
Marius usher
I am Professor of Psychology at the Tel-Aviv University (School of Psychology and Sagol School of Neuroscience) and a researcher in the field of cognitive neuroscience. My research interests are: decision-making and numerical processing (the algorithm the mind/brain uses to generate preferences and make decisions
less
Related Authors
Cees van Leeuwen
KU Leuven
Javad Salehi Fadardi
Ferdowsi University of Mashhad
Marta Jankowska
University of Warsaw
Andrey Nikolaev
KU Leuven
Uploads
Papers by Marius usher
accounting for vast amounts of behavioural and neural data. However, this mechanism has been challenged by non-integration
heuristics, and tracking decision boundaries has proven elusive. Here we first show that the decision boundary can be monitored
using a model-free behavioural method termed decision classification boundary, which extracts decision boundaries by
optimizing choice classification based on the accumulated evidence. Using this method, we provide direct support for evidence
integration over non-integration heuristics, show that the decision boundaries collapse across time and identify an integration
bias whereby incoming evidence is modulated based on its consistency with preceding information. This consistency bias,
which is a form of pre-decision confirmation bias, was supported in four cross-domain experiments, showing that choice accuracy
and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, the consistency
bias fosters performance by enhancing robustness to integration noise.
we develop and test a novel method to quantify global/local processing tendencies, in which we directly set in opposition the local/global information instead of instructing subjects to specifcally attend to one processing level. We apply our novel method to two different domains: (1) a numerical cognition task, and (2) a preference task. Using computational modeling, we account for classical effects in choice and numerical-cognition. Global/local tendencies in both tasks were quantified
using a salience parameter. Critically, the salience parameters extracted from the numerical cognition and preference tasks were highly correlated, providing support for robust perceptual organization tendencies within an individual.
accounting for vast amounts of behavioural and neural data. However, this mechanism has been challenged by non-integration
heuristics, and tracking decision boundaries has proven elusive. Here we first show that the decision boundary can be monitored
using a model-free behavioural method termed decision classification boundary, which extracts decision boundaries by
optimizing choice classification based on the accumulated evidence. Using this method, we provide direct support for evidence
integration over non-integration heuristics, show that the decision boundaries collapse across time and identify an integration
bias whereby incoming evidence is modulated based on its consistency with preceding information. This consistency bias,
which is a form of pre-decision confirmation bias, was supported in four cross-domain experiments, showing that choice accuracy
and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, the consistency
bias fosters performance by enhancing robustness to integration noise.
we develop and test a novel method to quantify global/local processing tendencies, in which we directly set in opposition the local/global information instead of instructing subjects to specifcally attend to one processing level. We apply our novel method to two different domains: (1) a numerical cognition task, and (2) a preference task. Using computational modeling, we account for classical effects in choice and numerical-cognition. Global/local tendencies in both tasks were quantified
using a salience parameter. Critically, the salience parameters extracted from the numerical cognition and preference tasks were highly correlated, providing support for robust perceptual organization tendencies within an individual.