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Journal articles on the topic 'Human behaviour'

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Published: 4 June 2021
Last updated: 1 August 2024

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1

Singh, Debashis. "Human behaviour." BMJ 319, Suppl S4 (October 1, 1999): 9910385b. http://dx.doi.org/10.1136/sbmj.9910385b.

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2

Fuentes, Agustin. "Human niche, human behaviour, human nature." Interface Focus 7, no. 5 (August 18, 2017): 20160136. http://dx.doi.org/10.1098/rsfs.2016.0136.

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The concept of a ‘human nature’ or ‘human natures’ retains a central role in theorizing about the human experience. In Homo sapiens it is clear that we have a suite of capacities generated via our evolutionary past, and present, and a flexible capacity to create and sustain particular kinds of cultures and to be shaped by them. Regardless of whether we label these capacities ‘human natures’ or not, humans occupy a distinctive niche and an evolutionary approach to examining it is critical. At present we are faced with a few different narratives as to exactly what such an evolutionary approach entails. There is a need for a robust and dynamic theoretical toolkit in order to develop a richer, and more nuanced, understanding of the cognitively sophisticated genus Homo and the diverse sorts of niches humans constructed and occupied across the Pleistocene, Holocene, and into the Anthropocene. Here I review current evolutionary approaches to ‘human nature’, arguing that we benefit from re-framing our investigations via the concept of the human niche and in the context of the extended evolutionary synthesis (EES). While not a replacement of standard evolutionary approaches, this is an expansion and enhancement of our toolkit. I offer brief examples from human evolution in support of these assertions.
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3

Pate, Larry E. "Understanding Human Behaviour." Management Decision 25, no. 6 (June 1987): 58–64. http://dx.doi.org/10.1108/eb001477.

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4

Ferguson, Neil. "Capturing human behaviour." Nature 446, no. 7137 (April 2007): 733. http://dx.doi.org/10.1038/446733a.

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5

Mukherjee, Mr Utsab, and Prof Samir Kumar Bandyopadhyay. "Detection of Human Behaviour byObject Recognitionusing Deep Learning:A Review." International Journal of Innovative Research in Computer Science & Technology 8, no. 2 (March 2020): 16–19. http://dx.doi.org/10.21276/ijircst.2020.8.2.2.

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6

Brunarska, Zuzanna. "SOCIO-ECONOMIC DEPENDENCE ON THE STATE AND VOTING BEHAVIOUR IN RUSSIA." Studia Humanistyczne AGH 19, no. 1 (2020): 105. http://dx.doi.org/10.7494/human.2020.19.1.105.

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7

Samsuddin, Nor Malyana, Roshana Takim, and Abdul Hadi Nawawi. "Human Behaviour and Resilience Hospital." Asian Journal of Behavioural Studies 2, no. 5 (January 1, 2017): 21. http://dx.doi.org/10.21834/ajbes.v2i5.46.

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Hospitals themselves vulnerable to numerous types of disasters and can get damaged risking the lives of human being. To a certain extent, despite hard resilience (structural and non-structural), human behavior is one of the contributing factors affecting the hospital’s capability in achieving disaster resilience. Hence, the objectives of the paper are twofold: to explore human weaknesses; and to investigate strategies for achieving disaster resilience for existing public hospitals. Qualitative research techniques employed in the form of focus group approach. The findings revealed that six human weaknesses and five strategies have been asserted by the respondents as of utmost critical.2398-4295 © 2017 The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK.. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, UniversitiTeknologi MARA, Malaysia.Keywords: Disaster Resilience; Hard and soft Resilience; Hospital; Human Behaviour
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8

Boffi, Mario, and Matteo Colleoni. "Human behaviour and GIS." Netcom, no. 28-1/2 (December 16, 2014): 131–44. http://dx.doi.org/10.4000/netcom.1584.

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9

Knight, Chris, and Derek Bickerton. "Language and Human Behaviour." Journal of the Royal Anthropological Institute 5, no. 4 (December 1999): 655. http://dx.doi.org/10.2307/2661182.

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10

Jones, Dan. "Human behaviour: Killer instincts." Nature 451, no. 7178 (January 2008): 512–15. http://dx.doi.org/10.1038/451512a.

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11

Senekal, A. "Directionality in human behaviour." South African Journal of Sociology 20, no. 1 (February 1989): 11–17. http://dx.doi.org/10.1080/02580144.1989.10432897.

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12

Schlebusch, Lourens. "Book Review: Human behaviour." South African Journal of Psychology 21, no. 1 (March 1991): 67–68. http://dx.doi.org/10.1177/008124639102100111.

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13

Ruddoza, Bada. "Genetics of Human Behaviour." Anthropologist 2, no. 2 (April 2000): 103–6. http://dx.doi.org/10.1080/09720073.2000.11890636.

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14

Berenbaum, Sheri A. "Hormones in human behaviour." Lancet Neurology 4, no. 6 (June 2005): 339. http://dx.doi.org/10.1016/s1474-4422(05)70091-0.

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15

WALDRON, H. A. "LEAD AND HUMAN BEHAVIOUR*." Journal of Intellectual Disability Research 22, no. 1 (June 28, 2008): 69–78. http://dx.doi.org/10.1111/j.1365-2788.1978.tb00963.x.

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&NA;. "Alarms and Human Behaviour." Survey of Anesthesiology 51, no. 6 (December 2007): 324–25. http://dx.doi.org/10.1097/01.sa.0000280276.97659.69.

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17

Pumpa, Malcolm. "What Motivates Human Behaviour?" Aboriginal Child at School 20, no. 2 (May 1992): 38–51. http://dx.doi.org/10.1017/s031058220000780x.

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The broad outcomes of 25 years of state education for A.T.S.I. people are not encouraging. Whilst it would be premature to say that the system has failed these people, it is obvious that the outcome has not met the expectations of many educators and many A.T.S.I. people.
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18

Ball, Philip. "Human behaviour—it's complicated." Lancet 390, no. 10106 (October 2017): 1943. http://dx.doi.org/10.1016/s0140-6736(17)32674-0.

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19

Moncrieff, Joanna. "Genetics of human behaviour." Psychiatric Bulletin 22, no. 3 (March 1998): 158–61. http://dx.doi.org/10.1192/pb.22.3.158.

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Ascribing human behaviour to the influence of genetic factors is a reductionist idea that inappropriately applies the explanations of physical science to the human social world. The result is that the designation of some behaviour as deviant is disguised as objective science. The high profile of genetic research means that we must be alert to the potential social consequences of this type of reasoning.
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Farmer, Anne, Michael Owen, and Peter McGuffin. "Genetics of human behaviour." Psychiatric Bulletin 22, no. 8 (August 1998): 518. http://dx.doi.org/10.1192/pb.22.8.518.

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21

St John, Freya A. V., Aidan M. Keane, Gareth Edwards-Jones, Lauren Jones, Richard W. Yarnell, and Julia P. G. Jones. "Identifying indicators of illegal behaviour: carnivore killing in human-managed landscapes." Proceedings of the Royal Society B: Biological Sciences 279, no. 1729 (July 27, 2011): 804–12. http://dx.doi.org/10.1098/rspb.2011.1228.

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Managing natural resources often depends on influencing people's behaviour, however effectively targeting interventions to discourage environmentally harmful behaviours is challenging because those involved may be unwilling to identify themselves. Non-sensitive indicators of sensitive behaviours are therefore needed. Previous studies have investigated people's attitudes, assuming attitudes reflect behaviour. There has also been interest in using people's estimates of the proportion of their peers involved in sensitive behaviours to identify those involved, since people tend to assume that others behave like themselves. However, there has been little attempt to test the potential of such indicators. We use the randomized response technique (RRT), designed for investigating sensitive behaviours, to estimate the proportion of farmers in north-eastern South Africa killing carnivores, and use a modified logistic regression model to explore relationships between our best estimates of true behaviour (from RRT) and our proposed non-sensitive indicators (including farmers' attitudes, and estimates of peer-behaviour). Farmers' attitudes towards carnivores, question sensitivity and estimates of peers' behaviour, predict the likelihood of farmers killing carnivores. Attitude and estimates of peer-behaviour are useful indicators of involvement in illicit behaviours and may be used to identify groups of people to engage in interventions aimed at changing behaviour.
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22

Garcia-Marques, Leonel, and Mário B. Ferreira. "Is Observing Behaviour the Best Way to Understand Behaviour?" Social Psychological Bulletin 13, no. 2 (May 29, 2018): e26076. http://dx.doi.org/10.5964/spb.v13i2.26076.

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Dolinski (2018, this issue) argues that Social Psychology may hardly be considered a science of behaviour anymore, given the rarity of published studies in which the dependent measures involve behaviours other than the completion of surveys, pressing of keys on a computer keyboard, or clicking a mouse. In the present, we comment on this void of empirical studies in which “real” human behaviours are examined to put forward the following points: i) Key-pressing can be a human behaviour as meaningful as any other more complex behaviour (i.e., behavioural complexity is not a good criterion for meaningfulness), ii) Lessons learned from past research in social psychology have shown us that studying “real” behaviour introduces a number of well-known complications, iii) Improvement in the comprehension of human behaviour depends more on a strong theoretical lens constrained by results obtained via rigorous experimentation than on the complexity of people’s observed actions.
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23

Schenk, Paulina M., Robert West, Oscar Castro, Emily Hayes, Janna Hastings, Marie Johnston, Marta M. Marques, et al. "An ontological framework for organising and describing behaviours: The Human Behaviour Ontology." Wellcome Open Research 9 (May 8, 2024): 237. http://dx.doi.org/10.12688/wellcomeopenres.21252.1.

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Background Human behaviours have been classified in areas such as health, occupation and sustainability. We aimed to develop a more broadly applicable framework for behaviours to facilitate integrating evidence across domains. Methods The Human Behaviour Ontology (HBO), a part of the Behaviour Change Intervention Ontology (BCIO), was developed by: (1) specifying the ontology’s scope, (2) identifying candidate classes from existing classifications, (3) refining the ontology by applying it to code behaviours in relevant literature, (4) conducting a stakeholder review with behavioural and ontology experts, (5) testing the inter-rater reliability of its use in annotating research reports, (6) finalising classes and adding relations between classes, and (7) publishing the ontology’s computer-readable version. Results A class labelled ‘individual human behaviour’ was defined as “A bodily process of a human that involves co-ordinated contraction of striated muscles controlled by the brain.” In Steps 1-4, the ontology’s initial version was developed, with 128 classes. The inter-rater reliability for applying this version in annotations was 0.63 for researchers familiar with it and, after minor adjustments to the ontology and annotation guidance, 0.74 for researchers unfamiliar with it. Following Steps 5-6, the ontology was published with 177 classes, including 128 individual human behaviour classes organised under upper-level classes relating to (1) experiences (e.g., playing), (2) expressive (e.g., laughing), (3) harm (e.g., self-injury behaviour), (4) health (e.g., undergoing vaccination), (5) life-function (e.g., breathing behaviour), (6) interacting with materials (e.g., consumption), (7) bodily care (e.g., washing), (8) position (e.g., walking), and (9) social environments (e.g., communication). The remaining 49 classes included: ‘individual human behaviour pattern’ for repeated behaviours, ‘population behaviour’, ‘population behaviour pattern’, behavioural attributes (e.g., impulsiveness), and abstinence from behaviour. Relations were also defined to represent timings, locations, participants, mental processes, functions, goals, and outcomes. Conclusions The HBO potentially provides a coherent framework for describing human behaviours.
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24

Adeoluwa Adewumi, Samson, Taiwo Ajadi, and Bhekabantu Ntshangase. "Green human resource management and green environmental workplace behaviour in the eThekwini municipality of South Africa." International Journal of Research in Business and Social Science (2147- 4478) 11, no. 4 (June 5, 2022): 159–70. http://dx.doi.org/10.20525/ijrbs.v11i4.1720.

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Conversations on green human resource management (GHRM) are relatively new in the canon of human resource discourse. Existing studies have been focused on industrial and service industries of the private sector leaving a research gap in the public sector establishment, especially at the municipality level of governance. This study aims to address this gap by interrogating the perception and understanding of green behaviour and the evaluation of green behaviour performance. A total of 12 unit managers and employees of the human resource management department of the eThekwini municipality were purposively recruited based on existing knowledge of the green behaviour concept. The semi-structured interview approach was used in eliciting qualitative data. The NVivo (v.12) qualitative software was employed for the identification of themes and the content qualitative analytical tool was employed to make sense of the various themes. The findings show a range of perceptions and understanding of green behavior including change in management behaviour towards environmental sustainability goals, moving towards a paperless environment, re-engineering and automation of work process and safeguarding the environment through pro-environmental behaviours. The evaluation of employees’ green behaviour performance can be achieved through regular introspection and monitoring of the operations of green environmental activities, the integration of green behaviour targets with key performance indicators (KPI) and the benefits of green behaviour to the organisation. The study echoes the need for robust managerial support and massive organizational awareness of the importance of green behaviours for a sustainable work environment.
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25

Shum, Leia C., Reza Faieghi, Terry Borsook, Tamim Faruk, Souraiya Kassam, Hoda Nabavi, Sofija Spasojevic, James Tung, Shehroz S. Khan, and Andrea Iaboni. "Indoor Location Data for Tracking Human Behaviours: A Scoping Review." Sensors 22, no. 3 (February 5, 2022): 1220. http://dx.doi.org/10.3390/s22031220.

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Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.
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St John, Freya A. V., Gareth Edwards-Jones, and Julia P. G. Jones. "Conservation and human behaviour: lessons from social psychology." Wildlife Research 37, no. 8 (2010): 658. http://dx.doi.org/10.1071/wr10032.

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Despite increased effort from non-governmental organisations, academics and governments over recent decades, several threats continue to cause species declines and even extinctions. Resource use by a growing human population is a significant driver of biodiversity loss, so conservation scientists need to be interested in the factors that motivate human behaviour. Economic models have been applied to human decision making for many years; however, humans are not financially rational beings and other characteristics of the decision maker (including attitude) and the pressure that people perceive to behave in a certain way (subjective norms) may influence decision making; these are characteristics considered by social psychologists interested in human decision making. We review social-psychology theories of behaviour and how they have been used in the context of conservation and natural-resource management. Many studies focus on general attitudes towards conservation rather than attitudes towards specific behaviours of relevance to conservation and thus have limited value in designing interventions to change specific behaviours (e.g. reduce hunting of a threatened species). By more specifically defining the behaviour of interest, and investigating attitude in the context of other social-psychological predictors of behaviour (e.g. subjective norms, the presence of facilitating factors and moral obligation), behaviours that have an impact on conservation goals will be better understood, allowing for the improved design of interventions to influence them.
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27

Konsolakis, Kostas, Hermie Hermens, and Oresti Banos. "A Novel Framework for the Holistic Monitoring and Analysis of Human Behaviour." Proceedings 31, no. 1 (November 20, 2019): 43. http://dx.doi.org/10.3390/proceedings2019031043.

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Recent technological advances have enabled the continuous and unobtrusive monitoring of human behaviour. However, most of the existing studies focus on detecting human behaviour under the limitation of one behavioural aspect, such as physical behaviour and not addressing human behaviour in a broad sense. For this reason, we propose a novel framework that will serve as the principal generator of knowledge on the user’s behaviour. The proposed framework moves beyond the current trends in automatic behaviour analysis by detecting and inferring human behaviour automatically, based on multimodal sensor data. In particular, the framework analyses human behaviour in a holistic approach, focusing on different behavioural aspects at the same time; namely physical, social, emotional and cognitive behaviour. Furthermore, the suggested framework investigates user’s behaviour over different periods, introducing the concept of short-term and long-term behaviours and how these change over time.
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28

Blaukopf, Clare L., and Gregory J. DiGirolamo. "Reward, Context, and Human Behaviour." Scientific World JOURNAL 7 (2007): 626–40. http://dx.doi.org/10.1100/tsw.2007.122.

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Animal models of reward processing have revealed an extensive network of brain areas that process different aspects of reward, from expectation and prediction to calculation of relative value. These results have been confirmed and extended in human neuroimaging to encompass secondary rewards more unique to humans, such as money. The majority of the extant literature covers the brain areas associated with rewards whilst neglecting analysis of the actual behaviours that these rewards generate. This review strives to redress this imbalance by illustrating the importance of looking at the behavioural outcome of rewards and the context in which they are produced. Following a brief review of the literature of reward-related activity in the brain, we examine the effect of reward context on actions. These studies reveal how the presence of reward vs. rewardandpunishment, or being conscious vs. unconscious of reward-related actions, differentially influence behaviour. The latter finding is of particular importance given the extent to which animal models are used in understanding the reward systems of the human mind. It is clear that further studies are needed to learn about the human reaction to reward in its entirety, including any distinctions between conscious and unconscious behaviours. We propose that studies of reward entail a measure of the animal's (human or nonhuman) knowledge of the reward and knowledge of its own behavioural outcome to achieve that reward.
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Pigott, B. "Fire Detection And Human Behaviour." Fire Safety Science 2 (1989): 573–81. http://dx.doi.org/10.3801/iafss.fss.2-573.

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30

Chanchlani, Prof Akshita. "Predicting Human Behaviour through Handwriting." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 849–54. http://dx.doi.org/10.22214/ijraset.2018.6129.

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Konsolakis, Kostas, Hermie Hermens, Claudia Villalonga, Miriam Vollenbroek-Hutten, and Oresti Banos. "Human Behaviour Analysis through Smartphones." Proceedings 2, no. 19 (October 30, 2018): 1243. http://dx.doi.org/10.3390/proceedings2191243.

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Human behaviour analysis through smartphone devices has been an active field for more than a decade and there are still a lot of key aspects to be addressed. This paper surveys the state-of-the-art in human behaviour analysis based on smartphones. We categorise prior works into four main sensing modalities related to physical, cognitive, emotional and social behaviour. Finally, we conclude with the outcomes of this survey and we illustrate our ideas for future research in the area of human behaviour understanding.
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32

Smith, Louise, Tao Zhong, and Parveen Bawa. "Nonlinear behaviour of human motoneurons." Canadian Journal of Physiology and Pharmacology 73, no. 1 (January 1, 1995): 113–23. http://dx.doi.org/10.1139/y95-016.

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When ramp-and-hold currents are injected into a motoneuron of an anesthetized cat, the motoneuron responds with a high initial firing rate (dynamic phase), which then adapts to a lower steady-state firing rate. The firing rates during the dynamic and the steady-state phases are linearly related to the rate of change and the magnitude of the injected current, respectively. In human subjects, where inputs to the motoneurons are not accessible, force parameters are used to describe motoneuron behaviour. Population responses of human motoneurons, measured in terms of gross electromyographic (EMG) activity, increase linearly with the magnitude and the rate of change of force. No study has attempted to examine the question of linearity of single motor units during the dynamic as well as the steady-state phases. The following study recorded single motor unit and EMG activities simultaneously from the flexor carpi radialis muscle in human subjects completing ramp-and-hold force trajectories. Although the results confirmed the linear relationship between EMG activities and the rate and magnitude of the force, a nonlinear activity pattern was observed between the single motor unit firing and the force parameters, suggesting that recruitment must be responsible for the linear behaviour of EMG activity. Comparisons of different background activity levels on the firing patterns of a given motor unit, as well as comparisons of two simultaneously recorded units, further supported nonlinear response patterns of single motor units.Key words: human, motoneurons, motor units, nonlinearity, force trajectory, repetitive firing.
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33

Cooke, P. "Human Chromosomes. Structure, Behaviour, Effects." Journal of Medical Genetics 24, no. 7 (July 1, 1987): 447. http://dx.doi.org/10.1136/jmg.24.7.447.

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34

Harrington, Anne. "Human behaviour: Guns and roses." Nature 544, no. 7650 (April 2017): 294–95. http://dx.doi.org/10.1038/544294a.

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35

Kingstone, Alan. "Everyday human cognition and behaviour." Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale 74, no. 4 (December 2020): 267–74. http://dx.doi.org/10.1037/cep0000244.

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Rosen, Julia. "Human behaviour: Find your voice." Nature 540, no. 7631 (November 30, 2016): 157–59. http://dx.doi.org/10.1038/nj7631-157a.

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37

Rommel, Ward. "Sexual Selection and Human Behaviour." Social Science Information 41, no. 3 (September 2002): 439–65. http://dx.doi.org/10.1177/0539018402041003005.

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This article reviews some recent evolutionary psychological theories about the interaction between environmental factors and sexual strategies. Evolutionary psychology explains sexual strategies in terms of innate information-processing mechanisms. The most important theoretical instrument relating to this topic is the theory of sexual selection and parental investment. Because of the unequal parental investment of the sexes, their sexual strategies differ. This is an important source of conflict between the sexes. Humans evolved in a complex social environment. As a consequence, human psychic mechanisms produce a wide variety of sexual strategies. Two dimensions along which human sexual strategies vary are considered here. First, people's mating strategies range from striving for a lifelong pair bond to aiming at a single act of copulation with someone. Second, strategies situated at the long-term end of the continuum can be polygynous, monogamous or polyandrous. The choices made by a concrete individual are influenced by several factors such as the personal life history, the general availability and predictability of resources, the distribution of political and economic power between men and women, the distribution of political and economic power between men themselves, the production mode of a society and finally the content of cultural representations in a society. It is shown that evolutionary psychology can be important for the explanation of contemporary behavioural differences. Some methodological problems of evolutionary psychology are reviewed and an evolutionary psychological perspective on the sex/gender distinction is considered.
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OLSSON, GUNNAR, and STEPHEN GALE. "SPATIAL THEORY AND HUMAN BEHAVIOUR." Papers in Regional Science 21, no. 1 (January 14, 2005): 229–42. http://dx.doi.org/10.1111/j.1435-5597.1968.tb01450.x.

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39

Gefter, Amanda. "Another explanation of human behaviour." New Scientist 210, no. 2809 (April 2011): 50. http://dx.doi.org/10.1016/s0262-4079(11)60937-8.

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40

Humphrey, Louise T. "Weaning behaviour in human evolution." Seminars in Cell & Developmental Biology 21, no. 4 (June 2010): 453–61. http://dx.doi.org/10.1016/j.semcdb.2009.11.003.

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41

Mansfield-Devine, Steve. "Bad behaviour: exploiting human weaknesses." Computer Fraud & Security 2017, no. 1 (January 2017): 17–20. http://dx.doi.org/10.1016/s1361-3723(17)30008-8.

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42

Conning, D. M. "Food composition and human behaviour." Food Chemistry 33, no. 1 (January 1989): 33–41. http://dx.doi.org/10.1016/0308-8146(89)90098-8.

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43

Neill, David. "Cortical evolution and human behaviour." Brain Research Bulletin 74, no. 4 (September 2007): 191–205. http://dx.doi.org/10.1016/j.brainresbull.2007.06.008.

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44

Sterelny, Kim. "Evolutionary explanations of human behaviour." Australasian Journal of Philosophy 70, no. 2 (June 1992): 156–73. http://dx.doi.org/10.1080/00048409212345051.

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Ingham, Barbara. "Human behaviour in development economics." European Journal of the History of Economic Thought 6, no. 4 (December 1999): 606–23. http://dx.doi.org/10.1080/10427719900000045.

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46

Sokas, Patrick. "Physiological Correlates of Human Behaviour." Journal of Nervous and Mental Disease 174, no. 3 (March 1986): 188–89. http://dx.doi.org/10.1097/00005053-198603000-00017.

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47

Peck, David F. "Health care and human behaviour." Journal of Psychosomatic Research 30, no. 1 (January 1986): 107. http://dx.doi.org/10.1016/0022-3999(86)90075-9.

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48

Marks, David F., and Gillian M. Pow. "Cannabis and human social behaviour." Human Psychopharmacology: Clinical and Experimental 4, no. 4 (December 1989): 283–90. http://dx.doi.org/10.1002/hup.470040408.

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Hussain, Madhumita, and Asha Sharma. "Space attachment and human behaviour." Khoj:An International Peer Reviewed Journal of Geography 10, no. 1 (2023): 66–77. http://dx.doi.org/10.5958/2455-6963.2023.00005.x.

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Craig, Ian W., and Kelly E. Halton. "Genetics of human aggressive behaviour." Human Genetics 126, no. 1 (June 9, 2009): 101–13. http://dx.doi.org/10.1007/s00439-009-0695-9.

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