Table 2. Factors influencing RT.

We used two sets of GLMs to predict the normalized RT as a function of the absolute difference in the actual (A) or estimated (B) probability of reward on the two objects presented on a given trial (absolute difference in actual/subjective reward probability), the trial number within a block of the experiment, the difference between BIC per trial (BIC_p) based on the best feature-based and object-based models (i.e., model-adoption index) for a given subject, and the reward outcome on the preceding trial. Reported values are the normalized regression coefficients (±s.e.m.), p-values for each coefficient (two-sided t-test), and adjusted R-squared for each experiment. No interaction term was statistically significant and thus, interactions terms are not reported here.

A
Regressor	Abs. difference in actual reward prob.	Trial number	BICp (Ft)–BICp (Obj)	Reward outcome on prev. trial	R2
Exp. 1	-0.05±0.005 (p = 10−16)	-0.15±0.006 (p = 10−16)	-0.02±0.005 (p = 4.7*10−6)	-0.03±0.006 (p = 1.9*10−6)	0.027
Exp. 2	-0.07±0.008 (p = 10−16)	-0.11±0.008 (p = 10−16)	-0.02±0.008 (p = 0.003)	-0.04±0.008(p = 10−16)	0.020
Exp. 3	-0.06±-0.008 (p = 10−12)	-0.16±0.008 (p = 10−16)	-0.03±0.008 (p = 0.004)	-0.05±0.008 (p = 1.9*10−7)	0.031
Exp. 4	-0.04±-0.007 (p = 10−16)	-0.23±0.007 (p = 10−16)	-0.01±0.007 (p = 0.01)	-0.03±0.007 (p = 7.8*10−4)	0.057
B
Regressor	Abs. difference in subjective reward prob.	Trial number	BICp (Ft)–BICp (Obj)	Reward outcome on prev. trial	R2
Exp. 1	-0.13±0.006 (p = 10−16)	-0.16±0.005 (p = 10−16)	-0.02±0.005 (p = 4.4*10−4)	-0.05±0.006 (p = 10−16)	0.041
Exp. 2	-0.13±0.008 (p = 10−16)	-0.12±0.008 (p = 10−16)	-0.03±0.008 (p = 4.6*10−5)	-0.07±0.008 (p = 10−16)	0.031
Exp. 3	-0.21±-0.008 (p = 10−16)	-0.14±0.008 (p = 10−16)	-0.06±0.008 (p = 0.005)	-0.04±0.008 (p = 1.4*10−9)	0.073
Exp. 4	-0.18±-0.007 (p = 10−16)	-0.21±0.007 (p = 10−16)	-0.01±0.007 (p = 0.03)	-0.03±0.007 (p = 1.6*10−5)	0.089