Stefanopoulos Leandros, Kim Byoung-Woo, Sheppard John, Azcona Emanuel A, Vike Nicole L, Bari Sumra, Lalvani Shamal, Woodward Sean, Maglaveras Nicos, Block Martin, Katsaggelos Aggelos K, Breiter Hans C
Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA.
School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Cogn Process. 2025 May;26(2):257-281. doi: 10.1007/s10339-024-01250-9. Epub 2024 Dec 7.
Operant keypress tasks in a reinforcement-reward framework where behavior is shaped by its consequence, show lawful relationships in human preference behavior (i.e., approach/avoidance) and have been analogized to "wanting". However, they take 20-40 min as opposed to short non-operant rating tasks, which can be as short as 3 min and unsupervised, thus more readily applied to internet research. It is unknown if non-operant rating tasks where each action does not have a consequence, analogous to "liking", show similar lawful relationships. We studied non-operant, picture-rating data from three independent population cohorts (N = 501, 506, and 4019 participants) using the same 7-point Likert scale for negative to positive preferences, and the same categories of images from the International Affective Picture System. Non-operant picture ratings were used to compute location, dispersion, and pattern (entropy) variables, that in turn produced similar value, limit, and trade-off functions to those reported for operant keypress tasks, all with individual R > 0.80. For all three datasets, the individual functions were discrete in mathematical formulation. They were also recurrent or consistent across the cohorts and scaled between individual and group curves. Behavioral features such as risk aversion and other interpretable features of the graphs were also consistent across cohorts. Together, these observations argue for lawfulness in the modeling of the ratings. This picture rating task demonstrates a simple, quick, and low-cost framework for quantitatively assessing human preference without forced choice decisions, games of chance, or operant keypressing. This framework can be easily deployed on any digital device worldwide.
在强化奖励框架下的操作性按键任务中，行为由其后果塑造，在人类偏好行为（即趋近/回避）中呈现出规律关系，并被类比为“想要”。然而，与短时间的非操作性评分任务相比，它们需要20 - 40分钟，而非操作性评分任务最短可达3分钟且无需监督，因此更易于应用于互联网研究。尚不清楚每个动作没有后果的非操作性评分任务（类似于“喜欢”）是否呈现类似的规律关系。我们研究了来自三个独立人群队列（分别有501、506和4019名参与者）的非操作性图片评分数据，使用相同的7点李克特量表来衡量从负面到正面的偏好，以及来自国际情感图片系统的相同类别的图像。非操作性图片评分用于计算位置、离散度和模式（熵）变量，这些变量进而产生了与操作性按键任务所报告的类似的值、极限和权衡函数，所有这些函数的个体相关系数R均> 0.80。对于所有三个数据集，个体函数在数学公式上是离散的。它们在各队列中也是反复出现或一致的，并且在个体曲线和群体曲线之间进行缩放。风险规避等行为特征以及图表的其他可解释特征在各队列中也保持一致。总之，这些观察结果支持了评分建模中的规律性。这种图片评分任务展示了一个简单、快速且低成本的框架，用于在没有强制选择决策、机会游戏或操作性按键的情况下定量评估人类偏好。这个框架可以轻松部署在全球任何数字设备上。
