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Neuronal network and awareness measures of post-decision wagering behavior in detecting masked emotional faces

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Neuronal network and awareness measures of post-decision wagering behavior in detecting masked emotional faces

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dc.contributor.author Szczepanowski, Remigiusz pl
dc.contributor.author Wierzchoń, Michał [SAP11018430] pl
dc.contributor.author Szulżycki, Marcin pl
dc.date.accessioned 2017-09-16T10:02:36Z
dc.date.available 2017-09-16T10:02:36Z
dc.date.issued 2017 pl
dc.identifier.issn 1866-9956 pl
dc.identifier.uri http://ruj.uj.edu.pl/xmlui/handle/item/44261
dc.language eng pl
dc.rights Udzielam licencji. Uznanie autorstwa 3.0 Polska pl
dc.rights.uri http://creativecommons.org/licenses/by/3.0/pl/legalcode pl
dc.title Neuronal network and awareness measures of post-decision wagering behavior in detecting masked emotional faces pl
dc.type JournalArticle pl
dc.description.physical 457-467 pl
dc.abstract.en Awareness can be measured by investigating the patterns of associations between discrimination performance (first-order decisions) and confidence judgments (knowledge). In a typical post-decision wagering (PDW) task, participants judge their performance by wagering on each decision made in a detection task. If participants are aware, they wager advantageously by betting high whenever decisions are correct and low for incorrect decisions. Thus, PDW - like other awareness measures with confidence ratings - quantifies if the knowledge upon which they make their decisions is conscious. The present study proposes a new method of assessing the association between advantageous wagering and awareness in the PDW task with a combination of log-linear (LLM) modeling and neural network simulation to reveal the computational patterns that establish this association. We applied the post-decision wagering measure to a backward masking experiment in which participants made first-order decisions about whether or not a masked emotional face was present, and then used imaginary or real monetary stakes to judge the correctness of their initial decisions. The LLM analysis was then used to examine whether advantageous wagering was aware by testing a hypothesis of partial associations between metacognitive judgments and accuracy of first-order decisions. The LLM outcomes were submitted into a feed-forward neural network. The network served as a general approximator that was trained to learn relationships between input wagers and the output of the corresponding log-linear function. The simulation resulted in a simple network architecture that successfully accounted for wagering behavior. This was a feed-forward network unit consisting of one hidden neuron layer with four inputs and one output. In addition, the study indicated no effect of the monetary incentive cues on wagering strategies, although we observed that only low-wager input weights of the neural network considerably contributed to advantageous wagering. pl
dc.subject.en awareness pl
dc.subject.en post-decision wagering pl
dc.subject.en metacognition pl
dc.subject.en log-linear pl
dc.subject.en connectionist model pl
dc.description.volume 9 pl
dc.description.number 4 pl
dc.identifier.doi 10.1007/s12559-017-9456-6 pl
dc.identifier.eissn 1866-9964 pl
dc.title.journal Cognitive Computation pl
dc.language.container eng pl
dc.affiliation Wydział Filozoficzny : Instytut Psychologii pl
dc.subtype Article pl
dc.rights.original CC-BY; otwarte repozytorium; ostateczna wersja wydawcy; w momencie opublikowania; 0; pl
.pointsMNiSW [2017 A]: 20


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Udzielam licencji. Uznanie autorstwa 3.0 Polska Except where otherwise noted, this item's license is described as Udzielam licencji. Uznanie autorstwa 3.0 Polska