Download Cognition: From Memory to Creativity by Robert W. Weisberg, Lauretta M. Reeves PDF

By Robert W. Weisberg, Lauretta M. Reeves

The method of cognition permits us to operate in existence; it interprets inputs from the realm with a purpose to realize the sound of the alarm clock, consider the day of the week, and judge which outfits to wear.
Cognition: From reminiscence to Creativity offers readers with a transparent, research-based, and well-illustrated presentation of the sphere, beginning with memory—the such a lot available beginning point—to extra complicated features and examine in info processing. Authors Robert Weisberg and Lauretta Reeves contain the most recent neurological findings that aid us comprehend the human tactics that let for cognition.
Unique in its association, Cognition contains either classical and sleek examine and gives demonstration experiments for college students to behavior with basic materials.
Cognition explores:
* types of reminiscence and reminiscence systems
* Encoding and retrieval
* Forgetting vs. fake memory
* visible cognition
* consciousness and imagery
* Sounds, phrases, and meaning
* Logical considering and choice making
* challenge fixing and inventive pondering

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Additional info for Cognition: From Memory to Creativity

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Technological advances in the fields that fall under the cognitive science umbrella have also led to theoretical and empirical advances in the study of cognition. Notable contributions have come from computer science, with the advent of parallel distributed processing computer models, and from neuroscience, with the invention of neuroimaging techniques, which provide a window on the brain’s activity. DISTRIBUTED MODELS OF COGNITION Parallel Distributed Processing Models We have seen how the development of the computer served to stimulate research in cognitive psychology.

The coverage in Neisser’s book was most heavily concentrated at the perceptual end of the information-processing sequence, such as pattern recognition and attention. , memory, concept formation, and problem solving). Neisser acknowledged this lack of balance, and commented that at the time not very much was known about the higher processes. This book, on the other hand, will have about two-thirds of its pages devoted to the higher processes. This is because we have learned much about these topics over the years since Neisser’s pioneering book was published.

Then . . ,” or Bayesian ) reasoning, has become popular in recent years in multiple areas, to explain topics as diverse as visual scene perception (Yuille & Kersten, 2006), inductive learning (Tenenbaum, Griffiths, & Kemp, 2006), and semantic memory (Steyvers, Griffiths, & Dennis, 2006). These models are called Bayesian models because they are based on an early theory of conditional reasoning that was developed by Thomas Bayes (c. 1701–1761; Bayes’s work was published in 1763). As do connectionist models, these Bayesian models “learn” complex information from simpler data.

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