Not my future? Core values and the neural representation of future events.
نویسندگان
چکیده
Individuals with pronounced self-transcendence values have been shown to put greater weight on the long-term consequences of their actions when making decisions. Using functional magnetic resonance imaging, we investigated the neural mechanisms underlying the evaluation of events occurring several decades in the future as well as the role of core values in these processes. Thirty-six participants viewed a series of events, consisting of potential consequences of climate change, which could occur in the near future (around 2030), and thus would be experienced by the participants themselves, or in the far future (around 2080). We observed increased activation in anterior VMPFC (BA11), a region involved in encoding the personal significance of future events, when participants were envisioning far future events, demonstrating for the first time that the role of the VMPFC in future projection extends to the time scale of decades. Importantly, this activation increase was observed only in participants with pronounced self-transcendence values measured by self-report questionnaire, as shown by a statistically significant interaction of temporal distance and value structure. These findings suggest that future projection mechanisms are modulated by self-transcendence values to allow for a more extensive simulation of far future events. Consistent with this, these participants reported similar concern ratings for near and far future events, whereas participants with pronounced self-enhancement values were more concerned about near future events. Our findings provide a neural substrate for the tendency of individuals with pronounced self-transcendence values to consider the long-term consequences of their actions.
منابع مشابه
Availability Prediction of the Repairable Equipment using Artificial Neural Network and Time Series Models
In this paper, one of the most important criterion in public services quality named availability is evaluated by using artificial neural network (ANN). In addition, the availability values are predicted for future periods by using exponential weighted moving average (EWMA) scheme and some time series models (TSM) including autoregressive (AR), moving average (MA) and autoregressive moving avera...
متن کاملStatistical Prediction of Probable Seismic Hazard Zonation of Iran Using Self-organized Artificial Intelligence Model
The Iranian plateau has been known as one of the most seismically active regions of the world, and it frequently suffers destructive and catastrophic earthquakes that cause heavy loss of human life and widespread damage. Earthquakes are regularly felt on all sides of the region. Prediction of the occurrence location of the future earthquakes along with determining the probability percentage can...
متن کاملNamed Entity Recognition in Persian Text using Deep Learning
Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefi...
متن کاملThe Morphology of Qom; The Study on Spatial Configuration Changes of The City (1956-2021)
The present study attempts to investigate the relationship between spatial configuration changes of Qom and the central core in the last half century. The main objective of this study is to review the Spatial form of Qom, and analyze the effect of changes in Urban configuration on the central core of the city and the main axes of urban activities during 1946 to 2011 and ultimately, Forecast the...
متن کاملNeuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion
In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Cognitive, affective & behavioral neuroscience
دوره شماره
صفحات -
تاریخ انتشار 2018