail party problem is now open to a plethora of binary classification techniques in neural networks and machine learning. I will discuss recent speech segregation algorithms that adopt the binary classification formulation, and the segregation performance of these systems represents considerable progress towards solving the cocktail party problem.
Per B. Sederberg
The Ohio State University, Psychology Department
Title: Tracking the role of context and prediction in perception and memory
Much of what defines us are the experiences etched into the tabula rasa we're handed at birth. Who we are, in turn, shapes our perception and what we learn from our experiences. In an attempt to grapple with this circular problem at the core of our cognition, I will present a computational theory of perception and episodic memory that provides a mechanistic account of how we process and learn from the events of our lives. Inspired by theories of temporal context and reinforcement learning, the model states that we are essentially prediction machines, employing our memory system in service of our goals. At each moment, we use our current state, which is the recency-weighted running average of experience that defines our mental context, to make a prediction of what will happen next. Depending on the observed outcome, and the error in our prediction, we modulate how we process incoming stimuli and what we encode from the experience. I will provide both behavioral and neural evidence in support of the model, which suggests that context and prediction play critical roles in shaping our mental representation of experience and, thus, who we are and will become in the future.
Edward Gibson
Brain and Cognitive Sciences, MIT
Title: The communicative basis of word order
Some recent evidence suggests that subject-object-verb (SOV) may be the default word order for human language. For example, SOV is the preferred word order in a task where participants gesture event meanings (Goldin-Meadow et al. 2008). Critically, SOV gesture production occurs not only for speakers of SOV languages, but also for speakers of SVO languages, such as English, Chinese, Spanish (Goldin-Meadow et al. 2008) and Italian (Langus & Nespor, 2010). The gesture-production task therefore plausibly reflects default word order independent of native language. However, this leaves open the question of why there are so many SVO languages (41.2% of languages; Dryer, 2005). We propose that the high percentage of SVO languages cross-linguistically is due to communication pressures over a noisy channel (Jelinek, 1975; Brill & Moore, 2000; Levy et al. 2009). In particular, we propose that people understand that the subject will tend to be produced before the object (a near universal cross-linguistically; Greenberg, 1963). Given this bias, people will produce SOV word order – the word order that Goldin-Meadow et al. show is the default – when there are cues in the input that tell the comprehender who the subject and the object are. But when the roles of the event participants are not disambiguated by the verb, then the noisy channel model predicts either (i) a shift to the SVO word order, in order to minimize the confusion between SOV and OSV, which are minimally different; or (ii) the invention of case marking, which can also disambiguate the roles of the event participants. We test the predictions of this hypothesis and provide support for it using gesture experiments in English, Japanese and Korean. We also provide evidence for the noisy channel model in language understanding in English.
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