Wednesday, June 20, 2018

Mind wandering is fine in some situations, Harvard-based study says



******************************************************
Kevin McGrew, PhD
Educational Psychologist
Director, Institute for Applied Psychometrics
IAP
******************************************************

Wednesday, June 13, 2018

Do early life non-cognitive skills matter? A systematic review and meta-analysis of early life effects on academic achievement, psychosocial, language and cognitive, and health outcomes



Do early life non-cognitive skills matter? A systematic review and meta-analysis of early life effects on academic achievement, psychosocial, language and cognitive, and health outcomes
https://www.biorxiv.org/content/early/2017/03/10/115691

Sent from Flipboard


******************************************************
Kevin McGrew, PhD
Educational Psychologist
Director, Institute for Applied Psychometrics
IAP
******************************************************

Saturday, June 09, 2018

Mental rotation and fluid intelligence: A brain potential analysis

File under Gv and Gf

Mental rotation and fluid intelligence: A brain potential analysis
Intelligence 69 (2018) 146–157. Article link.

Vincenzo Varrialea, Maurits W. van der Molenb, Vilfredo De Pascalis


ABSTRACT

The current study examined the relation between mental rotation and fluid intelligence using performance measures augmented with brain potential indices. Participants took a Raven's Progressive Matrices Test and performed on a mental rotation task presenting upright and rotated letter stimuli (60°, 120° or 180°) in normal and mirror image requiring a response execution or inhibition depending on instructions. The performance results showed that the linear slope relating performance accuracy, but not speed, to the angular rotation of the stimuli was related to individual differences in fluid intelligence. For upright stimuli, P3 amplitude recorded at frontal and central areas was positively associated with fluid intelligence scores. The mental rotation process was related to a negative shift of the brain potential recorded over the parietal cortex. The linear function relating the amplitude of the rotation-related negativity to rotation angle was associated with fluid intelligence. The slope was more pronounced for high- relative to low-ability participants suggesting that the former flexibly adjust their expenditure of mental effort to the mental rotation demands while the latter ones are less proficient in doing so.


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Rapid and widespread white matter plasticity during an intensive reading intervention



Rapid and widespread white matter plasticity during an intensive reading intervention
https://www.nature.com/articles/s41467-018-04627-5

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Tuesday, June 05, 2018

21 factors that can impact working memory: A formative list and taxonomy

An interesting list and logically based taxonomy in need of empirical validation.


Why Is Working Memory Performance Unstable? A Review of 21 Factors
Rachael N. Blasiman, Christopher A. Wasa

Europe's Journal of Psychology, 2018, Vol. 14(1), 188–231, doi:10.5964/ejop.v14i1.1472

Abstract

In this paper, we systematically reviewed twenty-one factors that have been shown to either vary with or influence performance on working memory (WM) tasks. Specifically, we review previous work on the influence of intelligence, gender, age, personality, mental illnesses/ medical conditions, dieting, craving, stress/anxiety, emotion/motivation, stereotype threat, temperature, mindfulness training, practice, bilingualism, musical training, altitude/hypoxia, sleep, exercise, diet, psychoactive substances, and brain stimulation on WM performance. In addition to a review of the literature, we suggest several frameworks for classifying these factors, identify shared mechanisms between several variables, and suggest areas requiring further investigation. This review critically examines the breadth of research investigating WM while synthesizing the results across related subfields in psychology.

Keywords: working memory, individual differences

Click on image to enlarge



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Sunday, June 03, 2018

Visualization, inductive reasoning, and memory span as components of fluid intelligence: Implications for technology education

File under CHC domains of Gf, Gwm, Gc and STEM

Visualization, inductive reasoning, and memory span as components of fluid intelligence: Implications for technology education. Article link.

Jeffrey Buckleya, Niall Seerya, Donal Cantyc, Lena Gumaelius

International Journal of Educational Research, 90 (2018) 64–77

ABSTRACT

The philosophy and epistemology of technology education are relatively unique as the subject largely focusses on acquiring task specific relevant knowledge rather than having an explicit epistemological discipline boundary. Additionally, there is a paucity of intelligence research in technology education. To support research on learning in technology education, this paper describes two studies which aimed to identify cognitive factors which are components of fluid intelligence. The results identify that a synthesis of visualization, short-term memory span and inductive reasoning can account for approximately 28% to 43% of the variance in fluid intelligence. A theoretical rationale for the importance of these factors in technology education is provided with a discussion for their future consideration in cognitive interventions.


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Saturday, June 02, 2018

Can emotional intelligence (Gei) be trained: A meta-analysis

Can emotional intelligence be trained? A meta-analysis

Please cite this article as: Mattingly, V., Human Resource Management Review (2018), https://doi.org/10.1016/j.hrmr.2018.03.002

Victoria Mattingly, Kurt Kraiger

Keywords: Emotional intelligence, Training Meta-analysis

A B S T R A C T

Human resource practitioners place value on selecting and training a more emotionally in-telligent workforce. Despite this, research has yet to systematically investigate whether emo-tional intelligence can in fact be trained. This study addresses this question by conducting a meta-analysis to assess the effect of training on emotional intelligence, and whether effects are mod-erated by substantive and methodological moderators. We identified a total of 58 published and unpublished studies that included an emotional intelligence training program using either a pre-post or treatment-control design. We calculated Cohen's d to estimate the effect of formal training on emotional intelligence scores. The results showed a moderate positive effect for training, regardless of design. Effect sizes were larger for published studies than dissertations. Effect sizes were relatively robust over gender of participants, and type of EI measure (ability v. mix-edmodel). Further, our effect sizes are in line with other meta-analytic studies of competency-based training programs. Implications for practice and future research on EI training are discussed.

See prior Gei posts here and here.


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Evidence of a Flynn Effect in Children's Human Figure Drawings (1902-1968).



Evidence of a Flynn Effect in Children's Human Figure Drawings (1902-1968).
https://www.ncbi.nlm.nih.gov/pubmed/29799338

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Friday, May 25, 2018

The Role of Executive Functions in Reading Comprehension - excellent overview of major models of reading comprehension

The Role of Executive Functions in Reading Comprehension. Article or link.

Reese Butterfuss and Panayiota Kendeou

ABSTRACT

Our goal in this paper is to understand the extent to which, and under what conditions, executive functions (EFs) play a role in reading comprehension processes. We begin with a brief review of core components of EF (inhibition, shifting, and updating) and reading comprehension. We then discuss the status of EFs in process models of reading comprehension. Next, we review and synthesize empirical evidence in the extant literature for the involvement of core components of EF in reading comprehension processes under different reading conditions and across different populations. In conclusion, we propose that EFs may help explain complex interactions between the reader, the text, and the discourse situation, and call for both existing and future models of reading comprehension to include EFs as explicit components.

Keywords Executive functions . Reading comprehension . Discourse processes


This article includes an excellent summary of the major models of reading comprehension. This post includes that select material.

The Status of Executive Functions in Models of Reading Comprehension

Reading comprehension is one of the most complex and important cognitive activities humans perform (Kendeou et al. 2016). Given its importance and complexity, researchers have sought to understand reading comprehension via the development and specification of a multitude of models and frameworks that account for various processes and mechanisms of reading.

Generally, reading comprehension refers to the construction of a mental representation of what the text is about (Kintsch and V an Dijk 1978). Although most models of reading comprehension converge on this general idea, the processes and assumptions by which readers construct such representations differ across models and frameworks. It is also important to note that a unified, comprehensive model of reading comprehension has yet to be established. McNamara and Magliano (2009) reviewed and compared one set of models, which are concerned primarily with the construction of the mental representation during reading: The Construction-Integration Model (Kintsch 1988), the Structure-Building Framework (Gernsbacher 1991), the Resonance Model (Albrecht and O'Brien 1993), the Event-Indexing Model (Zwaan et al. 1995), the Causal Network Model (Trabasso et al. 1989), the Constructionist Theory (Graesser et al. 1994), and the Landscape Model (van den Broek et al. 1999). In this review, we investigate the status of EFs in each of these models.

Among this set of models, the Construction-Integration (CI) model (Kintsch 1988) is perhaps the most comprehensive, and it is considered the best approximation to a true theory of reading comprehension (Kendeou and O'Brien 2017). According to the CI model, comprehension is the result of two processes, construction and integration. Construction refers to the activation of information in the text and background knowledge. There are four potential sources of activation: the current text input, the prior sentence, background knowledge, and prior text. As this information is activated, it is connected into a network of concepts. Integration refers to the continuous spread of activation within this network until activation settles. Activation sources from the construction process are iteratively integrated, and only those concepts that are connected to many others are maintained in the network. At the completion of reading, the result is a complete network or a mental representation of what the text is about. This mental representation has been termed the situation model. Even though the initial model makes no explicit reference to EFs, in a subsequent revision, Kintsch (1998) included a suppression mechanism in the CI model by adopting inhibitory links. Specifically, the CI model relies on links between information units to promote an appropriate representation of a text and inhibit inappropriate representations. In this context, facilitatory links connect related information units, and inhibitory (or negative) links connect conflicting or inappropriate information units. Inhibitory links serve to suppress or inhibit inappropriate representations (Kintsch 1998).

The Structure-Building Framework (Gernsbacher 1991) describes comprehension as the result of three processes. The first process, laying a foundation, involves using initial information from a text to lay the groundwork for a mental representation to be constructed. The second process, mapping, involves mapping information from the text onto that foundation to create structures. The third process, shifting, involves a shift to begin building a new structure when readers are unable to map information onto an existing structure. Irrelevant information that does not cohere with a current structure is suppressed. Thus, within the Structure-Building Framework, the suppression mechanism attempts to account for individual differences in comprehension ability. Specifically, the model posits that if incoming information is related to the current structure, then activation of that information is enhanced, resulting in its incorporation into the current structure. When information is not related to the current structure, then activation to that information is suppressed, or, alternatively, readers may shift and use that information to begin building a new structure. The suppression mechanism is the result of readers' ability to inhibit irrelevant information. This ability moderates reading comprehension in that skilled readers have a strong suppression mechanism and can therefore suppress irrelevant information, whereas less-skilled readers lack a strong suppression mechanism. As a result, less-skilled comprehenders' poor suppression ability may lead them to shift too often, which impairs comprehension because more information is competing for limited resources.

The Resonance Model (Myers and O'Brien 1998) attempts to account for factors that influence the activation of information during comprehension, particularly information that is no longer active in working memory. The model emphasizes automatic, memory-based retrieval mechanisms as fundamental assumptions. Specifically, the model assumes that information in working memory serves as a signal to all of memory, which activates information that resonates with the signal. Elements resonate as a function of the number of features that overlap with the contents of working memory. Even though the model has not explicitly incorporated any EFs, O'Brien et al. (1995) found that suppression was involved in processes relevant to the Resonance Model. Specifically, O'Brien et al. found that when an anaphoric phrase reactivated more than one potential antecedent from the text, the selected target antecedent was strengthened in long-term memory, whereas potential, but non-target, antecedents that interfered with the target antecedent were suppressed.

The Event-Indexing Model (Zwaan et al. 1995) was developed as an attempt to account more fully for processes involved with situation model construction of narrative texts. It operates under the assumption that readers monitor and establish coherence along five dimensions of continuity, and thus situation model construction: time, space, causality, motivation, and agents. Thus, within the event-indexing model, EFs such as shifting attention from one dimension to another as well as updating the construction of the situation model account for individual differences in comprehension ability. For example, Bohn-Gettler et al. (2011) found that there are developmental differences in children's ability to monitor the shifts in each of these dimensions.

The Causal Network Model (Trabasso et al. 1989) accounts for how readers generate causal inferences and represent causality during reading. Causal inferences are at the core of building a coherent representation of a story. Narrative elements can be categorized as either settings, events, goals, attempts, outcomes, or reactions. Also, there are assumed to be four types of causal relations: enabling, psychological, motivational, and physical. The model also provided a discourse analysis tool, Causal Network Analysis, to identify the causal structure that underlies story constituents. Overall, the model accounts for the importance of causal relations in memory for the text, but makes no assumptions about specific EFs. The Constructionist theory (Graesser et al. 1994) attempts to account for factors that predict inference generation during reading. The theory emphasizes the role of top-down, strategic processes in the construction of meaning, what has been termed Bsearch after meaning.^ Three assumptions define search after meaning. The first is the reader goal assumption, which suggests that readers construct meaning in accordance with their reading goals. The second is the coherence assumption, which suggests that readers construct meaning at both local and global levels. The third is the explanation assumption, which suggests that readers are driven to construct meaning that explains events they read. Even though the theory makes no concrete assumptions about EFs, it is reasonable to assume that shifting attention likely exerts an influence on the top-down, strategic processes that govern search after meaning.

Lastly, the Landscape Model (van den Broek et al. 1999) simulates the fluctuation of concept activation during reading. The Landscape Model is similar to the CI Model in that it assumes the same four sources of activation. The model also includes two important mechanisms, cohort activation and coherence-based retrieval. Cohort activation assumes that when a concept is activated, all other concepts that are also activated become associated with it (McClelland and Rumelhart 1985). Coherence-based retrieval assumes that the activation of text elements is in accordance with the readers' standards of coherence. In turn, standards of coherence refer to readers' implicit or explicit criteria for comprehension. Even though the Landscape Model makes no concrete assumptions about EFs, it is reasonable to assume that shifting likely exerts an influence on readers' standards of coherence, directing attention to information that aligns with readers' standards.



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File under evidence-based instructional interventions: Studying and Constructing Concept Maps: a Meta-Analysis

Studying and Constructing Concept Maps: a Meta-Analysis. Article link.

Noah L. Schroeder, John C. Nesbit, Carlos J. Anguiano & Olusola O. Adesope



Abstract A concept map is a node-link diagram in which each node represents a concept and each link identifies the relationship between the two concepts it connects. We investigated how using concept maps influences learning by synthesizing the results of 142 independent effect sizes (n = 11,814). A random-effects model meta-analysis revealed that learning with concept and knowledge maps produced a moderate, statistically significant effect (g = 0.58, p < 0.001). A moderator analysis revealed that creating concept maps (g = 0.72, p < 0.001) was associated with greater benefit relative to respective comparison conditions than studying concept maps (g = 0.43, p < 0.001). Additional moderator analyses indicated learning with concept maps was superior to other instructional comparison conditions, and was effective across science, technology, engineering, and math (STEM) and non-STEM knowledge domains. Further moderator analyses, as well as implications for theory and practice, are provided.

Keywords Concept map . Knowledge map . Meta-analysis . cmap . kmap



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Saturday, May 19, 2018

The Relation between Intelligence and Adaptive Behavior: A Meta-Analysis 

Very important meta-analysis of AB IQ relation. Primary finding on target with prior informal synthesis by McGrew (2015)

The Relation between Intelligence and Adaptive Behavior: A Meta-Analysis   
 
Ryan M. Alexander 
 
ABSTRACT 
 
Intelligence tests and adaptive behavior scales measure vital aspects of the multidimensional nature of human functioning. Assessment of each is a required component in the diagnosis or identification of intellectual disability, and both are frequently used conjointly in the assessment and identification of other developmental disabilities. The present study investigated the population correlation between intelligence and adaptive behavior using psychometric meta-analysis. The main analysis included 148 samples with 16,468 participants overall. Following correction for sampling error, measurement error, and range departure, analysis resulted in an estimated population correlation of ρ = .51. Moderator analyses indicated that the relation between intelligence and adaptive behavior tended to decrease as IQ increased, was strongest for very young children, and varied by disability type, adaptive measure respondent, and IQ measure used. Additionally, curvilinear regression analysis of adaptive behavior composite scores onto full scale IQ scores from datasets used to report the correlation between the Wechsler Intelligence Scales for Children- Fifth edition and Vineland-II scores in the WISC-V manuals indicated a curvilinear relation—adaptive behavior scores had little relation with IQ scores below 50 (WISC-V scores do not go below 45), from which there was positive relation up until an IQ of approximately 100, at which point and beyond the relation flattened out. Practical implications of varying correlation magnitudes between intelligence and adaptive behavior are discussed (viz., how the size of the correlation affects eligibility rates for intellectual disability).
 
Other Key Findings Reported
 
McGrew (2012) augmented Harrison's data-set and conducted an informal analysis including a total of 60 correlations, describing the distributional characteristics observed in the literature regarding the relation. He concluded that a reasonable estimate of the correlation is approximately .50, but made no attempt to explore factors potentially influencing the strength of the relation.
 
Results from the present study corroborate the conclusions of Harrison (1987) and McGrew (2012) that the IQ/adaptive behavior relation is moderate, indicating distinct yet related constructs. The results showed indeed that the correlation is likely to be stronger at lower IQ levels—a trend that spans the entire ID range, not just the severe range. The estimated true mean population is .51, and study artifacts such as sampling error, measurement error, and range departure resulted in somewhat attenuated findings in individual studies (a difference of about .05 between observed and estimated true correlations overall).
 
 
The present study found the estimated true population mean correlation to be .51, meaning that adaptive behavior and intelligence share 26% common variance. In practical terms, this magnitude of relation suggests that an individual's IQ score and adaptive behavior composite score will not always be commensurate and will frequently diverge, and not by a trivial amount. Using the formula Ŷ = Ȳ + ρ (X - X ̅ ), where Ŷ is the predicted adaptive behavior composite score, Ȳ  is the mean adaptive behavior score in the population, ρ  is the correlation between adaptive behavior and intelligence, X is the observed IQ score for an individual, and X ̅ is the mean IQ score, and accounting for regression to the mean, the predicted adaptive behavior composite score corresponding to an IQ score of 70, given a correlation of .51, would be 85 —a score that is a full standard deviation above an adaptive behavior composite score of 70, the cut score recommended by some entities to meet ID eligibility requirements. With a correlation of .51, and accounting for regression to the mean, an IQ score of 41 would be needed in order to have a predicted adaptive behavior composite score of 70. Considering that approximately 85% of individuals with ID have reported IQ scores between 55 and 70±5 (Heflinger et al., 1987; Reschly, 1981), the eligibility implications, especially for those with less severe intellectual impairment, are alarming. In fact, derived from calculations by Lohman and Korb (2006), only 17% of individuals obtaining an IQ score of 70 or below would be expected to also obtain an adaptive behavior composite score of 70 or below when the correlation between the two is .50. 
 
 
The purpose of this study was to investigate the relation between IQ and adaptive behavior and variables moderating the relation using psychometric meta-analysis. The findings contributed in several ways to the current literature with regard to IQ and adaptive behavior. First, the estimated true mean population correlation between intelligence and adaptive behavior following correction for sampling error, measurement error, and range departure is moderate, indicating that intelligence and adaptive behavior are distinct, yet related, constructs. Second, IQ level has a moderating effect on the relation between IQ and adaptive behavior. The correlation is likely to be stronger at lower IQ levels, and weaker as IQ increases. Third, while not linear, age has an effect on the IQ/adaptive behavior relation. The population correlation is highest for very young children, and lowest for children between the ages of five and 12. Fourth, the magnitude of IQ/adaptive behavior correlations varies by disability type. The correlation is weakest for those without disability, and strongest for very young children with developmental delays. IQ/adaptive behavior correlations for those with ID are comparable to those with autism when not matched on IQ level. Fifth, the IQ/adaptive correlation when parents/caregivers serve as adaptive behavior respondents is comparable to when teachers act as respondents, but direct assessment of adaptive behavior results in a stronger correlation. Sixth, an individual's race does not significantly alter the correlation between IQ and adaptive behavior, but future research should evaluate the influence of race of the rater on adaptive behavior ratings. Seventh, the correlation between IQ and adaptive behavior varies depending on IQ measure used—the population correlation when Stanford-Binet scales are employed is significantly higher than when Wechsler scales are employed. And eighth, the correlation between IQ and adaptive behavior is not significantly different between adaptive behavior composite scores obtained from the Vineland, SIB, and ABAS families of adaptive behavior measures, which are among those that have been deemed appropriate for disability identification. Limitations of this study notwithstanding, it is the first to employ meta-analysis procedures and techniques to examine the correlation between intelligence and adaptive behavior and how moderators alter this relation. The results of this study provide information that can help guide practitioners, researchers, and policy makers with regard to the diagnosis or identification of intellectual and developmental disabilities.


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Thursday, May 17, 2018

Interactive Metronome study: Clapping in time parallels literacy and calls upon overlapping neural mechanisms in early readers

Clapping in time parallels literacy and calls upon overlapping neural mechanisms in early readers

Annals of the New York Academy Of Science. Article link here.

Link to complete paper at IM site.

Silvia Bonacina Jennifer Krizman Travis White‐Schwoch Nina Krau

Abstract

The auditory system is extremely precise in processing the temporal information of perceptual events and using these cues to coordinate action. Synchronizing movement to a steady beat relies on this bidirectional connection between sensory and motor systems, and activates many of the auditory and cognitive processes used when reading. Here, we use Interactive Metronome, a clinical intervention technology requiring an individual to clap her hands in time with a steady beat, to investigate whether the links between literacy and synchronization skills, previously established in older children, are also evident in children who are learning to read. We tested 64 typically developing children (ages 5–7 years) on their synchronization abilities, neurophysiological responses to speech in noise, and literacy skills. We found that children who have lower variability in synchronizing have higher phase consistency, higher stability, and more accurate envelope encoding—all neurophysiological response components linked to language skills. Moreover, performing the same task with visual feedback reveals links with literacy skills, notably processing speed, phonological processing, word reading, spelling, morphology, and syntax. These results suggest that rhythm skills and literacy call on overlapping neural mechanisms, supporting the idea that rhythm training may boost literacy in part by engaging sensory‐motor systems.


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Wednesday, May 16, 2018

MindHub Pub #3: WJ IV Norm-Based and Supplemental Clinical Test Groupings for “Intelligent” Intelligence Testing with the WJ IV



I am pleased to announce the availability of MindHub Pub #3 (WJ IV Norm-Based and Supplemental Clinical Test Groupings for "Intelligent" Intelligence Testing with the WJ IV).  Click the link to view or download.

The material in this document is based on my work during the development of the WJ IV as well as significant post-WJ IV publication analyses.  I have been completing considerable post-WJ IV data analysis in response to questions on listservs and to develop advanced and clinical interpretation information for convention presentations and workshops.  In the past I had the luxury of time to write professional books re: clinical "intelligent" intelligence testing with the WJ (1986) and WJ-R (1984).  I was unable to find time for the WJ III nor the WJ IV.  So much to do....so little time.

I have presented early versions of this material at conventions and workshops.  However, I never felt comfortable with the final product.  The most important reason for not distributing widely was my knowledge that the CHC model was in the process of responding to new research and insights--to be published this fall 2018 in a chapter by Joel Schneider and myself.  I only wanted this"supplemental grouping strategy" worksheet material (ala, Dr. Alan Kaufman's shared ability approach to test interpretation) to be made available once the revised CHC model had been described.  This event will occur this August with the publication of our chapter.  An early visual-graphic overview of the chapter, presented in a nifty animated YouTube video was released at this blog approximately a week ago.

So...enjoy the material.  This is not a book or article--more of a detailed PPT presentation.  It should be understandable to clinicians familiar with the WJ IV, CHC theory, and Kaufman's "intelligent" intelligence test interpretation approach.

Below is a sample worksheet--for Gc related tests.  Click on images to enlarge.




Higher intelligence related to more efficiently organized brains-bigger/larger/more not always better




Click on image to enlarge

Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Article link.

Erhan Genç, Christoph Fraenz, Caroline Schlüter, Patrick Friedrich, Rüdiger Hossiep, Manuel C. Voelkle, Josef M. Ling, Onur Güntürkün, & Rex E. Jung

Abstract

Previous research has demonstrated that individuals with higher intelligence are more likely to have larger gray matter volume in brain areas predominantly located in parieto-frontal regions. These findings were usually interpreted to mean that individuals with more cortical brain volume possess more neurons and thus exhibit more computational capacity during reasoning. In addition, neuroimaging studies have shown that intelligent individuals, despite their larger brains, tend to exhibit lower rates of brain activity during reasoning. However, the microstructural architecture underlying both observations remains unclear. By combining advanced multi-shell diffusion tensor imaging with a culture-fair matrix-reasoning test, we found that higher intelligence in healthy individuals is related to lower values of dendritic density and arborization. These results suggest that the neuronal circuitry associated with higher intelligence is organized in a sparse and efficient manner, fostering more directed information processing and less cortical activity during reasoning.

From discussion

Taken together, the results of the present study contribute to our understanding of human intelligence differences in two ways. First, our findings confirm an important observation from previous research, namely, that bigger brains with a higher number of neurons are associated with higher intelligence. Second, we demonstrate that higher intelligence is associated with cortical mantles with sparsely and well-organized dendritic arbor, thereby increasing processing speed and network efficiency. Importantly, the findings obtained from our experimental sample were confirmed by the analysis of an independent validation sample from the Human Connectome Project25



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Tuesday, May 08, 2018

Gates, Zuckerberg team up on new education initiative



Gates, Zuckerberg team up on new education initiative

From Education, a Flipboard topic

Tech moguls Bill Gates and Mark Zuckerberg said Tuesday they will team up to help develop new technologies for kids with trouble learning — an…

Read it on Flipboard

Read it on foxbusiness.com




NEUROSCIENCE & SOCIETY: Ethics, Law, and Technology Confrence - Neuroethics & Law Blog

NEUROSCIENCE & SOCIETY: Ethics, Law, and Technology Confrence - Neuroethics & Law Blog

NEUROSCIENCE & SOCIETY: Ethics, Law, and Technology Confrence

NEUROSCIENCE & SOCIETY: Ethics, Law, and Technology
24-25 August 2018
Sydney, NSW, Australia

Advances in brain scanning and intervention technologies are transforming our ability to observe, explain, and influence human thought and behaviour. Potential applications of such technologies (e.g. brain-based pain detection in civil lawsuits, medications to help criminal offenders become less impulsive, prediction of future behaviour through neuroimaging) and their ethical, clinical, legal, and societal implications, fuel important debates in neuroethics. However, many factors beyond the brain – factors targeted by different emerging technologies – also influence human thought and behaviour. Sequencing the human genome and gene-editing technologies like CRISPR Cas-9 offer novel ways to explain and influence human thought and behaviour. Analysis of data about our offline and online lives (e.g. from fitness trackers, how we interact with our smartphone apps, and our social media posts and profiles) also provide striking insights into our psychology. Such intimate information can be used to predict and influence our behaviour, including through bespoke advertising for goods and services that more effectively exploits our psychology and political campaigns that sway election results. Although such methods often border on manipulation, they are both difficult to detect and potentially impossible to resist. The use of such information to guide the design of online environments, artifacts, and smart cities lies at the less nefarious – and potentially even socially useful and morally praiseworthy – end of the spectrum vis à vis the potential applications of such emerging "moral technologies".

At this year's Neuroscience & Society conference we will investigate the ethical, clinical, legal, and societal implications of a wide range of moral technologies that target factors beyond, as well as within, the brain, in order to observe, explain, and influence human thought and behaviour. Topics will include, but are not limited to:

  • cognitive and moral enhancement
  • neurolaw and neuro-evidence
  • brain-computer interfaces
  • neuro-advertising
  • neuromorphic engineering and computing
  • mental privacy and surveillance
  • social media and behaviour prediction/influence
  • implicit bias and priming
  • technological influences on human behaviour
  • nudging, environment and technology design, and human behaviour
  • artificial intelligence and machine learning
  • technology and the self
  • (neuro)technology and society

We invite abstracts from scholars, scientists, technology designers, policy-makers, practitioners, clinicians and graduate students, interested in presenting talks or posters on any of the above or related topics.

Abstracts of 300 words should be emailed to Cynthia Forlini <cynthia.forlini@sydney.edu.au> in Microsoft Word format by Thursday, 31 May 2018. Submissions will be peer reviewed, and authors of successful submissions will be notified via email by Friday, 15 June 2018.

In addition to keynote presentations (to be announced shortly), contributed talks, and a poster session, the conference program will also include three sessions on the following topics:

  • highlights from- and information about enhancements to the Australian Neurolaw Database
  • book symposium on Neuro-Interventions and The Law: Regulating Human Mental Capacity
  • panel on the topic of remorse
For enquiries about matters other than abstract submission, please email Adrian Carter <adrian.carter@monash.edu.au> or Jeanette Kennett <jeanette.kennett@mq.edu.au>
Neuroscience & Society is supported by the ARC Centre of Excellence for Integrative Brain Function Neuroethics Program, and the Centre for Agency Values and Ethics at Macquarie University.



Impact of Cognitive Abilities and Prior Knowledge on Complex Problem Solving Performance – Empirical Results and a Plea for Ecologically Valid Microworlds



Impact of Cognitive Abilities and Prior Knowledge on Complex Problem Solving Performance – Empirical Results and a Plea for Ecologically Valid Microworlds

Read it on Flipboard

Read it on frontiersin.org




Sunday, May 06, 2018

The salience brain network and personality (self-directedness; cognitive control)

Abstract:

A prevailing topic in personality neuroscience is the question how personality traits are
reflected in the brain. Functional and structural networks have been examined by functional and structural magnetic resonance imaging, however, the structural correlates of functionally defined networks have not been investigated in a personality context. By using the Temperament and Character Inventory (TCI) and Diffusion Tensor Imaging (DTI), the present study assesses in a sample of 116 healthy participants how personality traits proposed in the framework of the biopsychosocial theory on personality relate to white matter pathways delineated by functional network imaging. We show that the character trait self-directedness relates to the overall microstructural integrity of white matter tracts constituting the salience network as indicated by DTI-derived measures. Self-directedness has been proposed as the executive control component of personality and describes the tendency to stay focused on the attainment of long-term goals. The present finding corroborates the view of the salience network as an executive control network that serves maintenance of rules and task-sets to guide ongoing behavior.

Click here for info regarding one of the better brain network overview articles by Bressler and Menon.


Click on image to enlarge



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Research suggests that Gq acquired knowledge has distinct neuro basis from other types of semantic knowledge (Gc)

Thanks to my colleague Joel Schneider for making me aware of this article which provides support for Gq acquired knowledge systems being distinct from Gc (as per CHC theory)

Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks

Cite this article: Amalric M, Dehaene S. 2017
Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks. Phil. Trans. R. Soc. B373: 20160515.

Marie Amalric and Stanislas Dehaene

Abstract

Is mathematical language similar to natural language? Are language areas used by mathematicians when they do mathematics? And does the brain comprise a generic semantic system that stores mathematical knowledge alongside knowledge of history, geography or famous people? Here, we refute those views by reviewing three functional MRI studies of the representation and manipulation of high-level mathematical knowledge in professional mathematicians. The results reveal that brain activity during professional mathematical reflection spares perisylvian language-related brain regions as well as temporal lobe areas classically involved in general semantic knowledge. Instead, mathematical reflection recycles bilateral intra-parietal and ventral temporal regions involved in elementary number sense. Even simple fact retrieval, such as remembering that ‘the sine function is periodical' or that ‘London buses are red', activates dissociated areas for math versus non-math knowledge. Together with other fMRI and recent intra-cranial studies, our results indicated a major separation between two brain networks for mathematical and non-mathematical semantics, which goes a long way to explain a variety of facts in neuroimaging, neuropsychology and developmental disorders. This article is part of a discussion meeting issue ‘The origins of numerical abilities'.

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Monday, April 30, 2018

Prominent psychologist resigns as journal editor over allegations over self-citation



Prominent psychologist resigns as journal editor over allegations over self-citation

Robert Sternberg, professor of human development at Cornell University, resigned last week as editor of…

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Sunday, April 29, 2018

The Science of Mind Wandering

As per usual, another great summary by Dr. Jon Lieff.


The Science of Mind Wandering

Some feel that spontaneous thought occurring without specific stimulation is closest to understanding how we define ourselves. These seemingly random self-produced…

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Saturday, April 28, 2018

Stability of intelligence from infancy through adolescence

Stability of intelligence from infancy through adolescence: An autoregressive latent variable model (article link)

Huihui Yua, D. Betsy McCoach, Allen W. Gottfriedc, Adele Eskeles Gottfriedd

a Yale University, United States b University of Connecticut, United States c Fullerton Longitudinal Study, California State University, Fullerton, United States d California State University, Northridge, United States

A B S T R A C T

This study examined the stability of the latent construct of intelligence from infancy through adolescence, using latent variable modeling to account for measurement error. Based on the Fullerton Longitudinal Study data, the present study modeled general intelligence across four developmental periods from infancy through adolescence. The Fullerton Longitudinal Study included twelve assessments of intellectual performance over a sixteen-year interval. Three assessments of intellectual performance at each of four developmental periods served as in-dicators of latent intelligence during infancy (1, 1.5, and 2 years old), preschool (2.5, 3, and 3.5 years old), childhood (6, 7, and 8 years old), and adolescence (12, 15, and 17 years old). Intelligence exhibited a high degree of stability across the four developmental periods. For instance, infant intelligence revealed a strong cross-time correlation with preschool intelligence (r = 0.91) and moderate correlations with childhood and adolescent intelligence (r = 0.69 and 0.57, respectively). Intelligence followed a stage-autoregressive pattern whereby correlations between IQ scores decreased as the timespan between assessment waves increased. Further, from infancy to adolescence, the effect of intelligence during earlier periods was completely mediated by intelligence during the adjacent developmental period. In contrast to much prior research, this study demonstrated the stability of general intelligence, beginning in infancy.

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Knowledge acquisition is governed by striatal prediction errors



Knowledge acquisition is governed by striatal prediction errors

• Alex Pine, • Noa Sadeh, • Aya Ben-Yakov, • Yadin Dudai & • Avi Mendelsohn ORCID: orcid.org/0000-0003-4582-2668 Nature…

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Thursday, April 26, 2018

Practice effects and progressive error practice effects on speeded tests

Journal of Intelligence

Response Time Reduction Due to Retesting in Mental Speed Tests: A Meta-Analysis (article link)

Jana Scharfen, Diego Blum and Heinz Holling


Abstract

As retest effects in cognitive ability tests have been investigated by various primary and meta-analytic studies, most studies from this area focus on score gains as a result of retesting. To the best of our knowledge, no meta-analytic study has been reported that provides sizable estimates of response time (RT) reductions due to retesting. This multilevel meta-analysis focuses on mental speed tasks, for which outcome measures often consist of RTs. The size of RT reduction due to retesting in mental speed tasks for up to four test administrations was analyzed based on 36 studies including 49 samples and 212 outcomes for a total sample size of 21,810. Significant RT reductions were found, which increased with the number of test administrations, without reaching a plateau. Larger RT reductions were observed in more complex mental speed tasks compared to simple ones, whereas age and test-retest interval mostly did not moderate the size of the effect. Although a high heterogeneity of effects exists, retest effects were shown to occur for mental speed tasks regarding RT outcomes and should thus be more thoroughly accounted for in applied and research settings.

Keywords: meta-analysis; mental speed; processing speed; retest effect; practice effect; response time; reaction time; automatization


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Meta-analytic SEM of literacy and language development relations

Using Meta-analytic Structural Equation Modeling to Study Developmental Change in Relations Between Language and Literacy. Article link.

Jamie M. Quinn Richard K. Wagner

The purpose of this review was to introduce readers of Child Development to the meta-analytic structural equa-tion modeling (MASEM) technique. Provided are a background to the MASEM approach, a discussion of its utility in the study of child development, and an application of this technique in the study of reading compre-hension (RC) development. MASEM uses a two-stage approach: first, it provides a composite correlation matrix across included variables, and second, it fits hypothesized a priori models. The provided MASEM application used a large sample (N = 1,205,581) of students (ages 3.5–46.225) from 155 studies to investigate the factor structure and relations among components of RC. The practical implications of using this technique to study development are discussed.

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Thursday, April 19, 2018

The Flynn Effect and IQ Disparities Among Races, Ethnicities, and Nations: Are There Common Links? | Psychology Today

The Flynn Effect and IQ Disparities Among Races, Ethnicities, and Nations: Are There Common Links? | Psychology Today

The Flynn Effect and IQ Disparities Among Races, Ethnicities, and Nations: Are There Common Links?

Connecting the Flynn Effect to racial, ethnic, and national disparities in IQ

The 20th century witnessed a dramatic increase in IQ, as much as 3 points per decade (see Are you smarter than Aristotle? Part I). The fact that IQ scores increased so much in such a short amount of time has raised many issues about the nature of intelligence, and what intelligence tests are measuring. For instance, while an individual's IQ test performance within a particular generation tends to be relatively stable and is determined by a complex mix of nature and nurture, such dramatic increases across generations demonstrates the potent influence of the environment on the development of cognitive abilities.

Multiple researchers have proposed theories to explain the Flynn effect. One of the most elaborate is Dickens and Flynn's 'social multiplier effect'. Their proposed effect takes into account the importance of culture in influencing what particular forms of intelligence it educates, spotlights, and nurtures.

Source:

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I like to use breakdancing as an example (see IQ Bashing, The Flynn Effect, and Genes). Within a particular generation, really athletic individuals will tend to score higher on a wide variety of tests that require athleticism (a trait that is influenced both by genetic and environmental factors). Athletic individuals will tend to run faster, life heavier weights, swim faster, and probably even look better breakdancing. But imagine that breakdancing suddenly became an Olympic sport (I can only dream). In this imaginary world, society suddenly shifts interest in basketball to breakdancing. We drop more money into educating everyone in the fine art of the baby-freeze, the windmill, and the headstand. Breakdancing becomes a craze, appearing in grade school classrooms, on streets, and on all sorts of job applications. What would come about as a result?

This sort of situation would up the ante on breakdancing skills. Sure, those naturally inclined toward athleticism would still have a breakdancing advantage, but the average standard of breakdancing performance would be greatly increased. In order to remain competitive, aspiring breakdancers would have to step their game up and learn increasingly complex moves. Given enough generations with such high levels of breakdancing training, you would start to see a rise in mean scores on tests of breakdancing ability.

This breakdancing example also applies to the rise seen in IQ scores across generations. Within each generation, people who tend to do well on one test of cognitive ability will tend to do well on other tests that tap to some extent complex reasoning ability. But across generations, the particular types of tests that show the most dramatic increases indicate to a considerable degree our cultural priorities. The Flynn Effect serves as a reminder that when we give people more opportunities to prosper, more people do prosper. We've come quite a long way since the pre-industrial revolution in terms of our cultural emphasis on reading, writing, abstract reasoning, and scientific thinking. The Flynn Effect is a partial indicator of this progress (see Are you smarter than Aristole?: On the Flynn Effect and the Aristotle Paradox).

Over the years, various 'social multipliers' (Dickens & Flynn, 2006) have been proposed to account for the Flynn Effect, including increased nutrition, increased test familiarity, heterosis, increased scientific education, video games, TV show complexity, modernization, and more. Surely a combination of factors contributed to the rise. In this post, I want to focus though on a few changes over the course of the past 100 years that have particular implications for understanding race, ethnic, and national disparities in IQ. First let's look at literacy.

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Literacy involves the ability to write, read, and comprehend information of varying levels of complexity. It is estimated that there are 774 million illiterate adults in the world, 65% whom are women (UNESCO Institute for Statistics, 2007). In the United States alone, 5% of the adult population is completely nonliterate (Kirsch, Jungeblut, Jenkins, & Kolstad, 1993). Self-reported literacy skills of both White and Black populations of the U.S. have been increasing steadily since 1870, however (National Center for Education Statistics, 1993). One study showed that the IQ and literacy scores of Blacks increased in parallel from 1980 to 2000 (Dickens & Flynn, 2006).

The importance of being able to read for performance on an IQ test cannot be understated. Instead of measuring 'intelligence' in an illiterate test-taker, the test is measuring that person's inability to read. While 'intelligence' may certainly influence an individual's ability to read, society has a lot of influence on how many inhabitants even get the chance to read in the first place regardless of the intelligence level of any single individual. Therefore, reading skills may exert important effects on particular races and nationalities that have historically undergone much discrimination and as a result, limited opportunity for literacy development.

An enormous body of evidence collected over the past 50 years shows that different ethnicities and races within a country tend to show substantial differences in their average level of IQ. Some researchers argue that this gap is narrowing (Dickens & Flynn, 2006) whereas others argue that the IQ gap has remained stable (Murray, 2006). IQ test score discrepancies are also found between nations. For instance, sub-Saharan African countries have demonstrated statistically significantly lower IQs than other nations (Lynn, 2006, 2008). These findings have led some researchers to propose that such IQ gaps found across ethnicities, races, and nationalities suggests a difference in innate brain capacity (see Lynn & Vanhanen, 2006).

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Until recently, the phenomenon of the Flynn Effect, and IQ gaps found between different ethnicities, races, and nationalities have not been tied together. For the first time ever, Psychologist David F. Marks systematically analyzed the association between literacy skills and IQ across time, nationality, and race (Marks, 2010).

If increasing literacy were really explaining a number of seemingly different IQ trends, then you would expect to see a few things. First, within a population you should expect increased education of literacy skills to be associated with an increase in the average IQ of that population. Second, IQ gains should be most pronounced in the lower half of the IQ bell curve since this is the section of the population that prior to the education would have obtained relatively lower scores due to their inability to comprehend the intelligence test's instructions. With increased literacy, you should expect to see a change in the skewness of the IQ distribution from positive to negative as a result of higher rates of literacy in the lower half of the IQ distribution (but very little change in the top half of the distribution). You should also expect to see differences on the particular intelligence test subscales, with increased literacy showing the strongest effects on verbal tests of intelligence and minimal differences on other tests of intelligence. If all these predictions hold up, there would be support for the notion that secular IQ gains and race differences are not different phenomena but have a common origin in literacy.

To test these predictions, Marks looked at samples representative of whole populations (rather than individuals), and used ecological methods to calculate statistical associations between IQ and literacy rates across different countries. Were Marks' findings consistent with the predictions?

Strikingly, yes. He found that the higher the literacy rate of a population, the higher that population's mean IQ, and the higher that population's mean IQ, the higher the literacy rate of that population. When literacy rates declined, mean IQ also declined. Marks also found evidence for unequal improvements across the entire IQ spectrum: the greatest effects of increased literacy rates were on those in the lower half of the IQ distribution. Interestingly, he also found that both the Flynn Effect and racial/national IQ differences showed the largest effects of literacy on verbal tests of intelligence, with the perceptual tests of intelligence showing no consistent pattern.

It must be noted that literacy wasn't the only factor responsible for the Flynn effect. Adopting the Cattell-Horn-Carroll (C-H-C) framework (McGrew, 2005, 2009) Marks found that Visual processing (Gv) and Processing Speed (Gs) also made important contributions.

It should also be noted that Mark's findings only speak to populations (not individuals) and do not say much about causation. The findings can only definitively say that some not-yet-identified variable is causing both literacy and IQ scores to change. To really test for causation, future experimental studies should be conducted to look at the effect of literacy intervention on IQ scores in comparison with a control group not receiving literacy intervention and should also investigate intervening variables that affect both literacy and IQ. Still, the result that population level literacy changes with population IQ is suggestive that increased literacy is causing increased IQ.

Even though there is still much work to be done, their findings have some very strong implications for our understanding of the Flynn effect, the nature of intelligence, and the origin of race and secular differences in intelligence.

Source:

In Hernstein & Murray's 1994 book The bell curve: intelligence and class structure in American life, most of their controversial claims about IQ differences, ethnicity, and social issues came from the United States Department of Labor's National Longitudinal Survey of Youth. This survey includes the Armed Forces Qualifications Test, which was developed by the Department of Defense and measures the ability of potential recruits to learn how to perform military duties. Since many of Hernstein & Murray's conclusions were based on this test, it's important to really examine what that test measures.

Marks did just that by scanning the literature for datasets containing test estimates for populations of groups taking both the Armed Forces Qualifications Test and tests of literacy. One study on nine groups of soliders differing in job and reading ability found a correlation of .96 between the Armed Forces Qualifications Test and reading achievement (Sticht, Caylor, Kern, & Fox, 1972). Another study showed significant improvements among Black and Hispanic populations in their Armed Forces Qualifications Test scores between 1980 and 1992 while Whites only showed a slight decrement (Kilburn, Hanser, & Klerman, 1998). Another study obtained reading scores for 17-year olds for those same ethnic groups and dates (Campbell et al., 2000) and found a correlation of .997 between reading scores and Armed Forces Qualifications Test scores. This nearly perfect correlation was based on six pairs of data points from six independent population samples evaluated by two separate groups of investigators. As Marks notes,

"On the basis of the studies summarized here, there can be little doubt that the Armed Forces Qualifications Test is a measure of literacy."

The Flynn Effect was intriguing all by itself. Now that researchers have shown common linkages between The Flynn Effect, race, ethnic, and nationality disparities, there are even more questions to be answered and potential research avenues to be explored. The Marks study suggests a crucial environmental factor is literacy. If this is so, then interventions that increase literacy will also narrow the IQ gap found between different races and nationalities.

Literacy intervention can take many forms though, both directly and indirectly. Researchers should consider not just improved access to schooling but also lots of other conditions that may affect literacy rates. For instance, recent research shows the important effects of parasites and pathogens on a nation's intelligence (see recent article in The Economist called Mens sana in corpore sano). Christopher Eppig and colleague's argue in their recent article in Proceedings of the Royal Society that the Flynn effect may be caused in part by the decrease in the intensity of infectious diseases as nations develop. Looking at data from 192 countries and 28 infectious diseases in those countries, they found that the higher the disease burden of that population, the lower that population's mean IQ level, with robust correlations ranging from -0.76 to -0.82. The chance that this correlation came about at random is reported by The Economist to be less than 10,000. Interestingly, when Eppig and colleagues controlled for other contributing variables to national differences in IQ (temperature, distance from Africa, gross domestic product per capita and various measures of education), infectious disease remained the most powerful predictor of average national IQ.

These results suggest that infections and parasites such as intestinal worms, malaria, and perhaps most importantly (according to Eppig and colleagues) bugs that cause diarrhea, can all have important effects on both literacy rates and IQ scores. The good news is that disease interventions such as vaccinations, clean water and proper sewage can have quite outstanding effects on multiple areas of cognition.

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This latest research on the environmental effects of nutrition (Colom et al., 2005, but see Flynn, 2009), disease, literacy, and more on both the rise in IQ and ethnic, racial, and national disparities in IQ point to the importance of the environment for developing intelligence as well as the importance for researchers to be very careful when they use intelligence test performance (especially verbal tests) to make inferences about hereditary differences between different ethnic groups and nationalities.

© 2010 by Scott Barry Kaufman

Note: Portions of this post originally appeared as a guest post on the blog Intelligent Insights on Intelligence Theories and Test (see original post here), which is run by legendary IQ test maker, theorist, and researcher Kevin McGrew. I'm a long time follower of his blog and am honored to guest post for him.

Acknowledgments: Thanks to Louisa Egan for bringing The Economist article to my attention.

***Update*** Over at Kevin McGrew's blog, Bob Williams wrote an extensive reply to my post. You can read his very different perspective here.

For more on the Flynn Effect, see:

References

Campbell, J. R., Hombo, C. M., & Mazzeo, J. (2000) Trends in academic progress: three decades of student performance, NCES 2000-469. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement, National Center for Education Statistics, NAEP 1999.

Colom, R., Lluis-Font, J. M., & Andrés-Pueyo, A. (2005) The generational intelligence gains are caused by decreasing variance in the lower half of the distribution: supporting evidence for the nutrition hypothesis. Intelligence, 33, 83-91.

Dickens, W. T., & Flynn, J. R. (2006) Black Americans reduce the racial IQ gap: evidence from standardization samples. Psychological Science, 17, 913-920.

Eppig, C., Fincher, C.L., & Thornhill, R. (2010). Parasite prevalence and the worldwide distribution of cognitive ability. Proceedings of the Royal Society B, doi: 10.1098/rspb.2010.0973.

Flynn, J. R. (2009) Requiem for nutrition as the cause of IQ gains: Raven's gains in Britain 1938 to 2008. Economics and Human Biology, 7, 18-27.

Herrnstein, R. J., & Murray, C. (1994) The bell curve: Intelligence and class structure in American life. New York: Free Press.

Kilburn, M. R., Hanser, L. M., & Klerman, J. A. (1998) Estimating AFQT scores for National Educational Longitudinal Study(NELS) respondents. Santa Monica, CA: RAND Distribution Services.

Kirsch, I. S., Jungeblut, A., Jenkins, L., & Kolstad, A. (1993) Adult literacy in America: A first look ook at the results of the National Adult Literacy Survey. Princeton, NJ: Educational Testing Service.

Lynn, R. (2006) Race differences in intelligence: an evolutionary analysis. Augusta, GA: Washington Summit.

Lynn, R. (2008) The global bell curve. Augusta, GA: Washington Summit.

Lynn, R., & Vanhanen, T. (2002) IQ and the wealth of nations. Westport, CT: Praeger.

Marks, D.F. (2010). IQ variations across time, race, and nationality: An artifact of differences in literacy skills. Psychological Reports, 106, 3, 643-664.

McGrew, K. S. (2005) The Cattell-Horn-Carroll theory of cognitive abilities: past, present, and future. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: theories, tests, and issues. (2nd ed.) New York: Guilford. Pp. 136-182.

McGrew, K. (2009). Editorial. CHC theory and the human cognitive abilities project. Standing on the shoulders of the giants of psychometric intelligence research, Intelligence, 37, 1-10.

Murray, C. (2006) Changes over time in the Black-White difference on mental tests: evidence from the children of the 1979 cohort of the National Longitudinal Survey of Youth. Intelligence, 34, 527-540.

National Center for Education Statistics. (1993) 120 years of American educ ation: a statistical portrait. (T. Snyder, Ed.) Washington, DC: U.S. Department of Education, Institute of Education Sciences, NCES 1993.

Sticht, T. G., Caylor, J. S., Kern, R. P., & Fox, L. C. (1972) Project REALISTIC: determination of adult functional literacy skill levels. Reading Research Quarterly, 7, 424-465.




Wednesday, April 18, 2018

Early developmental trajectories of number knowledge and math achievement from 4 to 10 years: Low-persistent profile and early-life predictors



Early developmental trajectories of number knowledge and math achievement from 4 to 10 years: Low-persistent profile and early-life predictors

From Twitter, a Flipboard magazine by Journal of Sch Psych

Little is known about the development of number knowledge…

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Kevin McGrew, PhD
Educational Psychologist
Director, Institute for Applied Psychometrics
IAP
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Tuesday, April 17, 2018

The Flynn Effect Reference Project updated

The Flynn Effect Reference Project document, maintained at the Intellectual Competence and the Death Penalty blog, was just updated today.  This information can be found here.