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Journal articles on the topic 'Human biases'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Biswas, Mriganka, and John Murray. "Can Cognitive Biases in Robots Make More ‘Likeable' Human-Robot Interactions than the Robots Without Such Biases." International Journal of Artificial Life Research 6, no. 1 (January 2016): 1–29. http://dx.doi.org/10.4018/ijalr.2016010101.

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The research presented in the paper aims to develop long-term companionship between cognitively imperfect robots and humans. In order to develop cognitively imperfect robot, the research suggests to implement various cognitive biases in a robot's interactive behaviours. In the authors' understanding, such cognitively biased behaviours in robot will help the participants to relate with it easily. In the current paper, they show comparative results of the experiments using five biased and one non-biased algorithms in a 3D printed humanoid robot MARC. The results from the experiments show that the participants initially liked the robot with biased and imperfect behaviours than the same robots without any mistakes and biases.

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Sibbel, Rainer, and Angelina Huber. "How Human Decision-making Biases Influence Health Outcomes in Patient Care." European Journal of Management Issues 29, no. 2 (April 28, 2021): 64–72. http://dx.doi.org/10.15421/192106.

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Purpose: Medical treatments and medical decision making are mostly human based and therefore in risk of being influenced by cognitive biases. The potential impact could lead to bad medical outcome, unnecessary harm or even death. The aim of this comprehensive literature study is to analyse the evidence whether healthcare professionals are biased, which biases are most relevant in medicine and how these biases may be reduced. Approach/Findings: The results of the comprehensive literature based meta-analysis confirm on the one hand that several biases are relevant in the medical decision and treatment process. On the other hand, the study shows that the empirical evidence on the impact of cognitive biases on clinical outcome is scarce for most biases and that further research is necessary in this field. Value/Practical Implications: Nevertheless, it is important to determine the extent to which biases in healthcare professionals translate into negative clinical outcomes such as misdiagnosis, delayed diagnosis, or mistreatment. Only this way, the importance of incorporating debiasing strategies into the clinical setting, and which biases to focus on, can be properly assessed. Research Limitations/Future Research: Though recent literature puts great emphasis on cognitive debiasing strategies, there are still very few approaches that have proven to be efficient. Due to the increasing degree of specialization in medicine, the relevance of the different biases varies. Paper type: Theoretical.

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Webster, Matthew T., and Nick G. C. Smith. "Fixation biases affecting human SNPs." Trends in Genetics 20, no. 3 (March 2004): 122–26. http://dx.doi.org/10.1016/j.tig.2004.01.005.

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4

Thompson, Bill, and Thomas L. Griffiths. "Human biases limit cumulative innovation." Proceedings of the Royal Society B: Biological Sciences 288, no. 1946 (March 10, 2021): 20202752. http://dx.doi.org/10.1098/rspb.2020.2752.

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Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large ( n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific and educational contexts.

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Dumbalska, Tsvetomira, Vickie Li, Konstantinos Tsetsos, and Christopher Summerfield. "A map of decoy influence in human multialternative choice." Proceedings of the National Academy of Sciences 117, no. 40 (September 21, 2020): 25169–78. http://dx.doi.org/10.1073/pnas.2005058117.

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Human decisions can be biased by irrelevant information. For example, choices between two preferred alternatives can be swayed by a third option that is inferior or unavailable. Previous work has identified three classic biases, known as the attraction, similarity, and compromise effects, which arise during choices between economic alternatives defined by two attributes. However, the reliability, interrelationship, and computational origin of these three biases have been controversial. Here, a large cohort of human participants made incentive-compatible choices among assets that varied in price and quality. Instead of focusing on the three classic effects, we sampled decoy stimuli exhaustively across bidimensional multiattribute space and constructed a full map of decoy influence on choices between two otherwise preferred target items. Our analysis reveals that the decoy influence map is highly structured even beyond the three classic biases. We identify a very simple model that can fully reproduce the decoy influence map and capture its variability in individual participants. This model reveals that the three decoy effects are not distinct phenomena but are all special cases of a more general principle, by which attribute values are repulsed away from the context provided by rival options. The model helps us understand why the biases are typically correlated across participants and allows us to validate a prediction about their interrelationship. This work helps to clarify the origin of three of the most widely studied biases in human decision-making.

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Silk, Joan B. "Nepotistic cooperation in non-human primate groups." Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1533 (November 12, 2009): 3243–54. http://dx.doi.org/10.1098/rstb.2009.0118.

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Darwin was struck by the many similarities between humans and other primates and believed that these similarities were the product of common ancestry. He would be even more impressed by the similarities if he had known what we have learned about primates over the last 50 years. Genetic kinship has emerged as the primary organizing force in the evolution of primate social organization and the patterning of social behaviour in non-human primate groups. There are pronounced nepotistic biases across the primate order, from tiny grey mouse lemurs ( Microcebus murinus ) that forage alone at night but cluster with relatives to sleep during the day, to cooperatively breeding marmosets that rely on closely related helpers to rear their young, rhesus macaque ( Macaca mulatta ) females who acquire their mother's rank and form strict matrilineal dominance hierarchies, male howler monkeys that help their sons maintain access to groups of females and male chimpanzees ( Pan troglodytes ) that form lasting relationships with their brothers. As more evidence of nepotism has accumulated, important questions about the evolutionary processes underlying these kin biases have been raised. Although kin selection predicts that altruism will be biased in favour of relatives, it is difficult to assess whether primates actually conform to predictions derived from Hamilton's rule: br > c . In addition, other mechanisms, including contingent reciprocity and mutualism, could contribute to the nepotistic biases observed in non-human primate groups. There are good reasons to suspect that these processes may complement the effects of kin selection and amplify the extent of nepotistic biases in behaviour.

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Mellem, Monika S., Sophie Wohltjen, Stephen J. Gotts, Avniel Singh Ghuman, and Alex Martin. "Intrinsic frequency biases and profiles across human cortex." Journal of Neurophysiology 118, no. 5 (November 1, 2017): 2853–64. http://dx.doi.org/10.1152/jn.00061.2017.

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Recent findings in monkeys suggest that intrinsic periodic spiking activity in selective cortical areas occurs at timescales that follow a sensory or lower order-to-higher order processing hierarchy (Murray JD, Bernacchia A, Freedman DJ, Romo R, Wallis JD, Cai X, Padoa-Schioppa C, Pasternak T, Seo H, Lee D, Wang XJ. Nat Neurosci 17: 1661–1663, 2014). It has not yet been fully explored if a similar timescale hierarchy is present in humans. Additionally, these measures in the monkey studies have not addressed findings that rhythmic activity within a brain area can occur at multiple frequencies. In this study we investigate in humans if regions may be biased toward particular frequencies of intrinsic activity and if a full cortical mapping still reveals an organization that follows this hierarchy. We examined the spectral power in multiple frequency bands (0.5–150 Hz) from task-independent data using magnetoencephalography (MEG). We compared standardized power across bands to find regional frequency biases. Our results demonstrate a mix of lower and higher frequency biases across sensory and higher order regions. Thus they suggest a more complex cortical organization that does not simply follow this hierarchy. Additionally, some regions do not display a bias for a single band, and a data-driven clustering analysis reveals a regional organization with high standardized power in multiple bands. Specifically, theta and beta are both high in dorsal frontal cortex, whereas delta and gamma are high in ventral frontal cortex and temporal cortex. Occipital and parietal regions are biased more narrowly toward alpha power, and ventral temporal lobe displays specific biases toward gamma. Thus intrinsic rhythmic neural activity displays a regional organization but one that is not necessarily hierarchical. NEW & NOTEWORTHY The organization of rhythmic neural activity is not well understood. Whereas it has been postulated that rhythms are organized in a hierarchical manner across brain regions, our novel analysis allows comparison of full cortical maps across different frequency bands, which demonstrate that the rhythmic organization is more complex. Additionally, data-driven methods show that rhythms of multiple frequencies or timescales occur within a particular region and that this nonhierarchical organization is widespread.

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Cuturi, Luigi F., and Paul R. MacNeilage. "Systematic Biases in Human Heading Estimation." PLoS ONE 8, no. 2 (February 15, 2013): e56862. http://dx.doi.org/10.1371/journal.pone.0056862.

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9

Maldonado, Antonio, Andrés Catena, José César Perales, and Antonio Cándido. "Cognitive Biases in Human Causal Learning." Spanish Journal of Psychology 10, no. 2 (November 2007): 242–50. http://dx.doi.org/10.1017/s1138741600006508.

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The main aim of this work was to look for cognitive biases in human inference of causal relationships in order to emphasize the psychological processes that modulate causal learning. From the effect of the judgment frequency, this work presents subsequent research on cue competition (overshadowing, blocking, and super-conditioning effects) showing that the strength of prior beliefs and new evidence based upon covariation computation contributes additively to predict causal judgments, whereas the balance between the reliability of both, beliefs and covariation knowledge, modulates their relative weight. New findings also showed “inattentional blindness” for negative or preventative causal relationships but not for positive or generative ones, due to failure in codifying and retrieving the necessary information for its computation. Overall results unveil the need of three hierarchical levels of a whole architecture for human causal learning: the lower one, responsible for codifying the events during the task; the second one, computing the retrieved information; finally, the higher level, integrating this evidence with previous causal knowledge. In summary, whereas current theoretical frameworks on causal inference and decision-making usually focused either on causal beliefs or covariation information, the present work shows how both are required to be able to explain the complexity and flexibility involved in human causal learning.

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Ester, Edward F., Thomas C. Sprague, and John T. Serences. "Categorical Biases in Human Occipitoparietal Cortex." Journal of Neuroscience 40, no. 4 (December 20, 2019): 917–31. http://dx.doi.org/10.1523/jneurosci.2700-19.2019.

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Abrahamyan, Arman, Laura Luz Silva, Steven C. Dakin, Matteo Carandini, and Justin L. Gardner. "Adaptable history biases in human perceptual decisions." Proceedings of the National Academy of Sciences 113, no. 25 (June 2, 2016): E3548—E3557. http://dx.doi.org/10.1073/pnas.1518786113.

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When making choices under conditions of perceptual uncertainty, past experience can play a vital role. However, it can also lead to biases that worsen decisions. Consistent with previous observations, we found that human choices are influenced by the success or failure of past choices even in a standard two-alternative detection task, where choice history is irrelevant. The typical bias was one that made the subject switch choices after a failure. These choice history biases led to poorer performance and were similar for observers in different countries. They were well captured by a simple logistic regression model that had been previously applied to describe psychophysical performance in mice. Such irrational biases seem at odds with the principles of reinforcement learning, which would predict exquisite adaptability to choice history. We therefore asked whether subjects could adapt their irrational biases following changes in trial order statistics. Adaptability was strong in the direction that confirmed a subject’s default biases, but weaker in the opposite direction, so that existing biases could not be eradicated. We conclude that humans can adapt choice history biases, but cannot easily overcome existing biases even if irrational in the current context: adaptation is more sensitive to confirmatory than contradictory statistics.

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Liu, Siyuan, Jakob Seidlitz, Jonathan D. Blumenthal, Liv S. Clasen, and Armin Raznahan. "Integrative structural, functional, and transcriptomic analyses of sex-biased brain organization in humans." Proceedings of the National Academy of Sciences 117, no. 31 (July 20, 2020): 18788–98. http://dx.doi.org/10.1073/pnas.1919091117.

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Humans display reproducible sex differences in cognition and behavior, which may partly reflect intrinsic sex differences in regional brain organization. However, the consistency, causes and consequences of sex differences in the human brain are poorly characterized and hotly debated. In contrast, recent studies in mice—a major model organism for studying neurobiological sex differences—have established: 1) highly consistent sex biases in regional gray matter volume (GMV) involving the cortex and classical subcortical foci, 2) a preponderance of regional GMV sex differences in brain circuits for social and reproductive behavior, and 3) a spatial coupling between regional GMV sex biases and brain expression of sex chromosome genes in adulthood. Here, we directly test translatability of rodent findings to humans. First, using two independent structural-neuroimaging datasets (n> 2,000), we find that the spatial map of sex-biased GMV in humans is highly reproducible (r> 0.8 within and across cohorts). Relative GMV is female biased in prefrontal and superior parietal cortices, and male biased in ventral occipitotemporal, and distributed subcortical regions. Second, through systematic comparison with functional neuroimaging meta-analyses, we establish a statistically significant concentration of human GMV sex differences within brain regions that subserve face processing. Finally, by imaging-transcriptomic analyses, we show that GMV sex differences in human adulthood are specifically and significantly coupled to regional expression of sex-chromosome (vs. autosomal) genes and enriched for distinct cell-type signatures. These findings establish conserved aspects of sex-biased brain development in humans and mice, and shed light on the consistency, candidate causes, and potential functional corollaries of sex-biased brain anatomy in humans.

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13

Peterson, Sharyl Bender, and Traci Kroner. "Gender Biases in Textbooks for Introductory Psychology and Human Development." Psychology of Women Quarterly 16, no. 1 (March 1992): 17–36. http://dx.doi.org/10.1111/j.1471-6402.1992.tb00237.x.

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Because of spreading concerns about gender stereotypes in textbooks, in 1975 an American Psychological Association Task Force recommended changes in the type of language that should be used to report research findings and in the type of information that should be included in reports of psychological research. These recommendations also applied to the presentation of psychological information in textbooks. Studies in the subsequent 7 years indicated some decrease in the use of sex-stereotyped language and some decrease in gender-biased content. Researchers typically concluded, however, that although some improvements had occurred, gender biases and stereotypes still occurred in texts. The present study reports an extensive content analysis of current textbooks for introductory psychology and human development courses. Representation of the work, theory, and behavior of males continues to significantly exceed the representation of the work, theory, and behavior of females, and females continue to be portrayed in negative and gender-biased ways.

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14

Zhang, Qing, David Elsweiler, and Christoph Trattner. "Visual Cultural Biases in Food Classification." Foods 9, no. 6 (June 23, 2020): 823. http://dx.doi.org/10.3390/foods9060823.

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This article investigates how visual biases influence the choices made by people and machines in the context of online food. To this end the paper investigates three research questions and shows (i) to what extent machines are able to classify images, (ii) how this compares to human performance on the same task and (iii) which factors are involved in the decision making of both humans and machines. The research reveals that algorithms significantly outperform human labellers on this task with a range of biases being present in the decision-making process. The results are important as they have a range of implications for research, such as recommender technology and crowdsourcing, as is discussed in the article.

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15

Hood, Bruce M., Marc D. Hauser, Linda Anderson, and Laurie Santos. "Gravity biases in a non-human primate?" Developmental Science 2, no. 1 (March 1999): 35–41. http://dx.doi.org/10.1111/1467-7687.00051.

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Vintch, B., and J. L. Gardner. "Cortical Correlates of Human Motion Perception Biases." Journal of Neuroscience 34, no. 7 (February 12, 2014): 2592–604. http://dx.doi.org/10.1523/jneurosci.2809-13.2014.

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Duan, Guangyou, Chongyang Han, Qingli Wang, Shanna Guo, Yuhao Zhang, Ying Ying, Penghao Huang, et al. "A SCN10A SNP biases human pain sensitivity." Molecular Pain 12 (January 2016): 174480691666608. http://dx.doi.org/10.1177/1744806916666083.

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18

Hunt, Laurence T., Robb B. Rutledge, W. M. Nishantha Malalasekera, Steven W. Kennerley, and Raymond J. Dolan. "Approach-Induced Biases in Human Information Sampling." PLOS Biology 14, no. 11 (November 10, 2016): e2000638. http://dx.doi.org/10.1371/journal.pbio.2000638.

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19

Peters, Uwe. "Human thinking, shared intentionality, and egocentric biases." Biology & Philosophy 31, no. 2 (December 1, 2015): 299–312. http://dx.doi.org/10.1007/s10539-015-9512-0.

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20

Ayton, Peter, and Eva Pascoe. "Bias in human judgement under uncertainty?" Knowledge Engineering Review 10, no. 1 (March 1995): 21–41. http://dx.doi.org/10.1017/s0269888900007244.

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AbstractThe claim is frequently made that human judgement and reasoning are vulnerable to cognitive biases. Such biases are assumed to be inherent in that they are attributed to the nature of the mental processes that produce judgement. In this paper, we review the psychological evidence for this claim in the context of the debate concerning human judgemental competence under uncertainty. We consider recent counter-arguments which suggest that the evidence for cognitive biases may be dependent on observations of performance on inappropriate tasks and by comparisons with inappropriate normative standards. We also consider the practical implications for the design of decision support systems.

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Clark, Cory J., Brittany S. Liu, Bo M. Winegard, and Peter H. Ditto. "Tribalism Is Human Nature." Current Directions in Psychological Science 28, no. 6 (August 20, 2019): 587–92. http://dx.doi.org/10.1177/0963721419862289.

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Humans evolved in the context of intense intergroup competition, and groups comprised of loyal members more often succeeded than groups comprised of nonloyal members. Therefore, selective pressures have sculpted human minds to be tribal, and group loyalty and concomitant cognitive biases likely exist in all groups. Modern politics is one of the most salient forms of modern coalitional conflict and elicits substantial cognitive biases. The common evolutionary history of liberals and conservatives gives little reason to expect protribe biases to be higher on one side of the political spectrum than the other. This evolutionarily plausible null hypothesis has been supported by recent research. In a recent meta-analysis, liberals and conservatives showed similar levels of partisan bias, and several protribe cognitive tendencies often ascribed to conservatives (e.g., intolerance toward dissimilar other people) were found in similar degrees in liberals. We conclude that tribal bias is a natural and nearly ineradicable feature of human cognition and that no group—not even one’s own—is immune.

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22

Maiello, Guido, Vivian C. Paulun, Lina K. Klein, and Roland W. Fleming. "Object Visibility, Not Energy Expenditure, Accounts For Spatial Biases in Human Grasp Selection." i-Perception 10, no. 1 (January 2019): 204166951982760. http://dx.doi.org/10.1177/2041669519827608.

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Humans exhibit spatial biases when grasping objects. These biases may be due to actors attempting to shorten their reaching movements and therefore minimize energy expenditures. An alternative explanation could be that they arise from actors attempting to minimize the portion of a grasped object occluded from view by the hand. We reanalyze data from a recent study, in which a key condition decouples these two competing hypotheses. The analysis reveals that object visibility, not energy expenditure, most likely accounts for spatial biases observed in human grasping.

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Batt, Robert J., and Jordan D. Tong. "Mean Service Metrics: Biased Quality Judgment and the Customer–Server Quality Gap." Manufacturing & Service Operations Management 22, no. 5 (September 2020): 975–95. http://dx.doi.org/10.1287/msom.2019.0783.

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Problem definition: People often make service-quality judgments based on information about the quality of each server even though they care primarily about the quality each customer experiences. When and how do server-level quality metrics differ from customer-experienced ones? Can people properly account for these differences, or do they drive human judgment and decision biases? Academic/practical relevance: Biased judgments about service quality can cause governments to fund programs suboptimally, organizations to promote the wrong employees, and customers to make disappointing purchases. We further our understanding of the role that cognitive biases play in services and how to manage quality information in light of them. Methodology: We use a mathematical model to define the gap between server-level and customer-experienced quality metrics. We use secondary data in the context of the higher-education industry to quantify the customer–server quality gap in practice. We construct a behavioral model to derive hypotheses about how environmental factors impact the direction and magnitude of judgment biases. Controlled laboratory experiments test the hypothesized biases and mitigation techniques. Results: Our empirical study reveals that the two measures differ enough to drive significant differences in the rank order of school majors, teachers, and airports. Our experiments support our main conjecture that judgments and decisions about customer-experienced metrics are biased toward server-level metrics. Consequently, (1) judgments about customer-experienced quality are biased high/low when quality and server load are negatively/positively correlated, (2) judgments about a server’s absolute impact on customer experience are biased high/low when a server has a smaller/larger load than average, and (3) providing customer-experienced quality metrics mitigate these biases. Managerial implications: Our results help identify when and why service-quality metrics are likely to mislead judgments and bias decisions as well as who is likely to benefit from such biases. The results also guide system designers on how to report metrics when seeking to help support effective decision making.

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Hall, Thomas W., Andrew W. Higson, Bethane Jo Pierce, Kenneth H. Price, and Christopher J. Skousen. "Haphazard Sampling: Selection Biases Induced by Control Listing Properties and the Estimation Consequences of these Biases." Behavioral Research in Accounting 24, no. 2 (January 1, 2012): 101–32. http://dx.doi.org/10.2308/bria-50132.

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ABSTRACT Haphazard sampling is a nonstatistical technique used by auditors to simulate a variety of random sampling techniques when testing the error status of accounting populations. In this study, we compare the properties of haphazard samples selected from control listings with the properties of simple random samples. We hypothesize that control listing entries exhibit salience values that result from the effort required to locate entries and the visual properties of entries. We further hypothesize these salience values influence sample selections and result in sample properties that are different from those of simple random samples. To test these hypotheses, we examine the properties of haphazard samples selected by three participant groups. In each group, sample properties differ from those of simple random sampling and include a lack of independence across sample selections and biased sample inclusion probabilities. We also develop models showing how biased sample inclusion probabilities influence error projections and discuss the estimation consequences of these biases. Data Availability: For information about data availability, please contact the first author.

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Radzevick, Joseph R., and Don A. Moore. "Myopic biases in competitions." Organizational Behavior and Human Decision Processes 107, no. 2 (November 2008): 206–18. http://dx.doi.org/10.1016/j.obhdp.2008.02.010.

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Lakshminaryanan, Venkat, M. Keith Chen, and Laurie R. Santos. "Endowment effect in capuchin monkeys." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1511 (October 6, 2008): 3837–44. http://dx.doi.org/10.1098/rstb.2008.0149.

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In humans, the capacity for economically rational choice is constrained by a variety of preference biases: humans evaluate gambles relative to arbitrary reference points; weigh losses heavier than equally sized gains; and demand a higher price for owned goods than for equally preferred goods that are not yet owned. To date, however, fewer studies have examined the origins of these biases. Here, we review previous work demonstrating that human economic biases such as loss aversion and reference dependence are shared with an ancestrally related New World primate, the capuchin monkey ( Cebus apella ). We then examine whether capuchins display an endowment effect in a token-trading task. We identified pairs of treats (fruit discs versus cereal chunks) that were equally preferred by each monkey. When given a chance to trade away their owned fruit discs to obtain the equally valued cereal chunks (or vice versa), however, monkeys required a far greater compensation than the equally preferred treat. We show that these effects are not due to transaction costs or timing issues. These data suggest that biased preferences rely on cognitive systems that are more evolutionarily ancient than previously thought—and that common evolutionary ancestry shared by humans and capuchins may account for the occurrence of the endowment effect in both species.

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Senkfor, A. J. "Perceptual biases expressed during observation of human movement." Journal of Vision 8, no. 6 (April 8, 2010): 914. http://dx.doi.org/10.1167/8.6.914.

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Hunt, Laurence T., Robb B. Rutledge, W. M. Nishantha Malalasekera, Steven W. Kennerley, and Raymond J. Dolan. "Correction: Approach-Induced Biases in Human Information Sampling." PLOS Biology 15, no. 11 (November 30, 2017): e1002618. http://dx.doi.org/10.1371/journal.pbio.1002618.

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Garcia-Alamino, Josep M. "Human biases and the SARS-CoV-2 pandemic." Intensive and Critical Care Nursing 58 (June 2020): 102861. http://dx.doi.org/10.1016/j.iccn.2020.102861.

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Choi, Moo-jin, and Jongpil Park. ""Designing Fear Appeal Cues leveraging from Human Cognitive Biases"." Regional Industry Review 43, no. 3 (August 30, 2020): 259–72. http://dx.doi.org/10.33932/rir.43.3.12.

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Zamboni, Elisa, Timothy Ledgeway, Paul V. McGraw, and Denis Schluppeck. "Do perceptual biases emerge early or late in visual processing? Decision-biases in motion perception." Proceedings of the Royal Society B: Biological Sciences 283, no. 1833 (June 29, 2016): 20160263. http://dx.doi.org/10.1098/rspb.2016.0263.

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Visual perception is strongly influenced by contextual information. A good example is reference repulsion, where subjective reports about the direction of motion of a stimulus are significantly biased by the presence of an explicit reference. These perceptual biases could arise early, during sensory encoding, or alternatively, they may reflect decision-related processes occurring relatively late in the task sequence. To separate these two competing possibilities, we asked (human) subjects to perform a fine motion-discrimination task and then estimate the direction of motion in the presence or absence of an oriented reference line. When subjects performed the discrimination task with the reference, but subsequently estimated motion direction in its absence, direction estimates were unbiased. However, when subjects viewed the same stimuli but performed the estimation task only, with the orientation of the reference line jittered on every trial, the directions estimated by subjects were biased and yoked to the orientation of the shifted reference line. These results show that judgements made relative to a reference are subject to late, decision-related biases . A model in which information about motion is integrated with that of an explicit reference cue, resulting in a late, decision-related re-weighting of the sensory representation, can account for these results.

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Ghilardi, M. F., J. Gordon, and C. Ghez. "Learning a visuomotor transformation in a local area of work space produces directional biases in other areas." Journal of Neurophysiology 73, no. 6 (June 1, 1995): 2535–39. http://dx.doi.org/10.1152/jn.1995.73.6.2535.

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1. The dependence of directional biases in reaching movements on the initial position of the hand was studied in normal human subjects moving their unseen hand on a horizontal digitizing tablet to visual targets displayed on a vertical computer screen. 2. When initial hand positions were to the right of midline, movements were systematically biased clockwise. Biases were counterclockwise for starting points to the left. Biases were unaffected by the screen location of the starting and target positions. 3. Vision of the hand in relation to the target before movement, as well as practice with vision of the cursor during the movement, temporarily eliminated these biases. The spatial organization of the biases suggests that, without vision of the limb, the nervous system underestimates the distance of the hand from an axis or plane that includes its most common operating location. 4. To test the hypothesis that such an underestimate might represent an adaptation to a local area of work space or range effect, subjects were trained to reach accurately from right or left positions. After training, movements initiated from other locations, including ones that were previously error free, showed new biases that again represented underestimates of the distance of the initial hand position from the new trained location. 5. We conclude that hand path planning is dependent on learned representations of the location of the hand in the work space.

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Vragov, Roumen. "Detecting Behavioral Biases in Mixed Human-Proxy Online Auction Markets." International Journal of Strategic Information Technology and Applications 4, no. 4 (October 2013): 60–79. http://dx.doi.org/10.4018/ijsita.2013100104.

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Currently many auction websites directly or indirectly provide support for the use of automated proxies or agents. Buyers can use proxies to monitor auctions and bid at the appropriate time and with the appropriate bid price, sellers can use proxies to set prices or negotiate deals. Proxy complexity varies, however most proxies first require some input on the part of the human trader and then perform the trading task autonomously. This paper proposes and tests a theoretical model of human behavior that can be used to detect behavioral biases in electronic market environments populated by humans and software agents. The paper also quantifies the effect of these biases on individual and business profits.

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Pooresmaeili, Arezoo, Aurel Wannig, and Raymond J. Dolan. "Receipt of reward leads to altered estimation of effort." Proceedings of the National Academy of Sciences 112, no. 43 (October 12, 2015): 13407–10. http://dx.doi.org/10.1073/pnas.1507527112.

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Effort and reward jointly shape many human decisions. Errors in predicting the required effort needed for a task can lead to suboptimal behavior. Here, we show that effort estimations can be biased when retrospectively reestimated following receipt of a rewarding outcome. These biases depend on the contingency between reward and task difficulty and are stronger for highly contingent rewards. Strikingly, the observed pattern accords with predictions from Bayesian cue integration, indicating humans deploy an adaptive and rational strategy to deal with inconsistencies between the efforts they expend and the ensuing rewards.

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Oberai, Himani, and Ila Mehrotra Anand. "Unconscious bias: thinking without thinking." Human Resource Management International Digest 26, no. 6 (August 13, 2018): 14–17. http://dx.doi.org/10.1108/hrmid-05-2018-0102.

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Purpose Unconscious biases are often ignored in organizations; thus, it becomes more important to identify them so that we can build strong and competent organizations. In present dynamic and competitive business situations, you need to be well aware about those concerns of the organizations where these biases exist. Design/methodology/approach This paper identifies various types of unconscious biases prevailing at workplace. It also identifies different strategies which can be used to avoid them so that we can gain a competitive edge over others in market. Findings Unconscious biases are a fact of life; no one can deny them. Thus, it is important to identify them so that they can be eliminated, and our businesses can avoid their detrimental effects. Research limitations/implications Unconscious biases narrow down the pool of people in an organization and ultimately destabilize an organization’s base. Practical implications Unconscious biases narrow down the pool of people in the organization and ultimately destabilize an organization’s base. Originality/value This paper can help managers and executives to highlight the areas where these biases lie so that they can be removed easily.

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Asher, Lucy, Mary Friel, Kym Griffin, and Lisa M. Collins. "Mood and personality interact to determine cognitive biases in pigs." Biology Letters 12, no. 11 (November 2016): 20160402. http://dx.doi.org/10.1098/rsbl.2016.0402.

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Cognitive bias has become a popular way to access non-human animal mood, though inconsistent results have been found. In humans, mood and personality interact to determine cognitive bias, but to date, this has not been investigated in non-human animals. Here, we demonstrate for the first time, to the best of our knowledge, in a non-human animal, the domestic pig ( Sus scrofa domesticus ), that mood and personality interact, impacting on judgement. Pigs with a more proactive personality were more likely to respond optimistically to unrewarded ambiguous probes (spatially positioned between locations that were previously rewarded and unrewarded) independent of their housing (or enrichment) conditions. However, optimism/pessimism of reactive pigs in this task was affected by their housing conditions, which are likely to have influenced their mood state. Reactive pigs in the less enriched environment were more pessimistic and those in the more enriched environment, more optimistic. These results suggest that judgement in non-human animals is similar to humans, incorporating aspects of stable personality traits and more transient mood states.

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Steed, Ryan, and Aylin Caliskan. "A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild." AI and Ethics 1, no. 3 (January 12, 2021): 249–60. http://dx.doi.org/10.1007/s43681-020-00035-y.

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AbstractResearch in social psychology has shown that people’s biased, subjective judgments about another’s personality based solely on their appearance are not predictive of their actual personality traits. But researchers and companies often utilize computer vision models to predict similarly subjective personality attributes such as “employability”. We seek to determine whether state-of-the-art, black box face processing technology can learn human-like appearance biases. With features extracted with FaceNet, a widely used face recognition framework, we train a transfer learning model on human subjects’ first impressions of personality traits in other faces as measured by social psychologists. We find that features extracted with FaceNet can be used to predict human appearance bias scores for deliberately manipulated faces but not for randomly generated faces scored by humans. Additionally, in contrast to work with human biases in social psychology, the model does not find a significant signal correlating politicians’ vote shares with perceived competence bias. With Local Interpretable Model-Agnostic Explanations (LIME), we provide several explanations for this discrepancy. Our results suggest that some signals of appearance bias documented in social psychology are not embedded by the machine learning techniques we investigate. We shed light on the ways in which appearance bias could be embedded in face processing technology and cast further doubt on the practice of predicting subjective traits based on appearances.

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Suddendorf, Thomas. "Evolution, lies, and foresight biases." Behavioral and Brain Sciences 34, no. 1 (February 2011): 38–39. http://dx.doi.org/10.1017/s0140525x10002128.

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AbstractHumans are not the only animals to deceive, though we might be the only ones that lie. The arms race von Hippel & Trivers (VH&T) propose may have only started during hominin evolution. VH&T offer a powerful theory, and I suggest it can be expanded to explain why there are systematic biases in human foresight.

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Fournier, Susan, and Giana Eckhardt. "Managing the Human in Human Brands." GfK Marketing Intelligence Review 10, no. 1 (May 1, 2018): 30–33. http://dx.doi.org/10.2478/gfkmir-2018-0005.

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Abstract The physical and social realities, mental biases and limitations of being human differentiate human brands from others. It is their very humanness that introduces risk while generating the ability for enhanced returns. Four particular human characteristics can create imbalance or inconsistency between the person and the brand: mortality, hubris, unpredictability and social embeddedness. None of these qualities manifest in traditional non-human brands, and all of them present risks requiring active managerial attention. Rather than treating humans as brands and making humans into brands for sale in the commercial marketplace, our framework forces a focus on keeping a balance between the person and the personified object.

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Ryan, Michael J., Nicole M. Kime, and Gil G. Rosenthal. "Patterns of evolution in human speech processing and animal communication." Behavioral and Brain Sciences 21, no. 2 (April 1998): 282–83. http://dx.doi.org/10.1017/s0140525x98481172.

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We consider Sussman et al.'s suggestion that auditory biases for processing low-noise relationships among pairs of acoustic variables is a preadaptation for human speech processing. Data from other animal communication systems, especially those involving sexual selection, also suggest that neural biases in the receiver system can generate strong selection on the form of communication signals.

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Treviño, Mario, Santiago Castiello, Oscar Arias-Carrión, Braniff De la Torre-Valdovinos, and Ricardo Medina Coss y León. "Isomorphic decisional biases across perceptual tasks." PLOS ONE 16, no. 1 (January 22, 2021): e0245890. http://dx.doi.org/10.1371/journal.pone.0245890.

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Humans adjust their behavioral strategies to maximize rewards. However, in the laboratory, human decisional biases exist and persist in two alternative tasks, even when this behavior leads to a loss in utilities. Such biases constitute the tendency to choose one action over others and emerge from a combination of external and internal factors that are specific for each individual. Here, we explored the idea that internally-mediated decisional biases should stably occur and, hence, be reflected across multiple behavioral tasks. Our experimental results confirm this notion and illustrate how participants exhibited similar choice biases across days and tasks. Moreover, we show how side-choice behavior in a two alternative choice task served to identify participants, suggesting that individual traits could underlie these choice biases. The tasks and analytic tools developed for this study should become instrumental in exploring the interaction between internal and external factors that contribute to decisional biases. They could also serve to detect psychopathologies that involve aberrant levels of choice variability.

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Zhuang, Qinwei Kim-Wee, Jose Hector Galvez, Qian Xiao, Najla AlOgayil, Jeffrey Hyacinthe, Teruko Taketo, Guillaume Bourque, and Anna K. Naumova. "Sex Chromosomes and Sex Phenotype Contribute to Biased DNA Methylation in Mouse Liver." Cells 9, no. 6 (June 9, 2020): 1436. http://dx.doi.org/10.3390/cells9061436.

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Sex biases in the genome-wide distribution of DNA methylation and gene expression levels are some of the manifestations of sexual dimorphism in mammals. To advance our understanding of the mechanisms that contribute to sex biases in DNA methylation and gene expression, we conducted whole genome bisulfite sequencing (WGBS) as well as RNA-seq on liver samples from mice with different combinations of sex phenotype and sex-chromosome complement. We compared groups of animals with different sex phenotypes, but the same genetic sexes, and vice versa, same sex phenotypes, but different sex-chromosome complements. We also compared sex-biased DNA methylation in mouse and human livers. Our data show that sex phenotype, X-chromosome dosage, and the presence of Y chromosome shape the differences in DNA methylation between males and females. We also demonstrate that sex bias in autosomal methylation is associated with sex bias in gene expression, whereas X-chromosome dosage-dependent methylation differences are not, as expected for a dosage-compensation mechanism. Furthermore, we find partial conservation between the repertoires of mouse and human genes that are associated with sex-biased methylation, an indication that gene function is likely to be an important factor in this phenomenon.

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Blevins, Sydney J., Brian G. Pierce, Nishant K. Singh, Timothy P. Riley, Yuan Wang, Timothy T. Spear, Michael I. Nishimura, Zhiping Weng, and Brian M. Baker. "How structural adaptability exists alongside HLA-A2 bias in the human αβ TCR repertoire." Proceedings of the National Academy of Sciences 113, no. 9 (February 16, 2016): E1276—E1285. http://dx.doi.org/10.1073/pnas.1522069113.

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How T-cell receptors (TCRs) can be intrinsically biased toward MHC proteins while simultaneously display the structural adaptability required to engage diverse ligands remains a controversial puzzle. We addressed this by examining αβ TCR sequences and structures for evidence of physicochemical compatibility with MHC proteins. We found that human TCRs are enriched in the capacity to engage a polymorphic, positively charged “hot-spot” region that is almost exclusive to the α1-helix of the common human class I MHC protein, HLA-A*0201 (HLA-A2). TCR binding necessitates hot-spot burial, yielding high energetic penalties that must be offset via complementary electrostatic interactions. Enrichment of negative charges in TCR binding loops, particularly the germ-line loops encoded by the TCR Vα and Vβ genes, provides this capacity and is correlated with restricted positioning of TCRs over HLA-A2. Notably, this enrichment is absent from antibody genes. The data suggest a built-in TCR compatibility with HLA-A2 that biases receptors toward, but does not compel, particular binding modes. Our findings provide an instructional example for how structurally pliant MHC biases can be encoded within TCRs.

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Croson, Rachel, and James Konow. "Social preferences and moral biases." Journal of Economic Behavior & Organization 69, no. 3 (March 2009): 201–12. http://dx.doi.org/10.1016/j.jebo.2008.10.007.

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Brunkhorst, Robert, Waltraud Pfeilschifter, Sammy Patyna, Stefan Büttner, Timon Eckes, Sandra Trautmann, Dominique Thomas, Josef Pfeilschifter, and Alexander Koch. "Preanalytical Biases in the Measurement of Human Blood Sphingolipids." International Journal of Molecular Sciences 19, no. 5 (May 7, 2018): 1390. http://dx.doi.org/10.3390/ijms19051390.

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Gardner, Justin L., and Brett Vintch. "Early human visual cortex encodes biases in speed perception." Neuroscience Research 71 (September 2011): e48. http://dx.doi.org/10.1016/j.neures.2011.07.204.

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Wang, H. X., E. P. Merriam, J. Freeman, and D. J. Heeger. "Motion Direction Biases and Decoding in Human Visual Cortex." Journal of Neuroscience 34, no. 37 (September 10, 2014): 12601–15. http://dx.doi.org/10.1523/jneurosci.1034-14.2014.

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Reiman, Teemu, and Carl Rollenhagen. "Human and organizational biases affecting the management of safety." Reliability Engineering & System Safety 96, no. 10 (October 2011): 1263–74. http://dx.doi.org/10.1016/j.ress.2011.05.010.

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Mazzilli, Giacomo, and Andrew J. Schofield. "A Cue-Free Method to Probe Human Lighting Biases." Perception 42, no. 9 (January 2013): 932–40. http://dx.doi.org/10.1068/p7517.

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Ester, Edward, Jordan Camp, Tayna Latortue, Tommy Sprague, and John Serences. "Rapid onset of category-selective biases in human cortex." Journal of Vision 19, no. 10 (September 6, 2019): 249b. http://dx.doi.org/10.1167/19.10.249b.

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