A computational examination of retrospective evaluation of sequential events and the influence of preference-dependent working memory for peak-end rule using behavioral and MEG methods

Abstract

Humans organize sequences of events into a single overall experience, and evaluate the aggregated experience as a whole, such as a generally pleasant dinner, movie, or trip. However, such evaluations are potentially computationally taxing, and so our brains must employ heuristics. For example, the peak-end rule hypothesis suggests that we average the peaks and end of a sequential event versus integrating every moment. However, there is no general model to test viable hypotheses quantitatively, and there is no neuroimaging evidence related to sequential experience evaluation. In chapter 1, we propose a general model and test among multiple specific ones, while also examining the role of working memory. The models were tested with a novel picture-rating task. We first compared averaging across entire sequences versus the peak-end heuristic. Correlation tests indicated that averaging prevailed, with peak and end both still having significant prediction power. Given this, we developed “windowed” models, i.e., evaluation within a specified window. The windowed preference-dependent (WP) model explained the empirical data with long sequences better than without windowing. However, because fixed-windowed models harbor their own limitations—including an inability to capture peak-event influences beyond a fixed window—we then developed discounting models. With preference-dependence added to the discounting rate, the results showed that the discounting model reflected the actual working memory of the participants, and that the preference-dependent discounting (PD) model described different features from the WP model. Taken together, we propose the combined WP-PD model as a means by which people evaluate experiences, suggesting preference-dependent working-memory as a significant factor underlying our evaluations. In chapter 2, we conducted MEG experiments, measuring the brain activations during our sequence-rating and working-memory task. We found parietal activation correlated to single event evaluation, left/right parietal and right frontotemporal activations correlated to working memory ability, and parietal, central and left frontal activations correlated to the sequential event evaluation. We also developed a principal component regression model to predict the sequence ratings. The results show that the model coefficients depend on the working memory ability. The results show that there are significant brain signals during the sequence rating task, and the working memory ability is a factor of the sequential experience evaluation.