1
|
Lalani B, Gray S, Mitra-Ganguli T. Systems Thinking in an era of climate change: Does cognitive neuroscience hold the key to improving environmental decision making? A perspective on Climate-Smart Agriculture. Front Integr Neurosci 2023; 17:1145744. [PMID: 37181865 PMCID: PMC10174047 DOI: 10.3389/fnint.2023.1145744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/02/2023] [Indexed: 05/16/2023] Open
Abstract
Systems Thinking (ST) can be defined as a mental construct that recognises patterns and connections in a particular complex system to make the "best decision" possible. In the field of sustainable agriculture and climate change, higher degrees of ST are assumed to be associated with more successful adaptation strategies under changing conditions, and "better" environmental decision making in a number of environmental and cultural settings. Future climate change scenarios highlight the negative effects on agricultural productivity worldwide, particularly in low-income countries (LICs) situated in the Global South. Alongside this, current measures of ST are limited by their reliance on recall, and are prone to possible measurement errors. Using Climate-Smart Agriculture (CSA), as an example case study, in this article we explore: (i) ST from a social science perspective; (ii) cognitive neuroscience tools that could be used to explore ST abilities in the context of LICs; (iii) an exploration of the possible correlates of systems thinking: observational learning, prospective thinking/memory and the theory of planned behaviour and (iv) a proposed theory of change highlighting the integration of social science frameworks and a cognitive neuroscience perspective. We find, recent advancements in the field of cognitive neuroscience such as Near-Infrared Spectroscopy (NIRS) provide exciting potential to explore previously hidden forms of cognition, especially in a low-income country/field setting; improving our understanding of environmental decision-making and the ability to more accurately test more complex hypotheses where access to laboratory studies is severely limited. We highlight that ST may correlate with other key aspects involved in environmental decision-making and posit motivating farmers via specific brain networks would: (a) enhance understanding of CSA practices (e.g., via the frontoparietal network extending from the dorsolateral prefrontal cortex (DLPFC) to the parietal cortex (PC) a control hub involved in ST and observational learning) such as tailoring training towards developing improved ST abilities among farmers and involving observational learning more explicitly and (b) motivate farmers to use such practices [e.g., via the network between the DLPFC and nucleus accumbens (NAc)] which mediates reward processing and motivation by focussing on a reward/emotion to engage farmers. Finally, our proposed interdisciplinary theory of change can be used as a starting point to encourage discussion and guide future research in this space.
Collapse
Affiliation(s)
- Baqir Lalani
- Natural Resources Institute, University of Greenwich, Chatham Maritime, United Kingdom
- *Correspondence: Baqir Lalani
| | - Steven Gray
- Department of Community Sustainability, Michigan State University, East Lansing, MI, United States
| | | |
Collapse
|
2
|
Zhao H, Zhang T, Cheng T, Chen C, Zhai Y, Liang X, Cheng N, Long Y, Li Y, Wang Z, Lu C. Neurocomputational mechanisms of young children's observational learning of delayed gratification. Cereb Cortex 2022; 33:6063-6076. [PMID: 36562999 DOI: 10.1093/cercor/bhac484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/13/2022] [Accepted: 11/17/2022] [Indexed: 12/24/2022] Open
Abstract
The ability to delay gratification is crucial for a successful and healthy life. An effective way for young children to learn this ability is to observe the action of adult models. However, the underlying neurocomputational mechanism remains unknown. Here, we tested the hypotheses that children employed either the simple imitation strategy or the goal-inference strategy when learning from adult models in a high-uncertainty context. Results of computational modeling indicated that children used the goal-inference strategy regardless of whether the adult model was their mother or a stranger. At the neural level, results showed that successful learning of delayed gratification was associated with enhanced interpersonal neural synchronization (INS) between children and the adult models in the dorsal lateral prefrontal cortex but was not associated with children's own single-brain activity. Moreover, the discounting of future reward's value obtained from computational modeling of the goal-inference strategy was positively correlated with the strength of INS. These findings from our exploratory study suggest that, even for 3-year-olds, the goal-inference strategy is used to learn delayed gratification from adult models, and the learning strategy is associated with neural interaction between the brains of children and adult models.
Collapse
Affiliation(s)
- Hui Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
| | - Tengfei Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
| | - Tong Cheng
- Research Center for Child Development, School of Psychology, Capital Normal University, Beijing 100048, P.R. China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, CA 92697, United States
| | - Yu Zhai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
| | - Xi Liang
- Research Center for Child Development, School of Psychology, Capital Normal University, Beijing 100048, P.R. China
| | - Nanhua Cheng
- Research Center for Child Development, School of Psychology, Capital Normal University, Beijing 100048, P.R. China
| | - Yuhang Long
- Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Ying Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
| | - Zhengyan Wang
- Research Center for Child Development, School of Psychology, Capital Normal University, Beijing 100048, P.R. China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, P.R. China
| |
Collapse
|
3
|
Doublet T, Ghestem A, Bernard C. Deficit in observational learning in experimental epilepsy. Epilepsia 2022; 63:e150-e155. [PMID: 36197904 PMCID: PMC10092486 DOI: 10.1111/epi.17421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/26/2022] [Accepted: 09/26/2022] [Indexed: 01/11/2023]
Abstract
Individuals use the observation of a conspecific to learn new behaviors and skills in many species. Whether observational learning is affected in epilepsy is not known. Using the pilocarpine rat model of epilepsy, we assessed learning by observation in a spatial task. The task involves a naive animal observing a demonstrator animal seeking a reward at a specific spatial location. After five observational sessions, the observer is allowed to explore the rewarded space and look for the reward. Although control observer rats succeed in finding the reward when allowed to explore the rewarded space, epileptic animals fail. However, epileptic animals are able to successfully learn the location of the reward through their own experience after several trial sessions. Thus, epileptic animals show a clear deficit in learning by observation. This result may be clinically relevant, in particular in children who strongly rely on observational learning.
Collapse
Affiliation(s)
- Thomas Doublet
- Institute of Systems Neuroscience, Aix-Marseille University, Marseille, France
| | - Antoine Ghestem
- Institute of Systems Neuroscience, Aix-Marseille University, Marseille, France
| | - Christophe Bernard
- Institute of Systems Neuroscience, Aix-Marseille University, Marseille, France
| |
Collapse
|
4
|
Kang W, Pineda Hernández S, Wang J, Malvaso A. Instruction-based learning: A review. Neuropsychologia 2022; 166:108142. [PMID: 34999133 DOI: 10.1016/j.neuropsychologia.2022.108142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/22/2021] [Accepted: 01/03/2022] [Indexed: 10/19/2022]
Abstract
Humans are able to learn to implement novel rules from instructions rapidly, which is termed "instruction-based learning" (IBL). This remarkable ability is very important in our daily life in both learning individually or working as a team, and almost every psychology experiment starts with instructing participants. Many recent progresses have been made in IBL research both psychologically and neuroscientifically. In this review, we discuss the role of language in IBL, the importance of the first trial performance in IBL, why IBL should be considered as a goal-directed behavior, intelligence and IBL, cognitive flexibility and IBL, how behaviorally relevant information is processed in the lateral prefrontal cortex (LPFC), how the lateral frontal cortex (LFC) networks work as a functional hierarchy during IBL, and the cortical and subcortical contributions to IBL. Finally, we develop a neural working model for IBL and provide some sensible directions for future research.
Collapse
Affiliation(s)
- Weixi Kang
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, UK.
| | | | - Junxin Wang
- School of Nursing, Beijing University of Chinese Medicine, China
| | - Antonio Malvaso
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| |
Collapse
|