Computational meaningfulness as the source of beneficial cognitive biases.

The human brain has evolved to solve the problems it encounters in multiple environments. In solving these challenges, it forms mental simulations about multidimensional information about the world. These processes produce context-dependent behaviors. The brain as overparameterized modeling organ is an evolutionary solution for producing behavior in a complex world. One of the most essential characteristics of living creatures is that they compute the values of information they receive from external and internal contexts. As a result of this computation, the creature can behave in optimal ways in each environment. Whereas most other living creatures compute almost exclusively biological values (e.g., how to get food), the human as a cultural creature computes meaningfulness from the perspective of one's activity. The computational meaningfulness means the process of the human brain, with the help of which an individual tries to make the respective situation comprehensible to herself to know how to behave optimally. This paper challenges the bias-centric approach of behavioral economics by exploring different possibilities opened up by computational meaningfulness with insight into wider perspectives. We concentrate on confirmation bias and framing effect as behavioral economics examples of cognitive biases. We conclude that from the computational meaningfulness perspective of the brain, the use of these biases are indispensable property of an optimally designed computational system of what the human brain is like. From this perspective, cognitive biases can be rational under some conditions. Whereas the bias-centric approach relies on small-scale interpretable models which include only a few explanatory variables, the computational meaningfulness perspective emphasizes the behavioral models, which allow multiple variables in these models. People are used to working in multidimensional and varying environments. The human brain is at its best in such an environment and scientific study should increasingly take place in such situations simulating the real environment. By using naturalistic stimuli (e.g., videos and VR) we can create more realistic, life-like contexts for research purposes and analyze resulting data using machine learning algorithms. In this manner, we can better explain, understand and predict human behavior and choice in different contexts.

Human's Intuitive Mental Models as a Source of Realistic Artificial Intelligence and Engineering.

Despite the success of artificial intelligence (AI), we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models' perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, we follow Bayesian inference to combine intuitive models and new information to make decisions. We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that the probability calculation in Bayesian sense is sensitive to semantic properties of the objects' combination formed by observation and prior experience. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. First, intuitive models support an individual to use information meaningful ways in a current context. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision making. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. Finally, a progress in data analysis techniques in AI has allowed us to decipher how the human brain valuates different options in complex situations. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. We describe these solutions by reviewing the current research from this perspective. In this study, we outline the basic aspects for human-like AI and we discuss on how science can benefit from AI. The better we understand human's brain mechanisms, the better we can apply this understanding for building new AI. Both development of AI and understanding of human behavior go hand in hand.

The Consumer Contextual Decision-Making Model.

Consumers can have difficulty expressing their buying intentions on an explicit level. The most common explanation for this intention-action gap is that consumers have many cognitive biases that interfere with rational decision-making. The current resource-rational approach to understanding human cognition, however, suggests that brain environment interactions lead consumers to minimize the expenditure of cognitive energy according to the principle of Occam's Razor. This means that the consumer seeks as simple of a solution as possible for a problem requiring decision-making. In addition, this resource-rational approach to decision-making emphasizes the role of inductive inference and Bayesian reasoning. Together, the principle of Occam's Razor, inductive inference, and Bayesian reasoning illuminate the dynamic human-environment relationship. This paper analyzes these concepts from a contextual perspective and introduces the Consumer Contextual Decision-Making Model (CCDMM). Based on the CCDMM, two hypothetical strategies of consumer decision-making will be presented. First, the SIMilarity-Strategy (SIMS) is one in which most of a consumer's decisions in a real-life context are based on prior beliefs about the role of a commodities specific to real-life situation being encountered. Because beliefs are based on previous experiences, consumers are already aware of the most likely consequences of their actions. At the same time, they do not waste time on developing contingencies for what, based on previous experience, is unlikely to happen. Second, the What-is-Out-there-in-the-World-Strategy (WOWS) is one in which prior beliefs do not work in a real-life situation, requiring consumers to update their beliefs. The principle argument being made is that most experimental consumer research describes decision-making based on the WOWS, when participants cannot apply their previous knowledge and situation-based strategy to problems. The article analyzes sensory and cognitive biases described by behavioral economists from a CCDMM perspective, followed by a description and explanation of the typical intention-action gap based on the model. Prior to a section dedicated to discussion, the neuroeconomic approach will be described along with the valuation network of the brain, which has evolved to solve problems that the human has previously encountered in an information-rich environment. The principles of brain function will also be compared to CCDMM. Finally, different approaches and the future direction of consumer research from a contextual point of view will be presented.

Predictive modeling for trustworthiness and other subjective text properties in online nutrition and health communication.

While the internet has democratized and accelerated content creation and sharing, it has also made people more vulnerable to manipulation and misinformation. Also, the received information can be distorted by psychological biases. This is problematic especially in health-related communications which can greatly affect the quality of life of individuals. We assembled and analyzed 364 texts related to nutrition and health from Finnish online sources, such as news, columns and blogs, and asked non-experts to subjectively evaluate the texts. Texts were rated for their trustworthiness, sentiment, logic, information, clarity, and neutrality properties. We then estimated individual biases and consensus ratings that were used in training regression models. Firstly, we found that trustworthiness was significantly correlated to the information, neutrality and logic of the texts. Secondly, individual ratings for information and logic were significantly biased by the age and diet of the raters. Our best regression models explained up to 70% of the total variance of consensus ratings based on the low-level properties of texts, such as semantic embeddings, presence of key-terms and part-of-speech tags, references, quotes and paragraphs. With a novel combination of crowdsourcing, behavioral analysis, natural language processing and predictive modeling, our study contributes to the automated identification of reliable and high-quality online information. While critical evaluation of truthfulness cannot be surrendered to the machine only, our findings provide new insights into automated evaluation of subjective text properties and analysis of morphologically-rich languages in regards to trustworthiness.

Default Mode and Executive Networks Areas: Association with the Serial Order in Divergent Thinking.

Scientific findings have suggested a two-fold structure of the cognitive process. By using the heuristic thinking mode, people automatically process information that tends to be invariant across days, whereas by using the explicit thinking mode people explicitly process information that tends to be variant compared to typical previously learned information patterns. Previous studies on creativity found an association between creativity and the brain regions in the prefrontal cortex, the anterior cingulate cortex, the default mode network and the executive network. However, which neural networks contribute to the explicit mode of thinking during idea generation remains an open question. We employed an fMRI paradigm to examine which brain regions were activated when participants (n = 16) mentally generated alternative uses for everyday objects. Most previous creativity studies required participants to verbalize responses during idea generation, whereas in this study participants produced mental alternatives without verbalizing. This study found activation in the left anterior insula when contrasting idea generation and object identification. This finding suggests that the insula (part of the brain's salience network) plays a role in facilitating both the central executive and default mode networks to activate idea generation. We also investigated closely the effect of the serial order of idea being generated on brain responses: The amplitude of fMRI responses correlated positively with the serial order of idea being generated in the anterior cingulate cortex, which is part of the central executive network. Positive correlation with the serial order was also observed in the regions typically assigned to the default mode network: the precuneus/cuneus, inferior parietal lobule and posterior cingulate cortex. These networks support the explicit mode of thinking and help the individual to convert conventional mental models to new ones. The serial order correlated negatively with the BOLD responses in the posterior presupplementary motor area, left premotor cortex, right cerebellum and left inferior frontal gyrus. This finding might imply that idea generation without a verbal processing demand reflecting lack of need for new object identification in idea generation events. The results of the study are consistent with recent creativity studies, which emphasize that the creativity process involves working memory capacity to spontaneously shift between different kinds of thinking modes according to the context.