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Attentional Bias

Attentional bias refers to the tendency for an individual's attention to be drawn towards certain stimuli or information, often based on personal experiences, emotions, or motivations.
This cognitive bias can have significant impacts on decision-making, perception, and behavior.
Researchers utilize various methods, such as dot-probe tasks and visual search paradigms, to measure and study attentional bias.
Understaning the mechanisms and implications of attentional bias is an important area of research in psychology, cognition, and neuroscience, with applications in clincial settings and human performance optimization.
Identifying and mitigating attentional bias can help improve accuracy, reproducibilty, and efficiency in a wide range of contexts.

Most cited protocols related to «Attentional Bias»

Measuring Fear of COVID-19 Pandemic

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Publication 2020

Attentional Bias Coronavirus Fear Infection Virus

Eye Tracking in Anxiety and Depression

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Publication 2012

Adult Anxiety Attention Attentional Bias Electrooculography Emotions Fear Phobias

Examining Attentional Bias Towards Threat

The present study utilized existing datasets from three studies of attentional bias that each used a dot-probe task with emotional and neutral face stimuli. Consistent with the typical usage of the dot-probe in affective research, the speed with which responses to the “probe” were made was examined as a function of the stimulus type (i.e., threat or neutral) that was presented and replaced by the probe. This analysis of RT data is designed to provide an index of attentional bias towards threat. Across all studies, three trial types were available and defined as follows (also see Figure 1): “Incongruent” trials = trials where the dot replaced the neutral face in a threat/neutral face pair; “Congruent” trials = trials where the dot replaced the threat face in a threat/neutral face pair; and “Neutral” trials = trials containing neutral stimuli only, with no threat face [e.g., two images of the same neutral face (Studies 1 & 2) or two blank ovals (Study 3)]. Details of the samples, task parameters, and study procedure differed across the three studies, allowing for an examination of the robustness of patterns in stability across studies. Procedures for all three studies were approved by the Institutional Review Board of the relevant institution. Informed consent was obtained from adult participants while informed parental consent and child assent were obtained for pediatric participants (Study 3).

Price R.B., Kuckertz J.M., Siegle G.J., Ladouceur C.D., Silk J.S., Ryan N.D., Dahl R.E, & Amir N. (2014). Empirical Recommendations for Improving the Stability of the Dot-Probe Task in Clinical Research. Psychological assessment, 27(2), 365-376.

Publication 2014

Adult Attentional Bias Child Emotions Ethics Committees, Research Face Parent

Attentional Bias Measurement via Visual Dot Probe

Attentional bias was measured by a visual dot probe task that was operated using E-prime experiment generation software (Schneider et al., 2002) and performed on a personal computer. Participants were seated in front of the computer monitor with their chins resting in a headrest, which was used to stabilize head movement and maintain a constant eye-to-screen distance of 74 cm. During the task, two 13 cm × 18 cm pictures (a neutral and an alcohol-related image) were presented side-by-side, 3 cm apart, on a computer screen. Upon offset of the picture pair, a visual target appeared. The participant was instructed to respond to the target by pressing one of two response keys on the keyboard to indicate on which side the target appeared. The task compares the reaction times of responding to a target replacing an alcohol-related image versus a target replacing a neutral image and has been used in other research (e.g., 3).
The task stimuli consisted of twenty alcohol-related images that were matched with twenty neutral (i.e. non-alcohol-related) images. Half of the alcohol and neutral images were complex scenes and the other half were simple images. Simple alcohol images depicted a single, solitary image of an alcoholic beverage. These images were matched with simple, neutral images consisting of non-alcoholic drinks (e.g. a can of beer matched with a can of soda). All simple images were photographed against the same background: a bare, neutral colored wall. Complex alcohol images depicted real-life scenes involving alcohol. Examples included bar and party scenes showing people consuming alcohol. These images were matched with complex neutral images that also included groups of people and consumptive activities, such as eating food. A complex alcohol image was always paired with a complex neutral image and a simple alcohol image was always paired with a simple neutral image.
The 20 image pairs were presented four times, once for each of the four possible picture/target combinations (i.e. left and right picture location and left and right target probe location) for a total of 80 test trials. In addition, there were 80 filler trials which consisted of 20 pairs of neutral images presented four times each. The filler trials are commonly included in tests of attentional bias to reduce possible habituation to alcohol stimuli that might otherwise occur if all trials contained alcohol-related images [5 (link)]. The 80 filler trials were randomly intermixed among the 80 test trials for a total of 160 trials.
For each presentation, a fixation point (+) appeared at the center of the screen for 500 ms, followed by a pair of images displayed for 1000 ms. Once the images disappeared, a visual probe (an “X”) appeared on the left or right side of the screen, in the position where one of the pictures was previously displayed. Participants were required to press one of two keys ( “>” or “/”) on the keyboard to indicate the location of the target. They had 1000 ms to respond before the probe display was offset and the next trial began.

Miller M.A, & Fillmore M.T. (2010). The effect of image complexity on attentional bias toward alcohol-related images in adult drinkers. Addiction (Abingdon, England), 105(5), 883-890.

Publication 2009

Alcoholic Beverages Attentional Bias Beer Chin Ethanol Food Head Movements

Dot-Probe Attention Bias Measurement Protocol

The dot-probe task (Bar-Haim et al., 2007 (link); MacLeod et al., 1986 (link)) measures attention biases toward or away from threatening stimuli. The task comprises 160 trials beginning with a fixation cross (“+”) presented in the center of the screen for 500 milliseconds (ms), after which two words in size 12 Arial font immediately appeared for 500 ms, one above and one below the location of the fixation cross, separated by 1.5 centimeters. Following the words, a target probe (letter E or F) appeared in the location occupied by one of the words, and remained until participants responded. Participants were instructed to identify the probe using a designated mouse button as quickly as possible. There were 128 trials that included one threat and one neutral word; 32 trials included two neutral words. Stimuli were 32 trauma-related and 64 neutral words, selected from a list developed by MacLeod, Rutherford, Campbell, Ebsworthy, and Holker (2002) (link) for salience to traumatic life events (e.g., “harm,” “suffer”). Word pairs were matched for first letter, number of letters, and frequency of usage in the English language (MacLeod et al., 2002 (link)) and presented in random order using E-Prime 2.0 software (Psychology Software Tools, Inc., 2012 ).
To calculate attention bias, RTs from the threat-neutral trials were analyzed. Keeping with standard practice for maintaining data integrity for this task, trials with an incorrect response or in which the RT was extremely short (<150 ms) or long (>2000 ms) were excluded, as were trials in which RT was outside ±2 SDs of the participant's mean for each of the two conditions (probes behind threat words or neutral words; O'Toole & Dennis, 2012 (link); Roy et al., 2008 (link)). Attention bias was calculated as the difference between mean RT to probes behind neutral words and threat words. Attention bias toward threat is indicated when the mean RT to threat words is shorter than to neutral words, the opposite reflects attention bias away from threat.
To calculate attention-bias variability, dot-probe trials were split into eight sequential bins, and attention-bias scores were calculated for each bin. The SD of attention-bias scores across bins was calculated and divided by mean RT to correct for variance in RTs (Epstein et al., 2011 (link); Ode et al., 2011 (link)). The resulting attention-bias variability score provides an index of within-session stability of attention biases.

Iacoviello B.M., Wu G., Abend R., Murrough J.W., Feder A., Fruchter E., Levinstein Y., Wald I., Bailey C.R., Pine D.S., Neumeister A., Bar-Haim Y, & Charney D.S. (2014). Attention Bias Variability and Symptoms of Posttraumatic Stress Disorder. Journal of traumatic stress, 27(2), 232-239.

Publication 2014

Attentional Bias Mus Wounds and Injuries

Most recents protocols related to «Attentional Bias»

Spatial Attention Cues in Visual Search

To guarantee consistent prefocusing in the focus condition of experiment 1 (and experiment 2), we permanently marked the location of the upcoming items with location placeholders that were replaced by the onset of the search items. It has been shown in macaques that the attention-related neural responses to spatial (but not feature) cues disappear in area V4 when emptying the displays after cue offset (62 (link)). This suggested that spatial attentional biases to symbolic cues are not consistently maintained and require a fresh rebuilt upon stimulus onset to become effective. To avoid “spurious” shifts of attention due to such breakdown of the cuing state, we use placeholders permanently highlighting the item positions. Note that a previous N2pc study of cued visual search showed that placeholders do effectively anchor the spatial focus before search frame onset (63 (link)).

Bartsch M.V., Merkel C., Strumpf H., Schoenfeld M.A., Tsotsos J.K, & Hopf J.M. (2023). A cortical zoom-in operation underlies covert shifts of visual spatial attention. Science Advances, 9(10), eade7996.

Publication 2023

Attention Attentional Bias Catabolism Cortical Area V4 Macaca Nervousness Reading Frames

Morningness-Eveningness and Sleep Cognitions

Brief Horne-Östberg Morningness-Eveningness Questionnaire: The reduced Horne-Östberg-Morningness-Eveningness Questionnaire is a widely used measure of the morningness-eveningness dimension [34 (link)]. Its five items refer to rising time, peak time, retiring time, morning freshness, and self-evaluated chronotype. A composite score from 4 to 25. Higher levels of morningness are reflected by higher scores.
Dysfunctional Beliefs and Attitudes about Sleep: Participants’ sleep-related cognitions will be measured using the questionnaire Dysfunctional Beliefs and Attitudes about Sleep-16 [35 (link)], which consists of 16 items assessing sleep-related cognitions (e.g., faulty beliefs an appraisals, unrealistic expectations, perceptual and attentional bias).
Ford Insomnia Response to Stress Scale: Sleep reactivity will be measured using the Ford Insomnia Response to Stress Scale [36 (link)]. Its 9 items assess the vulnerability to situational insomnia under 9 different stressful conditions (i.e., sleep reactivity). The items are scored on a 4-point scale, ranging from ‘Not likely’ to ‘Very likely’.

Hrozanova M., Meisingset I., Kallestad H., Pallesen S., Nordstoga A.L, & Skarpsno E.S. (2023). Group-delivered cognitive behavioural therapy versus waiting list in the treatment of insomnia in primary care: study protocol for a pragmatic, multicentre randomized controlled trial. BMC Primary Care, 24, 61.

Publication 2023

Attentional Bias Chronotype Cognition Sleep Sleeplessness Stress Disorders, Traumatic

Evaluating Attentional Bias in Disordered Eating

SPSS 22.0 was used for statistical analysis. Measurement data were expressed as mean ± standard deviation (M ± SD), and count data were expressed as number of cases (percentage). Differences in group characteristics were assessed using the Chi-square test and independent t-tests two-tailed with a significance level of 0.05. The analysis of ECT results was based on the average RT of the correct response to the target under different experimental conditions, that is, press the “A” key for the left target and the “L” key for the right target. The correct response with RT less than 200 ms or more than 750 ms was excluded because they might be caused by continuous keystrokes, distractions, etc. (36 (link)).
A 2 × 2 × 2 (Group × Cue Stimulus × Cue Validity) mixed-factor ANOVA was conducted, with the between-subjects factor of Group (DEB, HC) and the within-subject factor of Cue Stimulus (food, neutral picture), and Cue Validity (valid, invalid). Then, another set of 2 × 2 (Group × Cue Stimulus) mixed-factor ANOVA was conducted to explore whether subjects show attentional bias to food pictures vs. neutral pictures. The confidence interval percentage was 95%, and p < 0.05 was considered statistically significant.
Following indices of AB were calculated (25 (link), 37 (link), 38 (link)):
(1) Attentional engagement = RT valid neutral cue—RT valid food cue. A positive score indicates that attention is easier directed at the location of the food cue as compared to the neutral cue. A negative score indicates decreased attentional engagement with the food cue.
(2) Attentional disengagement = RT invalid food cue—RT invalid neutral cue. A positive score indicates slower disengagement of attention and thus a reduced ability to shift attention away from the food as compared to the neutral cue, difficulty in disengagement toward food cue stimulus. A negative score indicates faster disengagement of attention from the food cue.

Jin L., Han W, & Zheng Z. (2023). Attentional vigilance of food information in disordered eating behaviors. Frontiers in Psychiatry, 14, 1108995.

Publication 2023

Attention Attentional Bias Food neuro-oncological ventral antigen 2, human Training Programs

Examining Victimization, Cognitive Biases, and Resilience

Early victimization. Early victimization was assessed by the Chinese version of the junior version of the Olweus Bully/Victim Questionnaire [54 (link)]. Six items related to verbal, relational, and physical victimization were used to measure participants’ past victimization experience in primary and secondary schools (e.g., “Some classmates gave me bad names to scold me, or made fun of me and satirized me”). The instruction was set to “Please recall if you had any of the following experiences in primary and secondary school”. Participants rated these items on a four-point scale (1 = never, 4 = always). Average scores of all items were computed, with higher scores indicating higher frequencies of early victimization. In the present study, Cronbach’s α for the questionnaire was 0.84.
Negative cognitive processing bias. The negative cognitive processing bias was measured by the Negative Cognitive Processing Bias Questionnaire developed by Yan et al. [31 (link)]. Participants answered 23 items assessing four dimensions of negative cognitive processing bias: negative attention bias, negative memory bias, negative interpretation bias, and negative rumination bias (e.g., “I vividly remember a time when I was laughed at”). Each item was answered on a four-point scale (1 = not at all true, 4 = completely true). Average scores of all items were computed, with higher scores representing a higher tendency for negative cognitive processing bias. In the present study, Cronbach’s α for the questionnaire was 0.89.
Resilience. Resilience was assessed using the Resilient Trait Scale for Chinese Adults [55 ]. The scale includes 30 items measuring five dimensions of resilience: internal locus of control, problem-focused coping style, optimism, predisposition to accepting and utilizing social supports, and acceptance (e.g., “I can overcome difficulties mainly because of my strong ability”). Participants rated their perceptions of resilience on a four-point scale (1 = not at all true, 4 = completely true). Average scores of all items were computed, with higher scores reflecting higher levels of resilience. In the present study, Cronbach’s α for the questionnaire was 0.91.
Core self-evaluations. Core self-evaluations were measured by the Chinese version of the Core Self-Evaluations Scale (CSES) [56 ,57 (link)]. The CSES consists of 10 items (e.g., “I am confident I can succeed in life”). Each item was rated on a five-point scale (1 = strongly disagree, 5 = strongly agree). Average scores of all items were computed, with higher scores indicating higher levels of core self-evaluations. In the present study, Cronbach’s α for the questionnaire was 0.86.

Wang L., Zhou S., Xue Y., Li Q., Cao M, & Gu C. (2023). Does Early Victimization of School Bullies Affect Core Self-Evaluations in Young Adulthood? A Moderated Mediation Model. International Journal of Environmental Research and Public Health, 20(4), 3338.

Publication 2023

Adult Attentional Bias Bullying Chinese Memory Optimism Physical Examination Rumination, Digestive Self-Evaluation Self Confidence Susceptibility, Disease Victimization

Hybrid Lattice-Aware Transformer for NLP

Since lexical information is more concerned with the relationship between words and characters, it is easier to identify local information, such as word position and boundaries, while syntactic information is more concerned with the overall information of the sentence, and the segmentation errors introduced by lexical information are corrected by syntactic constraints. After obtaining the embedding of syntactic information, the lexical and syntactic data are fused using a cross-transformer. The cross-transformer network is shown in Figure 4.
The input (

Q_{x}^{L}

K_{x}^{L}

V_{x}^{L}

) of the left transformer encoder is obtained from lattice embedding, which contains lexical information by linear transformation as follows:

[Q_{x}^{L}, K_{x}^{L}, V_{x}^{L}] = E_{x}^{L} [W_{Q}^{L}, W_{K}^{L}, W_{V}^{L}]

where

E_{x}^{L}

is the lattice embedding,

E_{x}^{L} \in \{x_{1}, \dots, x_{i}, \dots, x_{N}\}

x_{i}

is the lexical representation of the input and N is the length of the input sequence X, each

W^{L}

is a learnable parameter. The

Q_{s}^{R}

K_{s}^{R}

, and

V_{s}^{R}

of the left transformer encoder are obtained from the embedding of syntactic information and by linear transformation as follows:

[Q_{s}^{R}, K_{s}^{R}, V_{s}^{R}] = E_{s}^{R} [W_{Q}^{R}, W_{K}^{R}, W_{V}^{R}]

where

E_{s}^{R}

is the syntactic embedding,

E_{s}^{R} \in \{s_{1}, \dots, s_{i}, \dots, s_{N}\}

s_{i}

is the syntactic representation of the input, we keep the length of the input sequence at N to ensure that the input of the cross-transformer has the same length at both ends; each

W^{R}

is a learnable parameter. The cross-transformer consists of two transformer encoders. Each encoder is composed of self-attention and feed-forward network (FFN) layers, followed by residual connection and layer normalization. FFN is a position-wise multi-layer perceptron (MLP) with the nonlinear transformation of the semantic space. The self-attention layer is used to extract semantic-level information. The attention score of the vanilla transformer is calculated as:

A t t (A, V) = s o f t m a x (A) V

A_{i, j} = (\frac{Q_{i} K_{j}^{T}}{\sqrt{d_{k}}})

where

d_{k}

is the dimension of K. To cross the syntactic information and lattice information obtained from Equations (7) and (8), we use a variant of self-attention [35 ] with the relative position encoding in the FLAT model, which is computed as follows:

{A t t}^{L} = s o f t m a x (A^{R}) V^{L}

{A t t}^{R} = s o f t m a x (A^{L}) V^{R}

where

A^{R}

is the syntactic attention score and

A^{L}

is the lattice attention score.

A^{R}

is computed by

A_{i, j}^{R} = {(Q_{i}^{R} + u^{R})}^{T} K_{j}^{R} + {(Q_{i}^{R} + v^{R})}^{T} (R_{i, j}^{R} W_{r}^{R})

where

u^{R}

and

v^{R}

are attention bias,

W_{r}^{R}

are learnable parameters.

R_{i, j}^{R}

is the relative position encoding, which is computed by

R_{i, j}^{R} = R e L U (W_{r} (p_{h_{i} - h_{j}} \oplus p_{t_{i} - h_{j}} \oplus p_{h_{i} - t_{j}} \oplus p_{t_{i} - t_{j}}))

The relative position encoding

R_{i, j}

is used to avoid the loss of directionality caused by the inner dot-product of the vector. Each p denotes a relative distance. The calculation of

{A t t}^{L}

is essentially the same.

Deng Z., Tao Y., Lan R., Yang R, & Wang X. (2023). Kcr-FLAT: A Chinese-Named Entity Recognition Model with Enhanced Semantic Information. Sensors (Basel, Switzerland), 23(4), 1771.

Publication 2023

Attention Attentional Bias Character Cloning Vectors Vanilla

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More about "Attentional Bias"

Attentional Bias: Unraveling the Cognitive Puzzle Attentional bias, a well-documented phenomenon in the realms of psychology, cognition, and neuroscience, refers to the tendency of an individual's attention to be drawn towards certain stimuli or information.
This cognitive bias, often influenced by personal experiences, emotions, and motivations, can have significant impacts on decision-making, perception, and behavior.
Researchers have utilized various methods, such as dot-probe tasks and visual search paradigms, to measure and study attentional bias.
These techniques, often facilitated by software like SPSS (Statistical Package for the Social Sciences) in versions 22.0, 24, 23, and 20, as well as E-Prime, allow researchers to delve into the underlying mechanisms and implications of this cognitive phenomenon.
Understanding attentional bias is crucial, as it can affect accuracy, reproducibility, and efficiency in a wide range of contexts.
From clinical settings, where identifying and mitigating attentional bias can improve patient outcomes, to human performance optimization, where attentional focus plays a pivotal role, this area of research holds immense value.
Advancements in technology, such as the GoPro Hero 5 camera, have also enabled researchers to explore attentional bias in more natural, real-world settings, enhancing our understanding of this complex cognitive process.
By recognizing the impact of attentional bias and leveraging tools like SPSS Statistics version 21 and 28, researchers can work towards improving the accuracy, reproducibility, and efficiency of their studies, ultimately advancing our knowledge and application of this fascinating aspect of human cognition.