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. 2013 Jul;51(8):1397-407.
doi: 10.1016/j.neuropsychologia.2013.04.014. Epub 2013 May 2.

An electrophysiological index of changes in risk decision-making strategies

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An electrophysiological index of changes in risk decision-making strategies

Dandan Zhang et al. Neuropsychologia. 2013 Jul.

Abstract

Human decision-making is significantly modulated by previously experienced outcomes. Using event-related potentials (ERPs), we examined whether ERP components evoked by outcome feedbacks could serve as biomarkers to signal the influence of current outcome evaluation on subsequent decision-making. In this study, 18 adult volunteers participated in a simple monetary gambling task, in which they were asked to choose between two options that differed in risk. Their decisions were immediately followed by outcome presentation. Temporospatial principle component analysis (PCA) was applied to the outcome-onset locked ERPs in the 200-1000 ms time window. The PCA factors that approximated classical ERP components (P2, feedback-related negativity, P3a, and P3b) in terms of time course and scalp distribution were tested for their association with subsequent decision-making strategies. Our results revealed that a fronto-central PCA factor approximating the classical P3a was related to changes of decision-making strategies on subsequent trials. The decision to switch between high- and low-risk options resulted in a larger P3a relative to the decision to retain the same choice. According to the results, we suggest that the amplitude of the fronto-central P3a is an electrophysiological index of the influence of current outcome on subsequent risk decision-making. Furthermore, the ERP source analysis indicated that the activations of the frontopolar cortex and sensorimotor cortex were involved in subsequent changes of strategies, which enriches our understanding of the neural mechanisms of adjusting decision-making strategies based on previous experience.

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Conflict of interest statement

Conflict of Interest

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1. Schematic diagram of two adjacent experimental trials in the monetary gambling task
The three major factors (outcome valence, outcome magnitude, and subsequent strategy) in data analysis are indexed in red. In this example, the participant has chosen “99” (high-risk option) in the current trial, and the outcome valence is “−” (negative), so the outcome in the current trial is “-99” (lose 99 points). The participant’s choice in the subsequent trial is “99”, which means he/she decided to stay in the high-risk option rather than switch to the low-risk option. RT = response time.
Figure 2
Figure 2. The illustration of the main effects of the three major factors on the conventionally-averaged ERPs evoked by outcome presentation (from left to right: outcome magnitude, outcome valence, subsequent strategy)
The scalp topographies of every condition are available on the right side of the corresponding ERP waveforms. For the FRN component, the scalp topography of the “loss-win” difference wave is also provided, so as to demonstrate that the spatial distribution of the FRN is similar to reports from classical studies (Holroyd & Krigolson, 2007). The waveforms of FRN and the P3 were derived from Cz and FCz, respectively (as indexed by the white triangles in the topographies). The light grey areas indicate the time window for the analyses on the FRN and the P3. Asterisks indicate significant effects (* p < 0.05).
Figure 3
Figure 3. The illustration of the main effects of the three major factors on grand-mean PCA yielding factors, as well as the corresponding scalp topographies
The waveforms of the PCA components were derived from FCz, except the PCA-P3b, which was derived from CPz (as indexed by the white triangles). Asterisks indicate significant effects (* p < 0.05).
Figure 4
Figure 4. Temporal summarization of the five PCA factors (upper panels) and the conventionally-averaged ERPs (the lowest panel; divided by the purple line)
* p < 0.05 at FCz; # p < 0.05 at CPz.
Figure 5
Figure 5. Spatial summarization of the PCA factors (upper panels) and the conventionally-averaged ERPs (the lowest panel; divided by the purple line) in response to the effect of subsequent strategy
The first three PCA factors, which accounted for more than 50% variances in the ERP data, are summarized. Asterisks indicate significant main effects (*p < 0.05).
Figure 6
Figure 6. sLORETA images of the standardized current density maximum in response to the main effect of subsequent strategy
The results at the peak latency of PCA-P3a (372 ms) are illustrated. The color scale is equal in all the maps, of which the strongest activations are indexed in yellow. A = anterior; P = posterior; R = right; L = left; LH = left hemisphere; RH = right hemisphere; LV = left view; RV = right view; TV = top view; BV = bottom view.
Fig. 7
Fig. 7. The influence of subsequent strategy on the PCA-P3b, PCA-FN, and PCA-P3a for each participant
The difference waves between “switch” and “stay” conditions (switch waveform - stay waveform) are illustrated. Results from each of the 18 participants are displayed in distinct colors.

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