Publications of P Dayan

Working Paper (14)

541.
Working Paper
Jain , Y.; Callaway, F.; Griffiths, T.; Dayan, P.; Krueger, P.; Lieder, F.: A Computational Process-Tracing Method for Measuring People’s Planning Strategies and How They Change Over Time. (submitted)
542.
Working Paper
Kastner, D.; Miller, E.; Yang, Z.; Roumis, D.; Frank, L.; Dayan, P.: Spatial preferences account for inter-animal variability during the continual learning of a dynamic cognitive task. (submitted)
543.
Working Paper
Castro-Rodrigues, P.; Akam, T.; Snorasson, I.; Camacho, M.; Paixão, V.; Barahona-Corrêa, B.; Dayan, P.; Simpson, H.; Costa, R.; Oliveira-Maia, A.: Explicit knowledge of task structure is the primary determinant of human model-based action. (submitted)
544.
Working Paper
Dezfouli, A.; Nock, R.; Arabzadeh, E.; Dayan, P.: Neural Network Poisson Models for Behavioural and Neural Spike Train Data. (submitted)
545.
Working Paper
Mancinelli, F.; Roiser, J.; Dayan, P.: Subjective Beliefs In, Out, and About Control: A Quantitative Analysis. (submitted)
546.
Working Paper
Kastner, D.; Miller, E.; Yang, Z.; Roumis, D.; Liu, D.; Frank, L.; Dayan, P.: Dynamic preferences account for inter-animal variability during the continual learning of a cognitive task. (submitted)
547.
Working Paper
Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dayan, P.; Dolan, R.: Hippocampal-midbrain circuit enhances the pleasure of anticipation in the prefrontal cortex. (submitted)
548.
Working Paper
Zhao, S.; Chait, M.; Dick, F.; Dayan, P.; Furukawa, S.; Liao, H.-I.: Phasic norepinephrine is a neural interrupt signal for unexpected events in rapidly unfolding sensory sequences: evidence from pupillometry. (submitted)
549.
Working Paper
Danihelka, I.; Lakshminarayanan, B.; Uria, B.; Wierstra, D.; Dayan, P.: Comparison of Maximum Likelihood and GAN-based training of Real NVPs. (submitted)
550.
Working Paper
Guez, A.; Silver, D.; Dayan, P.: Better Optimism By Bayes: Adaptive Planning with Rich Models. (submitted)
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