We are testing a new system for linking grants to scientists.
The funding information displayed below comes from the
NIH Research Portfolio Online Reporting Tools and the
NSF Award Database.
The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
You can help! If you notice any innacuracies, please
sign in and mark grants as correct or incorrect matches.
Sign in to see low-probability grants and correct any errors in linkage between grants and researchers.
High-probability grants
According to our matching algorithm, Mark A. Pitt is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
1993 — 1997 |
Pitt, Mark A |
R29Activity Code Description: Undocumented code - click on the grant title for more information. |
Recognition of Spoken Words
Theories of auditory word recognition posit multiple stages of mental representation to describe the transformation of speech into words; the final stage is the lexicon, whose operation and functions differ across theories. The proposed research examines three issues concerning the function of the lexicon in word recognition, testing predictions of classes of theories that are divided on each issue: (1) whether or not lexical knowledge is used to facilitate speech perception; (2) which portion of a spoken word first makes contact with its representation in the lexicon; (3) the process by which a lexical entry is selected that best matches the speech signal. Experimental manipulations are designed to reveal the operation of the lexicon. Stimuli will consist of words and sentences, and the acoustic, phonological, and semantic characteristics of words will be varied. Multiple experimental tasks, such as phoneme monitoring, phoneme identification, and cross-modal priming, will be used to probe processing at different stages in the recognition system and to provide converging evidence on the issues that will be studied. Specialized computer hardware, software, and audio equipment will control stimulus construction, presentation and data collection. The findings should contribute to an understanding of the role of the lexicon in word recognition.
|
1 |
2001 — 2006 |
Pitt, Mark A |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Recognizing Phonological Variants of Spoken Words
DESCRIPTION (provided by applicant): The over-arching goal of this program of research is to understand how listeners recognize phonological variants of spoken words (e.g., [went, went]) as the words intended by the talker (e.g., [went]). Experimentation can be most productive and performed most knowledgeably with an accurate description (acoustic and phonological) of pronunciation variation in English. The aim of the present proposal is to make it possible to develop such descriptions by completing phonetic transcription of a 300,000-word corpus of conversational speech. When transcription is finished, not only will we use it for this purpose, but the corpus will be made available to researchers in the speech sciences. User-friendly software to analyze and search the corpus, along with a host of supporting material (instruction manuals, tutorials, user forum) will also be provided to facilitate and encourage its use.
|
1 |
2011 — 2015 |
Pitt, Mark A |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Adaptive Experimental Methods For Evaluating Computational Models of Cognition
DESCRIPTION (provided by applicant): The long-term goal of this program of research is to improve scientific inference in psychological science. The topic is investigated in the context of computational models of cognition, which can be extremely difficult to distinguish experimentally because of their complexity and the extent to which they mimic each other. Statistical methods (goodness-of-fit, Akaike Information Criterion) have been the dominant means of model evaluation and selection, and are applied after data have been collected in an experiment. The current project explores a new approach to improving inference by developing corresponding statistical methods that are applied on the front-end of an experiment, while the experiment is being designed. In this approach, dubbed adaptive design optimization (ADO), an experiment is divided into a series of mini-experiments. The design of each mini-experiment is updated based on performance in the preceding mini-experiment. The choice of design values is dictated by a sophisticated search algorithm that constantly pressures the models of interest to fit more and more challenging data points until one model emerges as superior. The adaptive nature of the methodology ensures the design is optimal throughout the testing session, and thereby maximizes the informativeness of the experimental results. Furthermore, the focus on optimizing the design simultaneously ensures that the experiment is highly efficient (e.g., fewer trials and participants). The three specific aims of the proposal are to (1) develop ADO so that it is applicable to a broad range of problems (e.g., various experimental designs, different modeling goals) in the discipline; (2) improve the ADO algorithm so that it can be used in real-time experiments; (3) develop web-based resources to enable researchers to learn about and take advantage of the methodology. The achievement of these three goals is intended to provide researchers with a new technology that can accelerate scientific discovery.
|
1 |