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The time course of brain signals reflect different cognitive processes during human decision making
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Michael D. Nunez, Kiana A. Scambray, Kitty K. Lui, Joachim Vandekerckhove, and Ramesh Srinivasan
Department of Cognitive Sciences, University of California, Irvine
Hypothesized theory of Neural Decision Making
A theory of decision making predicts distinct time periods that contribute to a response time
(RT): visual encoding time (VET; figure-ground segregation), visual evidence accumulation
(VEA), motor evidence accumulation (MEA), and motor execution time (MET). It is our
goal to accurately measure these time periods within participants using EEG and human be-
havior. Decision time (DT; evidence accumulation) and Non-Decision Time (NDT) can be
estimated directly from accuracy and RT. EEG could further differentiate these time periods.
New evidence that N200 ERP latencies track VET
Visual ERPs to the target Gabor were reduced to single time series per EEG session and con-
dition by SVD (PCA) from 128 electrodes. Latencies of early negative peaks (N200s with
weight in occipital electrodes) were obtained from conditions in which participants were
thought to be cognitively engaged in the difficult task (accuracy > 60 %; N=201). Some new
evidence exists for a slope-of-one relationship between N200 latencies and mean re-
sponse times. Mean RTs are likely to better reflect NDT in this cognitive task than ear ly
RT percentiles due to the time pressure cutoffs.
Spatial Frequency Differentiation Task (time pressured 2AFC)
On every trial, participants (N= 32 with 2 EEG sessions each) matched a randomly-rotated
Gabor to previously-studied high or low spatial frequencies. Participants responded during
the response interval by pressing one of two buttons with their right or left hands (3 blocks
each). Feedback was given based on 3 different time pressure cutoffs (2 blocks each; 6
unique blocks during an EEG session). Distracting visual noise onset before the Gabor.
Testing via Hierarchical Bayesian (HB) Parameter Posteriors
(RT,Acc)ijk ~ (1 - θjk)*DDM(δjk, αjk, τjk) + ( θjk )*Uniform( -max(RT)jk , max(RT)jk )
Drift-diffusion model with lapse trials for trial i per EEG session j and condition k
δjk ~ Normal(η(δ)k + γ(δ)k * (ERP_time) , σ2(δ)) Evidence accumulation rate prior
αjk ~ Normal(η(α)k + γ(α)k * (ERP_time) , σ2(α)) Evidence boundary prior
τjk ~ Normal(η(τ)k+γ(τ)k * (ERP_time) , σ2(τ))
Non-decision time prior
θjk ~ Normal(η(θ)k+γ(θ)k * (ERP_time) , σ2(θ))
Lapse-probability prior
γ(*)k ~ Normal( µ(*) , σ2(γ) ) Effect of ERP
µ(*) ~ Normal( 1, 32 ) Overall effect prior
Previous evidence for this theory
Drift-diffusion model theory (Ratcliff & McKoon, 2008) and neural evidence accumulation
theory (Shadlen & Kiani, 2013)
P300 ERP latencies (~ 300 to 1000 ms) reflect visual evidence accumulation (O’Connell et
al., 2012; Philiastides et al., 2014)
Readiness potentials (RP) reflect decision-time related to motor evidence accumulation
(tinyurl.com/RP-DecisionTime)
N200 ERP latencies (~ 150 to 275 ms) reflect visual encoding time (tinyurl.com/N200-
Evidence)
Ability to measure chronometric differences
Simple regression analysis yielded further evidence that visual event-related N200
latencies reflect visual encoding times. Some initial evidence was found that
event-related P300 latencies reflect visual evidence accumulation.
Preregistration: https://osf.io/78dgw/ Support: NSF Grant #1658303 Correspondence: mdnunez1@uci.edu
References: Ratcliff, R. & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. ; Shadlen, M. N. &
Kiani, R. (2013). Decision making as a window on cognition ; O’Connell, R. G., Dockree, P. M., Kelly, S. P. (2012). A supramodal accumulation-to-
bound signal that determines perceptual decisions in humans. ; Philiastides M. G., Heekeren, H. R., Sajda, P. (2014) Human scalp potentials reflect a
mixture of decision-related signals during perceptual choices.
hnl.ss.uci.edu www.cidlab.com
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No evidence of hypothesized condition effects on brain signals
Better estimates of P300, RP, and beta desynchronization may need to be found in
order to provide evidence for or against neural reflection of evidence accumulation.