Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Daniel J. Navarro"'
Publikováno v:
Judgment and Decision Making, Vol 8, Pp 498-511 (2013)
The illusion of control refers to the inference of action-outcome contingency in situations where outcomes are in fact random. The strength of this illusion has been found to be affected by whether the frequency of successes increases or decreases ov
Externí odkaz:
https://doaj.org/article/39090292eb524f9a860e6891e3c35dc2
Publikováno v:
Journal of doctoral nursing practice. 12(1)
BackgroundOnce a person is diagnosed with diabetes, aggressive management is imperative to minimize poor glycemic control devastating outcomes. However, for some patients reaching optimum blood glucose levels is challenging due to the complexity of d
Publikováno v:
Psychological Review. 124:410-441
Recent debates in the psychological literature have raised questions about the assumptions that underpin Bayesian models of cognition and what inferences they license about human cognition. In this paper we revisit this topic, arguing that there are
Publikováno v:
Behavior Research Methods. 49:2219-2234
As Bayesian methods become more popular among behavioral scientists, they will inevitably be applied in situations that violate the assumptions underpinning typical models used to guide statistical inference. With this in mind, it is important to kno
The study of semi-supervised category learning has generally focused on how additional unlabeled information with given labeled information might benefit category learning. The literature is also somewhat contradictory, sometimes appearing to show a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba1ebf7725b15fd9566a5b99c52e8443
https://psyarxiv.com/6ygzj
https://psyarxiv.com/6ygzj
Publikováno v:
Cognitive Psychology
How do people solve the explore–exploit trade-off in a changing environment? In this paper we present experimental evidence from an “observe or bet” task, in which people have to determine when to engage in information-seeking behavior and when
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dad836a481bbc4025a229e4724064104
https://doi.org/10.31234/osf.io/dv8qg
https://doi.org/10.31234/osf.io/dv8qg
Autor:
Daniel J. Navarro, Michael D. Lee
Publikováno v:
NIPS
This paper develops a new representational model of similarity data that combines continuous dimensions with discrete features. An algorithm capable of learning these representations is described, and a Bayesian model selection approach for choosing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a881cf487e943eb5c5eb010b741013e
https://doi.org/10.31234/osf.io/qejyb
https://doi.org/10.31234/osf.io/qejyb
Autor:
Daniel J. Navarro, Amy Perfors
We consider the situation in which a learner must induce the rule that explains an observed set of data but the hypothesis space of possible rules is not explicitly enumerated or identified. The first part of the article demonstrates that as long as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8077ee3d751185f008b70f4f9b876f0
https://psyarxiv.com/rj9kt
https://psyarxiv.com/rj9kt
© 2013 Taylor & Francis In contrast to noun categories, little is known about the graded structure of adjective categories. In this study, we investigated whether adjective categories show a similar graded structure and what determines this structur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a5fa740a2a0a5c22cc29a4e05a1db1c4
https://psyarxiv.com/9bfrd
https://psyarxiv.com/9bfrd
A robust finding in category-based induction tasks is for positive observations to raise the willingness to generalize to other categories while negative observations lower the willingness to generalize. This pattern is referred to as monotonic gener
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2c96289d0731cf8600e8c80346186db
https://doi.org/10.31234/osf.io/urgxh
https://doi.org/10.31234/osf.io/urgxh