Grant: Discovery Grants Program - Individual
University of Lethbridge
$24,745.00
Tacit sensitivity to structure and judgements of style in everyday behaviour
FY 2010-11
Summary
People become sensitive to the structural regularities in everyday experiences through incidental exposure to individual examples. Thus, we develop sensitivity to other people's personal styles and the styles in the works of individual artists and composers, a sense of what does and does not belong to a particular genre of writing, the likely conclusion to and appropriate sequences of a favorite televison series, what drivers in then left lane are likely to do that drivers in the right are not, and what a normal interaction with a teller at our bank is like. Often this sensitivity develops effortlessly and without any intention on our part to learn these regularities, and even without any awareness that there are any such regularities to learn. Yet, both common experience and laboratory research alike demonstrate that this tacit sensitivity influences our performance and expectations in virtually every task we undertake. Many non-human animals, such as chimpanzees, rats, pigeons, and even fish can demonstrate similar sensitivities to style in art, music, and even handwriting. The proposed research explores the source of this tacit, structural knowledge and how it comes to influence our performance in classification, recognition, and recall, and how it influences our and other animal's biases and preferences. Previous research suggests that the knowledge that is the source of these influences is not in the form of high-level abstractions such as rules or prototypes, but rather remains latent in and distributed over our memory for specific episodes of experience. These memories are recruited collectively in response to a similar current event to provide a feeling of familiarity or ""rightness"" or a fluidity to our responding to events consistent with the structure of those we've experienced in similar situations. This retrieval also provides for the ability to detect when an event is inconsistent with structure, even if we are unable to articulate how we ""know"" it. The proposed research explores a model of such memory and retrieval processes that would allow for this implicit learning and use of structure in both people and animals. The model is in the form of artificial neural network applied to low-level representations of the individual events, such as pixel-maps of images or digitised sounds.
Program
Discovery Grants Program - Individual
Recipient
Location
Alberta, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
2609-2007
Name nom
Vokey, John
Department département
Psychology
Organization id
12
Institution établissement
University of Lethbridge
Province en
Alberta
Province fr
Alberta
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2010
Competition year année de concours
2007
Award amount
24745
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
12
Committee name en
Psychology: Brain, Behaviour & Cognitive Sciences
Committee name fr
Psychologie: Cerveau, comportement et science cognitive
Area of application code
1210
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Psychology
Area of application fr
Psychologie
Research subject code
6304
Research subject group en
Psychology
Research subject group fr
Psychologie
Research subject en
Cognitive science -- fundamental
Research subject fr
Science cognitive -- principes fondamentaux
Partie
4
Nb partie
5
Application title
Tacit sensitivity to structure and judgements of style in everyday behaviour
Keyword
NSERC
Application summary
People become sensitive to the structural regularities in everyday experiences through incidental exposure to individual examples. Thus, we develop sensitivity to other people's personal styles and the styles in the works of individual artists and composers, a sense of what does and does not belong to a particular genre of writing, the likely conclusion to and appropriate sequences of a favorite televison series, what drivers in then left lane are likely to do that drivers in the right are not, and what a normal interaction with a teller at our bank is like. Often this sensitivity develops effortlessly and without any intention on our part to learn these regularities, and even without any awareness that there are any such regularities to learn. Yet, both common experience and laboratory research alike demonstrate that this tacit sensitivity influences our performance and expectations in virtually every task we undertake. Many non-human animals, such as chimpanzees, rats, pigeons, and even fish can demonstrate similar sensitivities to style in art, music, and even handwriting. The proposed research explores the source of this tacit, structural knowledge and how it comes to influence our performance in classification, recognition, and recall, and how it influences our and other animal's biases and preferences. Previous research suggests that the knowledge that is the source of these influences is not in the form of high-level abstractions such as rules or prototypes, but rather remains latent in and distributed over our memory for specific episodes of experience. These memories are recruited collectively in response to a similar current event to provide a feeling of familiarity or ""rightness"" or a fluidity to our responding to events consistent with the structure of those we've experienced in similar situations. This retrieval also provides for the ability to detect when an event is inconsistent with structure, even if we are unable to articulate how we ""know"" it. The proposed research explores a model of such memory and retrieval processes that would allow for this implicit learning and use of structure in both people and animals. The model is in the form of artificial neural network applied to low-level representations of the individual events, such as pixel-maps of images or digitised sounds.