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  1. Judgment under Uncertainty: Heuristics and Biases.Amos Tversky & Daniel Kahneman - 1974 - Science 185 (4157):1124-1131.
    This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...)
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  • (1 other version)Finding Structure in Time.Jeffrey L. Elman - 1990 - Cognitive Science 14 (2):179-211.
    Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...)
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  • Attention to action: willed and automatic control of behavior.D. Norman & T. Shallice - 1986 - In R. Davidson, R. Schwartz & D. Shapiro (eds.), Consciousness and Self-Regulation: Advances in Research and Theory IV. Plenum Press.
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  • Descartes' Error: Emotion, Reason, and the Human Brain.Antonio R. Damasio - 1994 - Putnam.
    Linking the process of rational decision making to emotions, an award-winning scientist who has done extensive research with brain-damaged patients notes the dependence of thought processes on feelings and the body's survival-oriented regulators. 50,000 first printing.
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  • The Neural Basis of Error Detection: Conflict Monitoring and the Error-Related Negativity.Nick Yeung, Matthew M. Botvinick & Jonathan D. Cohen - 2004 - Psychological Review 111 (4):931-959.
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  • The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity.Clay B. Holroyd & Michael G. H. Coles - 2002 - Psychological Review 109 (4):679-709.
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  • (1 other version)Spiking Phineas Gage: A Neurocomputational Theory of Cognitive-Affective Integration in Decision Making.Brandon M. Wagar & Paul Thagard - 2004 - Psychological Review 111 (1):67-79.
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  • An Integrative Theory of Prefrontal Cortex Function.Earl K. Miller & Jonathan D. Cohen - 2001 - Annual Review of Neuroscience 24 (1):167-202.
    The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of (...)
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  • Prospect Theory: An Analysis of Decision Under Risk.D. Kahneman & A. Tversky - 1979 - Econometrica: Journal of the Econometric Society:263--291.
    The following values have no corresponding Zotero field: PB - JSTOR.
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  • Insensitivity to future consequences following damage to human prefrontal cortex.Antoine Bechara, Antonio R. Damasio, Hanna Damasio & Steven W. Anderson - 1993 - Cognition 50 (1-3):7-15.
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  • On the control of automatic processes: A parallel distributed processing account of the Stroop effect.Jonathan D. Cohen, Kevin Dunbar & James L. McClelland - 1990 - Psychological Review 97 (3):332-361.
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  • (1 other version)Spiking Phineas Gage: A Neurocomputational Theory of Cognitive-Affective Integration in Decision Making.Paul Thagard & Brandon M. Wagar - 2004 - Psychological Review 111 (1):67-79.
    The authors present a neurological theory of how cognitive information and emotional information are integrated in the nucleus accumbens during effective decision making. They describe how the nucleus accumbens acts as a gateway to integrate cognitive information from the ventromedial prefrontal cortex and the hippocampus with emotional information from the amygdala. The authors have modeled this integration by a network of spiking artificial neurons organized into separate areas and used this computational model to simulate 2 kinds of cognitive–affective integration. The (...)
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  • (1 other version){Finding structure in time}.J. Elman - 1993 - {Cognitive Science} 48:71-99.
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  • Six principles for biologically based computational models of cortical cognition.Randall C. O'Reilly - 1998 - Trends in Cognitive Sciences 2 (11):455-462.
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