Results 1 
6 of
6
Semirational Models of Conditioning: The Case of Trial Order
, 2007
"... Bayesian treatments of animal conditioning start from a generative model that specifies precisely a set of assumptions about the structure of the learning task. Optimal rules for learning are direct mathematical consequences of these assumptions. In terms of Marr’s (1982) levels of analyses, the mai ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
Bayesian treatments of animal conditioning start from a generative model that specifies precisely a set of assumptions about the structure of the learning task. Optimal rules for learning are direct mathematical consequences of these assumptions. In terms of Marr’s (1982) levels of analyses, the main task at the computational level
Bayesian clustering of huge friendship networks
, 2007
"... Because of the recent growth in popularity of social websites, such as MySpace, Facebook and Last.fm, there is an increasing interest in ways to analyze extremely large friendship networks with even millions of nodes. These huge networks provide a practical test ground for new network algorithms. Th ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
Because of the recent growth in popularity of social websites, such as MySpace, Facebook and Last.fm, there is an increasing interest in ways to analyze extremely large friendship networks with even millions of nodes. These huge networks provide a practical test ground for new network algorithms. The network analysis methods can also be applied to other networks than social networks, such as interactions between proteins and links between web pages. Social networks have typically structure: there are dense groups of nodes and some nodes have disproportionately many connections. The structure emerges, because friendships are not formed randomly. Instead, people tend to become friends with those who are similar to themselves. This can be called homophily. There are also other factors that guide the formation of friendships, such as geographical location and membership in common activities. The M0 algorithm finds clustering structure in networks with homophily by Bayesian statistical inference. The algorithm is based on a generative model for creating the edges
Efficient Boundary Tracking Through Sampling
"... The proposed algorithm for image segmentation is inspired by an algorithm for autonomous environmental boundary tracking. The algorithm relies on a tracker that traverses a boundary between regions in a sinusoidallike path. Boundary tracking is done by efficiently sampling points, resulting in a si ..."
Abstract
 Add to MetaCart
The proposed algorithm for image segmentation is inspired by an algorithm for autonomous environmental boundary tracking. The algorithm relies on a tracker that traverses a boundary between regions in a sinusoidallike path. Boundary tracking is done by efficiently sampling points, resulting in a significant savings in computation time. Page’s cumulative sum (CUSUM) procedure and other methods are adapted to handle a high level of noise. Applications to large data sets such as hyperspectral are of particular interest. Irregularly shaped boundaries such as fractals are also treated at very fine detail.
Computational [Principles of] Psychology∗
, 2014
"... This course states, motivates, and offers detailed support to the observation that cognition is fundamentally a computational process [28]. Students are introduced to a number of conceptual tools for thinking about cognitive information processing, including statistical learning from experience and ..."
Abstract
 Add to MetaCart
(Show Context)
This course states, motivates, and offers detailed support to the observation that cognition is fundamentally a computational process [28]. Students are introduced to a number of conceptual tools for thinking about cognitive information processing, including statistical learning from experience and the use of patterns distilled from past experience in guiding future actions. The application of these tools to the understanding of natural minds and to advancing the goals of artificial intelligence is illustrated on selected examples drawn from the domains of perception, memory, motor control, language, action planning, problem solving, decision making, reasoning, and creativity. The material is conceptually advanced and moderately technical. It is aimed at advanced undergrad
Evolving useful delusions: Subjectively rational selfishness leads to objectively irrational cooperation
"... We introduce a framework within evolutionary game theory for studying the distinction between objective and subjective rationality and apply it to the evolution of cooperation on 3regular random graphs. In our simulations, agents evolve misrepresentations of objective reality that help them cooper ..."
Abstract
 Add to MetaCart
(Show Context)
We introduce a framework within evolutionary game theory for studying the distinction between objective and subjective rationality and apply it to the evolution of cooperation on 3regular random graphs. In our simulations, agents evolve misrepresentations of objective reality that help them cooperate and maintain higher social welfare in the Prisoner’s dilemma. These agents act rationally on their subjective representations of the world, but irrationally from the perspective of an external observer. We model misrepresentations as subjective perceptions of payoffs and quasimagical thinking as an inferential bias, finding that the former is more conducive to cooperation. This highlights the importance of internal representations, not just observed behavior, in evolutionary thought. Our results provide support for the interface theory of perception and suggest that the individual’s interface can serve not only the individual’s aims, but also society as a whole, offering insight into social phenomena such as religion.