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High-probability grants
According to our matching algorithm, John K. Kruschke is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
1994 — 1998 |
Kruschke, John K |
R29Activity Code Description: Undocumented code - click on the grant title for more information. |
Explorations of a Connectionist Category Learning Model @ Indiana University Bloomington
The phrase "category learning," as used in this proposal, refers to any situation in which a person must learn to classify stimuli, such as learning to classify lists of symptoms into the correct disease category. Theories of category learning have hypothesized various underlying representations ranging from rules to prototypes to exemplars, and various learning processes ranging from gradual association building to all-or- none rule acquisition. The proposed research explores applications of successively greater extensions of the ALCOVE model of category learning (Kruschke 1990, 1992). The ALCOVE model is a synthesis of three traditions in theories of category learning: It combines (i) exemplar-based representation with (ii) dimensional attention and (iii) error-driven learning. It is a theoretical synthesis that also quantitatively fits human-learning data in a variety of situations. The intermediate-range goal of this research is to explore the scope of applicability of ALCOVE and its extensions, thereby determining the scope of its underlying explanatory principles. The long-range goal of this research is to establish empirical evidence and theoretical accounts for relations between exemplar, rule-based, and other models of category learning. The significance of the research will be its role in covering a broad range of empirical data under a single explanatory umbrella, and in unifying alternative mechanisms and determining situations in which each seems to be dominant in human learning. All the proposed research is relevant to our knowledge of mental health insofar as it will help identify underlying learning mechanisms in normal adults. There is additional potential of direct relevance to mental health if it is found that exemplar and rule-based systems are dissociable components of category learning. For example, Pinker (1991) has argued that there are separate exemplar and rule systems in language learning, and has adduced behavioral effects of brain lesions to argue his case. The studies proposed here might lead to analogous decompositions for general category learning, which could in turn eventually have broad implications for teaching methods and therapies for both normals and the mentally challenged or brain damaged.
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1 |
2000 — 2004 |
Kruschke, John |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Attention in Associative Learning
Medical clinicians learn to associate certain symptoms with particular diseases. Seafarers learn to associate certain patterns of wind, wave and animal activity with particular future weather conditions. Stock traders learn to associate certain levels of value, earnings and market activity with increases or decreases in price. Law enforcement agents learn to associate features of people and the environment with particular crimes. Consumers learn to associate various products with different feelings. These examples illustrate that associative learning underlies much of human behavior, with potentially significant consequences for decisions in the real world.
People are adept at learning many arbitrary associations very quickly, without entirely forgetting previously learned associations. Despite this proficiency, people also show numerous learned behaviors that are suboptimal or irrational from a normative statistical perspective. The goal of the present research is to investigate one likely cause of these irrational behaviors, namely, selective attention. When people are learning to associate a complex array of information with an outcome, a subset of the information can be selectively attended to. By attending to distinctive aspects of the situation, the person can learn quickly without confusing it with previous knowledge. Selective attention, because it excludes some aspects of the situation, also implies incomplete knowledge. This side-effect of selective attention can then lead to numerous suboptimal behaviors in later circumstances.
The research involves (a) numerous experiments in human learning and (b) computer simulation of mathematical models of attentional shifts. A primary goal of the proposed research is to explain a wide spectrum of seemingly disparate and irrational effects in associative learning as the natural consequence of attention shifting. The attention shifting itself is actually an efficacious and adaptive response to the goal of learning quickly. The mathematical model reflects this goal by shifting attention to whatever features reduce predictive error most rapidly. The model mimics human learning in detail, and makes new predictions to be tested by the research. The model also accounts for several previously unexplained or disparately explained phenomena. The research will improve our understanding of the basic principles of associative learning. Future research can build on this understanding to develop interventions and applications in real-world environments.
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0.915 |