The bias–variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias–variance tradeoff is a general concept that can be applied to human cognition as well, and we discuss implications for research in cognitive science. In particular, we show how various strands of research in cognitive science can be interpreted (...) in light of the bias–variance tradeoff, giving insight into individual differences in learning, the nature of cognitive processes, and debates in cognitive science research. (shrink)

While theories of rationality and decision making typically adopt either a single-powertool perspective or a bag-of-tricks mentality, the research program of ecological rationality bridges these with a theoretically-driven account of when different heuristic decision mechanisms will work well. Here we described two ways to study how heuristics match their ecological setting: The bottom-up approach starts with psychologically plausible building blocks that are combined to create simple heuristics that fit specific environments. The top-down approach starts from the statistical problem facing the (...) organism and a set of principles, such as the bias– variance tradeoff, that can explain when and why heuristics work in uncertain environments, and then shows how effective heuristics can be built by biasing and simplifying more complex models. We conclude with challenges these approaches face in developing a psychologically realistic perspective on human rationality. (shrink)

Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scientists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent nature of this bias obscures the existence of the bias itself, (...) making it difficult to identify, mitigate, or evaluate using standard resources in epistemology and ethics. I demonstrate these points in the case of mitigation techniques by presenting what I call ‘the Proxy Problem’. One reason biases resist revision is that they rely on proxy attributes, seemingly innocuous attributes that correlate with socially-sensitive attributes, serving as proxies for the socially-sensitive attributes themselves. I argue that in both human and algorithmic domains, this problem presents a common dilemma for mitigation: attempts to discourage reliance on proxy attributes risk a tradeoff with judgement accuracy. This problem, I contend, admits of no purely algorithmic solution. (shrink)

Basic values explain more variance in political attitudes and preferences than other personality and sociodemographic variables. The values most relevant to the political domain are those likely to reflect the degree of negativity bias. Value conflicts that represent negativity bias clarify differences between what worries conservatives and liberals and suggest that relations between ideology and negativity bias are linear.

The increasing availability of passively observed data has yielded a growing interest in “data fusion” methods, which involve merging data from observational and experimental sources to draw causal conclusions. Such methods often require a precarious tradeoff between the unknown bias in the observational dataset and the often-large variance in the experimental dataset. We propose an alternative approach, which avoids this tradeoff: rather than using observational data for inference, we use it to design a more efficient experiment. We consider (...) the case of a stratified experiment with a binary outcome and suppose pilot estimates for the stratum potential outcome variances can be obtained from the observational study. We extend existing results to generate confidence sets for these variances, while accounting for the possibility of unmeasured confounding. Then, we pose the experimental design problem as a regret minimization problem subject to the constraints imposed by our confidence sets. We show that this problem can be converted into a concave maximization and solved using conventional methods. Finally, we demonstrate the practical utility of our methods using data from the Women’s Health Initiative. (shrink)

Women are under-represented in surgery, especially in leadership and academic roles, and face a gender pay gap. There has been little work on the role of implicit biases in women’s under-representation in surgery. Nor has the impact of epistemic injustice, whereby stereotyping influences knowledge or credibility judgements, been explored. This article reports findings of a qualitative in-depth interview study with women surgeons that investigates gender biases in surgery, including subtle types of bias. The study was conducted with 46 women surgeons (...) and trainees of the Royal Australasian College of Surgeons. Maximum variance sampling strategies ensured a comprehensive set of perspectives. Data were analysed using iterative thematic analysis to document and classify forms of gender bias experienced by the participants, including implicit bias and epistemic injustice. It found four types of bias affecting women surgeons: workplace factors such as access to parental leave and role models; epistemic injustices—unfair assessments of women surgeons’ credibility by patients and colleagues; stereotyped expectations that they will carry out more of surgery’s carework, such as meeting the emotional needs of patients and objectification. Implicit biases arose in each category. Given that many of the biases identified in this study are small, are harmless on their own and are not necessarily under anyone’s conscious control, important questions arise regarding how they cause harm and how to address them. I draw on theoretical work on cumulative harm to answer these questions. (shrink)

While rapid advances in artificial intelligence hiring tools promise to transform the workplace, these algorithms risk exacerbating existing biases against marginalized groups. In light of these ethical issues, AI vendors have sought to translate normative concepts such as fairness into measurable, mathematical criteria that can be optimized for. However, questions of disability and access often are omitted from these ongoing discussions about algorithmic bias. In this paper, I argue that the multiplicity of different kinds and intensities of people’s disabilities and (...) the fluid, contextual ways in which they manifest point to the limits of algorithmic fairness initiatives. In particular, existing de-biasing measures tend to flatten variance within and among disabled people and abstract away information in ways that reinforce pathologization. While fair machine learning methods can help mitigate certain disparities, I argue that fairness alone is insufficient to secure accessible, inclusive AI. I then outline a disability justice approach, which provides a framework for centering disabled people’s experiences and attending to the structures and norms that underpin algorithmic bias. (shrink)

A result from a standard linear model course is that the variance of the ordinary least squares (OLS) coefficient of a variable will never decrease when including additional covariates into the regression. The variance inflation factor (VIF) measures the increase of the variance. Another result from a standard linear model or experimental design course is that including additional covariates in a linear model of the outcome on the treatment indicator will never increase the variance of the OLS coefficient of the (...) treatment at least asymptotically. This technique is called the analysis of covariance (ANCOVA), which is often used to improve the efficiency of treatment effect estimation. So we have two paradoxical results: adding covariates never decreases the variance in the first result but never increases the variance in the second result. In fact, these two results are derived under different assumptions. More precisely, the VIF result conditions on the treatment indicators but the ANCOVA result averages over them. Comparing the estimators with and without adjusting for additional covariates in a completely randomized experiment, I show that the former has smaller variance averaging over the treatment indicators, and the latter has smaller variance at the cost of a larger bias conditioning on the treatment indicators. Therefore, there is no real paradox. (shrink)

Sequences of numbers representing prior resource size were presented to participants in a common-pool resource dilemma. The numbers were sampled from uniform probability distributions with either a low variance (low resource uncertainty) or a high variance (high resource uncertainty). Presentations were both sequential and simultaneous. Three groups of 16 undergraduates either estimated the size of the resource when it did not represent value to them; requested an amount from the resource, identified with a sum of money, when the outcome of (...) the requests only depended on resource size; or requested from the resource (sum of money) when the outcome of the requests depended on both resource size and how much others in a group requested. In support of an individual outcome-desirability bias due to selective recall of the number sequences, after sequential presentation larger requests were observed when resource uncertainty was high than when it was low. No effects of resource uncertainty or presentation were found on the estimates of resource size. Whether or not the outcome of the requests depended on others' requests made little difference. (shrink)

Previous research using immersive virtual reality (VR) has shown that after a short period of embodiment of White people in a Black virtual body their implicit racial bias against Black people diminishes. Here we tested the effects of some socio-cognitive variables that could contribute to enhancing or reducing the implicit racial bias. The first aim of the study was to assess the beneficial effects of cooperation within a VR scenario, the second aim was to provide preliminary testing of the hypothesis (...) that empathy and political attitudes could contribute to implicit bias about race, while the third aim was to explore the relationship between political attitudes and empathy. We had (Caucasian) participants embodied in a Black virtual body and engaged either in a cooperative (Coop group) or in a non-cooperative (Neutral group) activity with a confederate experimenter embodying another Black avatar. Before and after VR, we measured participants’ implicit racial bias by means of Implicit Association Test (IAT) and their perceived closeness toward the confederate experimenter. Before VR we also assessed participants’ political attitudes and empathy traits. Results revealed that, as compared to the Neutral group, the Coop group showed lower IAT scores after the social interaction. Interestingly, in the Neutral but not the Coop group the perceived closeness toward the confederate experimenter was associated with the initial racial bias: the more the participants reduced their distance, the more they reduced their IAT score. Moreover, reported traits of empathy and political attitudes significantly explained the variance observed in the initial implicit bias, with perspective-taking, empathic concern, and personal distress being significant predictors of the IAT scores. Finally, there was a relationship between political attitudes and empathy: the more participants considered themselves as left-wing voters, the higher their perspective-taking and empathic concern scores. We discuss these findings within the neuroscientific and social cognition field and encourage scholars from different domains to further explore whether and under which conditions a given manipulation for reducing racial bias could be efficiently transposed in VR. (shrink)

Historically, cognitivists considered moral choices to be determined by analytic processes. Recent theories, however, have emphasized the role of intuitive processes in determining moral choices. We propose that the engagement of analytic and intuitive processes is contingent on the type of tradeoff being considered. Specifically, when a tradeoff necessarily violates a moral principle no matter what choice is made, as in tragic tradeoffs, its resolution should result in greater moral conflict and less confidence in choice than when the (...) tradeoff offers a moral escape route, as in taboo tradeoffs. We manipulated tradeoff type in between subjects design and confirmed the prediction that tragic tradeoffs prompt more conflict and less confidence than taboo tradeoffs. The findings further revealed that moral conflict mediated the effect of tradeoff type on confidence. The study sheds light on the manner in which human minds resolve moral problems involving social agents. (shrink)

Two variables are usually recognised as determinants of human causal learning: the contingency between a candidate cause and effect, and the temporal and/or spatial contiguity between them. A common finding is that reductions in temporal contiguity produce concomitant decrements in causal judgement. This finding had previously (Shanks & Dickinson, 1987) been interpreted as evidence that causal induction is based on associative learning processes. Buehner and May (2002, 2003, 2004) have challenged this notion by demonstrating that the impact of temporal delay (...) depends on expectations about the timeframe between cause and effect. A corollary of this knowledge-mediation account is that in certain situations longer delays could facilitate, while shorter delays should impair, causal learning. Here we present two experiments involving a physical apparatus that demonstrate a detrimental effect of contiguity under certain conditions. In contrast to all previous studies, delays universally promoted causal learning. This evidence is clearly at variance with the notion of a bottom-up contiguity bias in causal induction. A new, more general timeframe bias is discussed. (shrink)

Field and Hineline have shown how pervasive and insidious is the tendency to make dispositional attributions, even among those who criticize the practice, and they identify a bias for models of contiguous causation as one reason for this tendency. They argue that order can be found at multiple scales of analysis and that in some cases a translation to a model of contiguous causation is impossible. I suggest that pragmatic considerations are sufficient to justify a particular scale of analysis and (...) observe that behavioral principles are fundamentally extended in time. However, I argue that accounting for variance is the goal of science; when events at one level are indeed mediated by those at another, more of the variance can be accounted for by considering both, and there is no principled reason for considering only one. (shrink)

Scholars have long debated the appropriate balance between efficiency and redistribution. But recently, a wave of critics has argued not only that efficiency is less important, but that efficiency analysis itself is fundamentally flawed. Some say that efficiency is incoherent because there is no neutral baseline from which to judge inefficiency. Others say that efficiency is biased toward those best able to pay (generally, the rich). This essay contends that efficiency is not meaningfully incoherent or biased. The most widely discussed (...) forms of efficiency do not require any particular baseline, and even those that do require a baseline can still serve as useful approximations of more theoretically sound but computationally demanding measures. Moreover, arguments of bias do not account for the source of funds in public projects, produce unintuitive results, and draw an arbitrary cutoff between bias and non-bias that elides important distributional details. Ultimately, the tradeoff between efficiency and redistribution remains the most useful frame for policy debate. (shrink)

Therapeutic reactivity among psychology trainees was ascertained by their response to 10 clinical vignettes depicting clients with HIV who are sexually active with uninformed partners. This construct accounts for the relative change in decisions to maintain the confidentiality of clients who acknowledge safe versus unsafe sexual behavior. As anticipated, an analysis of variance revealed a significant main effect for safety and a significant 3-way interaction. Subsequent analyses revealed that trainees exhibit the highest level of therapeutic reactivity toward heterosexual male clients, (...) and the lowest reactive stance toward heterosexual female clients. Although the decisional pattern evidenced toward heterosexuals seems congruent with epidemiological estimates of risk, the decisions toward lesbians and gay males appear more likely a function of bias. The ethical implications of these findings are discussed. (shrink)

The literature on algorithmic fairness has examined exogenous sources of biases such as shortcomings in the data and structural injustices in society. It has also examined internal sources of bias as evidenced by a number of impossibility theorems showing that no algorithm can concurrently satisfy multiple criteria of fairness. This paper contributes to the literature stemming from the impossibility theorems by examining how informational richness affects the accuracy and fairness of predictive algorithms. With the aid of a computer simulation, we (...) show that informational richness is the engine that drives improvements in the performance of a predictive algorithm, in terms of both accuracy and fairness. The centrality of informational richness suggests that classification parity, a popular criterion of algorithmic fairness, should be given relatively little weight. But we caution that the centrality of informational richness should be taken with a grain of salt in light of practical limitations, in particular, the so-called bias-variance trade off. (shrink)

Many cognitive and evolutionary theories of religion argue that supernatural explanations are byproducts of our cognitive adaptations. An influential argument states that our supernatural explanations result from a tendency to generate anthropomorphic explanations, and that this tendency is a byproduct of an error management strategy because agents tend to be associated with especially high fitness costs. We propose instead that anthropomorphic and other supernatural explanations result as features of a broader toolkit of well-designed cognitive adaptations, which are designed for explaining (...) the abstract and causal structure of complex, unobservable, and uncertain phenomena that have substantial impacts on fitness. Specifically, we argue that (1) mental representations about the abstract vs. the supernatural are largely overlapping, if not identical, and (2) when the data-generating processes for scarce and ambiguous observations are complex and opaque, a naive observer can improve a bias-variance trade-off by starting with a simple, underspecified explanation that Western observers readily interpret as “supernatural.” We then argue that (3) in many cases, knowledge specialists across cultures offer pragmatic services that involve apparently supernatural explanations, and their clients are frequently willing to pay them in a market for useful and effective services. We propose that at least some ethnographic descriptions of religion might actually reflect ordinary and adaptive responses to novel problems such as illnesses and natural disasters, where knowledge specialists possess and apply the best available explanations about phenomena that would otherwise be completely mysterious and unpredictable. (shrink)

Science is widely perceived as an especially systematic approach to knowing; engineering could be conceived as an especially systematic approach to willing. The transcendental precepts of Bernard Lonergan may be adapted to provide the backdrop for this assessment, which is manifest when the scientific and engineering methods are compared. In science, although the will is implicitly involved, the intellect is primary, because the goal is ideal—additional “objective” knowledge. In engineering, although the intellect is implicitly involved, the will is primary, because (...) the goal is pragmatic—some “subjective” outcome, which is often selected by a manager or client, rather than the engineer. Furthermore, engineering problems are rarely well-defined; uncertainties and resource constraints dictate that they be conceptualized and solved heuristically. As a result, different engineers will follow different design procedures and develop different models, none of which is uniquely “correct.” Because tradeoffs are always necessary, engineering decision-making—and human behavior in general—is more intentional than rational. Recognizing this can help today’s society to overcome its traditional bias in favor of knowing over willing and to engage engineers more explicitly in addressing the many challenges that it faces, technological and otherwise. (shrink)

Measures of algorithmic bias can be roughly classified into four categories, distinguished by the conditional probabilistic dependencies to which they are sensitive. First, measures of "procedural bias" diagnose bias when the score returned by an algorithm is probabilistically dependent on a sensitive class variable (e.g. race or sex). Second, measures of "outcome bias" capture probabilistic dependence between class variables and the outcome for each subject (e.g. parole granted or loan denied). Third, measures of "behavior-relative error bias" capture probabilistic dependence between (...) class variables and the algorithmic score, conditional on target behaviors (e.g. recidivism or loan default). Fourth, measures of "score-relative error bias" capture probabilistic dependence between class variables and behavior, conditional on score. Several recent discussions have demonstrated a tradeoff between these different measures of algorithmic bias, and at least one recent paper has suggested conditions under which tradeoffs may be minimized. -/- In this paper we use the machinery of causal graphical models to show that, under standard assumptions, the underlying causal relations among variables forces some tradeoffs. We delineate a number of normative considerations that are encoded in different measures of bias, with reference to the philosophical literature on the wrongfulness of disparate treatment and disparate impact. While both kinds of error bias are nominally motivated by concern to avoid disparate impact, we argue that consideration of causal structures shows that these measures are better understood as complicated and unreliable measures of procedural biases (i.e. disparate treatment). Moreover, while procedural bias is indicative of disparate treatment, we show that the measure of procedural bias one ought to adopt is dependent on the account of the wrongfulness of disparate treatment one endorses. Finally, given that neither score-relative nor behavior-relative measures of error bias capture the relevant normative considerations, we suggest that error bias proper is best measured by score-based measures of accuracy, such as the Brier score. (shrink)

It is widely believed that democracies require knowledgeable citizens to function well. But the most politically knowledgeable individuals also tend to be the most partisan, and the strength of partisan identity tends to corrupt political thinking. This creates a conundrum. On the one hand, an informed citizenry is allegedly necessary for a democracy to flourish. On the other hand, the most knowledgeable and passionate voters are also the most likely to think in corrupted, biased ways. What to do? This paper (...) examines this tension and draws out several lessons. First, it is not obvious that more knowledgeable voters will make better political decisions. Second, attempts to remedy voter ignorance are problematic because partisans tend to become more polarized when they acquire more information. Third, solutions to citizen incompetence must focus on the intellectual virtue of objectivity. Fourth, some forms of epistocracy are troubling, in part, because they would increase the political power of the most dogmatic and biased individuals. Fifth, a highly restrictive form of epistocracy may escape the problem of political dogmatism, but epistocrats may face a steeper tradeoff between inclusivity and epistemic virtue than many would like. (shrink)

As machine learning informs increasingly consequential decisions, different metrics have been proposed for measuring algorithmic bias or unfairness. Two popular “fairness measures” are calibration and equality of false positive rate. Each measure seems intuitively important, but notably, it is usually impossible to satisfy both measures. For this reason, a large literature in machine learning speaks of a “fairness tradeoff” between these two measures. This framing assumes that both measures are, in fact, capturing something important. To date, philosophers have seldom (...) examined this crucial assumption, and examined to what extent each measure actually tracks a normatively important property. This makes this inevitable statistical conflict – between calibration and false positive rate equality – an important topic for ethics. In this paper, I give an ethical framework for thinking about these measures and argue that, contrary to initial appearances, false positive rate equality is in fact morally irrelevant and does not measure fairness. (shrink)

Mental accounting is a concept associated with the work of Richard Thaler. According to Thaler, people think of value in relative rather than absolute terms. They derive pleasure not just from an object’s value, but also the quality of the deal – its transaction utility (Thaler, 1985). In addition, humans often fail to fully consider opportunity costs (tradeoffs) and are susceptible to the sunk cost fallacy. Why are people willing to spend more when they pay with a credit card than (...) cash (Prelec & Simester, 2001)? Why would more individuals spend $10 on a theater ticket if they had just lost a $10 bill than if they had to replace a lost ticket worth $10 (Kahneman & Tversky, 1984)? Why are people more likely to spend a small inheritance and invest a large one (Thaler, 1985)? According to the theory of mental accounting, people treat money differently, depending on factors such as the money’s origin and intended use, rather than thinking of it in terms of the “bottom line” as in formal accounting (Thaler, 1999). An important term underlying the theory is fungibility, the fact that all money is interchangable and has no labels. In mental accounting, people treat assets as less fungible than they really are. Even seasoned investors are susceptible to this bias when they view recent gains as disposable “house money” (Thaler & Johnson, 1990) that can be used in high-risk investments. In doing so, they make decisions on each mental account separately, losing out the big picture of the portfolio. (See also partitioning and pain of paying for ideas related to mental accounting.) . (shrink)

Judges in multiple US states, such as New York, Pennsylvania, Wisconsin, California, and Florida, receive a prediction of defendants’ recidivism risk, generated by the COMPAS algorithm. If judges act on these predictions, they implicitly delegate normative decisions to proprietary software, even beyond the previously documented race and age biases. Using the ProPublica dataset, we demonstrate that COMPAS predictions favor jailing over release. COMPAS is biased against defendants. We show that this bias can largely be removed. Our proposed correction increases overall (...) accuracy, and attenuates anti-black and anti-young bias. However, it also slightly increases the risk that defendants are released who commit a new crime before tried. We argue that this normative decision should not be buried in the code. The tradeoff between the interests of innocent defendants and of future victims should not only be made transparent. The algorithm should be changed such that the legislator and the courts do make this choice. (shrink)

This paper brings the intellectual tools of cognitive science to bear on resolving the “paradox of the active user” [Interfacing Thought: Cognitive Aspects of Human–Computer Interaction, Cambridge, MIT Press, MA, USA]—the persistent use of inefficient procedures in interactive tasks by experienced or even expert users when demonstrably more efficient procedures exist. The goal of this paper is to understand the roots of this paradox by finding regularities in these inefficient procedures. We examine three very different data sets. For each data (...) set, we first satisfy ourselves that the preferred procedures used by some subjects are indeed less efficient than the recommended procedures. We then amass evidence, for each set, and conclude that when a preferred procedure is used instead of a more efficient, recommended procedure, the preferred procedure tends to have two major characteristics: (1) the preferred procedure is a well‐practiced, generic procedure that is applicable either within the same task environment in different contexts or across different task environments, and (2) the preferred procedure is composed of interactive components that bring fast, incremental feedback on the external problem states. The support amassed for these characteristics leads to a new understanding of the paradox. In interactive tasks, people are biased towards the use of general procedures that start with interactive actions. These actions require much less cognitive effort as each action results in an immediate change to the external display that, in turn, cues the next action. Unfortunately for the users, the bias to use interactive unit tasks leads to a path that requires more effort in the long run. Our data suggest that interactive behavior is composed of a series of distributed choices; that is, people seldom make a once‐and‐for‐all decision on procedures. This series of biased selection of interactive unit tasks often leads to a stable suboptimal level of performance. (shrink)

A substantial portion of the knowledge base of psychology is based on subjective reports with a risk of information bias. The objective of the present study was to elucidate one contextual source of variance and potential bias in subjective reports: the influence of affective state at the time of responding to questionnaires. Employees were subjected to mood-induction procedures in the laboratory. Neutral, positive, and negative moods were induced by combinations of pictures from the international affective picture set and music. The (...) subjects responded to questions on visual analog scales in order to optimize sensitivity and attenuate short-term memory effects. Most subjects exhibited significant affective-state inductions with no change in arousal. The analyses took affective response to the manipulation into account. Only four of 20 questions were somewhat influenced by induced affective state: job overload, social support from co-workers, satisfaction with getting to develop personally, and an item measuring agreeableness. In general, responding to questions of work that were phrased for valence was little or insignificantly influenced by induced affective state. (shrink)

For scatterplots with gaussian distributions of dots, the perception of Pearson correlation r can be described by two simple laws: a linear one for discrimination, and a logarithmic one for perceived magnitude (Rensink & Baldridge, 2010). The underlying perceptual mechanisms, however, remain poorly understood. To cast light on these, four different distributions of datapoints were examined. The first had 100 points with equal variance in both dimensions. Consistent with earlier results, just noticeable difference (JND) was a linear function of the (...) distance away from r = 1, and the magnitude of perceived correlation a logarithmic function of this quantity. In addition, these laws were linked, with the intercept of the JND line being the inverse of the bias in perceived magnitude. Three other conditions were also examined: a dot cloud with 25 points, a horizontal compression of the cloud, and a cloud with a uniform distribution of dots. Performance was found to be similar in all conditions. The generality and form of these laws suggest that what underlies correlation perception is not a geometric structure such as the shape of the dot cloud, but the shape of the probability distribution of the dots, likely inferred via a form of ensemble coding. It is suggested that this reflects the ability of observers to perceive the information entropy in an image, with this quantity used as a proxy for Pearson correlation. (shrink)

A meta-analysis of 190 cross-cultural emotion studies, published between 1967 and 2000, was performed to examine (1) to what extent reported cross-cultural differences in emotion variables could be regarded as valid (substantive factors) or as method-related (statistical artefacts, cultural bias), and (2) which country characteristics could explain valid cross-cultural differences in emotion. The relative contribution of substantive and method-related factors at sample, study, and country level was investigated and country-level explanations for differences in emotions were tested. Results indicate that a (...) correction for statistical artefacts and method-related factors reduced the observed cross-cultural effect sizes considerably. After controlling for valence (positive vs. negative emotions) and kind of study (self-report vs. recognition studies), the remaining cross-cultural variance was associated with subsistence mode, political system, values, and religiosity. Values explained more variance than did ecological or sociopolitical variables. It was concluded that both method-related factors (13.8% of variance explained) and culture-level factors (27.9% of variance explained) underlie observed cross-cultural differences. (shrink)

Syntactic templates serve as schemas, allowing speakers to describe complex events in a systematic fashion. Motion events have long served as a prime example of how different languages favor different syntactic frames, in turn biasing their speakers toward different event conceptualizations. However, there is also variability in how motion events are syntactically framed within languages. Here, we measure the consistency in event encoding in two languages, Spanish and Swedish. We test a dominant account in the literature, namely that variability within (...) a language can be explained by specific properties of the events. This event-properties account predicts that descriptions of one and the same event should be consistent within a language, even in languages where there is overall variability in the use of syntactic frames. Spanish and Swedish speakers (N= 84) described 32 caused motion events. While the most frequent syntactic framing in each language was as expected based on typology (Spanish: verb-framed, Swedish: satellite-framed, cf. Talmy,2000), Swedish descriptions were substantially more consistent than Spanish descriptions. Swedish speakers almost invariably encoded all events with a single syntactic frame and systematically conveyed manner of motion. Spanish descriptions, in contrast, varied much more regarding syntactic framing and expression of manner. Crucially, variability in Spanish descriptions was not mainly a function of differences between events, as predicted by the event-properties account. Rather, Spanish variability in syntactic framing was driven by speaker biases. A similar picture arose for whether Spanish descriptions expressed manner information or not: Even after accounting for the effect of syntactic choice, a large portion of the variance in Spanish manner encoding remained attributable to differences among speakers. The results show that consistency in motion event encoding starkly differs across languages: Some languages (like Swedish) bias their speakers toward a particular linguistic event schema much more than others (like Spanish). Implications of these findings are discussed with respect to the typology of event framing, theories on the relationship between language and thought, and speech planning. In addition, the tools employed here to quantify variability can be applied to other domains of language. (shrink)

Few cross-national studies have been conducted on academic dishonesty. The aim of this study was to explore students’ disclosed levels of academic dishonesty between New Zealand and Nigeria. The measures obtained included incidence, acceptability, and justification of dishonest action. It was hypothesized that there would be differences between the two groups and that differences could be explained in terms of deontology, cultural relativism, utilitarianism, rational fair exchange, and/or response bias. There were 844 medical and health science students who participated in (...) the study and completed a questionnaire that explored incidence, acceptability, and justification of dishonest action. A binary logistic regression revealed that age and incidence were significant predictors of country allocation. Nigerian students reported more engagement in dishonest behaviors, and it is argued that these students have diverse cultural expectations, language variances, response strategies, norms, beliefs, and values. Educational interventions are essential to address concerns related to academic dishonesty such as promoting cross-cultural discourse, providing study skills programs, utilizing peer mentoring, creating formal exchange strategies, and endorsing honor codes. (shrink)

Ethical consumerism has been dramatically increasing in recent decades, but in service sector, fewer research has been conducted especially in the fast-food industry. In this paper, we determined empirically the consumer perceived ethicality effects on repurchase intentions as well as on word of mouth through brand image partial mediation and customer expertise moderation in fast-food sector. The data were collected from 307 consumers of the fast-food restaurants through self-administered questionnaires. Common method variance and social desirability bias were measured before testing (...) hypotheses, and the data were investigated by SPSS and AMOS 21st version. The empirical results demonstrated that consumer perceived ethicality effects on repurchase intentions directly and through brand image partial mediation indirectly. Moreover, the direct and positive effects of repurchase intentions on word of mouth were experienced. This study generalized the results of previous studies of consumer perceived ethicality conducted in western culture in Pakistani fast-food sector. This study also proposed that firms should tackle ethical concerns at corporate and operational strategy levels to improve consumers’ behavioral intentions. (shrink)

An important problem for decision-makers in society deals with the efficient and equitable allocation of scarce resources to individuals and groups. The significance of this problem is rapidly growing since there is a rising demand for scarce resources all over the world. Such resource dilemmas belong to a conceptually broader class of situations known as social dilemmas. In this type of dilemma, individual choices that appear ‘‘rational’’ often result in suboptimal group outcomes. In this article we study how people make (...) monetary allocation decisions between the community where they live and a neighbouring community, with the aim of finding out to what extent these decisions are subject to biased over-weighting. The manuscript reports four experiments that deal with the way individuals make such allocation decisions when the potential beneficiaries are such communities. The specific goal of these experiments is to gauge the amount of bias in the weights that people assign to the various beneficiaries. Taken together, the results from all the four experiments suggest that making the gain of the neighbouring community prominent to a higher extent de-biases the outcomes (the prominence effect) compared to when own community gain is made prominent. Place identity is discussed as a potentially important factor in this connection. Hence, it may be argued that there seems to be some kind of a pro-self component that is able to explain a large part of the variance observed for the prominence effect. Connections between such a factor and in-group favouritism are discussed. A strength of the study was that these major results appeared to be quite robust when considered as task effects, as the salience of the manipulated context factors in the studies (in terms of reliable main or interaction effects) did not distort them. (shrink)

ABSTRACTThis is the first study to investigate multiple cognitive biases in adolescence simultaneously, to examine whether anxiety and depression are associated with biases in attention and interpretation, and whether these biases are able to predict unique variance in self-reported levels of anxiety and depression. A total of 681 adolescents performed a Dot Probe Task, an Emotional Visual Search Task, and an Interpretation Recognition Task. Attention and interpretation biases were significantly correlated with anxiety. Mixed results were reported with regard to depression: (...) evidence was found for an interpretation bias, and for an attention bias as measured with the EVST but not with the DPT. Furthermore, interpretation and attention biases predicted unique variance in anxiety and depression scores. These results indicate that attention and interpretation biases are unique processes in anxiety and depression. They also suggest that anxiety and depression are partly based on similar underlying... (shrink)

In this article we attend to recent critiques of psychometric applications of life history theory to variance among humans and develop theory to advance the study of latent LH constructs. We then reanalyze data previously examined by Richardson et al., 2017, https://doi.org/10.1177/1474704916666840 to determine whether previously reported evidence of multidimensionality is robust to the modeling approach employed and the structure of LH indicators is invariant by sex. Findings provide further evidence that a single LH dimension is implausible and that researchers (...) should cease interpreting K-factor scores as empirical proxies for LH speed. In contrast to the original study, we detected a small inverse correlation between mating competition and Super-K that is consistent with a trade-off. Tests of measurement invariance across the sexes revealed evidence of metric invariance, consistent with the theory that K is a proximate cause of its indicators; however, evidence of partial scalar invariance suggests use of scores likely introduces bias when the sexes are compared. We discuss limitations and identify approaches that researchers may use to further evaluate the validity of the K-factor and other applications of LH to human variation. (shrink)

Previous studies suggest in line with dual process models that interoceptive skills affect controlled decisions via automatic or implicit processing. The “framing effect” is considered to capture implicit effects of task-irrelevant emotional stimuli on decision-making. We hypothesized that cardiac awareness, as a measure of interoceptive skills, is positively associated with susceptibility to the framing effect. Forty volunteers performed a risky-choice framing task in which the effect of loss versus gain frames on decisions based on identical information was assessed. The results (...) show a positive association between cardiac awareness and the framing effect, accounting for 24% of the variance in the framing effect. These findings demonstrate that good interoceptive skills are linked to poorer performance in risky choices based on ambivalent information when implicit bias is induced by task-irrelevant emotional information. These findings support a dual process perspective on decision-making and suggest that interoceptive skills mediate effects of implicit bias on decisions. (shrink)

This paper is intended to be a consideration of the role of multi-level modelling in educational research. It is not a guide on how to design or perform such an analysis. There are several references in the text to sources that teach the practicalities perfectly well, and the technique is anyway similar to other forms of regression and to analysis of variance. Rather, the paper describes what multi-level modelling is, why it is used, and what its limitations are. It does (...) so in the hope that some readers will be enthused sufficiently to become appropriately critical 'consumers' of research using this approach, so building research capacity, and easing pressures on 'specialist' reviewers. Anyone who can read or perform standard multivariate analyses can understand, referee, or conduct a multi-level model. Additionally, the paper makes three key points. The generally small sample size in each cluster at the lowest level of any multi-level model means that there is a danger of a greater bias in the results than in standard analyses that pool the data from all clusters. Even where there are genuine gains through the use of multi-level models these have to be set against a loss in simple intuitive grasp of the results, especially amongst policy-makers and practitioners. Therefore, long term, we are probably better advised to improve our research designs and the quality of the data we collect than to focus on more and more complex forms of analysis to overcome deficiencies in the datasets we already have. (shrink)