From smog to crime: How air pollution is shaping human behavior and public safety The Times of India
See the original post:
From smog to crime: How air pollution is shaping human behavior and public safety - The Times of India
Category Archives: Human Behavior
The Smell Of Death Has A Strange Influence On Human Behavior – IFLScience
The Smell Of Death Has A Strange Influence On Human Behavior IFLScience
Here is the original post:
The Smell Of Death Has A Strange Influence On Human Behavior - IFLScience
"WEIRD" in psychology literature oversimplifies the global diversity of human behavior. – Psychology Today
Scientists issue warning about increasingly alarming whale behavior due to human activity – Orcasonian
Does AI adoption call for a change in human behavior? – Fast Company
Dogs can smell human stress and it alters their own behavior, study reveals – New York Post
Trajectories of brain and behaviour development in the womb, at birth and through infancy – Nature.com
de Haan, M. D. H., Dumontheil, I. & Johnson, M. H. Developmental Cognitive Neuroscience: An Introduction 5th edn (Wiley, 2023).
Maccari, S. et al. Early-life experiences and the development of adult diseases with a focus on mental illness: the human birth theory. Neuroscience 342, 232251 (2017).
Article CAS PubMed Google Scholar
Tinbergen, N. The Study of Instinct (Oxford Univ. Press, 1951).
Gottlieb, G. Social induction of malleability in ducklings: sensory basis and psychological mechanism. Anim. Behav. 45, 707719 (1993).
Article Google Scholar
Lickliter, R. & Lewkowicz, D. J. Intersensory experience and early perceptual development: attenuated prenatal sensory stimulation affects postnatal auditory and visual responsiveness in bobwhite quail chicks (Colinus virginianus). Dev. Psychol. 31, 609618 (1995).
Lickliter, R. & Gottlieb, G. Visually imprinted maternal preference in ducklings is redirected by social interaction with siblings. Dev. Psychobiol. 19, 265277 (1986).
Article CAS PubMed Google Scholar
Bolhuis, J. J., Johnson, M. H. & Horn, G. Effects of early experience on the development of filial preferences in the domestic chick. Dev. Psychobiol. 18, 299308 (1985).
Article CAS PubMed Google Scholar
Spelke, E. S. & Kinzler, K. D. Core knowledge. Dev. Sci. 10, 8996 (2007).
Article PubMed Google Scholar
Fiser, J. & Aslin, R. N. Statistical learning of new visual feature combinations by infants. Proc. Natl Acad. Sci. USA 99, 1582215826 (2002).
Article CAS PubMed PubMed Central Google Scholar
Prechtl, H. F. R. Continuity of Neural Functions from Prenatal to Postnatal Life (Cambridge Univ. Press, 1984).
Lickliter, R. & Bahrick, L. E. in Fetal Development: Research on Brain and Behavior, Environmental Influences, and Emerging Technologies (eds Reissland, N. & Kisilevsky, B. S.) 314 (Springer, 2016).
Polese, D. et al. The newborns reaction to light as the determinant of the brains activation at human birth. Front. Integr. Neurosci. 16, 933426 (2022).
Article PubMed PubMed Central Google Scholar
Kisilvesky, B. S. & Muir, D. W. Human fetal and subsequent newborn responses to sound and vibration. Infant Behav. Dev. 14, 126 (1991).
Article Google Scholar
Stanojevic, M., Zaputovic, S. & Bosnjak, A. P. Continuity between fetal and neonatal neurobehavior. Semin. Fetal Neonatal Med. 17, 324329 (2012).
Article PubMed Google Scholar
De Asis-Cruz, J. et al. Global network organization of the fetal functional connectome. Cereb. Cortex 31, 30343046 (2021).
Article PubMed PubMed Central Google Scholar
Doria, V. et al. Emergence of resting state networks in the preterm human brain. Proc. Natl Acad. Sci. USA 107, 2001520020 (2010).
Article CAS PubMed PubMed Central Google Scholar
Stiles, J. & Jernigan, T. L. The basics of brain development. Neuropsychol. Rev. 20, 327348 (2010).
Article PubMed PubMed Central Google Scholar
Humphreys, K. L. & Salo, V. C. Expectable environments in early life. Curr. Opin. Behav. Sci. 36, 115119 (2020).
Article PubMed PubMed Central Google Scholar
Mayer, C. & Joseph, K. S. Fetal growth: a review of terms, concepts and issues relevant to obstetrics. Ultrasound Obstet. Gynecol. 41, 136145 (2013).
Article CAS PubMed Google Scholar
Iiguez, C. et al. Maternal smoking during pregnancy and fetal biometry: the INMA Mother and Child Cohort Study. Am. J. Epidemiol. 178, 10671075 (2013).
Article PubMed Google Scholar
Jaddoe, V. W. V. et al. Maternal smoking and fetal growth characteristics in different periods of pregnancy: the generation R study. Am. J. Epidemiol. 165, 12071215 (2007).
Article PubMed Google Scholar
Reissland, N. et al. Prenatal effects of maternal nutritional stress and mental health on the fetal movement profile. Arch. Gynecol. Obstet. 302, 6575 (2020).
Article CAS PubMed PubMed Central Google Scholar
De Asis-Cruz, J. et al. Examining the relationship between fetal cortical thickness, gestational age, and maternal psychological distress. Dev. Cogn. Neurosci. 63, 101282 (2023).
Article PubMed PubMed Central Google Scholar
Lebit, D. F.-D. & Vladareanu, P. D. R. The role of 4D ultrasound in the assessment of fetal behaviour. Maedica 6, 120127 (2011).
PubMed PubMed Central Google Scholar
Reissland, N., Froggatt, S., Reames, E. & Girkin, J. Effects of maternal anxiety and depression on fetal neuro-development. J. Affect. Disord. 241, 469474 (2018).
Article CAS PubMed Google Scholar
Parma, V., Brasselet, R., Zoia, S., Bulgheroni, M. & Castiello, U. The origin of human handedness and its role in pre-birth motor control. Sci. Rep. 7, 16804 (2017).
Article PubMed PubMed Central Google Scholar
Shuffrey, L. C. et al. Fetal heart rate, heart rate variability, and heart rate/movement coupling in the Safe Passage Study. J. Perinatol. 39, 608618 (2019).
Article PubMed PubMed Central Google Scholar
Dipietro, J. A., Voegtline, K. M., Pater, H. A. & Costigan, K. A. Predicting child temperament and behavior from the fetus. Dev. Psychopathol. 30, 855870 (2018).
Article PubMed PubMed Central Google Scholar
Namburete, A. I. L. et al. Learning-based prediction of gestational age from ultrasound images of the fetal brain. Med. Image Anal. 21, 7286 (2015).
Article PubMed PubMed Central Google Scholar
Gao, Y. & Alison Noble, J. in Medical Image Computing and Computer-Assisted InterventionMICCAI 2017, 305313 (Springer, 2017).
De Asis-Cruz, J., Barnett, S. D., Kim, J. H. & Limperopoulos, C. Functional connectivity-derived optimal gestational-age cut points for fetal brain network maturity. Brain Sci. 11, 20763425 (2021).
Article Google Scholar
Karolis, V. R. et al. Maturational networks of human fetal brain activity reveal emerging connectivity patterns prior to ex-utero exposure. Commun. Biol. 6, 661 (2023).
Article PubMed PubMed Central Google Scholar
Spann, M. N. et al. Association of maternal prepregnancy body mass index with fetal growth and neonatal thalamic brain connectivity among adolescent and young women. JAMA Netw. Open 3, e2024661 (2020).
Article PubMed PubMed Central Google Scholar
Norr, M. E., Hect, J. L., Lenniger, C. J., Van den Heuvel, M. & Thomason, M. E. An examination of maternal prenatal BMI and human fetal brain development. J. Child Psychol. Psychiatry 62, 458469 (2021).
Article PubMed Google Scholar
Thomason, M. E., Hect, J. L., Waller, R. & Curtin, P. Interactive relations between maternal prenatal stress, fetal brain connectivity, and gestational age at delivery. Neuropsychopharmacology 46, 18391847 (2021).
Article PubMed PubMed Central Google Scholar
van den Heuvel, M. I. et al. Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth. Sci. Rep. 11, 2228 (2021).
Article PubMed PubMed Central Google Scholar
Ji, L., Hendrix, C. L. & Thomason, M. E. Empirical evaluation of human fetal fMRI preprocessing steps. Netw. Neurosci. 6, 702721 (2022).
Article PubMed PubMed Central Google Scholar
Thomason, M., Austin, A. & Hendrix, C. Fetal amygdala functional connectivity relates to autism spectrum disorder traits at age 3. Biol. Psychiatry 89, S29 (2021).
Article Google Scholar
Alkandari, F., Ellahi, A., Aucott, L., Devereux, G. & Turner, S. Fetal ultrasound measurements and associations with postnatal outcomes in infancy and childhood: a systematic review of an emerging literature. J. Epidemiol. Community Health 69, 4148 (2015).
Article PubMed Google Scholar
Montagu, A. Touching: The Human Significance of the Skin (Harper and Row, 1978).
Zoia, S. et al. Evidence of early development of action planning in the human foetus: a kinematic study. Exp. Brain Res. 176, 217226 (2007).
Article PubMed Google Scholar
Reissland, N., & Austen, J. in Reach-to-Grasp Behavior: Brain, Behavior, and Modelling Across the Life Span (eds Corbetta, D. & Santello, M.) 317 (Routledge, 2018).
de Vries, J. I., Visser, G. H., Mulder, E. J. & Prechtl, H. F. Diurnal and other variations in fetal movement and heart rate patterns at 2022 weeks. Early Hum. Dev. 15, 333348 (1987).
Article PubMed Google Scholar
Kostovi, I. & Jovanov-Milosevi, N. The development of cerebral connections during the first 2045 weeks gestation. Semin. Fetal Neonatal Med. 11, 415422 (2006).
Article PubMed Google Scholar
Piontelli, A. et al. in Development of Normal Fetal Movements: The Last 15 Weeks of Gestation (ed. Piontelli, A.) 4151 (Springer, 2015).
Iasegi, I. ., Krsnik, . & Kostovi, I. in Factors Affecting Neurodevelopment (eds. Martin, C. R. et al.) 299307 (Elsevier, 2021).
Ustun, B., Covey, J. & Reissland, N. Chemosensory continuity from prenatal to postnatal life in humans: a systematic review and meta-analysis. PLoS ONE 18, e0283314 (2023).
Article CAS PubMed PubMed Central Google Scholar
Lagercrantz, H. in Infant Brain Development: Formation of the Mind and the Emergence of Consciousness (ed. Lagercrantz, H.) 4352 (Springer, 2016).
DiPietro, J. A., Hodgson, D. M., Costigan, K. A., Hilton, S. C. & Johnson, T. R. Development of fetal movementfetal heart rate coupling from 20 weeks through term. Early Hum. Dev. 44, 139151 (1996).
Article CAS PubMed Google Scholar
Lchinger, A. B., Hadders-Algra, M., van Kan, C. M. & de Vries, J. I. P. Fetal onset of general movements. Pediatr. Res. 63, 191195 (2008).
Article PubMed Google Scholar
Zoia, S. et al. The development of upper limb movements: from fetal to post-natal life. PLoS ONE 8, e80876 (2013).
Article PubMed PubMed Central Google Scholar
Ferrari, G. A. et al. Ultrasonographic investigation of human fetus responses to maternal communicative and non-communicative stimuli. Front. Psychol. 7, 354 (2016).
Article PubMed PubMed Central Google Scholar
Reissland, N., Francis, B., Buttanshaw, L., Austen, J. M. & Reid, V. Do fetuses move their lips to the sound that they hear? An observational feasibility study on auditory stimulation in the womb. Pilot Feasibility Stud. 2, 14 (2016).
Article PubMed PubMed Central Google Scholar
Myowa-Yamakoshi, M. & Takeshita, H. Do human fetuses anticipate self-oriented actions? A study by four-dimensional (4D) ultrasonography. Infancy 10, 289301 (2006).
Article Google Scholar
Reissland, N., Francis, B., Aydin, E., Mason, J. & Schaal, B. The development of anticipation in the fetus: a longitudinal account of human fetal mouth movements in reaction to and anticipation of touch. Dev. Psychobiol. 56, 955963 (2014).
Article CAS PubMed Google Scholar
Fagard, J., Esseily, R., Jacquey, L., ORegan, K. & Somogyi, E. Fetal origin of sensorimotor behavior. Front. Neurorobot. 12, 23 (2018).
AI model predicts human behavior from our poor decision-making – Big Think
Human beings behave irrationally or as an artificially intelligent robot might say, sub-optimally. Data, the emotionless yet affable android depicted in Star Trek: The Next Generation, regularly struggled to comprehend humans flawed decision-making. If he had been programmed with a new model devised by researchers at MIT and the University of Washington, he might have had an easier go of it.
In a paper published last month, Athul Paul Jacob, a Ph.D. student in AI at MIT, Dr. Jacob Andreas, his academic advisor, and Abhishek Gupta, an assistant professor in computer science and engineering at the University of Washington, described a new way to model an agents behavior. They then used their method to predict humans goals or actions.
Jacob, Andreas, and Gupta created what they termed a latent inference budget model. Its underlying breakthrough lies in inferring a human or machines computational constraints based on prior actions. These constraints result in sub-optimal choices. For example, a human constraint for decision decision-making is often time. When confronted with a difficult choice, we typically dont spend hours (or longer) gaming out every possible outcome. Instead, we make decisions quickly without taking the time to gather all the information available.
Models currently exist that account for irrational decision-making, but these only predict that errors will occur randomly. In reality, humans and machines screw up in more formulaic patterns. The latent inference budget model can quickly identify these patterns and then use them to forecast future behavior.
Across three tests, the researchers found that their new model generally outperforms the old models: It was as good or better at predicting a computer algorithms route when navigating a maze, a human chess players next move, and what a human speaker was trying to say from a quick utterance.
Jacob says that the research process made him realize how fundamental planning is to human behavior. Certain people are not inherently rational or irrational. Its just that some people take extra time to plan their actions while others take less.
At the end of the day, we saw that the depth of the planning, or how long someone thinks about the problem, is a really good proxy of how humans behave, he said in a statement.
Jacob envisions the model being used in futuristic robotic helpers or AI assistants.
If we know that a human is about to make a mistake, having seen how they have behaved before, the AI agent could step in and offer a better way to do it. Or the agent could adapt to the weaknesses that its human collaborators have, he said.
This is not scientists first attempt to develop tools that help AI predict human decision-making. Most researchers pursuing this goal envision positive futures. For example, we may someday see AI seamlessly coordinating their actions with ours, providing assistance in everyday tasks, boosting productivity at workplaces, and being our drinking buddies.
But there are more dystopian possibilities, too. AI models thoroughly designed to predict human behavior could also be used by bad actors to manipulate us. With enough data on how humans react to various stimuli, AI could be programmed to elicit responses that might not be in the targeted individuals best interest. Imagine if AI got really good at this. It would bring new urgency to the question of whether humans are agents with free will or simply automata reacting to external forces.
The rest is here:
AI model predicts human behavior from our poor decision-making - Big Think
ZkSync defends Sybil measures as Binance offers own ZK token airdrop – TradingView
Ethereum zero-knowledge layer-2 scaling platform zkSync has continued to defend criticism over the criteria for its zkSync (ZK) token airdrop, slated to launch on June 17.
In a frequently asked questions (FAQ) document updated on June 15, ZK Nation answered 15 questions explaining why some groups were eligible for the token while others werent.
One of its longer answers focused on its detection and prevention measures for Sybil attacks, which occur when one entity creates and uses a large number of wallets to farm an airdrop. This was a continuing pain point among airdrop claimants recently.
ZkSync reiterated it had used explicit Sybil detection in addition to a unique airdrop design to ensure the highest number of organic users were rewarded, but it also noted that this has led to some Sybil wallets being let through.
It explained some Sybils can employ sophisticated algorithmic strategies that are indistinguishable from real people.
They fund accounts from many distinct exchange addresses, never interact with each other, use randomized amounts, and use software to randomize daily patterns of human behavior, and even perform activities unique to the project (for example, using zkSync paymasters), it explained.
The majority of such bots are completely undetectable, even with the most advanced anti-Sybil methodology.
ZkSync claimed that being too aggressive with filtering could eliminate some Sybils but also might falsely flag many organic users, so it chose to reward organic users with high likelihood by using a combination of value scaling and multipliers.
Essentially, this assigned fewer eligibility points to wallets with low funding (a sign of Sybil behavior) but would give them a multiplying boost if there was onchain behavior that signaled human behavior.
ZkSync explained that Sybils typically creates many accounts but uses small amounts of crypto to fund each account to be capital efficient.
Introducing the ZK Token
Checker https://t.co/O2UonCvfzi
Announcement https://t.co/hjgI14PHoi
Its time to put the ZK token into the hands of the community. Its your turn to govern ZKsyncs future. pic.twitter.com/VD3fZgH5bf
Real people, on the other hand, tend to concentrate most of their wealth in just a few accounts, making their balances much larger compared to bots, it explained.
There will be Sybils in every airdrop, it concluded. However, for every example of Sybil that can be identified, there are hundreds that were excluded.
Binance offers ZK listing and distribution amid ongoing concerns
It comes as crypto exchange Binance has offered its own ZK airdrop targeted at those not eligible for the official airdrop on June 17.
The exchange said it will offer 10.5 million ZK tokens to over 52,000 Binance users in light of the ongoing concerns from the community around ZK token distribution.
Eligible claim addresses need to have initiated at least 50 transactions on zkSync Era between February 2023 and March 2024, conducted transactions in at least seven months in that period, arent a centralized exchange, bridge or contract address and werent eligible for the official airdrop.
However, each Binance user will only be given 200 ZK each.
The exchange will also open the ZK token for trading from June 17, including trading pairs with Bitcoin (BTC), Tether (USDT) and First Digital USD (FDUSD).
The ZK token airdrop will launch on June 17. According to Whales Pro, the ZK token istrading in the pre-market for $0.36 at the time of writing.
Originally posted here:
ZkSync defends Sybil measures as Binance offers own ZK token airdrop - TradingView
On TikTok, Goldendoodles Are People Trapped in Dog Bodies – The New York Times
He sits forlornly on a floating staircase, his body slightly slumped and his limbs in his lap, gazing out floor-to-ceiling windows into the summer foliage beyond. He seems to be contemplating something perhaps the meaning of life itself as the camera shifts around to the front to reveal his true condition. Hes not a man; hes a goldendoodle.
This video, taken by the dogs owner, Lawrence Skutelsky, is captioned Trying to find the zipper on my goldendoodle after this, and it has been viewed on TikTok more than 87 million times. Posted on May 24, it joined a pantheon of similar videos from other goldendoodle owners documenting the humanlike behavior of their pets and prompted a host of new additions to the genre.
Naturally, many viewers on TikTok are now joking that dogs particularly goldendoodles, a designer breed that is a cross between a golden retriever and a poodle may actually be people trapped in dog bodies.
Does anybody elses dog sit on them like a literal human child or is it just mine? Chloe Covington asked in a video she posted last year with her goldendoodle, Gemma, sitting upright on her lap. Others have shared clips of goldendoodles sitting on the stairs like Mr. Skutelskys dog or standing like a person.
Back in 2020, a woman named Molly Dolan, who lives in Charleston, S.C., posted a video of her goldendoodle walking upright on two hind legs across the entire street and it has been viewed about 6.5 million times to date.
Continued here:
On TikTok, Goldendoodles Are People Trapped in Dog Bodies - The New York Times