By Peter M. Todd, Visit Amazon's Thomas T. Hills Page, search results, Learn about Author Central, Thomas T. Hills, , Trevor W. Robbins
Over a century in the past, William James proposed that folks seek via reminiscence a lot as they rummage via a home trying to find misplaced keys. We scour our environments for territory, foodstuff, associates, and knowledge. We look for goods in visible scenes, for ancient evidence, and for the simplest bargains on websites; we look for new buddies so as to add to our social networks, and for ideas to novel difficulties. What we discover is usually ruled through how we seek and by way of the constitution of our surroundings. This ebook explores how we look for assets in our minds and on this planet. The authors learn the evolution and adaptive services of seek; the neural underpinnings of goal-searching mechanisms throughout species; mental versions of seek in reminiscence, determination making, and visible scenes; and purposes of seek habit in hugely advanced environments resembling the net. because the diversity of knowledge, social contacts, and items maintains to extend, how good we can seek and effectively locate what we search turns into more and more very important. even as, seek bargains cross-disciplinary insights to the clinical research of human cognition and its evolution. Combining views from researchers throughout quite a few domain names, this booklet furthers our knowing of the connection among seek and the human brain.
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Additional info for Cognitive Search: Evolution, Algorithms, and the Brain
1. X-axis values represent turning angles (negative = counterclockwise; positive = clockwise). Redrawn after Couzin (1999). increasingly platykurtic, tending toward a uniform distribution. Thus, there is typically some intermediate move-length that produces the most informative characterization of movement. Correlated Random Walk Models of Animal Search The pure random walk model, analogous to Brownian motion, is typically too simple to represent animal movement because it does not account for the correlation of an organism’s current direction with that of its previous direction (resulting from head-to-tail polarization).
First, it envisions an iterated rather than single-shot search, and this makes the handling time variable important. A long handling time increases the opportunity cost of accepting an item, because the time a forager spends handling is time it cannot spend searching for new items. Second, crudely speaking, the model predicts that environmental richness should determine a forager’s selectivity; this follows from the idea of iterated search and the opportunity costs of accepting an item with a long handling time.
2. 3. The environments in which organisms search both externally and internally share similar structural properties, and resources tend to be patchily distributed. Various search strategies often rely on this patchiness to focus search around areas where resources have been recently found, and thus to facilitate resource acquisition based on their nonrandom distribution. The neural mechanisms that control search—especially those involving dopamine, the prefrontal cortex, and the striatum—are often shared across species and search environments.