Learning-by-Doing in Non-Homogeneous Tasks: An Empirical Study of Content Creator Performance on A Music Streaming Platform

With the development of high-speed internet and better mobile connections, online streaming platforms with user-generated videos are becoming popular. The success of these platforms relies on content creators who can effectively enhance user engagement (e.g., subscribing to a content creator’s channel). As opposed to homogeneous production scenarios (e.g., assembling automobiles in a factory), creating user-generated videos is a more complex task in which learning might happen. In this study, we empirically test the effect of prior experience on content creators’ performance. Furthermore, we examine the role of specialization in learning. We use a dataset from NetEase Cloud Music, one of the most popular music streaming platforms in China, with 21,549 content creators and 252,762 user-generated videos. The findings indicate that: (1) prior experience has a positive effect on creators’ performance; (2) specialized experience across distinct video categories has a nonlinear effect on creators’ performance. These results have implications for improving user engagement for online user-generated video streaming platforms

The Gambler's Fallacy Is Associated with Weak Affective Decision Making but Strong Cognitive Ability

Humans demonstrate an inherent bias towards making maladaptive decisions, as shown by a phenomenon known as the gambler’s fallacy (GF). The GF has been traditionally considered as a heuristic bias supported by the fast and automatic intuition system, which can be overcome by the reasoning system. The present study examined an intriguing hypothesis, based on emerging evidence from neuroscience research, that the GF might be attributed to a weak affective but strong cognitive decision making mechanism. With data from a large sample of college students, we found that individuals’ use of the GF strategy was positively correlated with their general intelligence and executive function, such as working memory and conflict resolution, but negatively correlated with their affective decision making capacities, as measured by the Iowa Gambling Task. Our result provides a novel insight into the mechanisms underlying the GF, which highlights the significant role of affective mechanisms in adaptive decision-making

COMT Val158Met polymorphism interacts with stressful life events and parental warmth to influence decision making

Both genetic and environmental factors have been shown to influence decision making, but their relative contributions and interactions are not well understood. The present study aimed to reveal possible gene-environment interactions on decision making in a large healthy sample. Specifically, we examined how the frequently studied COMT Val(158)Met polymorphism interacted with an environmental risk factor (i.e., stressful life events) and a protective factor (i.e., parental warmth) to influence affective decision making as measured by the Iowa Gambling Task. We found that stressful life events acted as a risk factor for poor IGT performance (i.e., high reward sensitivity) among Met carriers, whereas parental warmth acted as a protective factor for good IGT performance (i.e., higher IGT score) among Val/Val homozygotes. These results shed some new light on gene-environment interactions in decision making, which could potentially help us understand the underlying etiology of several psychiatric disorders associated with decision making impairment

Component matrix for behavior measures.

<p>Extraction method: Principle component analysis. Rotation method: Varimax with Kaiser Normalization.</p

A proposed three-component model for model-based decision making, including an abstract world-model, the LPFC cognitive system and the MPFC affective system.

<p>Depending on the situations and the subjective world model, the LPFC and the MPFC could both play constructive and/or destructive roles.</p

Descriptions of major measures.

a<p>Cong: Congruent; Incong: Incongruent.</p>b<p>RA: Risk Advantageous; RD: Risk Disadvantageous.</p

Behavioral performance in the gambler’s fallacy task.

<p>A. Percentage of trials using the gambler’s fallacy strategy (i.e., deviating from computer’s last choice) as a function of streak length. Error bars (which is very small and invisible except streak 6) represent standard errors. B. Histogram of individual differences in the use of the gambler’s fallacy strategy under long streak (> = 4).</p

Correlations between different measures.

<p>Note: p<.05;</p>**<p>p<.01.</p