How do migratory birds, herding dogs, and navigating sea turtles do the amazing things that they do? For hundreds of years, scientists and philosophers have struggled over possible explanations. In time, one word came to dominate the discussion: instinct. It became the catch-all explanation for those adaptive and complex abilities that do not obviously result from learning or experience. Today, various animals are said to possess a survival instinct, migratory instinct, herding instinct, maternal instinct, or language instinct. But a closer look reveals that these and other 'instincts' are not satisfactorily described as inborn, pre-programmed, hardwired, or genetically determined. Rather, research in this area teaches us that species-typical behaviors develop-and they do so in every individual under the guidance of species-typical experiences occurring within reliable ecological contexts. WIREs Cogn Sci 2017, 8:e1371. doi: 10.1002/wcs.1371 For further resources related to this article, please visit the WIREs website.

This discussion paper carefully analyzes the cognition-related theories proposed for behavioral economics, to expand the concepts from human behaviors to those of plants. Behavioral economists analyze the roles of the intuitive sense and the rational thoughts affecting the human behavior, by employing the psychology-based models such as Two Minds theory (TMT) highlighting intuitive rapid thoughts (System 1) and rational slower thoughts (System 2) and Prospect theory (PT) with probability (<i>p</i>)-weighting functions explaining the human tendencies to overrate the low <i>p</i> events and to underrate the high <i>p</i> events. There are similarities between non-consciously processed System 1 (of TMT) and overweighing of low-<i>p</i> events (as in PT) and also, between the consciously processed System 2 (of TMT) and underrating of high-<i>p</i> events (as in PT). While most known <i>p</i>-weighting mathematical models employed single functions, we propose a pair of Hill-type functions reflecting the collective behaviors of two types of automata corresponding to intuition (System 1) and rationality (System 2), as a metaphor to the natural light processing in layered plant leaves. Then, the model was applied to two different TMT/PT-related behaviors, namely, preference reversal and habituation. Furthermore, we highlight the behaviors of plants through the above conceptual frameworks implying that plants behave as if they have Two Minds. Lastly, the possible evolutionary origins of the nature of Two Minds are discussed. There are similarities between non-consciously processed System 1 (of TMT) and overweighing of low- p events (as in PT) and also, between the consciously processed System 2 (of TMT) and underrating of high- p events (as in PT). While most known p -weighting mathematical models employed single functions, we propose a pair of Hill-type functions reflecting the collective behaviors of two types of automata corresponding to intuition (System 1) and rationality (System 2), as a metaphor to the natural light processing in layered plant leaves. Then, the model was applied to two different TMT/PT-related behaviors, namely, preference reversal and habituation. Kahneman (1934–2024; known for the 2022 Nobel Memorial Prize in Economic Sciences) and his colleagues have developed a key theory for human behaviors known as Two Minds theory (TMT) explaining the rapid and slow mental functions or thoughts 1 , 2 behind the cognitions 3 and morals. 4 In TMT, the rapid thought designated as System 1 is considered to be non-consciously processed and responsible for intuition and indignation, and the thought called System 2 represents the conscious, deliberate, and rational phase of mental functions. The concept of Two Minds was developed after the series of earlier works known as Prospect theory (PT) which were also documented by Kahneman’s team. 5 , 6 PT handles two distinct modes of cognitions, namely, the rapid and non-conscious cognitive mode responding to low-probability ( p ) events often overrating the values, and the slow but rational mode of cognition responding to high- p events often underrating the values. Due to the continuity of the series of studies by the same group, similarity could be found between System 1 (of TMT) and the cognition of low- p events (in PT), and between System 2 (of TMT) and the cognition of high- p events (in PT). (a) Based on similarity, system 1 (of TMT), basal four layers of cognitive processes in LRMB, and subitizing capability are aligned together (bottom) while system 2 (of TMT), top two layers of LRMB, and bounded rationality are aligned together (top). (b) Distinct roles for systems 1 and 2 in TMT, subconscious and conscious modes of intelligence in LRMB, and two distinct modes of probability sensing toward low and high input of p . In the above proposed LRMB framework, the non-conscious and the conscious processes are consisted of basal four layers (layers 1, 2, 3, and 4) and top two layers (layers 5 and 6), respectively. Kahneman known for PT 5 also proposed a dual system model called “Two Minds” or TMT focusing on two distinct modes of human cognitions for biases 3 and morals, 4 namely, System 1 typically responsible for intuition and indignation, and System 2 reflecting the rational mental functions. Therefore, human behaviors can be viewed as the integration of outputs by System 1 (non-conscious automatic processes) and System 2 (conscious deliberate processes). 1 , 2 System 1 reportedly activates a sequence of automatic actions while System 2 monitors System 1’s performance along with the existing plan and, at the same time, System 2 further activates future possible courses of actions. Unicellular eukaryotes such as Physarum polycephalum , an acellular slime mold popularly known as the blob, belonging to the Amoebozoa, which diverged from animals 89–92 In the Cellular Basis of Consciousness (CBC) theory, only the cells have the primary version of consciousness 82 , 85 , 86 whereas organisms based on multiple cells assemble the corporate version of consciousness and life. 93 Multicellular organisms do not have direct access to the primary cellular consciousness and perceive it only indirectly, as subconscious feelings-based intuitions of the System 1. Conclusion and perspectives This paper focused on the similarities among non-consciously processed rapid decisions through System 1, overweighing of p , and the subitizing toward p , followed by slow decision made by consciously processed System 2, underrating of p , and the rational analysis of p . While most of reported p -weighting models for PT employed single functions, this paper proposes a pair of Hill-type functions for weighting of p reflecting the collective behaviors of two types of automata corresponding to subitizing and rationality. The mathematical structure for the paired functions is inspired from the assorted photo-energy processing in the layered leaves of living plants.

Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers. The results show that brain region activity is more stable in the advanced phase of intuition training compared to the initial phase. Particularly in the alart state task, the JTF-MDBN demonstrated stronger connection strength. Multi-layer network analysis indicates that modularity is significantly higher for the non-alert state task than the alert state task in the alpha and beta bands. In the W4 time window (1 second before a collision), we identified significant features that can differentiate situations where a car collision is imminent from those where no collision occurs. Single-layer network analysis also revealed statistical differences in node strength and local efficiency for some EEG channels in the alpha and beta bands during the W4 and W5 time windows. Using these biomarkers to predict vehicle collision risk, the classification accuracy of a linear kernel SVM reached up to 87.5%, demonstrating the feasibility of predicting driving collisions through brain network biomarkers. These findings are important for the study of human intuition and the development of brain-computer interface-based intelligent driving hazard perception assistance systems. Intuition involves rapid judgment and processing of information, often occurring at a subconscious level. In psychology, intuitive decision-making is viewed as a swift cognitive process playing a key role in handling complex situations. Slovic and Västfjäll ( 2010 ) proposed that in hazardous situations, decisions are made through an automatic processing system reliant on emotions and experience, which is mostly irrational and faster than controlled processing systems. It is this rapid and unconscious decision-making process that plays a vital role in enhancing driving safety. Driving intuition is a typical manifestation of human brain intuition in real-world scenarios and is an important focus for studying the emergence and development of intuition in complex environments (Risen, 2017 ). Driving, a daily activity fraught with risks of injury, death, and associated costs, demands high levels of cognitive and sensory engagement from drivers (Abay and Mannering, 2016 ). Although many manage to maintain safety, the complexity and variability of the driving environment continually pose potential risks. Notably, intuition is a dynamic cognitive process involving rapid collaboration and reorganization among cerebral regions, and it can be enhanced through specific training (Fellnhofer et al., 2023 ). However, most related research focuses on brain activity at fixed time period lengths, neglecting the dynamics of cerebral region activities over time and the individual variability and learnability of intuitive capabilities, thereby failing to explore the temporal evolution of brain connectivity. To address these limitations, a novel approach has emerged: the multi-layer dynamic networks (Han et al., 2020 ; Chang et al., 2022 ). This stability may stem from the enhanced functioning of task-related brain regions and neuroplastic changes induced by repetitive task execution (Chu et al., 2012 ). These findings provide evidence for the plasticity of intuitive abilities, supporting the notion that intuition is not only innate but can also be enhanced through appropriate training (Hogarth, 2001 ; Fellnhofer et al., 2023 ). Given that the EEG data from the ITAP reflect a more mature and stable intuitive processing ability, our analysis is confined to this phase. ( 2007 ) noted that the prefrontal cortex is involved in the regulation of higher cognitive processes such as planning, decision making, and task switching, which are essential for maintaining high alertness in complex environments. In addition, Corbetta and Shulman ( 2002 )'s study emphasized the role of prefrontal vs. parietal networks in regulating attention and alertness, particularly in anticipating and responding to external stimuli. These studies echo our findings, suggesting that during the state of alertness, the PLI connectivity strength of brain networks increases, especially in prefrontal regions, possibly to enhance information processing and rapid response. Conversely, in the AS, due to the need for rapid response and stimulus processing, different brain regions may require closer cooperation, resulting in reduced Q-value. This tight network connectivity may facilitate rapid information transmission and integration, enabling the brain to effectively respond to urgent situations (Zhang et al., 2017 ). Therefore, the high Q-value under the NAS may reflect the independent processing characteristics of the brain in this state, while the low Q-value under the alertness state may be related to rapid response and decision-making processes. What sets our research apart is that we proposed a novel multi-layer network analysis method JTF-MDBN that can simultaneously analyze driving intuition in different bands and continuous time windows. It is well known that different bands of EEG act functionally differently and that brain networks for cognitive processing in the brain change rapidly, making our proposed method well-suited for such a study. And we combine the features of multi-layer and single-layer brain networks to provide insights from both global and local. Multi-layer network analysis provides a richer understanding of brain network dynamics in the spatial domain than static methods.