<p>Faces constitute one of the most important stimuli for humans. Studies show that women recognize more faces than men, and that females are particularly able to recognize female faces, thus exhibiting an own-sex bias. In the present thesis, three empirical studies investigated the generality of sex differences in face recognition and the female own-sex bias. <i>Study I</i> explored men’s and women’s face recognition performance for Bangladeshi and Swedish female and male faces of adults and children. Result showed sex differences, favoring women, for all face categories. <i>Study II </i>assessed boys’ and girls’ ability to recognize female and male faces from two age- and ethnic groups. The result demonstrated that girls recognize more faces than boys do, but that no sex differences were present for Swedish male faces. The results from <i>Study I</i> and <i>II</i> consistently demonstrate that females show reliable own-sex biases independent of whether the female faces were young, old, or of Bangladeshi or Swedish origin. In an attempt to explain the mechanisms of sex differences in face recognition and the female own-sex bias, <i>Study III</i> investigated men’s and women’s recognition performance for androgynous faces, either labeled “men”, “women”, or “faces”. The result showed that women told to remember “women” recognized more faces than women told to remember faces labeled “men” or “faces”, and that sex differences were present for androgynous faces, regardless of the label. Based on these findings, it is suggested that females’ attention is in particular directed towards other females, resulting in an own-sex bias. It is also suggested that there may be a difference in females’ and males’ orientation toward other individuals. This difference can have a biological base, which together with socialization may result in sex differences in face recognition. </p>

Faces constitute one of the most important stimuli for humans. Studies show that women recognize more faces than men, and that females are particularly able to recognize female faces, thus exhibiting an own-sex bias. In the present thesis, three empirical studies investigated the generality of sex differences in face recognition and the female own-sex bias. Study I explored men’s and women’s face recognition performance for Bangladeshi and Swedish female and male faces of adults and children. Result showed sex differences, favoring women, for all face categories. Study II assessed boys’ and girls’ ability to recognize female and male faces from two age- and ethnic groups. The result demonstrated that girls recognize more faces than boys do, but that no sex differences were present for Swedish male faces. The results from Study I and II consistently demonstrate that females show reliable own-sex biases independent of whether the female faces were young, old, or of Bangladeshi or Swedish origin. In an attempt to explain the mechanisms of sex differences in face recognition and the female own-sex bias, Study III investigated men’s and women’s recognition performance for androgynous faces, either labeled “men”, “women”, or “faces”. The result showed that women told to remember “women” recognized more faces than women told to remember faces labeled “men” or “faces”, and that sex differences were present for androgynous faces, regardless of the label. Based on these findings, it is suggested that females’ attention is in particular directed towards other females, resulting in an own-sex bias. It is also suggested that there may be a difference in females’ and males’ orientation toward other individuals. This difference can have a biological base, which together with socialization may result in sex differences in face recognition.

In the last few decades, vector autoregressive (VAR) models have gained tremendous popularity as an all-purpose tool in econometrics and other disciplines. Some of their most prominent uses are for forecasting, causality tests, tests of economic theories, hypothesis-seeking, data characterisation, innovation accounting, policy analysis, and cointegration analysis. Their popularity appears to be attributable to their flexibility relative to other models rather than to their virtues per se. In addition, analysts often use VAR models as benchmark models. VAR modeling has not gone uncriticised, though. A list of relevant arguments against VAR modelling can be found in Section 2.3 of this thesis. There is one additional problem which is rarely mentioned though, namely the often heavily biased estimates in VAR models. Although methods to reduce this bias have been available for quite some time, it has probably not been done before, at least not in any systematic way. The present thesis attempts to systematically examine the performance of bias-reduced VAR estimates, using two existing and one newly derived approximation to the bias. The thesis is orginanised as follows. After a short introductory chapter, a brief history of VAR modelling can be found in Chapter 2 together with a review of different representations and a compilation of criticisms against VAR models. Chapter 3 reports the results of very extensive Monte Carlo experiments serving dual purposes: Firstly, the simulations will reveal whether or not bias really poses a serious problem, because if it turns out that biases appear only by exception or are mainly insignificant, there would be little need to reduce the bias. Secondly, the same data as in Chapter 3 will be used in Chapter 4 to evaluate the bias approximations, allowing for direct comparison between bias-reduced and original estimates. Though Monte Carlo methods have been (rightfully) criticised for being too specific to allow for any generalisation, there seems to be no good alternative to analyse small-sample properties of complicated estimators such as these. Chapter 4 is in a sense the core of the thesis, containing evaluations of three bias approximations. The performance of the bias approximations is evaluated chiefly using single regression equations and 3D surfaces. The only truly new research result in this thesis can also be found in Chapter 4; a second-order approximation to the bias of the parameter matrix in a VAR(p) model. Its performance is compared with the performance of two existing first-order approximations, and all three are used to construct bias-reduced estimators, which are then evaluated. Chapter 5 holds an application of US money supply and inflation in order to find out whether the results in Chapter 4 can have any real impacts. Unfortunately though, bias reduction appears not to make any difference in this particular case. Chapter 6 concludes. / Diss. Stockholm : Handelshögsk.

Neurologically normal individuals show a leftward spatial bias and tend to collide with objects on the right side more frequently than on the left. The upper visual field is associated with extrapersonal space, and mediated by the ventral stream through parvocellular projections. The lower visual field is associated with peripersonal space, and mediated by the dorsal stream through magnocellular projections. Upper and lower visual field differences have been observed in perceptual asymmetries but results have been mixed. Object- and space-based coordinates also both influence the leftward bias; however their relative contributions are unknown as similar spatial conditions are often collapsed across. More left-side collisions emerged on a route following task in the lower visual field and more right-side collisions were seen in the upper visual field (Thomas, Stuckel, Gutwin, & Elias, 2009). Left-handers made more right-side collisions in the central condition, whereas right-handers showed no bias. Leftward biases on the greyscales task were stronger in the lower visual field; however no distance-based differences were observed (Thomas & Elias, 2010). A stronger spatial bias was found on the greyscales task, whereas a stronger object-based bias was found on the object luminosity task (Thomas & Elias, in press). When individual spatial conditions were examined, the image chosen most often was always located in the lower field. Stimulus type and spatial location interacted to determine which coordinate type contributes more strongly to leftward biases. We also found that the leftward bias on the greyscales task was stronger in the lower visual field during prolonged presentation and in the upper visual field during brief presentation. A global motion task was created to preferentially engage magnocellular projections to the dorsal stream. Isoluminant red/green and blue/yellow colour tasks, which preferentially engage parvocellular projections to the ventral stream, were also created. Leftward biases were seen on the greyscales and motion tasks. On an isoluminant colour task, biases were significantly weakened, suggesting leftward biases exhibited by neurologically normal people are mediated by magnocellular projections to the dorsal stream and this preferential processing leads to a lower visual field advantage on the greyscales task.

Previous studies examining memory biases for threatening faces in social anxiety (SA) have yielded inconclusive results. In the present study, memory and expectancy biases were tested within the context of a novel face recognition paradigm that was designed to offset some of the methodological challenges that have hampered previous research. Undergraduates with high (n = 40) and low (n = 40) levels of SA viewed a series of neutral faces randomly paired with phrases that communicated positive or negative social feedback. Participants’ recognition memory was tested for previously encountered faces, and for their categorization of each encoded face as having been associated with negative (mean) or positive (nice) interpersonal statements. For new faces, participants were asked whether the person seemed mean or nice. Results provided no evidence in support of a general memory bias for threatening (mean) faces among high SA individuals, but instead suggested that high SA individuals lack a positive expectancy bias to appraise new social partners as being nice. Implications are considered for cognitive behavioral and interpersonal models of SA.

Neurologically normal individuals show a leftward spatial bias and tend to collide with objects on the right side more frequently than on the left. The upper visual field is associated with extrapersonal space, and mediated by the ventral stream through parvocellular projections. The lower visual field is associated with peripersonal space, and mediated by the dorsal stream through magnocellular projections. Upper and lower visual field differences have been observed in perceptual asymmetries but results have been mixed. Object- and space-based coordinates also both influence the leftward bias; however their relative contributions are unknown as similar spatial conditions are often collapsed across. More left-side collisions emerged on a route following task in the lower visual field and more right-side collisions were seen in the upper visual field (Thomas, Stuckel, Gutwin, & Elias, 2009). Left-handers made more right-side collisions in the central condition, whereas right-handers showed no bias. Leftward biases on the greyscales task were stronger in the lower visual field; however no distance-based differences were observed (Thomas & Elias, 2010). A stronger spatial bias was found on the greyscales task, whereas a stronger object-based bias was found on the object luminosity task (Thomas & Elias, in press). When individual spatial conditions were examined, the image chosen most often was always located in the lower field. Stimulus type and spatial location interacted to determine which coordinate type contributes more strongly to leftward biases. We also found that the leftward bias on the greyscales task was stronger in the lower visual field during prolonged presentation and in the upper visual field during brief presentation. A global motion task was created to preferentially engage magnocellular projections to the dorsal stream. Isoluminant red/green and blue/yellow colour tasks, which preferentially engage parvocellular projections to the ventral stream, were also created. Leftward biases were seen on the greyscales and motion tasks. On an isoluminant colour task, biases were significantly weakened, suggesting leftward biases exhibited by neurologically normal people are mediated by magnocellular projections to the dorsal stream and this preferential processing leads to a lower visual field advantage on the greyscales task.

In this study, I examine the effects of audit guidance and estimation uncertainty on auditors’ confirmation bias and professional skepticism when evaluating fair value estimates. Fair value estimation is becoming more prevalent in financial reporting frameworks, and regulators warn that fair value estimation presents higher risk of material misstatement when greater judgment in estimation is involved. In addition recent evidence from the Public Company Accounting Oversight Board (PCAOB) indicates that some auditors may not be exercising sufficient professional skepticism when performing audit procedures in higher risk areas of the audit. Martin et al. (2006) suggest that it may be the audit standards themselves that orient auditors toward biased evaluation of management’s estimates, suggesting that such directional audit guidance leads to confirmation bias. Further, it is possible that because of auditors’ intolerance for ambiguity, that a greater degree of estimation uncertainty exacerbates the bias. Thus, I examine whether directional audit guidance (e.g., support management’s estimate, and oppose management’s estimate) versus non-directional audit guidance (e.g., develop own estimate) affects auditors’ confirmation bias differentially under varying degrees of uncertainty (e.g., low vs. high), and the extent to which this bias increases or decreases professional skepticism. The results show that auditors exhibit the greatest confirmation bias when they are directed to oppose versus support management’s estimate or generate their own estimate, and that this bias increases the degree of professional skepticism exercised by auditors. Further, the greatest extent of confirmation bias resulted when auditors were directed to oppose management’s estimate and estimation uncertainty was high. This study sheds light on the effects of directional versus non-directional audit guidance in the presence of uncertainty and should be informative to standard setters and practitioners as they press forward in issuing new audit guidance related to the evaluation of fair value estimates.

The human visual system has learned to assume that light originates from above, most likely because of the persistent natural overhead light source – the sun (Ramachandran, 1988). Asymmetries of perception in neurologically normal individuals, like assuming light is coming from above, in part result from efficiency measures of the visual system. Not only is light assumed to come from above, but light from above and to the left has been found to decrease reaction times in target finding as well as increase aesthetic preference (Sun & Perona, 1998; Smith & Elias, 2013). The underlying cause of the bias towards upper-left lighting is debated, and may have a relationship with another peculiar phenomenon in neurologically normal individuals where greater attention is paid to leftward space, called pseudoneglect (Bowers & Heilman, 1980). Alternatively, an explanation suggesting that directional reading influences lighting preferences has been proposed, as Smith and Elias (2013) found native right-to-left readers to be significantly different from leftward biased left-to-right readers. The current set of experiments used eye-tracking and a target finding paradigm to assess differences between left-to-right and right-to-left readers. Manipulating the position of the light illuminating a field of spheres generated targets, creating either 1 convex bubble among 15 concave depressions, or vice-versa. Results from these studies are mixed, and highlight differences between both upper and lower and lateral visual space. Light originating from above facilitated shorter average duration times for both groups, whereas left-to-right readers tended to prefer light from the upper-left, while right-to-left readers preferred light from the upper-right. No one target location in the array facilitated shorter average duration times for right-to-left readers, although left-to-right readers tended to exhibit shorter durations when identifying targets in the upper-left quadrant. Participants spent the greatest amount of time examining the upper quadrants of the array, tending to focus more on the side of the image that their native reading direction begins on. The influence of directional reading on light source perception, and the potential problems of using exclusively Western participant samples are discussed.

This thesis develops techniques for adjusting for selection bias using Gaussian process models. Selection bias is a key issue both in sample surveys and in observational studies for causal inference. Despite recently emerged techniques for dealing with selection bias in high-dimensional or complex situations, use of Gaussian process models and Bayesian hierarchical models in general has not been explored. Three approaches are developed for using Gaussian process models to estimate the population mean of a response variable with binary selection mechanism. The first approach models only the response with the selection probability being ignored. The second approach incorporates the selection probability when modeling the response using dependent Gaussian process priors. The third approach uses the selection probability as an additional covariate when modeling the response. The third approach requires knowledge of the selection probability, while the second approach can be used even when the selection probability is not available. In addition to these Gaussian process approaches, a new version of the Horvitz-Thompson estimator is also developed, which follows the conditionality principle and relates to importance sampling for Monte Carlo simulations. Simulation studies and the analysis of an example due to Kang and Schafer show that the Gaussian process approaches that consider the selection probability are able to not only correct selection bias effectively, but also control the sampling errors well, and therefore can often provide more efficient estimates than the methods tested that are not based on Gaussian process models, in both simple and complex situations. Even the Gaussian process approach that ignores the selection probability often, though not always, performs well when some selection bias is present. These results demonstrate the strength of Gaussian process models in dealing with selection bias, especially in high-dimensional or complex situations. These results also demonstrate that Gaussian process models can be implemented rather effectively so that the benefits of using Gaussian process models can be realized in practice, contrary to the common belief that highly flexible models are too complex to use practically for dealing with selection bias.

Previous studies examining memory biases for threatening faces in social anxiety (SA) have yielded inconclusive results. In the present study, memory and expectancy biases were tested within the context of a novel face recognition paradigm that was designed to offset some of the methodological challenges that have hampered previous research. Undergraduates with high (n = 40) and low (n = 40) levels of SA viewed a series of neutral faces randomly paired with phrases that communicated positive or negative social feedback. Participants’ recognition memory was tested for previously encountered faces, and for their categorization of each encoded face as having been associated with negative (mean) or positive (nice) interpersonal statements. For new faces, participants were asked whether the person seemed mean or nice. Results provided no evidence in support of a general memory bias for threatening (mean) faces among high SA individuals, but instead suggested that high SA individuals lack a positive expectancy bias to appraise new social partners as being nice. Implications are considered for cognitive behavioral and interpersonal models of SA.