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 (i.e., approximations). For example, the peak-end rule hypothesis suggests that we average the peaks and end of a sequential event vs. integrating every moment. However, there is no general model to test viable hypotheses quantitatively. Here, 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 vs. the peak-end heuristic. Correlation tests indicated that averaging prevailed, with peak and end both still having significant prediction power. Given this, we developed generalized order-dependent and relative-preference-dependent models to subsume averaging, peak and end. The combined model improved the prediction power. However, based on limitations of relative-preference-including imposing a potentially arbitrary ranking among preferences-we introduced an absolute-preference-dependent model, which successfully explained the remembered utilities. Yet, because using all experiences in a sequence requires too much memory as real-world settings scale, we then tested "windowed" models, i.e., evaluation within a specified window. The windowed (absolute) 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 (absolute) 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 a combined WP-PD model as a means by which people evaluate experiences, suggesting preference-dependent working-memory as a significant factor underlying our evaluations.
ABSTRACT
Our decisions have a temporally distributed order, and different choice orders (e.g., choosing preferred items first or last) can lead to vastly different experiences. We previously found two dominant strategies (favorite-first and favorite-last) in a preference-based serial choice setting (the 'sushi problem'). However, it remains unclear why these two opposite behavioral patterns arise: i.e., the mechanisms underlying them. Here we developed a novel serial-choice task, using pictures based on attractiveness, to test for a possible shared mechanism with delay discounting, the 'peak-end' bias (i.e., preference for experienced sequences that end well), or working-memory capacity. We also collected psychological and clinical metric data on personality, depression, anxiety, and emotion regulation. We again found the two dominant selection strategies. However, the results of the delay, peak-end bias, and memory capacity tasks were not related to serial choice, while two key psychological metrics were: emotion regulation and conscientiousness (with agreeableness also marginally related). Favorite-first strategists actually regulated emotions better, suggesting better tolerance of negative outcomes. Whereas participants with more varied strategies across trials were more conscientious (and perhaps agreeable), suggesting that they were less willing to settle for a single, simpler strategy. Our findings clarify mechanisms underlying serial choice and show that it may reflect a unique ability to organize choices into sequences of events.
Subject(s)
Delay Discounting/physiology , Emotional Regulation/physiology , Intuition/physiology , Adult , Healthy Volunteers , Humans , Male , Young AdultABSTRACT
RATIONALE AND OBJECTIVES: The aim of this study was to assess the feasibility of thermally cross-linked superparamagnetic iron oxide nanoparticle contrast (TCL-SPION) in magnetic resonance (MR) imaging (MRI) for the detection of lymph node metastasis in experimental model. MATERIALS AND METHODS: B16F1 human melanoma cells were subcutaneously injected into the thighs of C57BL/6 mice (n = 10). MRI was performed 21 days after tumor injection using a 4.7-T MR scanner. In vivo MRI was performed before and after the intravenous administration of TCL-SPION using T2 fast spin-echo and T2 gradient-echo pulse sequences. Then, ex vivo MR images were obtained for resected inguinal lymph nodes (n = 18) using the same pulse sequences as for in vivo imaging. On the basis of hematoxylin and eosin staining results, the lymph nodes were classified into three groups: group 1, nonmetastatic; group 2, tumor volume <50% of the resected sample; and group 3, tumor volume >50% of the resected sample. Size, signal-to-background ratio, and enhancement pattern were evaluated in each of the three groups on ex vivo images. RESULTS: The findings observed on ex vivo MR images of 18 inguinal lymph nodes were compared with histopathologic findings. All nodes were classified into three groups: group 1, n = 6; group 2, n = 5; and group 3, n = 7. The sizes of the lymph nodes in group 1 were significantly different from the sizes of those in group 3 (P = .014), but there was no significant difference in lymph node sizes between groups 1 and 2 (P = .792). Signal-to-background ratios of samples in groups 2 and 3 were significantly higher than those of samples in group 1 (P = .045 and P = .007, respectively). Each group of lymph nodes showed characteristic enhancement patterns that were well correlated between the images and pathology, except for one node. CONCLUSIONS: The features and extent of metastasis in the lymph nodes corresponded to those observed on TCL-SPION-enhanced MR images. TCL-SPION-enhanced MRI is useful for the detection and estimation of lymph node metastasis.
