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  1. Exploring the Strength of Association between the Components of Emotion Syndromes: The Case of Surprise.Rainer Reisenzein - 2000 - Cognition and Emotion 14 (1):1-38.
    A new experimental paradigm involving a computerised quiz was used to examine, on an intra-individual level, the strength of association between four components of the surprise syndrome: cognitive (degree of prospectively estimated unexpectedness), experiential (the feeling of surprise), behavioural (degree of response delay on a parallel task), and expressive (the facial expression of surprise). It is argued that this paradigm, together with associated methods of data analysis, effectively controls for most method factors that could in previous studies have lowered the (...)
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  • The Cognitive‐Evolutionary Model of Surprise: A Review of the Evidence. [REVIEW]Rainer Reisenzein, Gernot Horstmann & Achim Schützwohl - 2019 - Topics in Cognitive Science 11 (1):50-74.
    Research on surprise relevant to the cognitive-evolutionary model of surprise proposed by Meyer, Reisenzein, and Schützwohl is reviewed. The majority of the assumptions of the model are found empirically supported. Surprise is evoked by unexpected events and its intensity is determined by the degree if schema-discrepancy, whereas the novelty and the valence of the eliciting events probably do not have an independent effect. Unexpected events cause an automatic interruption of ongoing mental processes that is followed by an attentional shift and (...)
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  • Surprise, Curiosity, and Confusion Promote Knowledge Exploration: Evidence for Robust Effects of Epistemic Emotions.Elisabeth Vogl, Reinhard Pekrun, Kou Murayama, Kristina Loderer & Sandra Schubert - 2019 - Frontiers in Psychology 10.
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  • The Role of the Anterior Cingulate Cortex in Prediction Error and Signaling Surprise.William H. Alexander & Joshua W. Brown - 2019 - Topics in Cognitive Science 11 (1):119-135.
    In the past two decades, reinforcement learning has become a popular framework for understanding brain function. A key component of RL models, prediction error, has been associated with neural signals throughout the brain, including subcortical nuclei, primary sensory cortices, and prefrontal cortex. Depending on the location in which activity is observed, the functional interpretation of prediction error may change: Prediction errors may reflect a discrepancy in the anticipated and actual value of reward, a signal indicating the salience or novelty of (...)
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