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Cognition

Sections


Attention

Attention is a cognitive ability that allows a learner to concentrate on pertinent stimuli or information. Sustained/focused Attention refers to focus on a specific task without being distracted. Selective Attention refers to the ability to select relevant information while ignoring irrelevant information and is tightly coupled with inhibitory control.

Deficits in both visual and auditory selective Attention have been associated with reading disabilities (Stevens et al., 2012).

  • Visual Selective Attention mediates the selection of relevant and the filtering out of irrelevant information from cluttered visual scenes.
  • Auditory Selective Attention refers to the ability to focus on a specific source of sound or speech in a noisy environment.

Sustained Attention is an important component for literacy success because it can help students attend to classroom instruction. Evidence suggests that visual selective Attention is also an important skill for learning to read and that training auditory selective Attention processes can enhance early literacy skills.

Assessments

  • SWAN (Swanson et al., 2004): A teacher rating containing 18 items rated on a 7-point Likert scale that measure Attention and hyperactivity/impulsivity
  • Diagnosis of Attention Deficit/Hyperactivity Disorder
  • Behavior Rating Inventory of Executive Functions (BRIEF) (Goia, Isquith, Guy & Kenworthy, 2000): Parental rating of a child’s behavior using a 3-point Likert scale on 86 items related to executive functions. Has Behavior Regulation Index, which consists of three scales measuring: Inhibition, shifting of Attention, and Emotion control. Also includes a Metacognition Index measuring initiation ability, Working Memory, planning/organizational skills, and organization of materials.
  • Assessments of Visual Attention:
    • Visual array search tasks
    • Visual Attention span task: Letter strings are flashed briefly on a screen, and participants are asked to orally report either a single cued letter or the entire string.

Learner Factor Connections

  • Auditory Processing: The process of interpreting sounds, such as speech, as meaningful input
    • Auditory selective Attention requires Auditory Processing skills for attending to speech in a noisy environment.
  • Decoding: The ability to apply knowledge of relationships between letters and speech sounds to properly recognize and pronounce words
    • Poor Attention skills in preschool contribute to weak Decoding skills in kindergarten. Research suggests this is because students with Attention deficits have difficulty learning early literacy skills (e.g. Alphabet Knowledge, Phonological Awareness, and Vocabulary), which contribute to Decoding skills.
  • Emotion: A complex psychological state that involves a subjective experience and can result in a physiological and behavioral response. Emotion regulation is the ability to control emotional arousal in order to facilitate adaptive functioning.
    • Strong Emotion regulation skills help students focus Attention resources on learning classroom material.
  • Inhibition: The ability to suppress Attention to irrelevant stimuli to focus on pertinent stimuli/information, both controlling responses and Attention
    • Inhibition supports Attention by allowing students to focus on important stimuli by suppressing irrelevant information.
  • Physical Fitness: A state of overall health and physical well-being
    • Regular participation in physical activities enhances Attention skills, which contributes to enhanced academic performance.
  • Primary Language: The child’s native language they have been exposed to from birth
    • Evidence suggests that children who are bilingual/multilingual develop enhanced selective Attention skills relative to their monolingual peers because they must constantly select the appropriate language to use according to situational context.
  • Self-Regulation: The ability to alter responses and align them with standards, such as social expectations
    • Self-Regulation requires Attention for monitoring and altering responses.
  • Sleep: A naturally occurring state involving the suspension of consciousness during which the body and the brain rest. Both duration and quality contribute to obtaining sufficient Sleep.
    • Insufficient Sleep can negatively impact sustained Attention abilities.
  • Vision: The ability to use eyesight to perceive information about the world
    • Vision skills are necessary for visual selective Attention.
  • Visual Processing: The process of interpreting visual stimuli as meaningful input
    • Visual selective Attention requires Visual Processing skills, as they are important for attending to visual stimuli like text.
  • Working Memory: The type of memory that allows a person to temporarily hold and manipulate information for use in many complex cognitive processes
    • Attention helps information to be maintained Working Memory, and Working Memory affects control of Attention  (Awh, Vogel, & Oh, 2006).

Research Findings

Sustained/Focused Behavioral Attention

  • A study of students in 4th to 9th grade found that lower Attention ratings (as measured by the BRIEF and parental report) were associated with written language learning but not aural/oral language learning outcomes. However, measures of Inhibition (which is required for focused Attention) were predictive of oral language comprehension skills (Berninger, Abbott, Cook, & Nagy, 2016).
  • Miller and colleagues (2014) examined the impact of behavioral Attention (as measured by teacher rating) on reading comprehension in a group of 1st-graders who were identified as at risk for developing reading disabilities. They found that behavioral Attention was a significant predictor of responsiveness to reading instruction in the 1st grade. The researchers suggest that behavioral Attention measures be considered when evaluating at-risk students because it can be helpful in determining whether they will respond to reading interventions.
  • Pagani, Fitzpatrick, and Parent (2012) aimed to determine the relationship between Attention skills in kindergarten and the development of classroom engagement (as measured by observation of behaviors like whether the students stayed on task, completed work on time, and demonstrated self-control) throughout elementary school (up to 6th grade). Attention skills were measured by a teacher-rated questionnaire from the Social Behavior Questionnaire (SBQ) (Tremblay et al., 1991). Using semi-parametric mixture modeling of data from the Montreal Longitudinal Preschool Study (MLPS), they found that Attention skills in kindergarten were associated with a better trajectory in developing classroom engagement skills throughout elementary school. Attention was a better predictor of classroom engagement skill development than confounding child and family variables such as maternal education and whether the family is intact or not intact.
  • Dice and Schwanenflugel (2012) investigated the relationship between Attention skills in prekindergarten and Decoding abilities in kindergarten. To do this, they had a sample of 250 children and used structural equation modeling (a statistical technique that allows researchers to investigate the relationship between different variables of interest, in this case Attention skills and Decoding abilities). The children were assessed within one month of entering prekindergarten (Time 1) and also within a month of the end of their year of kindergarten (Time 2). Teacher ratings of Attention were measured using an experimental short form of the Teacher Rating Scale – Preschool of the Behavioral Assessment System for Children (TRS-P BASC) (Reynolds & Kamphaus, 1992) called the BASC screener (Yanosky, 2005; Yanosky et al., 2011), which contains 25 items. Decoding was assessed in kindergarten using the Early Decoding Test (Schwanenflugel et al., 2010). Receptive Vocabulary (measured using the Peabody Picture Vocabulary Test – III, Dunn & Dunn, 1997) and expressive Vocabulary (measured using the Expressive Vocabulary Test, EVT, Williams, 1997), Alphabet Knowledge (measured using identification of letter shapes/names/sounds), and Phonological Awareness (measured using the Phonological Awareness Test, PAT, Robertson & Salter, 1997) were also assessed. Their results indicated that poor Attention skills in prekindergarten contribute to weak emergent literacy skills (e.g., Phonological Awareness, Alphabet Knowledge, receptive/expressive Vocabulary), which lead to poor Decoding skills in kindergarten.
  • Sadeh and colleagues (2002) measured Sleep activity (using actigraphy) for five consecutive nights in a group of school-age children (7.2 – 12.7 years). The children were identified as either poor sleepers (more fragmented sleep or reduced sleep efficiency) or good sleepers. The good sleepers performed significantly better than the poor sleepers on a declarative memory task (serial digit learning test) in the morning but not the noon testing session. Performance on more complex tasks was correlated with poor Sleep. Also, they found a stronger correlation between Sleep and neurobehavioral functioning in younger children. Overall they found that poor Sleep was associated in difficulty with sustaining Attention and behavioral Inhibition.
  • Self-Regulation includes cognitive regulation, that is using executive functions (e.g., Working Memory, Inhibition, Attention) to inhibit impulses and attend to tasks (Williford, Whittaker, Vitiello, & Downer, 2013).
  • A smaller Working Memory capacity is associated with more “mind wandering,” possibly due to a lack of Attention control over intruding thoughts (McVay & Kane, 2011).
  • Rhoades and colleagues (2011) conducted a study examining the relationship between Emotion knowledge (measured with the Affect Knowledge Test, Emotion Situation Knowledge, and the Kusche Emotional Inventory ), Attention, and academic competence (measured by the Woodcock-Johnson Psycho-Educational Battery Revised; Woodcock & Johnson, 1990) in a group of economically disadvantaged preschool children (n = 341). They followed the children for three years (preschool, kindergarten, and first grade). Emotion knowledge in preschool was a significant predictor of academic achievement in the 1st grade (including Alphabet Knowledge). The authors discuss that the development of Emotion knowledge is important for academic success, but this relationship is mediated by Attention skills. They propose that children’s Emotion knowledge helps them regulate their Emotions so they can use their Attention skills to focus on learning in the classroom.
  • Many studies have found that bilingual/multilingual children have certain cognitive advantages relative to their monolingual peers (Morton & Harper, 2007; Bialystok & Senman, 2004; Foy & Mann, 2015). Specifically, these studies have found that bilinguals/multilinguals have better cognitive control abilities. Cognitive control is the ability to consciously act according to goals and resolve interference. It is achieved through a combination of cognitive skills including Inhibition and Attention. This is likely because children are required to actively inhibit the language(s) that is/are not currently in use to use the desired language. They also must monitor contextual cues and selectively attend to the appropriate language according to the particular situation. This constant monitoring, Inhibition, and selective Attention all serve to boost these cognitive skills. Further research is required to fully understand the details of this bilingual cognitive advantage (see Barac et al., 2014, for a review).

Auditory Selective Attention

  • A group of kindergarteners identified as being at risk of developing reading difficulties were given reading instruction using Early Reading Intervention (small group instruction in reading for 45 minutes, five days per week for eight weeks). By the end of intervention, early literacy skills of children in the at-risk group improved significantly so that there was no difference in scores between the typical and at-risk groups. Additionally, measures of neurophysiological activity during auditory selective Attention tasks (attending to a story presented over headphones in one ear, while inhibiting the story presented in the other ear) before and after the reading intervention demonstrated improvements in sensorineural processing. The researchers concluded that reading intervention programs may work in part by training selective Attention processes (Stevens et al., 2013).

Visual Attention

There are conflicting findings in this area with some researchers finding evidence that a deficit in visual Attention is associated with a diagnosis of dyslexia. This theory would predict a deficit of visual Attention for both letter strings and non-alphanumeric symbol strings. However, other researchers attribute differences in visual Attention tasks in this population to a deficit in symbol-sound mapping. According to these studies, individuals with dyslexia only have visual Attention deficits in letter strings. This research is summarized below:

  • A three-year longitudinal study of preschoolers found that visual spatial Attention (as measured by a serial visual search task and a spatial cueing facilitation task where children have to indicate what shape was presented previously) predicts reading acquisition in 1st and 2nd grades. Children who were classified as poor readers in the 2nd grade made two times as many errors in the serial visual search task in the prereading stage (Franceschini, Gori, Ruffino, Pedrolli, & Facoetti, 2012).
  • Another study found that children with dyslexia performed significantly worse than peers without dyslexia on visual Attention span tasks of both verbal and non-verbal stimuli. Children with dyslexia were not able to process as many visual elements as peers without dyslexia. The authors concluded that deficits in visual Attention contribute to reading impairments in dyslexia (Lobier, Zoubrinetzky, & Valdois, 2012).
  • Ziegler and colleagues (2010) examined visual Attention processing in children with dyslexia and only found a deficit in the processing of letter but not symbol strings.
  • Boets and colleagues (2008) examined Auditory Processing, Visual Processing, Phonological Awareness, and reading skills in a group of 62 5-year-old children. They found that dynamic Visual Processing was related to orthographic ability (as measured by a task where orthographic knowledge was separated statistically from phonological knowledge in order to construct a pure orthographic measure). Also, a relationship was found between dynamic Visual Processing skills and reading development (e.g., word-level reading, reading accuracy, reading speed). Thus, these results demonstrate that Visual Processing is an important factor in reading development.
  • Van der Niet and colleagues (2014) conducted a study where they modeled the relationship between Physical Fitness, academic achievement, and executive functioning in a group of 263 children between 7- to 12-year-olds living in the Netherlands. In order to assess executive functioning, they used the Tower of London test, which measures problem solving and planning abilities, and the Trailmaking text, which measures visual Attention and task switching skills. Physical Fitness was measured using the European Physical Fitness Test Battery (EUROFIT), including standing broad jump, sit-ups, and running. Academic achievement was assessed using standardized test scores in math, spelling, and reading comprehension.Their resulting model suggested that Physical Fitness was related to both executive functioning and academic achievement. Moreover, executive functioning was a mediator in the relationship between Physical Fitness and academic achievement. Note that this was a cross-sectional study and not a longitudinal study, so the causality between these different variables needs to be further studied in order to make a stronger statement about the specific relationship between these three factors.

References

Awh, E., Vogel, E. K., & Oh, S. H. (2006). Interactions between attention and working memory . Neuroscience, 139(1), 201-208.
Barac, R., Bialystok, E., Castro, D. C., & Sanchez, M. (2014). The cognitive development of young dual language learners: A critical review. Early Childhood Research Quarterly, 29 (4), 699-714.
Berninger, V., Abbott, R., Cook, C. R., & Nagy, W. (2016). Relationships of attention and executive functions to oral language, reading, and writing skills and systems in middle childhood and early adolescence . Journal of Learning Disabilities, 1-16.
Bialystok, E., & Senman, L. (2004). Executive processes in appearance–reality tasks: The role of inhibition of attention and symbolic representation . Child Development, 75 (2), 562-579.
Boets, B., Wouters, J., Van Wieringen, A., De Smedt, B., & Ghesquiere, P. (2008). Modelling relations between sensory processing, speech perception, orthographic and phonological ability, and literacy achievement . Brain and Language, 106(1), 29-40.
Dice, J. L., & Schwanenflugel, P. (2012). A structural model of the effects of preschool attention on kindergarten literacy . Reading and Writing, 25(9), 2205-2222.
Foy, J. G., & Mann, V. A. (2013). Bilingual children show advantages in nonverbal auditory executive function task . International Journal of Bilingualism, 18 (6), 717-729.
Franceschini, S., Gori, S., Ruffino, M., Pedrolli, K., & Facoetti, A. (2012). A causal link between visual spatial attention and reading acquisition . Current Biology, 22(9), 814-819.
Gioia, G. A., Isquith, P. K., Guy, S. C., & Kenworthy, L. (2000). Behavior rating inventory of executive function: BRIEF. Odessa, FL: Psychological Assessment Resources.
Lobier, M., Zoubrinetzky, R., & Valdois, S. (2012). The visual attention span deficit in dyslexia is visual and not verbal . Cortex, 48(6), 768-773.
McVay, J. C., & Kane, M. J. (2012). Why does working memory capacity predict variation in reading comprehension? On the influence of mind wandering and executive attention . Journal of experimental psychology: general, 141 (2), 302.
Miller, A. C., Fuchs, D., Fuchs, L. S., Compton, D., Kearns, D., Zhang, W., …, & Kirchner, D. P. (2014). Behavioral attention: A longitudinal study of whether and how it influences the development of word reading and reading comprehension among at-risk readers . Journal of Research on Educational Effectiveness, 7(3), 232-249.
Morton, J. B., & Harper, S. N. (2007). What did Simon say? Revisiting the bilingual advantage . Developmental Science, 10 (6), 719-726.
Pagani, L. S., Fitzpatrick, C., & Parent, S. (2012). Relating kindergarten attention to subsequent developmental pathways of classroom engagement in elementary school . Journal of Abnormal Child Psychology, 40(5), 715-725.
Rhoades, B. L., Warren, H. K., Domitrovich, C. E., & Greenberg, M. T. (2011). Examining the link between preschool social–emotional competence and first grade academic achievement: The role of attention skills . Early Childhood Research Quarterly, 26 (2), 182-191.
Sadeh, A., Gruber, R., & Raviv, A. (2002). Sleep, neurobehavioral functioning, and behavior problems in school‐age children . Child Development, 73 (2), 405-417.
Stevens, C., Harn, B., Chard, D. J., Currin, J., Parisi, D., & Neville, H. (2013). Examining the role of attention and instruction in at-risk kindergarteners: Electrophysiological measures of selective auditory attention before and after an early literacy intervention . Journal of Learning Disabilities, 46(1), 73-86.
Swanson, J. M., Schuck, S., Porter, M. M., Carlson, C., Hartman, C. A., Sergeant, J. A., … & Wigal, T. (2012). Categorical and dimensional definitions and evaluations of symptoms of ADHD: History of the SNAP and the SWAN rating scales . The International Journal of Educational and Psychological Assessment , 10(1), 51-70.
Van der Niet, A. G., Hartman, E., Smith, J., & Visscher, C. (2014). Modeling relationships between physical fitness, executive functioning, and academic achievement in primary school children . Psychology of Sport and Exercise, 15 (4), 319-325.
Williford, A. P., Vick Whittaker, J. E., Vitiello, V. E., & Downer, J. T. (2013). Children’s engagement within the preschool classroom and their development of self-regulation . Early Education and Development, 24 (2), 162-187.

Ziegler, J. C., Pech‐Georgel, C., Dufau, S., & Grainger, J. (2010). Rapid processing of letters, digits and symbols: What purely visual‐attentional deficit in developmental dyslexia? Developmental Science , 13(4), F8-F14.

Auditory Processing

Auditory Processing refers to the interpretation of sounds as meaningful input through the discrimination, recognition, and comprehension of auditory information. Difficulties with Auditory Processing can occur even when there are no hearing impairments.

Auditory Processing is essential for speech perception and therefore is an important component in oral language development, particularly developing Phonological Awareness. Children who have Auditory Processing impairments may have difficulty developing Phonological Awareness skills, which can negatively impact literacy learning.

Assessments

  • Dynamic Auditory Processing can be measured using a frequency modulation task where students are asked to detect a specific frequency modulation.
  • The brain’s response to auditory stimuli can be measured, even in young infants, using event-related brain potentials (ERPs). The brain’s response to varying auditory stimuli is recorded. ERPs can be used to determine how well someone can differentiate between different auditory stimuli.
  • Test of Auditory Processing Skills (Martin & Brownell, 2005): A formal assessment of Auditory Processing skills examining auditory attention, auditory memory, auditory cohesion, and phonological skills.

Learner Factor Connections

  • Attention: The ability to focus on a specific task without being distracted, as well as the ability to select relevant information while ignoring irrelevant information. Auditory selective Attention is the ability to focus on a specific source of sound or speech in a noisy environment.
    • Auditory selective Attention requires Auditory Processing skills for attending to speech in a noisy environment.
  • Hearing: The ability to hear sounds in the typical human range of approximately 20 – 20,000 Hz.
    • Hearing allows auditory information to enter the brain and be interpreted as meaningful input via Auditory Processing.
  • Phonological Awareness: The knowledge of and ability to manipulate and detect sounds in words
    • Auditory Processing is necessary for speech perception and therefore essential for developing Phonological Awareness.
  • Sensory Integration: The process of receiving, processing, and organizing multiple sources of sensory information from the environment
    • Some research suggests that Auditory Processing impairments can cause difficulties with Sensory Integration.

Research Findings

  • Boets and colleagues (2008) examined Auditory Processing, Visual Processing, Phonological Awareness, and reading skills in a group of 62 5-year-old children. They found that dynamic Auditory Processing was related to speech perception skills and that speech perception skills were a predictor of Phonological Awareness skills. There was also a direct link between Auditory Processing skills and Phonological Awareness, suggesting that the relationship between Auditory Processing and Phonological Awareness is only partially mediated by speech perception skills. Phonological Awareness skills were directly related to early literacy skills. Overall, these results suggest that Phonological Awareness deficits are likely preceded by deficits in Auditory Processing.
  • Leppanen and colleagues (2010) conducted a longitudinal study to examine whether Auditory Processing difficulties lead to dyslexia. Auditory Processing was examined using event-related brain potentials (a measure of the brain’s electrophysiological response to stimuli of interest) in a group of children at risk for developing dyslexia (at least one close relative was diagnosed with dyslexia) compared to a group of kids without a family risk. Then when the children reached 2nd grade, they were divided into four groups based on their reading skills: (1) at-risk group with reading disability, (2) at-risk group with typical reading skills, (3) without-risk group with reading disability, and (4) without-risk group without reading disability. They found that, as newborns, the ERPs in children in the without-risk group without a reading disability showed a clear ability to differentiate between auditory tones. However, the at-risk group with reading disability and the at-risk group without a reading disability did not show this ability to discriminate between auditory tones as newborns. Also, ERPs detecting passive change detection were correlated with phonological skills and Alphabet Knowledge at pre-school age, and they were also associated with reading speed and spelling accuracy in the 2nd grade (~ 9-year-olds). Specifically, the amplitudes of passive change detection ERPs were smaller in the at-risk groups compared to the without-risk without reading disability group. Thus, this study suggests that an Auditory Processing deficit at birth may lead to difficulty with phonological processing and reading development at a later age.
  • Brandwein and colleagues (2005) examined the relationship between Sensory Integration difficulties, Auditory Processing skills, and autism severity in a group of children with Autism Spectrum Disorder (ASD) (n = 52; 6- to 17-year-olds). The event-related brain potentials (a direct measure of electrophysiological processing in the brain) and behavioral measures were used. Their results suggest that aberrant auditory sensory processing may contribute to difficulties with Sensory Integration.

Research on Auditory Selective Attention

  • A group of kindergarteners identified as being at risk of developing reading difficulties were given reading instruction using Early Reading Intervention (small group instruction in reading for 45 minutes, five days per week for eight weeks). By the end of intervention, early literacy skills of children in the at-risk group improved significantly so that there was no difference in scores between the typical and at-risk groups. Additionally, measures of neurophysiological activity during auditory selective attention tasks (attending to a story presented over headphones in one ear, while inhibiting the story presented in the other ear) before and after the reading intervention demonstrated improvements in sensorineural processing. The researchers concluded that reading intervention programs may work in part by training selective Attention processes (Stevens et al., 2013).

References

Boets, B., Wouters, J., Van Wieringen, A., De Smedt, B., & Ghesquiere, P. (2008). Modelling relations between sensory processing, speech perception, orthographic and phonological ability, and literacy achievement. Brain and Language, 106(1), 29-40.
Brandwein, A. B., Foxe, J. J., Butler, J. S., Frey, H. P., Bates, J. C., Shulman, L. H., & Molholm, S. (2015). Neurophysiological indices of atypical auditory processing and multisensory integration are associated with symptom severity in autism. Journal of Autism and Developmental Disorders, 45(1), 230-244.
Leppänen, P. H., Hämäläinen, J. A., Salminen, H. K., Eklund, K. M., Guttorm, T. K., Lohvansuu, K., … Lyytinen, H. (2010). Newborn brain event-related potentials revealing atypical processing of sound frequency and the subsequent association with later literacy skills in children with familial dyslexia. Cortex, 46(10), 1362-1376.
Martin, N., & Brownell, R. (2005). Test of auditory processing skills. Novato.
Stevens, C., Harn, B., Chard, D. J., Currin, J., Parisi, D., & Neville, H. (2013). Examining the role of attention and instruction in at-risk kindergarteners electrophysiological measures of selective auditory attention before and after an early literacy intervention. Journal of Learning Disabilities, 46(1), 73-86.
Swanson, J., Schuck, S., Mann, M., Carlson, C., Hartman, K., Sergeant, J., . . . McCleary, R. (2004). Categorical and dimensional definitions and evaluations of symptoms of ADHD: The SNAP and the SWAN rating scales.

White-Schwoch, T., Carr, K. W., Thompson, E.C., Anderson, S., Nicol, T., Bradlow, A., … Kraus, N. (2015). Auditory processing in noise: A preschool biomarker for literacy. PLOS Biology, 13(7), 1-17.

 


Inhibition

Inhibition (also called executive control or cognitive control) is an executive function skill that begins to develop around age 3 or 4 (Barkley, 1997, 2003) and continues to develop throughout adolescence. It involves suppressing attention to irrelevant stimuli in order to focus on pertinent stimuli/information. Inhibition occurs at the behavioral (controlling responses) and cognitive levels. Behavioral and cognitive Inhibition are tightly coupled, as a lack of cognitive Inhibition often results in a lack of behavioral Inhibition (Altemeier, Abbott, & Berninger, 2008). There is evidence that Inhibition affects early literacy skills (Kegel, van der Kooy-Hofland, & Bus, 2009). Moreover, it is an important component of behavioral- and Self-Regulation (McClelland, Cameron, Connor, Farris, Jewkes & Morrison, 2007). Also, Inhibition is believed to affect Long-term Memory at the encoding and retrieval stages (de Ribaupierre, 2002) by allowing phonological codes for letters to be retrieved efficiently by suppressing incorrect codes

Assessments

  • Stroop Test: Participants are asked to indicate the color of the font that a word (e.g., red, blue, green) is printed in. Participants experience interference when the font color does not match the color word (e.g., red, blue, green), and this interference makes it harder for them to complete the task compared to conditions where the font color matches the color word. Under interference conditions, participants must inhibit the automatic recognition of the word meaning and instead say the font color; thus this task measures how well participants can inhibit irrelevant stimuli.
  • The Color-Word Interference test (based on the Stroop test) from the Delis-Kaplan Executive Function System (D-KEFS) (Delis, Kaplan & Kramer, 2001) has four tasks. Inhibition can be measured in the first task, which requires students name the color words are printed in rather than reading the word. Other tasks in this test include naming color patches, reading words, and switching between naming the color words are printed in and reading the word itself.
  • Flexibility Task: Requires participants to place hands on two separate response buttons while viewing a computer screen. A letter and a number are displayed on the screen, and the participant is instructed to press the button on the same side of the screen as the letter then press the button on the same side of the screen as the number. The position of the letters and numbers switch randomly with each trial. Accuracy and response times are measured.
  • Head-to-Toes Task: Measures Inhibitory control, Attention, and Working Memory by requiring children to perform the opposite of provided verbal instructions.
  • TEA-Ch Same Word-Opposite Word Task (Gerstadt, Hong, & Diamond, 1994): Requires children to say the opposite of each digit displayed in an array.
  • Eriksen Flanker Task (Eriksen & Eriksen, 1974): The ability to suppress an inappropriate response is measured. The subject is told to respond to a target stimulus (a variety of stimulus types can be used, e.g., letters, arrows facing to the left). The target is flanked by either congruent flankers (same as the target stimulus), incongruent flankers (opposite of the target stimulus), or neutral flankers.

Learner Factor Connections

  • Alphabet Knowledge: Familiarity with letter names, forms, and corresponding sounds
    • Inhibition skills in kindergarteners have been positively linked to Alphabet Knowledge.
  • Attention: The ability to focus on a specific task without being distracted, as well as the ability to select relevant information while ignoring irrelevant information
    • Inhibition supports Attention by allowing students to focus on important stimuli by suppressing irrelevant information.
  • Decoding: The ability to apply knowledge of relationships between letters and speech sounds to properly recognize and pronounce words
    • Inhibition supports Decoding by aiding in the retrieval of the target word by suppressing words similar to the target word.
  • Emotion: A complex psychological state that involves a subjective experience and can result in a physiological and behavioral response. Emotion regulation is the ability to control emotional arousal in order to facilitate adaptive functioning.
    • Inhibition allows emotional impulses to be regulated and controlled and is essential for Emotion regulation.
  • Long-term Memory: The ability to hold information for a long period of time, and possibly indefinitely
    • Inhibition can aid the encoding and retrieval of information in Long-term Memory. For example, if a student is trying to retrieve the meaning of a word stored in Long-term Memory, Inhibition will suppress incorrect words from being retrieved.
  • Self-Regulation: The ability to alter responses and align them with standards, such as social expectations
    • Cognitive and behavioral Inhibition are essential to Self-Regulation because they allow students to effectively monitor and suppress inappropriate behaviors and impulses.
  • Sleep: A naturally occurring state involving the suspension of consciousness during which the body and the brain rest. Both duration and quality contribute to obtaining sufficient Sleep.
    • Behavioral Inhibition declines when students do not obtain the optimal amount and quality of Sleep.
  • Social Awareness & Relationship Skills: Understanding social norms for behavior and understanding the perspective of others contributes to interpersonal skills
    • Inhibition subserves Relationship Skills such as sharing, likely because the choice to share requires students to inhibit the impulse to keep resources (food/toys etc.) for themselves.
  • Trauma: Emotional distress resulting from experiencing violence, abuse, a disaster, or an accident. Interpersonal trauma refers to Trauma that has occurred between people (e.g., assault or abuse), and non-interpersonal Trauma refers to Trauma inflicted by some other source (e.g., a motor vehicle accident, a natural disaster).
    • Children who experience Trauma, particularly those who are abused or witness abuse at home, are at risk for executive functioning impairments, including deficits to Inhibition.
  • Working Memory: The type of memory that allows a person to temporarily hold and manipulate information for use in many complex cognitive processes
    • Cognitive Inhibition aids the temporary storage of information in Working Memory by allowing for the suppression of irrelevant items that would interfere with relevant items in Working Memory.

Research Findings

  • McClelland and colleagues (2007) investigated the impact of behavioral Inhibition skills (as measured by the Head-to-Toes task) on Vocabulary and emergent literacy skills in a group of students in both the fall and spring of their prekindergarten year. They found that behavioral Inhibition predicted 4-year-olds’ Vocabulary and Print Awareness skills, even when controlling for gender in both the fall and spring. Moreover, growth in behavioral regulation performance predicted growth in Vocabulary and Print Awareness skills.
  • Reiter and colleagues (2005) found that children with dyslexia (26 boys and 16 girls, mean age of 10.8 years) have difficulties with behavioral/response Inhibition (as measured by a Stroop task and a Flexibility task) relative to a group of control participants.
  • Altemeier, Abbott, and Berninger (2008) examined the influence of Inhibition (as well as rapid automatic switching and Inhibition/switching) on literacy outcomes in a group of children with dyslexia and a group of typically developing children without dyslexia. In the children without dyslexia, Inhibition measured in 1st grade (as measured by D-KEFS color-word interference task) significantly predicted performance on reading and writing measures (e.g., reading comprehension, Decoding, writing skills) in the 4th grade, including phonemic Decoding, word reading skills, and spelling. Rapid automatic switching and Inhibition/switching were also found to predict literacy outcomes in each grade. Also, the authors found that Inhibition increased steadily between 1st to 6th grades. However, in the group of children with dyslexia, less of the variance in literacy outcomes were explained by multiple regression models. The authors suggested that children with dyslexia may not apply executive functions in the same way as typical readers.
  • Blair and Razza (2007) investigated the role of different aspects of Self-Regulation in 41 children in preschool and one year later in kindergarten. They measured Inhibition and Attention (executive functioning skills), as well as the ability to self-regulate Emotion in the classroom. Inhibitory control was measured using a peg-tapping procedure where children are given a wooden dowel and instructed to tap once when the experimenter taps twice and tap twice when the experimenter taps once. This measures how well children can inhibit the automatic impulse to tap the same amount of times as the experimenter. They measured children’s Alphabet Knowledge and Vocabulary, along with other academic measures (including math). They found that inhibitory control and Self-Regulation in preschool was a strong predictor of Alphabet Knowledge in kindergarten. The authors believe this indicates that inhibitory control is an important component for the acquisition of Alphabet Knowledge.
  • Cain (2006) examined the relationship between inhibitory processing and verbal Working Memory in a group of children with poor reading comprehension skills (but age appropriate word level reading skills) compared to a group of children with good reading comprehension skills. In a word-based Working Memory task, they found that the poor comprehenders were more likely to recall words from previous trials that the good comprehenders successfully inhibited. A sentence completion task followed by a memory task was also administered, where participants are asked to complete a sentence with an appropriate noun, and then they would hear the last word of the recording. They were also told they would be required to remember the last word of the recording rather than the last word that they provided. Later they were asked to recall the list of nouns on the recordings. On this task, the poor comprehenders were more likely than the good comprehenders to provide words that had been disconfirmed (words they provided but were different from the target word on the recording). However, the groups did not differ on measures of Short-term Memory (digit recall and word recall task). These results suggest that students with poor reading comprehension possess weaker inhibitory processing skills within Working Memory, even though their ability to store words in Short-term Memory is not impaired.
  • DePrince and colleagues (2009) tested executive functioning in three groups of children: those who had been exposed to familial Trauma, those exposed to non-familial Trauma, and those with no exposure to Trauma. Specifically, they measured Working Memory, Inhibition, auditory Attention, and interference control. They found a significant correlation between experiencing familial Trauma to executive functioning, even when controlling for SES, traumatic brain injury, and anxiety. They did not find this association in children who had been exposed to non-familial Trauma. They concluded that executive functioning impairments may contribute to the academic, psychological, and behavior problems often seen in children who have suffered abuse.
  • A study of students in 4th to 9th grade found that lower Attention ratings (as measured by the BRIEF and parental report) were associated with written language learning but not aural/oral language learning outcomes. However, measures of Inhibition (which is required for focused Attention) were predictive of oral language comprehension skills (Berninger, Abbott, Cook, & Nagy, 2016). Dice and Schwanenflugel (2012) investigated the relationship between Attention skills in prekindergarten and Decoding abilities in kindergarten. To do this, they had a sample of 250 children and used structural equation modeling (a statistical technique that allows researchers to investigate the relationship between different variables of interest – in this case Attention skills and Decoding abilities). The children were assessed within one month of entering prekindergarten (Time 1) and also within a month of the end of their year of kindergarten (Time 2). Teacher ratings of Attention were measured using an experimental short form of the the Teacher Rating Scale – Preschool of the Behavioral Assessment System for Children (TRS-P BASC) (Reynolds & Kamphaus, 1992) called the BASC screener (Yanosky, 2005; Yanosky et al., 2011) which contains 25 items. Decoding was assessed in kindergarten using the Early Decoding Test (Schwanenflugel et al., 2010). Receptive Vocabulary (measured using the Peabody Picture Vocabulary Test – III, Dunn & Dunn, 1997), expressive Vocabulary (measured using the Expressive Vocabulary Test, EVT, Williams, 1997), Alphabet Knowledge (measured using identification of letter shapes/names/sounds), and Phonological Awareness (measured using the Phonological Awareness Test, PAT, Robertson & Salter, 1997) were also assessed. Their results indicated that poor Attention skills in prekindergarten contribute to weak emergent literacy skills (e.g., Phonological Awareness, Alphabet Knowledge, receptive/expressive Vocabulary), which lead to poor Decoding skills in kindergarten.
  • Sadeh and colleagues (2002) measured Sleep activity (using actigraphy) for five consecutive nights in a group of school-age children (7.2 – 12.7 years). The children were identified as either poor sleepers (more fragmented sleep or reduced sleep efficiency) or good sleepers. The good sleepers performed significantly better than the poor sleepers on a declarative memory task (serial digit learning test) in the morning but not the noon testing session. Performance on more complex tasks was correlated with poor sleep. Also, they found a stronger correlation between Sleep and neurobehavioral functioning in younger children. Overall, they found that poor Sleep was associated with difficulty sustaining Attention and behavioral Inhibition.
  • Self-Regulation involves cognitive regulation: using executive functions (e.g., Working Memory, Inhibition, Attention) to inhibit impulses and attend to tasks (Williford, Whittaker, Vitiello, & Downer, 2013).
  • Carlson and Wang (2007) conducted a study with 53 children between the ages of 4 to 6 to determine the relationship between Emotion regulation (measured using a Disappointing Gift scenario and a Secret Keeping scenario, as well as a parent report questionnaire) and inhibitory Control of Attention (measured using a Simon Says game). Inhibition was significantly correlated with Emotion regulation skills. Parent report measures of Inhibition and Emotion regulation were also correlated with the behavioral results obtained by the researchers.  The authors discussed that this relationship did change as a function of age and gender. This relationship was significant in 4-year-olds but not 5-year-olds, and it was stronger in girls than in boys. The gender differences were possibly due to the fact that poor Emotion regulation skills are more rare in girls, so when girls display difficulty with Emotion regulation, it is likely indicative of a more severe problem.
  • Many studies have found that bilingual/multilingual children have certain cognitive advantages relative to their monolingual peers (Morton & Harper, 2007; Bialystok & Senman, 2004; Foy & Mann, 2015). Specifically, these studies have found that bilinguals/multilinguals have better cognitive control abilities. Cognitive control is the ability to consciously act according to goals and resolve interference. It is achieved through a combination of cognitive skills including Inhibition and Attention. This is likely because children are required to actively inhibit the language(s) that is/are not currently in use to use the desired language at that point in time. They also must monitor contextual cues and selectively attend to the appropriate language according to the particular situation. This constant monitoring, Inhibition, and selective Attention all serve to boost these cognitive skills. Further research is required to fully understand the details of this bilingual cognitive advantage (see Barac et al., 2014, for a review).
  • Aguilar-Pardo and colleagues (2013) examined altruistic sharing in a group of 4- to 6-year-olds and its relationship to Inhibition, Working Memory, and cognitive flexibility. They found that altruistic behavior was related to a child’s inhibitory control skills. They discuss that altruism, which is an important component of Social Awareness and Relationship Skills, is partially subserved by the ability to inhibit the impulse to preserve your own resources. Future research is needed to fully explore the cognitive underpinnings of relationship skills like altruistic sharing.

References

Aguilar-Pardo, D., Martínez-Arias, R., & Colmenares, F. (2013). The role of inhibition in young children’s altruistic behaviour. Cognitive Processing, 14(3), 301-307.
Altemeier, L. E., Abbott, R. D., & Berninger, V. W. (2008). Executive functions for reading and writing in typical literacy development and dyslexia. Journal of Clinical and Experimental Neuropsychology, 30(5), 588-606.
Barac, R., Bialystok, E., Castro, D. C., & Sanchez, M. (2014). The cognitive development of young dual language learners: A critical review. Early Childhood Research Quarterly, 29(4), 699-714.
Barkley, R. (1997). Behavioural inhibition, sustained attention, and executive functions. Psychological Bulletin, 121, 65-94.
Barkley, R. (2003). Attention-deficit/hyperactivity disorder. In E. J. Mash & R. Barkley (Eds.), Child psychopathology (2nd ed., pp. 75–143). New York: Guilford Press.
Berninger, V., Abbott, R., Cook, C. R., & Nagy, W. (2016). Relationships of attention and executive functions to oral language, reading, and writing skills and systems in middle childhood and early adolescence. Journal of Learning Disabilities, 1-16.
Bialystok, E., & Senman, L. (2004). Executive processes in appearance–reality tasks: The role of inhibition of attention and symbolic representation. Child Development, 75(2), 562-579.
Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78(2), 647-663.
Cain, K. (2006). Individual differences in children’s memory and reading comprehension: An investigation of semantic and inhibitory deficits. Memory, 14(5), 553-569.
Carlson, S. M., & Wang, T. S. (2007). Inhibitory control and emotion regulation in preschool children. Cognitive Development, 22(4), 489-510.
de Ribaupierre, A. (2002). Working memory and attentional processes across the lifespan. [The 2nd Tsukuba International Conference on Memory.] In P. Graf & N. Ohta (Eds.), Lifespan development of human memory (pp. 59–80). Cambridge, MA: The MIT Press.
Delis, D. C., Kaplan, E., & Kramer, J. H. (2001). The Delis–Kaplan executive function system: Examiner’s manual. San Antonio, TX: The Psychological Corporation.
DePrince, A. P., Weinzierl, K. M., & Combs, M. D. (2009). Executive function performance and trauma exposure in a community sample of children. Child Abuse & Neglect, 33(6), 353-361.
Dice, J. L., & Schwanenflugel, P. (2012). A structural model of the effects of preschool attention on kindergarten literacy. Reading and Writing, 25(9), 2205-2222.
Dunn, L. M., & Dunn, L. M. (1997). Peabody picture vocabulary test (3rd ed.). Circle Pines, MN: American Guidance Service.
Eriksen, B.A., & Eriksen, C.W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics, 16(1), 143–149.
Foy, J. G., & Mann, V. A. (2013). Bilingual children show advantages in nonverbal auditory executive function task. International Journal of Bilingualism, 18(6), 717-729.
Gerstadt, C. L., Hong, Y. J., & Diamond, A. (1994). The relationship between cognition and action: Performance of children 3 1/2–7 years old on a Stroop-like day-night test. Cognition, 53(2), 129–153.
Harms, T., Clifford, R. M., & Cryer, D. (1998). Early childhood environment rating scale. Revised edition. New York, NY: Teachers College Press, Columbia University.
Kegel, C. A. T., van der Kooy-Hofland, V. A. C., & Bus, A. G. (2009). Improving early phoneme skills with a computer program: Differential effects of regulatory skills. Learning and Individual Differences, 19(4), 549–554.
McClelland, M. M., Cameron, C. E., Connor, C. M., Farris, C. L., Jewkes, A. M., & Morrison, F. J. (2007). Links between behavioral regulation and preschoolers’ literacy, vocabulary, and math skills. Developmental Psychology, 43(4), 947–959.
Morton, J. B., & Harper, S. N. (2007). What did Simon say? Revisiting the bilingual advantage. Developmental Science, 10(6), 719-726.
Pianta, R. C., La Paro, K. M., & Hamre, B. K. (2008). Classroom assessment scoring system [CLASS] manual: Pre-K. Baltimore, MD: Brookes.
Reiter, A., Tucha, O., & Lange, K. W. (2005). Executive functions in children with dyslexia. Dyslexia, 11(2), 116-131.
Reynolds, C. R., & Kamphaus, R. W. (2004). Behavior assessment system for children: 2nd edition (BASC-2). Circle Pines, MN; AGS.
Sadeh, A., Gruber, R., & Raviv, A. (2002). Sleep, neurobehavioral functioning, and behavior problems in school‐age children. Child Development, 73(2), 405-417.
Schwanenflugel, P. J., Hamilton, C. E., Neuharth-Pritchett, S., Restrepo, M. A., Bradley, B. A., & Webb, M.-Y. (2010). PAVEd for Success: An evaluation of a comprehensive literacy program for 4-year-old children. Journal of Literacy Research, 42, 227–275.
Smith, M. W., Dickinson, D. K., Sangeorge, A., & Anastasopoulos, L. (2002). User’s guide to the early language & literacy classroom observation toolkit: Research edition. Baltimore, MD: Brookes.
Weiland, C., Ulvestad, K., Sachs, J., & Yoshikawa, H. (2013). Associations between classroom quality and children’s vocabulary and executive function skills in an urban public prekindergarten program. Early Childhood Research Quarterly, 28(2), 199-209.
Williams, K. T. (1997). Expressive vocabulary test. Circle Pines, MN: American Guidance Service.
Williford, A. P., Vick Whittaker, J. E., Vitiello, V. E., & Downer, J. T. (2013). Children’s engagement within the preschool classroom and their development of self-regulation. Early Education and Development, 24(2), 162-187.
Yanosky, D. (2005). Paper presented at the Georgia Educational Research Association Annual Meeting. Athens, Georgia.

Yanosky, D., Schwanenflugel, P. J., & Kamphaus, R. W. (2013). Psychometric properties of a proposed short form of the BASC teacher rating scale – Preschool. Journal of Psychoeducational Assessment, 31(4), 351-362.

 


Long-term Memory

Information can be stored in Long-term Memory indefinitely, which distinguishes Long-term Memory from Short-term and Working Memory, which store information temporarily. When memories stored in Short-term Memory are consolidated through rehearsal of information, they become part of Long-term Memory. The central executive component of Working Memory is believed to control and regulate the temporary activation of information stored in Long-term Memory (Baddeley, 1998).

Long-term Memory can be divided into explicit memory and implicit memory:

  • Explicit memory (declarative memory) refers to memories that can be consciously remembered. Explicit memory can be further divided into episodic and semantic memory:
    • Episodic memory is for the storage of daily personal experiences, that is specific events in time, such as what we ate for breakfast yesterday.
    • Semantic memory is for memories of factual/general knowledge about the world, such as the fact that Tokyo is a city in Japan. Information about the time and place that this knowledge was learned is not typically known.
  • Implicit memory (nondeclarative memory) does not require conscious thought and can be further divided into procedural and emotional memory:
    • Procedural memory involves learning a sequence of movements or actions, such as swinging a golf club or riding a bike. Procedural memories are automatically retrieved and used for the execution of these actions/tasks.
    • Emotional memory involves a change in how stimuli are approached based on a past negative or positive experience, such as someone avoiding food that previously made them ill.

Assessments

  • The Children’s Memory Scale (CMS): Can assess Long-term, Short-term, and immediate Memory in children. The test includes six subtests measuring three domains, including verbal Short-term and Long-term Memory, visual Short-term and Long-term Memory, and Attention/concentration.

Learner Factor Connections

  • Background Knowledge: The knowledge a student already knows about a topic
    • Long-term Memory is key for the storage and retrieval of Background Knowledge when reading.
  • Inhibition: The ability to suppress attention to irrelevant stimuli to focus on pertinent stimuli/information, both controlling responses and attention
    • Inhibition can aid the encoding and retrieval of information in Long-term Memory. For example, if a student is trying to retrieve the meaning of a word stored in Long-term Memory, Inhibition will suppress incorrect words from being retrieved.
  • Short-term Memory: The ability to hold information for a short period of time
    • Differences in verbal Short-term Memory between poor and skilled readers have been attributed to poor representations in Long-term Memory, particularly for less frequent words.
  • Sight Recognition: The ability to recognize a word by sight rather than needing to decode the word
    • Sight words are stored in Long-term Memory in order to be accessed during reading.
  • Speed of Processing: The time it takes to perceive information, process it, and/or formulate or enact a response
    • Speed of Processing is likely slowed down by poor representations of words in Long-term Memory because poor representations interfere with information retrieval.
  • Trauma: An experience of violence and abuse, which can lead to long-term negative consequences
    • Childhood trauma and maltreatment have been linked with reductions in adults in the brain structure involved in the consolidation of memories from Short- to Long-term Memory.
  • Vocabulary: Includes both the lexical representations of stored sounds (word forms) and the semantic meaning associated with each of those stored sounds
    • Once a word is sufficiently learned, that Vocabulary knowledge is stored in Long-term Memory.
  • Working Memory: The type of memory that allows a person to temporarily hold and manipulate information for use in many complex cognitive processes
    • The central executive component of Working Memory is believed to control and regulate the temporary activation of information stored in Long-term Memory.

Research Findings

  • Was and Woltz (2007) examined the relationship between available Long-term Memory, Working Memory, Background Knowledge, and listening comprehension in a group of adults. Available Long-term Memory was found to mediate the relationships between Working Memory and listening comprehension, as well as between Background Knowledge and listening comprehension.
  • In an examination of verbal Short-term Memory and Long-term Memory in a group of 9- to 14-year-olds with and without reading disabilities, Kibby (2009) found that impairments on a verbal Short-term Memory task could be attributed to weak representations in Long-term Memory. However, this effect was only found in less frequent words, not in common words.
  • McDougall and colleagues (1994, 2002) investigated the relationship between Long-term Memory and verbal Short-term Memory in good versus poor readers (mean age of poor readers and age-matched controls was 8 years). In their first study using high frequency common words, they did not find differences in Long-term Memory between good and poor readers. However, in their follow-up study (2002) using low-frequency words, they found a reduced verbal Short-term Memory span in poor relative to good readers. The difference in verbal Short-term Memory between high- and low-frequency words was attributed to poorer representations in Long-term Memory for low-frequency words.
  • Speed of Processing is likely influenced by phonological representations in Long-term Memory, as poor phonological representations will interfere with retrieval of this information (Simmons & Singleton, 2008).
  • Several studies have found reductions in hippocampus (a brain structure involved in the consolidation of information from Short-term to Long-term Memory) and amgydala (involved in mood, emotional and cognitive responses) volumes in adults who experienced childhood Trauma and maltreatment (Bremner, 2003; Samplin et al., 2013 Stein, 1997; Teicher et al., 2012).
    • For example, Samplin and colleagues examined structural MRI scans in a group of adults with a history of childhood abuse compared to those without. They compared the hippocampal volume, as well as measures of general cognitive ability (e.g., IQ, digit span), and subclinical psychopathology between both groups. They found reduced hippocampal volumes in males but not in females. However, both males and females experienced higher rates of subclinical psychiatric symptoms resulting from childhood maltreatment.
  • Sight Recognition refers to recognizing a word by sight rather than needing to decode the word. The term “sight words” can be used to refer to words that do not conform to rules of phonetic Decoding (e.g., light, could, was, said) and must be recognized by sight because they cannot be sounded out phonetically. Being able to recognize words by sight, rather than needing to decode them, improves reading fluency and efficiency (Johnston, 2000). In this process, as soon as the word is fixated, the meaning and pronunciation of the word are automatically activated (Ehri, 1995). This requires “sight words” to be stored in Long-term Memory in order to be retrieved during reading (Ehri, 2014).

References

Baddeley, A. D. (1998). The central executive: A concept and some misconceptions. Journal of the International Neuropsychological Society, 4(5), 523-526.
Bremner, J. D. (2003). Long-term effects of childhood abuse on brain and neurobiology. Child and Adolescent Psychiatric Clinics of North America, 12(2), 271-292.
Cohen, M. J. (1997). Examiner’s manual: Children’s memory scale. San Antonio, TX: Harcourt Brace & Company.
Ehri, L. C. (2014). Orthographic mapping in the acquisition of sight word reading, spelling memory, and vocabulary learning. Scientific Studies of Reading, 18(1), 5-21.
Ehri, L.C. (1995). Phases of development in learning to read words by sight. Journal of Research in Reading, 18(2), 116-125.
Johnston, F. R. (2000). Word learning in predictable text. Journal of Educational Psychology, 92(2), 248.
Kibby, M. Y. (2009). There are multiple contributors to the verbal short-term memory deficit in children with developmental reading disabilities. Child Neuropsychology, 15(5), 485-506.
McDougall, S. J., & Donohoe, R. (2002). Reading ability and memory span: Long-term memory contributions to span for good and poor readers. Reading and Writing, 15(3-4), 359-387.
McDougall, S., Hulme, C., Ellis, A., & Monk, A. (1994). Learning to read: The role of short-term memory and phonological skills. Journal of Experimental Child Psychology, 58(1), 112-133.
Samplin, E., Ikuta, T., Malhotra, A. K., Szeszko, P. R., & DeRosse, P. (2013). Sex differences in resilience to childhood maltreatment: effects of trauma history on hippocampal volume, general cognition and subclinical psychosis in healthy adults. Journal of Psychiatric Research, 47(9), 1174-1179.
Simmons, F. R., & Singleton, C. (2008). Do weak phonological representations impact on arithmetic development? A review of research into arithmetic and dyslexia. Dyslexia, 14(2), 77-94.
Stein, M. B., Koverola, C., Hanna, C., Torchia, M. G., & McClarty, B. (1997). Hippocampal volume in women victimized by childhood sexual abuse. Psychological Medicine, 27(04), 951-959.
Teicher, M. H., Anderson, C. M., & Polcari, A. (2012). Childhood maltreatment is associated with reduced volume in the hippocampal subfields CA3, dentate gyrus, and subiculum. Proceedings of the National Academy of Sciences, 109(9), E563-E572.

Was, C. A., & Woltz, D. J. (2007). Reexamining the relationship between working memory and comprehension: The role of available long-term memory. Journal of Memory and Language, 56(1), 86-102.

Sensory Integration

Sensory Integration is the process of receiving, processing, and organizing multiple sources of sensory information from the environment then transforming that information into an appropriate response. Sensory overload can occur when the body’s senses (e.g., touch, hearing, taste) are overly stimulated. People who have sensory processing disorders frequently experience sensory overload which causes discomfort. Some symptoms of sensory overload include irritability, avoiding touch, and withdrawing from participating in activities. Sensory Integration difficulties are associated with lower participation in school activities, struggles with peer relationships, social withdrawal, and getting inadequate Sleep (Koenig & Rudney, 2010). These can lead to difficulties with successful learning, including learning to read.

The neurological threshold for sensory information and the behavioral response can vary:

  • Neurological Threshold
    • High threshold: The nervous system is only stimulated with high levels of sensory input.
    • Low threshold: The nervous system is easily stimulated with low levels of sensory input.
  • Behavioral Response
    • Active: The child responds immediately to the sensory environment (e.g., they may leave a room if it is too loud).
    • Passive: The child does not respond immediately to the sensory environment, even though they may immediately perceive the environment as being uncomfortable (e.g., the child will passively remain in a room that is too loud).

Some basic patterns of Sensory Integration include:

  • Sensation seeking (high threshold/active)
  • Low registration (high threshold/passive)
  • Sensation avoiding (low threshold/active)
  • Sensory sensitivity (low threshold/passive)

Children who are hyposensitive to sensory stimuli may seek out higher levels of sensory input (e.g., requiring noise to be louder or needing more light). Children who are hypersensitive to sensory stimuli may experience sensory overload where senses are overstimulated, causing them to be irritable, withdraw from activities, and/or avoid touch. Some children may show a mixture of sensory seeking and sensory avoidance.

Assessments

  • The Sensory Profile 2: Family of assessments that allow for the assessment of sensory processing patterns in infants to 14-year-olds through caregiver and teacher questionnaires.
  • The Sensory Integration and Praxis Tests (SIPT) (Ayres, 1989): Collection of 17 tests that provides a comprehensive measure of visual, tactile, and kinesthetic processing and motor performance.

Learner Factor Connections

  • Auditory Processing: The process of interpreting sounds, such as speech, as meaningful input
    • Some research suggests that Auditory Processing impairments can cause difficulties with Sensory Integration.
  • Emotion: A complex psychological state that involve a subjective experience and can result in a physiological and behavioral response. Emotion regulation is the ability to control emotional arousal in order to facilitate adaptive functioning.
    • Sensory Integration difficulties can negatively impact Emotion regulation abilities.
  • Self-Regulation: The ability to alter responses and align them with standards, such as social expectations
    • Sensory Integration results from a combination of sensory processing and behavioral Self-Regulation.
  • Sleep: A naturally occurring state involving the suspension of consciousness during which the body and the brain rest. Both duration and quality contribute to obtaining sufficient Sleep.
    • Students with Sensory Integration difficulties (and particularly students with a tactile sensitivity) often experience Sleep disturbances.
  • Social Awareness & Relationship Skills: Understanding social norms for behavior and understanding the perspective of others contributes to interpersonal skills
    • Social Awareness & Relationship Skills are negatively impacted when a student has difficulties with Sensory Integration due to decreases in social participation with peers and quality/quantity of play skills.

Research Findings

  • Difficulty regulating sensory information can lead to several different behavioral patterns. It may lead to sensory avoidance, where environments with things like loud noises or bright lights are avoided. It can also lead to hyposensitivity to sensory stimuli, where high levels of sensory input are sought out (e.g., needing more light, requiring noise to be loud in order to register the sound). People with difficulty integrating sensory information may seek out sensory stimulation (e.g, banging toys or crashing into walls), avoid sensory stimulation, or display a mixture of sensory seeking and avoidance (Dunn, 2007).
  • Dunn (1997; 2007) proposed that sensory processing results from the interaction of a child’s neurological threshold and their behavioral responses/Self-Regulation strategies. The nervous system is activated when stimuli in the environment are encountered. The extent to which the nervous system is stimulated can be described by the neurological threshold, where children with low thresholds are sensitive to even low levels of sensory input and children with high thresholds are only sensitive to higher levels of input. Behavioral responses can also vary along a spectrum of behaviors. For example, even if they are in an environment that they perceived to be too loud or visually stimulating, some children would react passively and stay in that environment, whereas other children may lie on the other end of the spectrum and immediately react to the environment. These children would likely leave an area they found to be over-stimulating and uncomfortable.
  • Engel-Yeger and colleagues (2016) investigated the relationship between sensory processing and clinical conditions in individuals with major affective disorders in a group of adults (20- to 84-year-olds). Overall they found that sensory processing disorders are very common among patients with major affective disorders. About 70% of the participants in this study had reduced sensation seeking, and overall, sensory avoiding and low registration were twice as common relative to the general population. Specifically, they found that a sensation seeking profile predicted hyperthymic affective temperament (excessively positive) as well as lower severity of depression. A low registration profile predicted anxious and irritable affective temperaments. A sensory avoiding profile predicted depression and an anxious affective temperament.
  • Brandwein and colleagues (2015) examined the relationship between Sensory Integration difficulties, Auditory Processing skills, and autism severity in a group of children with Autism Spectrum Disorder (ASD) (n = 52; age range = 6 to 17 years). The event-related brain potentials (a direct measure of electrophysiological processing in the brain) and behavioral measures were used. Their results suggest that aberrant auditory sensory processing may contribute to difficulties with Sensory Integration.
  • In a review paper, Koenig and Rudney (2010) reviewed papers which examined Sensory Integration and its impact on Sleep, activities of daily living, social participation, and education in children and adolescents. This review suggests that children with tactile sensitivity exhibited a high rate of Sleep disturbances. Also, children with Sensory Integration difficulties show decreased quality/quantity of play skills and lower ratings of social competence. High sensory overresponsivity possess the worst Social Awareness and Relationship Skills. Moreover, children with these difficulties display lower participation in school activities. Thus, difficulty with Sensory Integration can lead to difficulties with social participation, Sleep, and participating in classroom activities.
  • Bolanos and colleagues (2015) examined a group of children (n = 36) at risk for developing sensory processing disorders (SPDs) because they showed sensory processing indicators and a group of children  (n = 15) not at risk for developing SPDs. They assessed the children with the Revised Profile of Developmental Behaviors (PCD-R) (a developmental scale) and the Sensory Profile, as well as conducted classroom observations. They found a significant correlation between sensory processing impairments and Emotion regulation difficulties. Moreover, their results suggested that children with sensory processing risk indicators also were affected in the areas of motor organization and expressive language.

References

Ayres, A. J. (1989). The sensory integration and praxis tests. Los Angeles, CA: Western Psychological Services.
Bolaños, C., Gomez, M. M., Ramos, G., & Rios del Rio, J. (2015). Developmental risk signals as a screening tool for early identification of sensory processing disorders. Occupational Therapy International, 23(2), 154-164.
Brandwein, A. B., Foxe, J. J., Butler, J. S., Frey, H. P., Bates, J. C., Shulman, L. H., & Molholm, S. (2015). Neurophysiological indices of atypical auditory processing and multisensory integration are associated with symptom severity in autism. Journal of Autism and Developmental Disorders, 45(1), 230-244.
Dunn, W. (1997). The impact of sensory processing abilities on the daily lives of young children and their families: A conceptual model. Infants & Young Children, 9(4), 23-35.
Dunn, W. (2007). Supporting children to participate successfully in everyday life by using sensory processing knowledge. Infants and Young Children, 20, 84-101.
Dunn, W., (2014). Sensory profile 2. San Antonio, TX: Pearson.
Engel-Yeger, B., Muzio, C., Rinosi, G., Solano, P., Geoffroy, P. A., Pompili, M., … Serafini, G. (2016). Extreme sensory processing patterns and their relation with clinical conditions among individuals with major affective disorders. Psychiatry Research, 236, 112-118.
Koenig, K. P., & Rudney, S. G. (2010). Performance challenges for children and adolescents with difficulty processing and integrating sensory information: A systematic review. American Journal of Occupational Therapy, 64(3), 430-442.

Thompson, S. D., & Raisor, J. M. (2013). Meeting the sensory needs of young children. Young Children, 68(2), 34-43.

 


Short-term Memory

Short-term Memory refers to the ability to hold information in memory for a short period of time. Unlike Working Memory, it does not refer to manipulating or inferring information. Rather, in Short-term Memory tasks, participants are asked to repeat the material of interest in the same order as it was presented.

Assessments

  • Digit Span Task: Determines Short-term Memory capacity.
  • Kaufman Assessment Battery for Children (Kaufman & Kaufman, 1983): The experimenter says a string of numbers, and the child is asked to repeat them in the same order.
  • Children’s Memory Scale (CMS) (Cohen, 1997): Assesses Long- and Short-term Memory in 5- to 16-year-olds in three domains: verbal, visual, and attention/concentration.
  • Test of Memory and Learning (TOMAL) (Reynolds & Voress, 2007): Measures verbal, nonverbal, and composite memory in children and adults (5- to 59-year-olds).

Learner Factor Connections

  • Alphabet Knowledge: Familiarity with letter names, forms, and corresponding sounds
    • Verbal Short-term Memory measured for 4-year-olds has been shown to be a significant predictor of Alphabet Knowledge by the end of kindergarten (almost 6-year-olds).
  • Decoding: The ability to apply knowledge of relationships between letters and speech sounds to properly recognize and pronounce words
    • Verbal Short-term Memory is important for Decoding novel words.
  • Long-term Memory: The ability to hold information for a long period of time, and possibly indefinitely
    • Differences in verbal Short-term Memory between poor and skilled readers have been attributed to poor representations of words in Long-term Memory, particularly for less frequent words.
  • Phonological Awareness: The knowledge of and ability to manipulate and detect sounds in words
    • Deficits in verbal Short-term Memory may result from poor Phonological Awareness.
  • Speed of Processing: The time it takes to perceive information, process it, and/or formulate or enact a response
    • Faster Speed of Processing is related to better Short-term Memory task performance.
  • Vocabulary: Includes both the lexical representations of stored sounds (word forms) and the semantic meaning associated with each of those stored sounds
    • Verbal Short-term Memory supports the development of Vocabulary.
  • Working Memory: The type of memory that allows a person to temporarily hold and manipulate information for use in many complex cognitive processes
    • Working Memory and Short-term Memory are co-contributors to deficits in reading skills.

Research Findings

  • Many studies have found that verbal Short-term Memory is impaired in children with reading disabilities (Kibby, 2009; Snowling, 1991). However, visuospatial Short-term Memory is often unimpaired in students with reading disabilities (Kibby & Cohen, 2008; Kibby, Marks, Morgan, & Long, 2004). It is possible that this deficit in verbal Short-term Memory originates from poor Phonological Awareness and processing (Liberman & Shankweiler, 1991; Wagener et al., 1994). However, other researchers suggest the verbal Short-term Memory deficit results from a less efficient storage buffer, slow articulation rate, and poorer quality representations in Long-term Memory (Kibby et al., 2004; McDougall & Donohoe, 2002).
  • Swanson, Zheng, and Jerman (2009) conducted a meta-analysis to compare students with age-appropriate reading skills to those with reading disabilities on measures of Short-term Memory and Working Memory. Overall, their results revealed that children with reading disabilities have deficits of both Short-term Memory and Working Memory, particularly when processing speech-based or verbal information. Short-term Memory and Working Memory made independent contributions to differences in effect size between students with reading disabilities and those without reading disabilities.
  • Rispens and Baker (2012) examined the roles of phonological Short-term Memory (measured using a digit span task) and phonological representations (measured using a minimal pair distinction task) on a nonword repetition task. Several groups of students participated in the study including, a group of children with specific language impairment (SLI), a group with reading impairment (RI), a group with both SLI and RI, a typically developing age-matched group (mean age = 7.8), and a group of language-matched typically developing children (mean age = 5.8). The group of children with both SLI and RI were significantly impaired on the nonword repetition task relative to the other groups. They also found that phonological Short-term Memory contributed significantly to nonword repetition. Phonological representations also contributed.
  • Kibby (2009) investigated the impact of Phonological Awareness, phonological storage, phonological rehearsal, articulation rate, and Long-term Memory on verbal Short-term Memory in a group of children with reading disabilities and a control group (all groups between 9- to 14-year-olds). Verbal Short-term Memory was measured using a serial recall task on common words, less frequent words, and nonwords. Kibby found that children with a reading disability performed similarly to controls on frequent words but performed significantly worse on less frequent and nonwords. This was attributed to poorer quality representations in Long-term Memory for less frequent words. Also, Phonological Awareness was impaired for items that were coded phonetically relative to items coded by their meaning. Kibby concluded that verbal Short-term Memory impacts reading ability for novel words that must be decoded.
  • Phonological memory (aka verbal Short-term Memory) measured at the beginning of preschool (4-year-olds) has been shown to be a significant predictor of Alphabet Knowledge by the end of kindergarten (almost 6-year-olds) (De Jong & Olson, 2004).
  • Alloway and Alloway (2010) examined the relationship between verbal Short-term Memory (as measured by digit and word recall tasks), Working Memory (as measured by a backward digit recall and listening recall task), IQ (measured using subtests from the Wechsler Preschool and Primary Scale of Intelligence – Revised), and literacy skills in a group of 98 children living in the UK. Literacy skills were assessed using the Wechsler Objective Reading Dimensions (WORD) (Wechsler, 1993). The Wechsler Objective Numerical Dimensions (WOND) was also used to assess numeracy skills including mathematical reasoning and number operations (Wechsler, 1996). The students were tested at Time 1 when they were between the ages of 4.3-5.7 (mean age =  five), and they were tested six years later at Time 2 when they were between the ages of 10-11.3 (mean age = 10.11). Literacy and numeracy skills were only assessed at Time 2. Working Memory at the age 5 is found to be an excellent predictor of literacy skills six years later. Working Memory was a more powerful predictor of academic success, relative to IQ, in children in early grades.
  • Short-term Memory (as measured by a digit span task) predicts Vocabulary skills (as measured by the PPVT) more so than intelligence in pre-K students (Davidse et al., 2010).

References

Alloway, T. P., & Alloway, R. G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 106(1), 20-29.
Cohen, M. J. (1997). Examiner’s manual: Children’s Memory Scale. San Antonio, TX: Harcourt Brace & Company.
Davidse, N. J., de Jong, M. T., Bus, A. G., Huijbregts, S. C. J., & Swaab, H. (2011). Cognitive and environmental predictors of early literacy skills. Reading and Writing, 24(4), 395-412.
De Jong, P. F., & Olson, R. K. (2004). Early predictors of letter knowledge. Journal of Experimental Child Psychology, 88(3), 254-273.
Kail, R., & Hall, L. K. (2001). Distinguishing short-term memory from working memory. Memory & Cognition, 29(1), 1–9.
Kaufman, A. S., & Kaufman, N. L. (1983). Kaufman assessment battery for children. Bloomington, MN: Pearson Assessments.
Kibby, M. Y. (2009). There are multiple contributors to the verbal short-term memory deficit in children with developmental reading disabilities. Child Neuropsychology, 15(5), 485-506.
Kibby, M. Y., & Cohen, M. J. (2008). Memory functioning in children with reading disabilities and/ or attention-deficit/hyperactivity disorder: A clinical investigation of their working memory and long-term memory functioning. Child Neuropsychology, 14(8), 525–546.
Kibby, M. Y., Marks, W., Morgan, S., & Long, C. J. (2004). Specific impairment in developmental reading disabilities: A working memory approach. Journal of Learning Disabilities, 37(4), 349–363.
Liberman, I. Y., Shankweiler, D., Liberman, A. M., Fowler, C., & Fischer, F. W. (1977). Phonetic segmentation and recoding in the beginning reader. In A. S. Reber & D. Scarborough (Eds.), Toward a psychology of reading: The proceedings of the C.U.N.Y. Conferences (pp. 207–225). Hillsdale, NJ: Erlbaum.
McDougall, S. J. P., & Donohoe, R. (2002). Reading ability and memory span: Long-term memory contributions to span for good and poor readers. Reading and Writing, 15(3), 359–387.
Rispens, J., & Baker, A. (2012). Nonword repetition: The relative contributions of phonological short-term memory and phonological representations in children with language and reading impairment. Journal of Speech, Language, and Hearing Research, 55(3), 683-694.
Snowling, M. J. (1991). Developmental reading disorders. Journal of Child Psychology and Psychiatry, 32(1), 49-77.
Swanson, H. L., Zheng, X., & Jerman, O. (2009). Working memory, short-term memory, and reading disabilities: A selective meta-analysis of the literature. Journal of Learning Disabilities, 42(3), 260-287.
Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1994). Development of reading-related phonological processing abilities: New evidence of bidirectional causality from a latent variable longitudinal study. Developmental Psychology, 30(1), 73-87.
Wechsler, D. (1991). Wechsler intelligence scale for children (3rd ed.). San Antonio, TX: Psychological Corp.
Wechsler, D. (1993). Wechsler objective reading dimensions. London: Pearson Assessment.

Wechsler, D. (1996). Wechsler objective numerical dimensions. London: Pearson Assessment.

 


Speed of Processing

Speed of Processing is an important factor in the development of reading skills because it helps students process many different types of information quickly and efficiently. Speed of Processing is the rate that information is perceived, processed, and an appropriate response is formulated.

Speed of Processing influences:

  • The rate students can recognize and pronounce words (Decoding);
  • The rate students can accurately read text (fluency); and
  • How well students can understand reading material (reading comprehension).

Speed of information processing is important in any task that requires processing different types of stimuli (Shaul & Nevo, 2015). Having a higher Speed of Processing is associated with increased Working Memory capacity and enhanced Verbal Reasoning skills, as well as a higher intelligence (Fry & Hale, 1996; Kail, 2007; Sheppard & Vernon, 2008). Working Memory is reliant on Speed of Processing because a faster processing speed will result in faster rehearsal (Leonard et al., 2007).

Assessments

  • Visual Matching subtest of the Woodcock-Johnson (Woodcock et al., 2001): Requires children to look at a sheet with rows containing six numbers, two of which are the same. The child is asked to draw circles around the two identical numbers in each row as fast as possible. The total time allowed is three minutes.
  • Cross-Out subtest of the Woodcock Johnson: Requires children to look at a sheet containing rows of 20 shapes each. Each row contains five shapes that are identical to the first shape in the row, and the child is asked to cross out these five shapes. The total time allowed is three minutes.
  • Rapid Automatic Naming (RAN) assessments  (e.g., Rapid Automatized Naming Test) (Van den Bos, Lutje Spelberg, & Ruizeveld-de Winter, 2008): Tests children’s ability to name different objects (e.g., colors, letters, digits, pictures of objects). Children are presented with a page containing several rows of items, and they are asked to name them as quickly as possible one after the other. This is a common measure of Speed of Processing, but it also requires linguistic skills (e.g., verbal retrieval, Vocabulary knowledge of test items).

Learner Factor Connections

  • Alphabet Knowledge: Familiarity with letter names, forms, and corresponding sounds
    • A faster Speed of Processing aids the acquisition of Alphabet Knowledge.
  • Decoding: The ability to apply knowledge of relationships between letters and speech sounds to properly recognize and pronounce words
    • A faster Speed of Processing makes Decoding more efficient.
  • Long-term Memory: The ability to hold information for a long period of time, and possibly indefinitely
    • Speed of Processing is likely slowed down by poor representations of words in Long-term Memory because poor representations interfere with information retrieval.
  • Print Awareness: Understanding the forms, functions, and conventions of print
    • A faster Speed of Processing helps students develop Print Awareness skills.
  • Short-term Memory: The ability to hold information for a short period of time.
    • Faster Speed of Processing is related to enhanced Short-term Memory.
  • Working Memory: The type of memory that allows a person to temporarily hold and manipulate information for use in many complex cognitive processes
    • A faster Speed of Processing enhances Working Memory capacity because it allows more information to enter Working Memory due to faster rehearsal.

Research Findings

  • It is likely that Speed of Processing is an essential skill underlying performance of many cognitive skills including Working Memory (Kail & Salthouse, 1994; Shaul & Nevo, 2015).
  • Shaul and Nevo (2015) examined the relationship between Speed of Processing (measured using Rapid Automatic Naming) and reading skills. They tested a group of 96 kindergarteners on measures of print knowledge, Alphabet Knowledge, Phonological Awareness, and rapid naming speed, then they tested the same group of students a year later on measures of rapid automatic naming and reading skills (Decoding, fluency, comprehension).  Speed of Processing ability was a strong predictor of early literacy skills including Alphabet Knowledge (measured by letter naming) and Print Awareness in kindergarten and reading abilities in the 1st grade. Slower processors have weaker reading comprehension and fluency skills, and faster processors have stronger comprehension and fluency skills. Slower processors also displayed weaker Decoding skills than faster processors (Shaul & Nevo, 2015).
  • Speed of Processing ability (as measured by Rapid Automatic Naming) at the age of 5 is a significant predictor of reading fluency ability at the age of 10 (Koponen, Salmi, Eklund, & Aro, 2013).
  • Speed of Processing is likely influenced by phonological representations in Long-term Memory, as poor phonological representations will interfere with retrieval of this information (Simmons & Singleton, 2008).
  • Weak reading skills in the 3rd grade are associated with a general deficit in Speed of Processing (as measured by reaction time and Rapid Automatic Naming tasks), thus suggesting that some children with weak reading skills also have a domain-general deficit in Speed of Processing (Catts, Gillispie, Leonard, Kail, & Miller, 2002).
  • Non-alphabetic Rapid Automatic Naming measured before reading instruction has begun and alphanumeric Rapid Automatic Naming measured after reading instruction has begun are both strong predictors of reading fluency, even when controlling for verbal abilities and phonemic awareness (Lervag & Hulme, 2009). Thus, Rapid Automatic Naming may be a useful measure to use when determining whether a student is at risk for developing reading difficulties.
  • Catts and colleagues (2001) conducted a longitudinal study of 604 children who were tested in kindergarten on measures of language, cognitive skills, and early literacy skills. In 2nd grade, these children completed an assessment of reading comprehension abilities. The researchers aimed to identify factors that would be predictive of future reading success. At the follow-up testing in 2nd grade, 183 of 604 children were identified as having reading difficulties. The authors used a logistic regression analysis to determine which factors, measured in kindergarten, were most predictive of reading difficulties in 2nd grade.They found that letter identification, sentence imitation, Phonological Awareness (measured using a phoneme/syllable deletion task), mother’s education, and rapid automatic naming ability (RAN) skills were the most predictive of future reading abilities. RAN was measured using a task where children were asked to name pictures of animals as quickly and accurately as possible. Thus, Speed of Processing in kindergarten, as measured by RAN, is an important predictor of future reading success.

References

Catts, H. W., Fey, M. E., Zhang, X., & Tomblin, J. B. (2001). Estimating the risk of future reading difficulties in kindergarten children: A research-based model and its clinical implementation. Language, Speech, and Hearing Services in Schools, 32(1), 38-50.
Catts, H. W., Gillispie, M., Leonard, L. B., Kail, R. V., & Miller, C. A. (2002). The role of speed of processing, rapid naming, and phonological awareness in reading achievement. Journal of Learning Disabilities, 35(6), 510-525.
Fry, A., & Hale, S. (1996). Processing speed, working memory, and fluid intelligence: Evidence for developmental cascade. Psychological Science, 7(4), 237-241.
Kail, R. V. (2007). Longitudinal evidence that increases in processing speed and working memory enhance children’s reasoning. Psychological Science, 18(4), 312-313.
Kail, R., & Hall, L. K. (2001). Distinguishing short-term memory from working memory. Memory & Cognition, 29(1), 1–9.
Kail, R., & Salthouse, T. A. (1994). Processing speed as a mental capacity. Acta Psychologica, 86(2-3), 199–225.
Koponen, T., Salmi, P., Eklund, K., & Aro, T. (2013). Counting and RAN: Predictors of arithmetic calculation and reading fluency. Journal of Educational Psychology, 105(1), 162-175.
Leonard, L. B., Weismer, S. E., Miller, C. A., Francis, D. J., Tomblin, J. B., & Kail, R. V. (2007). Speed of processing, working memory, and language impairment in children. Journal of Speech, Language, and Hearing Research, 50(2), 408-428.
Lervåg, A., & Hulme, C. (2009). Rapid automatized naming (RAN) taps a mechanism that places constraints on the development of early reading fluency. Psychological Science, 20(8), 1040-1048.
Shaul, S., & Nevo, E. (2015). Different speed of processing levels in childhood and their contribution to early literacy and reading abilities. Early Childhood Research Quarterly, 32, 193-203.
Sheppard, L. D., & Vernon, P. (2008). Intelligence and speed of information- processing: A review of 50 years of research. Personality and Individual Differences, 44(3), 535-551.
Simmons, F. R., & Singleton, C. (2008). Do weak phonological representations impact on arithmetic development? A review of research into arithmetic and dyslexia. Dyslexia, 14(2), 77-94.
Swanson, H. L., Zheng, X., & Jerman, O. (2009). Working memory, short-term memory, and reading disabilities: A selective meta-analysis of the literature. Journal of Learning Disabilities, 42(3), 260-287.
Van den Bos, K.P.S., Lutje Spelberg, H. C., & Ruizeveld-de Winter, E.L. (2008). Serieel benoemen en woorden lezen [Serial naming and word reading]. Groningen, the Netherlands.

Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). Woodcock-Johnson III tests of cognitive abilities. Itaska, IL: Riverside Publishing.

 


Visual Processing

Visual Processing refers to the interpretation of visual stimuli. Visual Processing is important for reading skills because it contributes to reading speed and accuracy.

Visual Processing involves several different skills, including:

  • Visual Discrimination: Discriminating visual stimuli. During reading, this allows students to distinguish different letters from one another.
  • Visual Sequencing: Determining the order of images, words, or symbols.
  • Visual Spatial Processing: Recognizing the relation of objects in space to one another and to oneself. During reading this skill is important for determining the arrangement of words on a page.

Visual selective attention is an important component in reading since reading requires visual information processing. Deficits in visual attention have been associated with dyslexia, which is an impairment of reading acquisition. However, some researchers have not found a link between a deficit in general Visual Processing and dyslexia (e.g., Ziegler et al., 2010), rather they have found deficits in visual attention to letter strings but not non-alphanumeric stimuli. These researchers attribute the deficit in dyslexia to a deficit in symbol-sound mapping rather than a deficit in visual attentional processing.

Assessments

  • Visual array search tasks
  • Visual attention span task: Letter strings are flashed briefly on a screen, and participants are asked to orally report either a single cued letter or the entire string.
  • The Florida Kindergarten Screening Battery (FKSB) (Satz & Fletcher, 1982): Contains a Recognition-Discrimination subtest that assesses Visual Processing.
  • Rey-Osterrieth Complex Figure Test (ROCF) (Rey, 1941): Evaluates Visual Spatial Processing, as well as memory for visual-spatial information. Students are given cards with complex drawings and are first asked to draw the figure they are looking at. Then the card is removed, and they are asked to draw the figure again from memory.
  • Visual Processing is often assessed in research studies using computer-based tasks where students are asked to remember or respond to the spatial position or some specific visual characteristic of presented symbols. Typically these tasks hone in on Visual Processing skills through the use of non-linguistic symbols so language skills will not impact scores.

Learner Factor Connections

  • Alphabet Knowledge: Familiarity with letter names, forms, and corresponding sounds
    • Visual Processing skills aid in the acquisition of Alphabet Knowledge.
  • Attention: The ability to focus on a specific task without being distracted, as well as the ability to select relevant information while ignoring irrelevant information. Visual Selective Attention is the ability to select relevant and filter out irrelevant information from cluttered visual scenes.
    • Visual Selective Attention, which is important for attending to visual stimuli like text, requires Visual Processing skills.
  • Decoding: The ability to apply knowledge of relationships between letters and speech sounds to properly recognize and pronounce words
    • Decoding uses Visual Processing skills to map visual stimuli to phonological information (speech sounds).
  • Vision: The ability to use eyesight to perceive information about the world
    • Vision allows visual information to enter the brain and be interpreted as meaningful input via Visual Processing.
  • Working Memory: The type of memory that allows a person to temporarily hold and manipulate information for use in many complex cognitive processes. The visual-spatial sketchpad is a component of Working Memory that processes visual and spatial information by displaying and manipulating information on what things look like. Also displays and manipulates information pulled from storage in long-term memory.
    • Visual Processing allows for information to enter the visual-spatial sketchpad in Working Memory.

Research Findings

Bosse and colleagues (2015) investigated the relationship between Visual Processing skills and orthographic learning in a group of French children (n = 88) in the 3rd to 5th grades. They examined the acquisition of orthographic knowledge using pseudowords. Gaining orthographic knowledge (Alphabet Knowledge) was aided by the ability to process an entire string of letters at once. Also, they discussed that Decoding depends on the successful mapping of visual input to phonological information.

There are conflicting findings in this area, with some researchers finding evidence that a deficit in visual attention is associated with a diagnosis of dyslexia. This theory would predict a deficit of visual attention for both letter strings and non-alphanumeric symbol strings. However, other researchers attribute differences in visual attention tasks in this population to a deficit in symbol-sound mapping. According to these studies, individuals with dyslexia only have visual attention deficits in letter strings. It should be noted that, historically, dyslexia has been incorrectly described as a vision problem, when it is actually due to processing difficulties.  Scientists continue to conduct research to describe and understand these processing difficulties in more detail. Research examining Visual Processing differences in children with dyslexia is summarized below:

  • A three year longitudinal study of preschoolers found that visual spatial attention (as measured by a serial visual search task and a spatial cueing facilitation task where children have to indicate what shape was presented previously) predicts reading acquisition in the 1st and 2nd grades. Children who were classified as poor readers in the 2nd grade made two times as many errors in the serial visual search task in the prereading stage (Franceschini, Gori, Ruffino, Pedrolli, & Facoetti, 2012).
  • Another study found that children with dyslexia performed significantly worse than peers without dyslexia on visual attention span tasks of both verbal and non-verbal stimuli. Children with dyslexia were not able to process as many visual elements as peers without dyslexia. The authors concluded that deficits in visual attention contribute to reading impairments in dyslexia (Lobier, Zoubrinetzky, & Valdois, 2012).
  • Ziegler and colleagues (2010) examined visual attention processing in children with dyslexia and only found a deficit in the processing of letter but not symbol strings.

Below is research investigating the relationship between Visual Processing and reading skills

  • Boets and colleagues (2008) examined Auditory Processing, Visual Processing, Phonological Awareness, and reading skills in a group of 62 5-year-olds. They found that dynamic Visual Processing was related to orthographic ability, as measured by a task where orthographic knowledge (e.g., Alphabet Knowledge) was separated statistically from phonological knowledge in order to construct a pure orthographic measure. Also, a relationship was found between dynamic Visual Processing skills and reading development (e.g., word-level reading, reading accuracy, reading speed). Thus, these results demonstrate that Visual Processing is an important factor in reading development.

Visual-motor integration is another important area of research. Visual-motor integration skills allow eyes and hands to work together in an organized and coordinated manner, and it relies on both visual spatial discrimination and motor skills. Many studies have found a relationship between visual perception and academic performance (Feagans & Merriwether, 1990; Kulp, 1999; Rosner & Rosner, 1987). However it should be noted that the field of research examining the relationship between visual function and academic performance is controversial (see Lack, 2010, for a review). Yet since a good amount of research exists suggesting Vision is an important factor in academic performance, that literature will be discussed briefly here.

  • Several researchers have found that visual-motor integration (VMI) is a predictor of academic performance. For example, Kulp (1999) examined VMI in children in kindergarten to 3rd grade and found a significant correlation between VMI and scores in reading, writing, spelling, and math. Also, Sortor and Kulp (2003) found that lower VMI scores was associated with poor performance in both math and reading. These associations were maintained after controlling for intelligence.
  • Goldstand, Koslowe, and Parush (2005) examined visual and visual-information processing skills in 7th grade students who were considered to either be proficient or nonproficient readers. The students were tested using the Modified Clinical Technique (MCT), an optometric vision screening task (Blum, Peters, & Bettman, 1959), which measured several aspects of vision (e.g., visual acuity near/far, color vision, visual tracking). Half of the students failed the MCT vision screening in both the proficient and nonproficient reader groups. However, nonproficient readers had significantly poorer scores on the visual screening.

References

Boets, B., Wouters, J., Van Wieringen, A., De Smedt, B., & Ghesquiere, P. (2008). Modelling relations between sensory processing, speech perception, orthographic and phonological ability, and literacy achievement. Brain and Language, 106(1), 29-40.
Bosse, M. L., Chaves, N., Largy, P., & Valdois, S. (2015). Orthographic learning during reading: The role of whole‐word visual processing. Journal of Research in Reading, 38(2), 141-158.
Feagans, L. V., & Merriwether, A. (1990). Visual discrimination of letter-like forms and its relationship to achievement over time in children with learning disabilities. Journal of Learning Disabilities, 23(7), 417-425.
Franceschini, S., Gori, S., Ruffino, M., Pedrolli, K., & Facoetti, A. (2012). A causal link between visual spatial attention and reading acquisition. Current Biology, 22(9), 814-819.
Goldstand, S., Koslowe, K. C., & Parush, S. (2005). Vision, visual-information processing, and academic performance among seventh-grade schoolchildren: A more significant relationship than we thought? American Journal of Occupational Therapy, 59(4), 377-389.
Kulp, M. T. (1999). Relationship between visual motor integration skill and academic performance in kindergarten through third grade. Optometry & Vision Science, 76(3), 159-163.
Lack, D. (2010). Another joint statement regarding learning disabilities, dyslexia, and vision—A rebuttal. Optometry-Journal of the American Optometric Association, 81(10), 533-543.
Lobier, M., Zoubrinetzky, R., & Valdois, S. (2012). The visual attention span deficit in dyslexia is visual and not verbal. Cortex, 48(6), 768-773.
Rey, A. (1941). L’examen psychologique dans les cas d’encephalopathie traumatique. (Les Problems). Archives de Psychologie, 28, 286–340.
Rosner, J., & Rosner, J. (1987). Comparison of visual characteristics in children with and without learning difficulties. Optometry & Vision Science, 64(7), 531-533.
Satz, P., & Fletcher, J. (1982). Florida kindergarten screening battery (FKSB). Psychological Assessment Resources.
Sortor, J. M., & Kulp, M. T. (2003). Are the results of the Beery-Buktenica Developmental Test of Visual-Motor Integration and its subtests related to achievement test scores? Optometry & Vision Science, 80(11), 758-763.

Ziegler, J. C., Pech‐Georgel, C., Dufau, S., & Grainger, J. (2010). Rapid processing of letters, digits and symbols: What purely visual‐attentional deficit in developmental dyslexia? Developmental Science, 13(4), F8-F14.

 


Working Memory

Working Memory is a type of memory that allows a person to temporarily hold and manipulate information for use in many complex cognitive processes. Although there are several models of Working Memory, one of the earliest and best known is Baddeley’s Working Memory Model (Baddeley, 1986, 2000).

This model consists of a central executive and three “slave” systems, including the visuospatial sketchpad, the phonological loop, and the episodic buffer. The central executive is responsible for directing the activities of these three systems and is responsible for shifting and focusing attention to these three components. It is thought to have a limited capacity. The visuospatial sketchpad processes visuospatial information and has both storage and rehearsal components, while the phonological loop processes phonological encoding and rehearsal. The episodic buffer is the least well-understood system and is responsible for linking visual, spatial, and verbal information, as well as allowing Long-term Memory to interact with the other components of the model. This model describes a reciprocal relationship between Working Memory (fluid systems) and Long-term Memory (crystallized systems).

Working Memory is likely required for retaining information during reading and manipulating words within a sentence or passage to understand how they all relate to one another. A low Working Memory span may result in less capacity to integrate information from the text with information in Long-term Memory (Background Knowledge). Children with weaker Working Memory skills may appear to have a poor attention span and be easily distracted; however, they do not engage in hyperactive/impulsive behavior that is more characteristic of children with Attention Deficit Hyperactivity Disorder (ADHD).

Assessments

  • Listening Span/Recall (e.g., Daneman & Carpenter’s task): Child listens to a sentence, verifies it, and remembers the final word.
  • Backward Digit Recall: Child is asked to recall a sequence of digits in reverse order.
  • Screening tools for use by teachers in the classroom:
    • Behavior Rating Inventory of Executive Function (BRIEF) (Gioia, Isquith, Guy & Kenworthy, 2000): As this is a long assessment, teachers likely will not complete this for every student due to time commitment.
    • Working Memory Rating Scale (WMRS) (Alloway, Gathercole, Kirkwood, & Elliott, 2009): A behavior rating scale based on patterns of behavior typically observed in children with poor Working Memory skills, this is a shorter assessment than BRIEF and has good internal reliability (Alloway et al., 2009). It can be rapidly administered, is simple to score, and does not require training in psychometric assessment. If child is identified as being likely to have poor Working Memory skills on the WMRS, they should be referred to an expert for further diagnostic tests.

Learner Factor Connections

  • Attention: The ability to focus on a specific task without being distracted, as well as the ability to select relevant information while ignoring irrelevant information
    • Working Memory affects control of Attention, and Attention helps information to be maintained Working Memory.
  • Decoding: The ability to apply knowledge of relationships between letters and speech sounds to properly recognize and pronounce words
    • The phonological loop component of Working Memory is essential to decoding.
  • Emotion: A complex psychological state that involve a subjective experience and can result in a physiological and behavioral response. Anxiety is intense worry or fear, and in individuals with an anxiety disorder, these feelings will not subside over time.
    • When children experience anxiety during testing, this can interfere with the efficiency of Working Memory processes causing them to underperform.
  • Inhibition: The ability to suppress attention to irrelevant stimuli to focus on pertinent stimuli/information, both controlling responses and attention
    • Cognitive Inhibition aids the temporary storage of information in Working Memory by allowing for the suppression of irrelevant items that would interfere with relevant items in Working Memory.
  • Long-term Memory: The ability to hold information for a long period of time, and possibly indefinitely
    • The central executive component of Working Memory is believed to control and regulate the temporary activation of information stored in Long-term Memory.
  • Physical Fitness: A state of overall health and physical well-being
    • Programs aimed at enhancing Physical Fitness in children have been found to also improve Working Memory processes.
  • Short-term Memory: The ability to hold information for a short period of time
    • Working Memory and Short-term Memory are co-contributors to deficits in reading skills.
  • Sleep: A naturally occurring state involving the suspension of consciousness during which the body and the brain rest. Both duration and quality contribute to obtaining sufficient Sleep.
    • Sleep, and especially the quality of Sleep, that a student receives is important for Working Memory processes to operate accurately and efficiently.
  • Socioeconomic Status (SES): A combination of factors including education and income of a family compared to other families
    • Children who are raised in low SES homes tend to have weaker Working Memory skills compared to children who live in middle or high SES homes.
  • Speed of Processing: The time it takes to perceive information, process it, and/or formulate or enact a response
    • A faster Speed of Processing enhances Working Memory capacity because it allows more information to enter Working Memory due to faster rehearsal.
  • Stereotype Threat: When a negative stereotype about a group results in suboptimal performance by members of that group, due to concern about being judged and confirming the negative stereotype about their group.
    • Research in older children and adults suggests that the uncertainty and concern over confirming Stereotypes negatively impacts Working Memory processes, and this is what leads to reduced academic performance.
  • Syntax: The rules and principles that govern the structure and word order of sentences
    • Working memory contributes to Syntax development in the phonological loop, which processes spoken and written information through temporary storage and repetition.
  • Trauma: Emotional distress resulting from experiencing violence, abuse, a disaster, or an accident. Interpersonal trauma refers to Trauma that has occurred between people (e.g., assault or abuse), and non-interpersonal Trauma refers to Trauma inflicted by some other source (e.g., a motor vehicle accident, a natural disaster).
    • Children who are abused or witness abuse in their homes are at greater risk for having Working Memory deficits.
  • Verbal Reasoning: Required to fully understand a text’s meaning, making inferences involves connecting and integrating information read within a text, and global inferencing requires integrating Background Knowledge
    • Working Memory is important for Verbal Reasoning because information can be held in Working Memory while it is related to the new information presented in the text.
  • Visual Processing: The process of interpreting visual stimuli as meaningful input
    • Visual Processing allows for information to enter the visual-spatial sketchpad in Working Memory.
  • Vocabulary: Includes both the lexical representations of stored sounds (word forms) and the semantic meaning associated with each of those stored sounds
    • The phonological loop is important for the acquisition of new vocabulary.

Research Findings

  • Alloway and Alloway (2010) examined the relationship between Short-term Memory (as measured by digit and word recall tasks), Working Memory (as measured by a backward digit recall and listening recall task), IQ (measured using subtests from the Wechsler Preschool and Primary Scale of Intelligence – Revised), and literacy skills in a group of 98 children living in the UK. Literacy skills were assessed using the Wechsler Objective Reading Dimensions (WORD) (Wechsler, 1993). The Wechsler Objective Numerical Dimensions (WOND) was also used to assess numeracy skills including mathematical reasoning and number Operations (Wechsler, 1996). The students were tested at Time 1 when they were between the ages of 4.3-5.7 (mean age = 5), and they were tested six years later at Time 2 when they were between the ages of 10-11.3 (mean age = 10 years and 11 months). Literacy and numeracy skills were only assessed at Time 2. Working Memory at the age of 5 is found to be an excellent predictor of literacy skills six years later. Working Memory was a more powerful predictor of academic success, relative to IQ, in children in early grades.
  • Dahlin (2010) investigated whether Working Memory training could improve reading development in a group of 57 children in grades 3 to 5 (age range = 9 to 12 years) in Sweden. All of the children had special education needs, as well as difficulties with Attention (e.g., an ADHD diagnosis). The treatment group included 42 children, and the control group included 15 children. A computerized training program (RoboMemo) was implemented that focuses on visuospatial and verbal Working Memory tasks. An average of 100 trials was completed each day by each student. The treatment was provided 30-40 minutes a day over the course of five weeks. Prior to training, the treatment group was tested on measures of reading (reading comprehension, Decoding, and orthographic knowledge), verbal Working Memory (digit span from WISC III; ), visual-spatial Working Memory (Span Board from the WAIS-NI), and nonverbal reasoning. The children in both the treatment and control groups were tested at the beginning of the study (prior to intervention), post-intervention (five to six weeks after treatment conclusion), and six to seven months later. The results revealed that the Working Memory training program improved reading comprehension development, suggesting that Working Memory training could benefit children with weak Working Memory skills. Moreover, Working Memory measures were related to measures of Decoding and reading comprehension. Specifically, visuospatial Working Memory was related to reading comprehension, and verbal Working Memory was related to Decoding.
  • Increased activity (as measured by BOLD signal) has been observed in the prefrontal cortex in healthy adults following Working Memory training (Olesen, Westerberg, & Klingberg, 2004)
  • Swanson, Howard, and Saez (2006) compared three groups of children with reading difficulties to a group of children who were skilled readers. Data from 66 children were collected (age range = 7.8 to 17 years; mean age = 12.45, SD = 2.42). Group 1 had children who had both word comprehension and recognition deficits. The children in group 2 only had comprehension deficits, and the children in group 3 had difficulties with word recognition and comprehension and also had a low verbal IQ. The children in Group 4 were skilled readers. All four groups were compared using measures of:
    • Working Memory (listening sentence span, backward digit span, semantic association task, digit/sentence span)
    • Short-term Memory (forward digit span from Digit Span subtest of the WISC-III, Wechsler, 1991)
    • Speed of Processing (rapid letter, digit, and object naming speed subtests of the CTOPP)
    • Executive processing (Inhibition and updating measures using the random generation of letters and numbers task)
    • Phonological processing (phonological deletion – Elision subtest from the CTOPP; Wagner et al., 2000)

    The skilled readers performed better than all of the less skilled reader groups on measures of Speed of Processing and Working Memory updating (monitoring and coding information to determine if it is relevant to the task at hand). Children in Group 2 outperformed children in Group 1 on measures of Working Memory, Short-term Memory, Speed of Processing, and phonological processing. The children in Group 3 outperformed children in Group 1 on measures of Working Memory and phonological processing. The authors concluded that the difference between skilled and less skilled readers could partially be explained by variations in Working Memory skills. They also concluded that Short-term Memory and updating contributed to differences in Working Memory.

  • Poor Working Memory skills in children are often associated with specific behaviors including difficulty with following instructions, completing multi-step tasks, and remembering detailed content of ongoing activities (Alloway et al., 2009)
  • A smaller Working Memory capacity is associated with more “mind wandering” possibly due to a lack of Attention control over intruding thoughts (McVay & Kane, 2011).
  • There is some evidence that Working Memory should be assessed in verbal and non-verbal domains because processing differences in verbal Working Memory (as measured by tasks such as nonword repetition tasks or the Competing Language Processing Task) is associated with language impairment (Leonard, Weismer, Miller, Francis, Tomblin, & Kail, 2007).
  • Being raised in a low Socioeconomic Status (SES) household is associated with reduced Working Memory and executive functioning abilities in 5- to 7-year-olds (Noble et al., 2005; Noble et al., 2007) and 10- to 13-year-olds (Farah et al., 2006).
  • It is likely that Speed of Processing is an essential skill that underlies performance of many cognitive skills, including Working Memory (Kail & Salthouse, 1994; Shaul & Nevo, 2015).
  • DePrince and colleagues (2009) tested executive functioning in 114 school-age children divided into three groups: those who had been exposed to familial Trauma (e.g., sexual abuse, physical abuse, or witnessing domestic violence), those exposed to non-familial Trauma, and those with no exposure to Trauma. Specifically, they measured Working Memory, Inhibition, auditory Attention, and interference control. Working Memory was measured using arithmetic, letter-number sequencing, and digit span subscales of the Wechsler Intelligence Scale for Children (WISC-IV) (Wechsler, 2003). Inhibition was measured using the Gordon Diagnostic System, where children are asked to look at strings of numbers and press a key whenever they see a certain sequence of numbers in the center of the number string. Auditory Attention was measured using the Brief Test of Attention, where the children are asked to listen to a series of letters and numbers then are asked to indicate how many numbers were presented without counting on their fingers. Finally, interference control was assessed using the Stroop task. They found a significant correlation (medium effect size) between experiencing familial Trauma to a composite score of executive functioning, even when controlling for SES, traumatic brain injury, and anxiety. They did not find this association in children who had been exposed to non-familial Trauma. They concluded that executive functioning impairments may contribute to the academic, psychological, and behavior problems often seen in children who have suffered abuse.
  • Holsgrove and Garton (2011) investigated the relationship between syntactic processing (Syntax), phonological processing, Working Memory, and reading comprehension. They researched these factors in a group of 13-year-old students. Both syntactic processing and phonological processing made independent contributions to reading comprehension skills. Overall, their results supported a model of reading comprehension where Working Memory contributes to syntactic processing skills through the phonological loop (part of Working Memory).
  • The relationship between Working Memory, Vocabulary, and inference-generation (Verbal Reasoning) skills has also been investigated. Currie and Cain (2015) measured these three variables in 5- to 6-year-olds, 7- to 8-year-olds, and 9- to 10-year-olds. The authors investigated both local inference and global inference generation. They found that both Vocabulary and Working Memory were correlated with inference generation (local and global). However, while Working Memory was associated with Verbal Reasoning abilities in 6- to 10-year-olds, this effect was mediated by Vocabulary. Thus, Vocabulary was a unique predictor of inference performance. The authors attributed their findings to the link between Vocabulary and Background Knowledge, which allows readers to form associations between semantically associated words and synonyms.
  • Beilock, Rydell, and McConnell (2007) aimed to investigate how Stereotype Threat leads to decreased academic performance by examining math performance in adult women. The participants were given a variety of math problems, which were designed to place various demands on Working Memory resources. They discovered that Stereotype Threat only resulted in decreased performance in problems that relied heavily on Working Memory resources, and particularly those relying on verbal Working Memory resources. They also examined a training program that was designed to alleviate the influence of Stereotype Threat on Working Memory by drawing on Long-term Memory resources instead of Working Memory resources (by repeating problems multiple times). The training program was successful at alleviating the negative effects of Stereotype Threat.
  • Schmader (2010) wrote a review paper discussing the cognitive factors that lead to the negative influences of Stereotype Threat on academic performance. In this review, the author explains that complex cognitive operations are required for successful testing performance across many academic domains. Students who are concerned about confirming negative stereotypes about their gender, race, or other important groups are unable to focus all of their Working Memory resources on completing the test. Instead, they use some of their Working Memory resources worrying about their performance, and this leads to decreased academic performance in students from groups at risk of being stereotyped. Stereotype Threat impairs Working Memory capacity most significantly in students who highly identify with, and are most invested in, the academic domain of interest.

References

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