Sequential Bias Research Articles

Testing for sequential bias in school inspections

ABSTRACT Inspectors are tasked with judging the quality of provision based on visits to schools. They conduct these inspections sequentially, completing one before moving on to the next. However, empirical research in a range of settings outside education suggests that prior judgements in a sequence can influence subsequent judgements, despite being logically irrelevant. We investigate whether school inspectors in England display such sequential bias by testing whether they judge similar schools differently, depending on the judgements they reached in prior inspections. We find only limited evidence of sequential bias in primary school inspections. In particular, an inspector reaching an ‘Inadequate’ judgement in their previous inspection is associated with a 42 per cent reduction in the odds of reaching another ‘Inadequate’ judgement in their next inspection. Only around 5 per cent of inspection judgements result in an ‘Inadequate’ and we do not find consistent evidence of sequential bias at other grades, meaning this bias only affects a small minority of judgements. We also do not find the same results for secondary schools, albeit in a much smaller sample.

The impact of task measurements on sequential dependence: a comparison between temporal reproduction and discrimination tasks

Decisions about a current stimulus are influenced by previously encountered stimuli, leading to sequential bias. However, the specific processing levels at which serial dependence emerges remain unclear. Despite considerable evidence pointing to contributions from perceptual and post-perceptual processes, as well as response carryover effects impacting subsequent judgments, research into how different task measurements affect sequential dependencies is limited. To address this gap, the present study investigated the role of task type in shaping sequential effects in time perception, employing a random-dot kinematogram (RDK) in a post-cue paradigm. Participants had to remember both the duration and the direction of the RDK movement and perform the task based on a post-cue, which was equally likely to be direction or duration. To delineate the task type, we employed the temporal bisection task in Experiment 1 and the duration reproduction task in Experiment 2. Both experiments revealed a significant sequential bias: durations were perceived as longer following longer previous durations, and vice versa. Intriguingly, the sequential effect was enhanced in the reproduction task following the same reproduction task (Experiment 2), but did not show significant variation by the task type in the bisection task (Experiment 1). Moreover, comparable response carryover effects were observed across two experiments. We argue that the differential impacts of task types on sequential dependence lies in the involvement of memory reactivation process in the decision stage, while the post-decision response carryover effect may reflect the assimilation by subjective, rather than objective, durations, potentially linking to the sticky pacemaker rate and/or decisional inertia.

Neural mechanisms of sequential dependence in time perception: the impact of prior task and memory processing.

Our perception and decision-making are susceptible to prior context. Such sequential dependence has been extensively studied in the visual domain, but less is known about its impact on time perception. Moreover, there are ongoing debates about whether these sequential biases occur at the perceptual stage or during subsequent post-perceptual processing. Using functional magnetic resonance imaging, we investigated neural mechanisms underlying temporal sequential dependence and the role of action in time judgments across trials. Participants performed a timing task where they had to remember the duration of green coherent motion and were cued to either actively reproduce its duration or simply view it passively. We found that sequential biases in time perception were only evident when the preceding task involved active duration reproduction. Merely encoding a prior duration without reproduction failed to induce such biases. Neurally, we observed activation in networks associated with timing, such as striato-thalamo-cortical circuits, and performance monitoring networks, particularly when a "Response" trial was anticipated. Importantly, the hippocampus showed sensitivity to these sequential biases, and its activation negatively correlated with the individual's sequential bias following active reproduction trials. These findings highlight the significant role of memory networks in shaping time-related sequential biases at the post-perceptual stages.

Systematic diurnal bias of the CMA-MESO model in southern China: Characteristics and correction

Model error is an important source of numerical weather prediction (NWP) errors. Among model errors, the systematic diurnal bias plays an important role in high-resolution numerical weather prediction models. The main purpose of this study is to explore the characteristics of the systematic diurnal bias of a high-resolution NWP model in southern China and reduce the diurnal bias to improve the forecast results, hence providing a better background field for data assimilation. Based on the China Meteorological Administration Meso-scale (CMA-MESO) high-resolution NWP model, a 15-day sequential numerical weather prediction experiment was performed in southern China, and the forecast results were analyzed. A sequential bias correction scheme (SBCS) based on analysis increments was designed to reduce the systematic diurnal bias of the CMA-MESO model, and 15-day sequential comparative experiments were carried out. The analysis results showed that the CMA-MESO model has apparent systematic diurnal biases, and the characteristics differ among variables. A large diurnal bias was mainly found in the lower model layers, and it was concentrated in areas with a complex underlying surface for the horizontal distribution, such as the Qinghai-Tibet Plateau and South China Coast. The results based on the 15-day sequential experiment showed that the sequential bias correction scheme partly reduced the systematic diurnal biases of the CMA-MESO model. The mean biases of meridional wind, zonal wind, potential temperature, and water vapor mixing ratio were reduced by 45%, 35%, 20%, and 10%, respectively, and the root mean square errors (RMSEs) were reduced by approximately 5%. This study revealed the characteristics of the systematic diurnal bias of CMA-MESO model in southern China, which may be caused by the diurnal variation in the thermal and dynamic exchange on underlying surfaces. The effectiveness of the sequential bias correction scheme was also verified, and the results had good prospects for providing more reference information for high-resolution numerical prediction models and data assimilation.

Sequential decision bias – evidence from grading exams

ABSTRACT Human perception is very comparative. A misperception of random sequential processes, however, can influence the outcome of important decisions in many areas of daily life, including the evaluation of exams in higher education. We investigate this effect by using an extensive dataset of more than 20,000 examination results that is analysed on whether a student’s performance in an exam has an impact on the evaluation of the one following, e.g. whether a poor performance lets the following shine in a better light, resulting in a better-than-expected grade. We conclude that there is evidence for sequential decision biases that do not necessarily occur generally, but rather after streaks of extreme events. An even greater effect size is detected when limiting the sample to exams with a low variance in grades and to exams that were evaluated later in the exam correction sequence. Therefore, there is evidence that under certain conditions, past evaluations may impact on current evaluations of student performances. This study should raise awareness of biases in grading against the background of their importance in the assessment of students’ educational performances, the admission to consecutive study programmes and as a key metric in the evaluation of job candidates.

Applying Behavioral Economics to microcredit in China’s rural areas

To improve the provision of microcredit to farmers, this paper describes and suggests which cognitive biases in Behavioral Economics may apply to and should be tested on borrowers (farmers) and lenders (workers at Rural Credit Cooperatives) in China’s rural market for microcredit. For example, some of the biases considered are loss aversion, ambiguity aversion and the sequential bias. If biases are found, this paper also suggests what would help mitigate the biases’ negative effects. This paper is the first to apply Behavioral Economics to the lenders in China’s rural credit market.

A neural-based account of sequential bias during perceptual judgment.

Sequential effects are prominent and pervasive phenomena that exist in most perceptual judgments. Of importance, these effects reflect dynamic aspects in our judgment bias induced by the recent context. When making successive judgments in response to a sequence of stimuli, two opposing consequences have frequently been observed: assimilation effects - current stimuli judged as being closer to preceding stimuli than they actually are, and contrast effects - current stimuli judged as being further from preceding stimuli than they actually are. Although several cognitive accounts have been previously proposed, there is still a lack of consensus on the underlying mechanism, particularly regarding the insights of the temporal dynamics. Building upon accumulating human M/EEG findings, I propose a framework to explain how sequential bias is generated, unfolded over time, and eventually incorporated into the formation of current biased judgment. By bringing sequential effects closer to a biologically plausible framework, this synthetic view could account for how the opposing consequences of sequential effects differentially evolve, distinguish the effects from other perceptual phenomena with similar behavioral outcomes (such as aftereffects and priming), and illuminate how perceptual judgment is adaptively adjusted under the impact of temporal context.

A flexible nickel phthalocyanine resistive random access memory with multi-level data storage capability

Metal phthalocyanine is considered one of the most promising candidates for the design and fabrication of flexible resistive random access memory (RRAM) devices due to its intrinsic flexibility and excellent functionality. However, performance degradation and the lack of multi-level capability, which can directly expand the storage capacity in one memory cell without sacrificing additional layout area, are the primary obstacles to the use of metal phthalocyanine RRAMs in information storage. Here, a flexible RRAM with pristine nickel phthalocyanine (NiPc) as the resistive layer is reported for multi-level data storage. Due to its high trap-concentration, the charge transport behavior of the device agrees well with classical space charge limited conduction controlled by traps, leading to an excellent performance, including a high on-off current ratio of 107, a long-term retention of 106 s, a reproducible endurance over 6000 cycles, long-term flexibility at a bending strain of 0.6 %, a write speed of 50 ns under sequential bias pulses and the capability of multi-level data storage with reliable retention and uniformity.

Testing for Jump Spillovers Without Testing for Jumps

This article develops statistical tools for testing conditional independence among the jump components of the daily quadratic variation, which we estimate using intraday data. To avoid sequential bias distortion, we do not pretest for the presence of jumps. If the null is true, our test statistic based on daily integrated jumps weakly converges to a Gaussian random variable if both assets have jumps. If instead at least one asset has no jumps, then the statistic approaches zero in probability. We show how to compute asymptotically valid bootstrap-based critical values that result in a consistent test with asymptotic size equal to or smaller than the nominal size. Empirically, we study jump linkages between US futures and equity index markets. We find not only strong evidence of jump cross-excitation between the SPDR exchange-traded fund and E-mini futures on the S&P 500 index, but also that integrated jumps in the E-mini futures during the overnight period carry relevant information. Supplementary materials for this article are available as an online supplement.

Evaluation and Postprocessing of Ensemble Fire Weather Predictions over the Northeast United States

AbstractThe Short-Range Ensemble Forecast (SREF) system is verified and bias corrected for fire weather days (FWDs) defined as having an elevated probability of wildfire occurrence using a statistical Fire Weather Index (FWI) over a subdomain of the northeastern United States (NEUS) between 2007 and 2014. The SREF is compared to the Rapid Update Cycle and Rapid Refresh analyses for temperature, relative humidity, specific humidity, and the FWI. An additive bias correction is employed using the most recent previous 14 days [sequential bias correction (SBC)] and the most recent previous 14 FWDs [conditional bias correction (CBC)]. Synoptic weather regimes on FWDs are established using cluster analysis (CA) on North American Regional Reanalysis sea level pressure, 850-hPa temperature, 500-hPa temperature, and 500-hPa geopotential height. SREF severely underpredicts FWI (by two indices at FWI = 3) on FWDs, which is partially corrected using SBC and largely corrected with CBC. FWI underprediction is associated with a cool (ensemble mean error of −1.8 K) and wet near-surface model bias (ensemble mean error of 0.46 g kg−1) that decreases to near zero above 800 hPa. Although CBC improves reliability and Brier skill scores on FWDs, ensemble FWI values exhibit underdispersion. CA reveals three synoptic weather regimes on FWDs, with the largest cool and wet biases associated with a departing surface low pressure system. These results suggest the potential benefit of an operational analog bias correction on FWDs. Furthermore, CA may help elucidate model error during certain synoptic weather regimes.

Sequential effects in Olympic synchronized diving scores

When judging performances in a sequence, the current score is often influenced by the preceding score. Where athletes are perceived to be similar, a judgement is assimilated towards the previous one. However, if judges focus on the differences between the two athletes, this will result in a contrasting influence on their scores. Here, I investigate sequential effects during synchronized diving events at the 2012 and 2016 Olympic Games. Although previous research found assimilation in scores of gymnasts, the current data showed contrast effects—current scores benefited from following a poor performance but were at a disadvantage if they followed a high-scoring performance. One explanation may be that the processes involved in judging synchronized pairs results in a focus on the differences between athletes, producing a contrast effect across dives. That the specific direction of this sequential bias may depend on the particular sport has implications for how judges might approach their roles in a context-dependent manner, as well as how such biases should be addressed.

SEQUENTIAL JUDGMENT EFFECTS IN THE WORKPLACE: EVIDENCE FROM THE NATIONAL BASKETBALL ASSOCIATION

This study investigates the impact of past performance evaluations on future decisions involving judgment. I analyze the decisions of highly skilled and highly monitored referees regarding offensive fouls and violations in the National Basketball Association. After testing for equilibrium adjustments in player behavior, findings support a hypothesis of increased referee scrutiny on one team following a potentially questionable call on the opposing team. Results are inconclusive for subsequent changes in scrutiny toward the original violating team. The analysis provides a nonexperimental test of sequential bias on elite employees working under strict performance standards, and suggests a likely role for sequential judgment effects in other areas of economic activity. (JEL D03, L83, J44, M59)

(Not Finding a) Sequential Order Bias in Elite Level Gymnastics

Sequential order bias is often used to refer to timing biases in sequential order judging. However, there are two distinct biases within this structure: overall order bias, a bias throughout the event, and a sequential order bias, a judgment biased by the immediately preceding performance. I independently test these forms of bias using uniquely suitable data from elite level gymnastics. After modeling overall order bias, I find evidence that this bias exists, as scores escalate throughout the competition. However, I find no evidence of a sequential order bias; scores are independent of the immediately preceding performer.

Experimental evaluation of sequential bias in online customer reviews

Explicit information used in the development of recommender systems includes online customer feed-back. Such explicit input from customers suffers from several types of bias, which directly affect the quality of the resulting recommender system. Recent research has identified sequential bias to be present in online customer reviews. However, to our knowledge, no study to date has confirmed its existence in this context. Given the nature of sequential bias, confirming its existence necessitates a controlled experimental study, which is lacking in extant published literature. We attempt to address this gap. Results from our study show evidence for the existence of sequential bias.

Asymmetric Predictability and Cognitive Competition in Football Penalty Shootouts

Sports provide powerful demonstrations of cognitive strategies underlying competitive behavior. Penalty shootouts in football (soccer) involve direct competition between elite players and absorb the attention of millions. The penalty shootout between Germany and England in the 1990 World Cup semifinal was viewed by an estimated 46.49% of the UK population. In a penalty shootout, a goalkeeper must defend their goal without teammate assistance while an opposing series of kickers aim to kick the ball past them into the net. As in many sports, the ball during a penalty kick often approaches too quickly for the goalkeeper to react to its direction of motion; instead, the goalkeeper must guess the likely direction of the kick, and dive in anticipation, if they are to have a chance of saving the shot. We examined all 361 kicks from the 37 penalty shootouts that occurred in World Cup and Euro Cup matches over a 36-year period from 1976 to 2012 and show that goalkeepers displayed a clear sequential bias. Following repeated kicks in the same direction, goalkeepers became increasingly likely to dive in the opposite direction on the next kick. Surprisingly, kickers failed to exploit these goalkeeper biases. Our findings highlight the importance of monitoring and predicting sequential behavior in real-world competition. Penalty shootouts pit one goalkeeper against several kickers in rapid succession. Asymmetries in the cognitive capacities of an individual versus a group could produce significant advantages over opponents.

순차바이어스를 이용한 반도체 레이더용 SSPA 설계

본 논문에서는 순차 바이어스를 이용한 반도체 레이더용 SSPA를 설계 하였다. 전력증폭기의 상승/하강 바이어스 지연에 의해 발생되는 신호의 왜곡을 제거하기 위하여 가변확장 펄스 생성기를 적용하였다. 최적화된 임피던스 매칭회로는 GaN-전력 소자의 높은 효율을 갖기 위하여 로드-풀 방식을 통한 마이크로파 특성 측정으로 설계되었다. 설계된 SSPA는 X밴드 반도체 레이더에 적용하기 위하여 전치 증폭기, 구동 증폭기 그리고 주 증폭기의 3개의 단으로 구성되었다. 그 결과로 200W 출력 펄스 최대 53.67dBm을 가지고 평균 52.85dBm의 SSPA를 만들 수 있었다. 본 논문에 제시된 반도체 펄스 압축 레이더 트랜시버 모듈의 최적화 설계는 추가적인 디지털 레이더에 대한 연구를 통해 소형화와 동작향상이 가능하다. In this paper, we present a design for solid-state radar SSPA with sequential bias. We apply to variable extension pulse generator to eliminate signal distortion which is caused by bias rising/falling delay of power amplifier. There is an optimum impedance matching circuit to have high efficiency of GaN-power device by measuring microwave characteristics through load-pull method. The designed SSPA is consisted of pre-amplifier, drive-amplifier and main-amplifier as a three stages to apply for X-Band solid-state radar. Thereby we made a 200W SSPA which has output pulse maximum power shows 53.67dBm and its average power is 52.85dBm. The optimum design of transceiver module for solid-state pulse compression radar which is presented in this dissertation, it can be available to miniaturize and to improve the radar performances through additional research for digital radar from now on.

(Not Finding a) Sequential Order Bias in Elite Level Gymnastics

Sequential order bias is often used to refer to timing biases in sequential order judging. However, there are two distinct biases within this structure: overall order bias, a bias throughout the event, and a sequential order bias, a judgment biased by the immediately preceding performance. I independently test these forms of bias using uniquely suitable data from elite level gymnastics. After modeling overall order bias, I find evidence this bias exists; scores escalate throughout the competition. However, I find no evidence of a sequential order bias; scores are independent of the immediately preceding performer.

Climate change impact and uncertainty analysis of extreme rainfall events in the Apalachicola River basin, Florida

Climate change impact on rainfall intensity–duration–frequency (IDF) curves at the Apalachicola River basin (Florida Panhandle coast) is assessed using an ensemble of regional climate models (RCMs) obtained from the North American Regional Climate Change Assessment Program. The suitability of seven RCMs on simulating temporal variation of rainfall at the fine-scale is assessed for the case study region. Two RCMs, HRM3–HADCM3 and RCM3–GFDL, are found to have good skill scores in generating high intensity events at the mid-afternoon (2:00–4:00 PM). These two RCMs are selected for assessing potential climate change impact on IDF curves. Two methods are used to conduct bias correction on future rainfall IDF curves, i.e., maximum intensity percentile-based method, and sequential bias correction and maximum intensity percentile-based method. Based on the projection by HRM3–HADCM3, there is no significant change in rainfall intensity at the upstream and middle stream stations but higher intensity at the downstream station. RCM3–GFDL projected increased rainfall intensity from upstream to downstream, particularly at the downstream. The potential temporal shift of extreme rainfall events coupled with overall increased intensities may exacerbate flood magnitudes and lead to increased sediment and nutrient loadings to the estuary, especially in light of sea level change.

Impact of Bias-Correction Type and Conditional Training on Bayesian Model Averaging over the Northeast United States

Abstract The performance of a multimodel ensemble over the northeast United States is evaluated before and after applying bias correction and Bayesian model averaging (BMA). The 13-member Stony Brook University (SBU) ensemble at 0000 UTC is combined with the 21-member National Centers for Environmental Prediction (NCEP) Short-Range Ensemble Forecast (SREF) system at 2100 UTC. The ensemble is verified using 2-m temperature and 10-m wind speed for the 2007–09 warm seasons, and for subsets of days with high ozone and high fire threat. The impacts of training period, bias-correction method, and BMA are explored for these potentially hazardous weather events using the most recent consecutive (sequential training) and most recent similar days (conditional training). BMA sensitivity to the selection of ensemble members is explored. A running mean difference between forecasts and observations using the last 14 days is better at removing temperature bias than is a cumulative distribution function (CDF) or linear regression approach. Wind speed bias is better removed by adjusting the modeled CDF to the observation. High fire threat and ozone days exhibit a larger cool bias and a greater negative wind speed bias than the warm-season average. Conditional bias correction is generally better at removing temperature and wind speed biases than sequential training. Greater probabilistic skill is found for temperature using both conditional bias correction and BMA compared to sequential bias correction with or without BMA. Conditional and sequential BMA results are similar for 10-m wind speed, although BMA typically improves probabilistic skill regardless of training.

Estimating sequential bias in online reviews: A Kalman filtering approach

Online reviews of products along with reviewer related data are regarded by many as one of the most significant knowledge base systems created by online commerce websites. They have played a big role in fueling the popularity and growth of electronic marketplaces like Amazon and eBay. Although the main attraction of online reviews is that they are perceived by most consumers to be independent and unbiased, many studies have shown the existence of various types of biases inherent in the product reviews. In this paper we present a novel approach of estimating the bias in reviews using Kalman filtering technique that is computationally feasible and can update the estimation of bias with every new review without having to store all the past ratings information. We further extend our model to study the existence of sequential bias in the reviews. We use panel data from 19 different products collected from Amazon.com and show the existence of sequential bias in ratings that depends on previous review and reviewer characteristics.