Research Alert: Combining Psychology With Artificial Intelligence: What Could Possibly Go Wrong?

Quick email-based Research Alert FYI.  This is an open access article👍

 

Combining Psychology With Artificial Intelligence: What Could Possibly Go Wrong? - Iris van Rooij, Olivia Guest, 2026 
https://journals.sagepub.com/doi/10.1177/09637214261438379

 

Abstract

The current AI hype cycle combined with psychology’s various crises make for a perfect storm. Psychology, on the one hand, has a history of weak theoretical foundations, a neglect for computational and formal skills, and a hyperempiricist privileging of experimental tasks and testing for effects. Artificial intelligence, on the other hand, has a history of conflating artifacts for theories of cognition, or even minds themselves, and its engineering offspring likes to move fast and break things. Many of our contemporaries now want to combine the worst of these two worlds. What could possibly go wrong? Quite a lot. Does this mean that psychology and artificial intelligence can best part ways? Not at all. There are very fruitful ways in which the two disciplines can interact and theoretically contribute to cognitive science, for instance, by studying the scope and limits of computational models of human cognition. But to reap the fruits, one needs to understand how to steer clear of potential traps.

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Research alert: #ExecutiveFunctions, #Metacognition, #Self-Regulation, and #Self-RegulatedLearning – What are We Talking About? A Review and Introduction of the #EMERGE Model

Quick email FYI research alert post.  

 

This is a much needed and interesting attempt to deal with the “jingle-jangle” fallacy amoung the cognition-related constructs of executive functions, metacognition, self-regulation, and self-regulated learning—via the proposed EMERGE model.  

 

I’m setting this aside for focused reflective reading.  Good news…it is open access and thus downloadable 👍

 

Executive Functions, Metacognition, Self-Regulation, and Self-Regulated Learning – What are We Talking About? A Review and Introduction of the EMERGE Model | Educational Psychology Review | Springer Nature Link 
https://link.springer.com/article/10.1007/s10648-026-10157-0

Abstract 

As students progress through school, they are expected to increasingly regulate their attention, behaviour, and learning. While some meet these demands with ease, others face ongoing challenges that can hinder their academic success. Research has identified four key concepts in this area: executive functions (EF), metacognition (MC), self-regulation (SR) and self-regulated learning (SRL). Although these constructs are conceptually related, they have often been examined in isolation due to disciplinary and methodological divides, resulting in fragmented accounts that obscure their dynamic interplay. This review addresses this issue by providing a comparative overview of EF, MC, SR and SRL in terms of their definitions, how they are operationalised and the research designs used. Based on this synthesis, we introduce the EMERGE model, which positions these constructs along a continuum ranging from more biologically grounded mechanics (e.g., EF) to more culturally shaped pragmatics (e.g., strategy knowledge in SRL). The model highlights both shared mechanisms and distinct functions and conceptualises SR in learning situa-tions as an integrative construct. Building on this framework, we propose two guid-ing hypotheses: the stage-setting hypothesis, which emphasises long-term develop-mental interplay; and the compensatory hypothesis, which focuses on short-term interactions that predict learning outcomes. Together, these perspectives highlight the need for longitudinal, experimental, and hybrid designs to capture developmen-tal and dynamic processes. The EMERGE model thus aims to bridge fragmented research traditions, improve diagnostics, and inform interventions that effectively support students in meeting the growing demands of self-regulated and adaptive learning.

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IQ McGrew’s Recommended Reading: How Human Personality [and Intelligence] Will [May?] Change With the Use of Artificial Intelligence - #recrdg #personality #intelligence #AI #CHC #artificialintellignece #psychology #schoolpsychology #schoolpsychologists

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I seldom designate an article as a recommended reading. I typically make FYI posts about new research I finding interesting in my small corner of the larger sandbox of psychology…more as FYI alerts.  I break with my typical FYI research alert blogging behavior for this article by Dr. John D. Mayer.  I recommend reading Mayer’s thought provoking article—especially since it is open source and can be downloaded and read for free (click here to access).

Why?  Because it is a well-reasoned “thought piece” about the many unanswered questions regarding the potential positive and negative impact of AI on humans, in this case, human personality and cognition.  I’m relatively new to the fast-moving AI movement and, as an educational psychologist, I’m interested in how certain cognitive abilities (especially CHC cognitive abilities) may become “skilled” or “deskilled” with greater reliance on AI.  

Abstract

People change as they form new habits, encounter new situations, and mature. As people interact with artificial intelligence (AI), their personalities will change, including their emotional responses to AI, their cognition, and their self-understanding. The present theoretical integration draws together empirical studies of how personality changes in response to technological innovations, and to AI in particular. Research studies reviewed were selected according to their relevance and quality. Some key points include that (a) as AI becomes increasingly human-like, and humans represent themselves online, humans and bots become increasingly difficult to distinguish; (b) as people rely on AI as a coach to guide them in interpersonal interactions, they may become socially deskilled; and, (c) as they rely on AI for work tasks, they may become cognitively deskilled in key areas. These changes in personality will entail an overall shift in people’s self-concepts. Psychologists can track these changes by classifying people’s types of AI interactions and relating them to relevant personality attributes.

Research Byte: Age-related change in #inhibitory processes when controlling for #workingmemory (#Gwm) capacity and #processingspeed (#Gs) - #cognition #intelligence #CHC #executivefunctions #Gwm #Gs #schoolpsychology

 

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This is a nice study/paper.  And it is open access and can be downloaded for reading by clicking here.

I recommend reading, if not the entire article, at least the introductory lit review.  The introductory lit review is worth a read if one wants to understand the basic literature re the definition, theories, and research regarding the relations between cognitive inhibition, working memory capacity (Gwm), and processing speed (Gs) in a developmental context.  

Abstract

The main purpose of this study was to examine the age-related changes in inhibitory control of 450 children at the ages of 7–8, 11–12, and 14–16 when controlling for working memory capacity (WMC) and processing speed to determine whether inhibition is an independent factor far beyond its possible reliance on the other two factors. This examination is important for several reasons. First, empirical evidence about age-related changes of inhibitory control is controversial. Second, there are no studies that explore the organization of inhibitory functions by controlling for the influence of processing speed and WMC in these age groups. Third, the construct of inhibition has been questioned in recent research. Multigroup confirmatory analyses suggested that inhibition can be organized as a one-dimension factor in which processing speed and WMC modulate the variability of some inhibition tasks. The partial reliance of inhibitory processes on processing speed and WMC demonstrates that the inhibition factor partially explains the variance of inhibitory tasks even when WMC and processing speed are controlled and some methodological concerns are addressed.

Meta-analysis does not support incremental predictive validity of cognitive measures for response to intervention (RTI)

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Beyond IQ Series #8: What is "academic goal setting" and why is it important for learning?

Background comment regarding this series

Interest in social-emotional learning and resiliency training (click here and here for just two examples) in education has shown a recent uptick on activity. Given this activity, IQs Corner is starting a series to explain the previously articulated Model of Academic Competence and Motivation (MACM), which was a model ahead of it's time (IMHO). The imporance of non-cognitive (conative) characteristics in learning have been articulated since the days of Spearman, the father of the construct of general intelligence. Richard Snow's work on the concept of "aptitude," which integrates cognitive and conative individual difference variables, is the foundation of the Beyond IQ MACM. Non-cognitive (cognitive) characteristics of learners are important for learning and are more manipulable (more likely to be modified via intervention) than intelligence. Thus, the MACM components make sense as potential levers for improving school learning and pursuing more well rounded life-long learners. This material comes a larger set of materials on the web (click here).

Current MACM Series Installment

This eighth installment in the Beyond IQ series defines academic goal setting and summarizes implications for learning. [All installments in this series (and other related posts and research) can be found by clicking here.
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Academic Goal Setting: Definition and Conceptual Background

A person’s ability to set, prioritize and monitor progress towards appropriate and realistic short-(proximal) and longterm (distal) academic goals that serve to direct attention,effort, energy, and persistence toward goal-relevant activities (and away from goal-irrelevant activities).

Goal setting is the ability to set, prioritize and monitor progress towards appropriate and realistic short-term (proximal) and long-term (distal) goals that serve to direct attention, effort, energy, and persistence toward goal-relevant activities (and away from goal-irrelevant activities) (Locke & Latham, 2002). Goals (e.g., academic goals) are the object or aim of an action or behavior and typically include a specified time limit and standard of proficiency. The act of setting goals is based on the assumption, supported by approximately 4 decades of research, that conscious goals will affect action or behavior (Locke & Latham, 2002). According to goal- setting theory, goal-setting facilitates higher levels of academic performance via: (a) direction of attention and efforts toward goal- relevant activities; (b) energizing effort; (c) increasing persistence and more sustained effort; and (d) indirectly leading to the discovery and use of task-relevant strategies (Locke & Latham, 2002).


Academic Goal Setting: Implications

Research has consistently suggested that the two types of academic goal orientations produce significantly different adaptive or nonadaptive learning-related behaviors (Maehr, 1999). According to Covington (2000), “one’s achievement goals are thought to influence the quality, timing, and appropriateness of cognitive strategies that, in turn, control the quality of one’s accomplishments” (p. 174). In general, the research suggests (Anderman et al., 2002; Covington, 2000; Eccles & Wigfield, 2002; Kaplan & Maehr, 1999; Linnenbrink & Pintrich, 2002b; Maehr, 1999; Newman, 2000; Pintrich, 2000b, 2000c; Skaalvik & Skaalvik, 2002; Snow et al., 1996):

A performance goal orientation is associated with nonadaptive learning behaviors which include hiding self-perceived incompetence, self-handicapping, greater worry and anxiety, increased behavior problems, a concern for establishing superiority relative to others, a focus on obtaining grades for grades' sake or other external reasons, less adaptive subsequent motivation, negative self-evaluations and affect, poorer and disorganized strategy use, and poorer academic performance. A performance goal orientation has been associated with students demonstrating a pattern of “helplessness” and the avoidance of challenging situations in order to maintain positive self-perceptions of ability (when compared to others). “Success…is evaluated in social comparison terms. In terms of developing self-esteem, this is a decidedly hazardous situation. By definition, success is a limited commodity. Only a few, at best, can win a competitive game” (Maehr, 1999, p. 331).

A learning goal orientation is associated with more adaptive learning behaviors: positive affect (e.g., pride and satisfaction), higher levels of efficacy, interest, task effort and engagement, the use of more creative and deep self-regulatory learning strategies, and better academic performance. When learning results in stress and frustration, learning goal oriented students tend to view the situation as a challenge, are often energized by the challenge, maintain a positive and optimistic outlook, persevere, and demonstrate the ability to be strategically flexible in their problem solving strategies.

The adoption of a particular learning goal orientation is predictive of, and related to, the attainment of important and valued educational outcomes for children and adolescents. According to Covington’s (2000) review, “the accumulated evidence overwhelmingly favors the goal-theory hypothesis that different reasons for achieving, nominally approach and avoidance, influence the quality of achievement striving via self-regulation mechanisms” (p. 178). A learning goal orientation is a key student attribute that should be assessed and fostered in learning environments. A learning goal orientation is associated with environments that define success as progress and improvement, value effort and learning, and accept mistakes as an inherent component of learning. Learning goal oriented environments stress personal goals, internal comparisons, and a focus on past performance as a frame of reference. In contrast, educational practices that encourage normative ability social comparisons (comparisons that highlight and accentuate competency differences) are believed to foster performance goal orientations and associated maladaptive learner behaviors. Classroom and school incentive systems, which specify how students are evaluated and how rewards (e.g., grades, praise) are distributed, can have a significant impact on a student’s adoption of a specific academic goal orientation.

The reader is referred to Covington (2002) for a summary of the research on the two major categories of classroom incentive structures (ability vs. equity game structures).

Recently, some goal achievement research has differentiated between two subtypes of performance goal orientation. Performance- approach goals are hypothesized to be present when a student’s purpose for learning is focused on demonstrating their competence and abilities. Performance-approach orientations have been associated with both adaptive and maladaptive learning outcomes. It is hypothesized that for some students, a focus on doing better than others and publicly demonstrating their competence (performance- approach) can contribute to higher levels of motivation, task engagement, and academic success, particularly when the student also displays intrinsic interest in the task. However, there is disagreement in the field regarding the positive and negative consequences of a performance-avoidance goal orientation (Eccles & Wigfield, 2002). A performance- avoidance goal orientation is present when a student’s purpose or goal for achievement is to avoid the demonstration of incompetence (i.e., avoid looking stupid). Performance- avoidance goals have been linked with maladaptive educational and behavioral outcomes.

Developmental research has revealed significant differences and changes in a student’s goal orientation over time, largely in response to students adapting to new environments. In general, the developmental goal orientation research literature suggests that changes occur more as a function of changing learning environment, and not enduring personality traits (Anderman et al., 2000).

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Beyond IQ Series # 3: Importance of individual learner characteristics (and relevant theories)

Background comment regarding this series

Interest in social-emotional learning and resiliency training (click here and here for just two examples) in education has shown a recent uptick on activity. Given this activity, IQs Corner is starting a series to explain the previously articulated Model of Academic Competence and Motivation (MACM), which was a model ahead of it's time (IMHO). The imporance of non-cognitive (conative) characteristics in learning have been articulated since the days of Spearman, the father of the construct of general intelligence. Richard Snow's work on the concept of "aptitude," which integrates cognitive and conative individual difference variables, is the foundation of the Beyond IQ MACM. Non-cognitive (cognitive) characteristics of learners are important for learning and are more manipulable (more likely to be modified via intervention) than intelligence. Thus, the MACM components make sense as potential levers for improving school learning and pursuing more well rounded life-long learners. This material comes a larger set of materials on the web (click here).

Current MACM Series Installment

This third installment in the Beyond IQ series builds on the prior "big picture" model of school learning post, and "drills down" on the learner characteristics component of most all models of school learning. [All installments in this series (and other related posts and research) can be found by clicking here].

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Current MACM Series Installment: Models of School Learning--The Importance of Learner Characteristics

Inspection of the previously posted figure that outlined the major models of school learning, and Walberg's theory of educational productivity in particular, indicates that despite differences among the major models of school learning, significant commonalities exits across the models. According to Walberg (1980), all models specify certain conditions prerequisite for effective instruction, characteristics of the teaching-learning process, and the quantifiable outcomes of schooling with which they are concerned. In addition, several theorists discuss environmental conditions which include teacher background, curriculum and institutional factors, and cultural context. All theorists recognize the contribution of certain intrinsic learner characteristics in the form of cognitive (e.g., aptitude, ability to comprehend instruction, prior achievement) and attitudinal (e.g., perseverance, motivation, self- concept as learner) variables. As summarized by Wang et al., the major categories of learner characteristics important for academic learning are learner demographics, history of educational placement, social and behavioral outcomes, motivation and affective, cognitive, metacognitive, and psychomotor abilities (Gerlach, Aaside, Humphreys, Gade, Paulson & Law, 2002).

Five of the seven learner characteristic domains (social and behavioral, motivation and affective, cognitive, metacognitive, and psychomotor) reflect intrinsic traits or states of the learner. Although serving a valuable heuristic function for model- based research and literature integration, each of these five learner characteristic categories refer to separate broad and complex multivariate domains of human behavior. For example, Carroll’s (1993) recent meta- analysis of the extant factor analysis research on human cognitive abilities suggests that the cognitive domain alone includes, under a single general intellectual ability (g), at least eight broad cognitive domains and 70+ narrow or specialized cognitive abilities. Similar broad multivariate taxonomies have been presented in the other broad learner characteristic domains. The breadth of potentially important learner characteristics (and potential valued educational outcomes) for learners with and without disabilities is staggering.

Clearly these non-cognitive characteristics are those that should be targeted for assessment, intervention, or that should be designated as valued outcomes of school learning, must be circumscribed and prioritized. An assumption of this author is that the identification of the broad and narrow MACM domains and proposed organizing framework must emerge from the extant empirical research and theoretical literature, and not from the advocacy, policy, nor political arenas.

A limitation of the Walberg model is the macro focus on monolithic domains, domains that fail to convey the richness, multivariate complexity, and specificity needed for scientifically-based applied research and intervention. A review of the literature reveals a burgeoning of MACM research during the past 30-40 years. The primary MACM variables identified in the current review are listed and briefly defined in the table below (double click to enlarge or click here for on-line version). The table reveals a richness of variables lurking beneath Walberg's broad domain of student characteristics.

A review of the extant literature reveals that most student characteristic related research has focused on single behaviors or traits in isolation, have studied the same characteristic at different levels of generality (e.g., motivation vs. intrinsic motivation), or have not been integrated into an overarching taxonomy or model. The diversity of behaviors and traits listed in the table below (double click on image to enlarge or click here for another on-line version) suggests an “embarrassment of riches” in our understanding of MACM variables.


The remainder of this series on the MACM framework articulates a model for identifying the non-cognitive (conative) characteristics that might be targeted in order for all learners to maximize their educational attainment. These collective essential learner facilitators are referred to under the umbrella term of "Model of Academic Competence and Motivation" (MACM).


Theoretical/Conceptual Foundations of MACM Domains

Finally, for those who want to know more about the theoretical/conceptual foundations listed in the above table, brief definitions of each are provided below:

Need for Achievement Theory. Originally proposed by McClelland (McClelland, Atkinson, Clark, & Lowell, 1953), this theory hypothesizes that all humans have a distinct internal motive to seek achievement, attainment of realistic (but challenging) goals, and advancement. Individuals are believed to posses a strong need for feedback regarding their achievement and progress, and a need for a sense of accomplishment.

Intrinsic Motivation Theory. Intrinsic motivation theory postulates that “when individuals are intrinsically motivated, they engage in an activity because they are interested in and enjoy the activity. When extrinsically motivated, individuals engage in activities for instrumental or other reasons, such as receiving a reward”(Eccles & Wigfield, 2002, p. 112).

Self-determination Theory. According to Deci and Ryan (1985), self-determination theory explains 2 main components of human motivation—“(a) humans are motivated to maintain an optimal level of stimulation (Hebb, 1955), and (b) humans have basic needs for competence (White, 1959) and personal causation or self-determination (deCharms, 1968)”(Eccles and Wigfield, 2002, p. 112). Deci and Ryan argue that self-determination plays a role in both intrinsic and extrinsic motivation. The basic premise of the theory, is that a person will feel a sense of self-determination when they are able to determine the activities they will engage in and feel competent with during task performance.

Goal Theory. Researchers have proposed a number of models to describe how individuals develop and display goal-directed behavior. Bandura (1997) and Shunk’s (1990) research suggests that “specific, proximal, and somewhat challenging goals promote both self-efficacy and improved performance” (Eccles & Wigfield, 2002, p. 115). Cognitive goal theory is based on the hypothesis that “all actions are given meaning, direction, and purpose by the goals that individuals seek out, and that the quality and intensity of behavior will change as these goals change”(Covington, 2000, p. 174). Goal theory focuses on the role that “purpose” plays in motivation attitudes and behavior (Anderman & Maehr, 1994; Eccles & Wigfield, 2002; Maehr, 1999; Snow et al., 1996; Urdan & Maehr, 1995). In an academic context, a person’s achievement goal orientation deals with a student’s reason for taking a course, wanting a desired grade, etc. (Anderman et al., 2002). Although the specific terminology may differ across researchers, goal theory typically proposes 2 general goal orientations (Covington, 2000; Linnenbrink & Pintrich, 2002a). The underlying commonality among the different models is a distinction between a goal orientation driven by a concern for personal ability and normative social comparison (performance goal orientation) versus an orientation with a focus on task completion, understanding, developing and learning new skills, and mastery (learning goal orientation).

Goal Setting Theory. According to Locke and Latham (2002), goal-setting theory, which is largely an inductively derived theory (emerged from empirical research), is based on the premise that conscious goals affect action. Goal setting theory focuses on understanding the relationship between conscious performance goals and subsequent levels of task performance.

Interest Theory. Contemporary interest theory makes a distinction between individual and situational interest. “Individual interest is a relatively stable evaluative orientation towards certain domains; situational interest is an emotional state aroused by specific features of an activity or a task” (Eccles & Wigfield, 2002, p. 114). The domain of individual interest is often differentiated further into the categories of feeling-related (based more on feelings) and value- related (based more on personal significance of a situation) interests (Eccles & Wigfield, 2002). For the most part, research on situational interest has focused on “characteristics of academic tasks that create interest (e.g., Hidi & Baird, 1986)” (Eccles &Wigfield, 2002, p. 115). Research on individual interest, on the other hand, has focused more on the quality of learning and how it is related to interest.

Expectancy and Value Theory. Contemporary expectancy-value theories of motivation are based in Atkinson’s (1964) expectancy- value model, in that they link achievement performance, persistence, and choice most directly to an individual’s expectancy-related and task-value beliefs. The expectancy component of the theory focuses on an individual’s beliefs about their competence, efficacy, expectations for success and failure, and feelings of control over the outcomes of situations. The value component focuses on an individual’s incentives, motivations, and reasons for engaging in activities. Most contemporary expectancy-value theories believe that expectancies and values are positively related.

Self-efficacy Theory. Self-efficacy theory can be traced to Bandura’s social cognitive model of motivation. “Bandura defined self- efficacy as individuals’ confidence in their ability to organize and execute a given course of action to solve a problem or accomplish a task; he characterized it as a multidimensional construct that varies in strength, generality, and level (or difficulty)”(Eccles & Wigfield, 2002, p. 110). The focus of self-efficacy theory is on expectations for success (outcome expectations—a belief that certain behaviors will result in certain outcomes) and efficacy expectations (beliefs of whether one can perform the behaviors necessary to attain a certain outcome).

Attribution Theory. Attribution theory deals primarily with an individual’s interpretation of their achieved outcomes, rather than how specific motivational dispositions or realized outcomes affect subsequent achievement strivings (Eccles & Wigfield, 2002). “Attribution models include beliefs about ability and expectancies for success, along with incentives for engaging in different activities, including valuing of achievement (see Graham & Taylor, 2001)”(Eccles & Wigfield, 2002, p. 117). The key achievement attributes, as identified by Weiner and associates, are ability, effort, task difficulty, and luck (Eccles & Wigfield, 2002). These attributes are further described along the dimensions of locus of control, stability, and controllability.

Control Theory. Control theory is another type of expectancy-value theory and focuses on the hypothesis that an individual can only be successful to the extent they feel they have control over a situation (Eccles &Wigfield, 2002). Connell & Wellborn (1991); have also integrated control beliefs into a broader framework that includes 3 basic psychological needs: competence, autonomy, and relatedness. This theory posits a link between control beliefs and competence needs—individuals who believe they are in control of their achievement outcomes will feel more competent.

Self-worth Theory. Self-worth theory seeks to link motivational behavior to ability-related and valued-related constructs, as well as focusing on mental health “as a key determinant of the relation of expectancies and values to achievement behaviors” (Eccles & Wigfield, 2002, p. 122). Covington (1992, 1998) hypothesized that establishing and maintaining a positive self- image (i.e., a positive view of self-worth) is a primary human motive.

Social Awareness Theory. According to Greenspan (1981a), “the term social awareness may be defined as the individual’s ability to understand people, social events, and the processes involved in regulating social events. The emphasis on interpersonal understanding as the core operation in social awareness indicates that this construct is a cognitive component of human competence” (p. 18). Social awareness is a multidimensional hierarchical construct that includes: social sensitivity (which subsumes the subdomains of role-taking and social inference); social insight (subdomains of social comprehension, psychological insight, and moral judgment); and social communication (subdomains of referential communication and social problem- solving). Social awareness is one component of a larger all-encompassing model of personal competence that also includes emotional competence, physical competence, conceptual intelligence, and practical intelligence.

Social Cognitive Theories of Self-Regulation, Volition, & Motivation. In general terms, social cognitive theories of self-regulation focus on “how motivation gets translated into regulated behavior, and how motivation and cognition are linked” (Eccles & Wigfield, 2002, p. 124). A self-regulated student would be described as an individual who is “metacognitively, motivationally, and behaviorally active in their own learning processes and in achieving their own goals” (Eccles & Wigfield, 2002, p. 124). Multiple determinants of self-regulation have been suggested and include environmental, personal, and behavioral components, as well as context. The primary processes hypothesized to occur during self-regulation include self- observation, self-judgment, and self-reactions (Eccles &Wigfield, 2002).


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Beyond IQ series: The "big picture" model of educational productivity context for the Model of Academic Competence and Motivation (MACM)

Background comment regarding this series

Interest in social-emotional learning and resiliency training (click here and here for just two examples) in education has shown a recent uptick on activity. Given this activity, IQs Corner is starting a series to explain the previously articulated Model of Academic Competence and Motivation (MACM), which was a model ahead of it's time (IMHO). The imporance of non-cognitive (conative) characteristics in learning have been articulated since the days of Spearman, the father of the construct of general intelligence. Richard Snow's work on the concept of "aptitude," which integrates cognitive and conative individual difference variables, is the foundation of the Beyond IQ MACM. Non-cognitive (cognitive) characteristics of learners are important for learning and are more manipulable (more likely to be modified via intervention) than intelligence. Thus, the MACM components make sense as potential levers for improving school learning and pursuing more well rounded life-long learners. This material comes a larger set of materials on the web (click here).

Current MACM Series Installment

This second installment in the Beyond IQ series provides the the over-arching empirical and theoretical backdrop that led to the development of the MACM framework. [All installments in this series (and other related posts and research) can be found by clicking here]. Research on models of school learning, and the seminal work represented by Walberg's theory of educational productivity, provided the "big picture" framework for the development of one component of this larger model of educational productivity--the MACM framework.

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Walberg's (1981) theory of educational productivity, which is one of the few empirically tested theories of school learning based on an extensive review and integration of over 3,000 studies (DiPerna, Volpe & Stephen, 2002). “Wang, Haertel, and Walberg (1997) analyzed the content of 179 handbook chapters and reviews and 91 research syntheses and surveyed educational researchers in an effort to achieve some consensus regarding the most significant influences on learning" (Greenberg et al., 2003, p. 470). Using a variety of methods, Wang, et al. (1977) identified 28 categories of learning influence. Of the 11 most influential domains of variables, 8 involved social-emotional influences: classroom management, parental support, student- teacher interactions, social- behavioral attributes, motivational- effective attributes, the peer group, school culture, and classroom climate (Greenberg et al., 2003). Distant background influences (e.g., state, district, or school policies, organizational characteristics, curriculum, and instruction) were less influential. Wang et al. (1997) concluded that "the direct intervention in the psychological determinants of learning promise the most effective avenues for reform" (p. 210). Wang et al.’s research review targeted student learning characteristics (i.e., social, behavioral, motivational, affective, cognitive, and metacognitive) as the set of variables with the most potential for modification that could, in turn, significantly and positively effect student outcomes (DiPerna et al., 2002).

More recently, Zins, Weissberg, Wang and Walberg, (2004) demonstrated the importance of the domains of motivational orientations, self-regulated learning strategies, and social/interpersonal abilities in facilitating academic performance. Zins et al. reported, based on the large-scale implementation of a Social-Emotional Learning (SEL) program, that student’s who became more self-aware and confident regarding their learning abilities, who were more motivated, who set learning goals, and who were organized in their approach to work (self- regulated learning) performed better in school. According to Greenberg, Weissberg, O'Brien, Zins, Fredericks, Resnick, & Elias, (2003), Zins et al. (2004) assert that “research linking social, emotional, and academic factors are sufficiently strong to advance the new term social, emotional, and academic learning (SEAL). A central challenge for researchers, educators, and policymakers is to strengthen this connection through coordinated multiyear programming"(p. 470).

Walberg and associates’ conclusions resonate with findings from other fields. For example, the "resilience" literature (Garmezy, 1993) grew from the observation that despite living in disadvantaged and risky environments, certain children overcame and attained high levels of achievement, motivation, and performance (Gutman, Sameroff & Eccles, 2002). Wach’s (2000) review of biological, social, and psychological factors suggested that no single factor could explain “how” and “why” these resilient children had been inoculated from the deleterious effects of their day- to-day environments. A variety of promotive (direct) and protective (interactive) variables were suggested, which included, aside from cognitive abilities, such conative characteristics as study habits, social abilities, and the absence of behavior problems (Guttman et al., 2003).

Haertel, Walberg, and Weinstein (1983) identified 8 major models of school learning that are either based on psychological learning theory (Glaser, 1976) or time-based models of learning (Bennett, 1978; Bloom, 1976; Carroll, 1963; Cooley & Leinhardt, 1975; Harnischfeger & Wiley, 1976). Despite variations in names of constructs, Haertel et al. (1983) found that most of the 8 theories included variables representing ability, motivation, quality of instruction, and quantity of instruction. Constructs less represented in the models were social environment of the classroom, home environment, peer influence, and mass media (Watson & Keith, 2002). Haertel et al.’s (1983) review of theories, multiple quantitative syntheses of classroom research, and secondary data analyses of large- scale national surveys (Reynolds & Walberg, 1992), generally support Walberg's global model of educational productivity. Walberg’s model specifies that:

Classroom learning is a multiplicative, diminishing-returns function of four essential factors—student ability and motivation, and quality and quantity of instruction—and possibly four supplementary or supportive factors—the social psychological environment of the classroom, education-stimulating conditions in the home and peer group, and exposure to mass media. Each of the essential factors appears to be necessary but insufficient by itself for classroom learning; that is, all four of these factors appear required at least at minimum level. It also appears that the essential factors may substitute, compensate, or trade off for one another in diminishing rates of return: for example, immense quantities of time may be required for a moderate amount of learning to occur if motivation, ability, or quality of instruction is minimal (Haertel et al., 1983, p. 76)

.

An important finding of the Walberg et al. large scale causal modeling research was that nine different educational productivity factors were hypothesized to operate vis- à-vis a complex set of interactions to account for school learning. Additionally, some student characteristic variables (motivation, prior achievement, attitudes) had indirect effects (e.g., the influence of the variable “went through” or was mediated via another variable).

The importance of the Walberg et al. group’s findings cannot be overstated. Walberg’s (1981) theory of educational productivity is one of the few empirically tested theories of school learning and is based on the review and integration of over 3,000 studies (DiPerna et al., 2002). Walberg et al. have identified key variables that effect student outcomes: student ability/prior achievement, motivation, age/developmental level, quantity of instruction, quality of instruction, classroom climate, home environment, peer group, and exposure to mass media outside of school (Walberg, Fraser & Welch, 1986). In the current context, the first three variables (ability, motivation, and age) reflect characteristics of the student. The fourth and fifth variables reflect instruction (quantity and quality), and the final four variables (classroom climate, home environment, peer group, and exposure to media) represent aspects of the psychological environment (DiPerna et al., 2002). Clearly student characteristics are important for school learning, but they only comprise a portion of the learning equation.

More recently, Wang, Haertel, and Walberg (1993) organized the relevant school learning knowledge base into major construct domains (State & District Governance & Organization, Home & Community Contexts, School Demographics, Culture, Climate, Policies & Practices, Design & Delivery of Curriculum & Instruction, Classroom Practices, Learner Characteristics) and attempted to establish the relative importance of 228 variables in predicting academic domains. Using a variety of methods, the authors concluded that psychological, instructional, and home environment characteristics (“proximal” variables) have a more significant impact on achievement than variables such as state-, district-, or school-level policy and demographics (“distal”variables). More importantly, in the context of the current document, student characteristics (i.e., social, behavioral, motivational, affective, cognitive, metacognitive) were the set of proximal variables with the most significant impact on learner outcomes (DiPerna et al., 2002).

A sampling of the major components of the school learning models summarized by Walberg and associates is presented in the figure below (double on figure to enlarge or click here for another on-line version). The student characteristic domain in the figure is the primary focus of this series and the MACM framework.




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CNTRICS: Consensus-based cognitive measurement in schizophrenia--a model worth examinig

[double click on image to enlarge]

Schizophrenia is not my cup of tea...but good cognitive measurement is. Thus, I was intrigued when doing my weekly "IQs Recent Literature of Interest" searching when I stumbled across an intriguing set of articles in the Schizophrenia Bulletin [Vol. 35 (1) 2009].

I was impressed to find that a group of scientists studying a common disorder (schizophrenia) had engaged in a consensus-building process to identify common sets of cognitive measures to use across their various research labs. This was all part of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative. What a good model for improving the quality of research across researchers!

Maybe this model consensus-building activity could be adapted by those of us studying intelligence and cognitive related disorders in education. Instead of our constant problem in comparing research studies with different measures used by different researchers, we could, at a minimum, at least establish a core set of "marker" measures to embed in each others favorite research batteries. Yes....at times I can be naive....but I believe in the power of consensus-building to improve research...and, more importantly, the probability of improving the quality of life for individuals with cognitive-related deficits and learning disorders. I've made a related plea for the adaptation of a common cognitive nomenclature/taxonomy (CHC theory) in many articles/chapters, most recently in the journal Intelligence.

Below are the abstracts. I've provided a link to the editorial introductory article. If anyone is interested in reading one or more of the other articles, articles that focus on measuring executive control, working memory, social cognitive and affective measures, promising paradigms, and control of attention, let me know...and I'd send a copy, but only in exchange for a guest blog post. The articles are worth a read, if for on other reason, for the nifty way many of the tasks discussed are presented via visual figures (see example of stroop task at the top of this post)--nice stuff.

Below are the abstracts:

Selecting Paradigms From Cognitive Neuroscience for Translation into Use in Clinical Trials: Proceedings of the Third CNTRICS Meeting (click here to read introductory editorial)

• This overview describes the goals and objectives of the third conference conducted as part of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) initiative. This third conference was focused on selecting specific paradigms from cognitive neuroscience that measured the constructs identified in the first CNTRICS meeting, with the goal of facilitating the translation of these paradigms into use in clinical trials contexts. To identify such paradigms, we had an open nomination process in which the field was asked to nominate potentially relevant paradigms and to provide information on several domains relevant to selecting the most promising tasks for each construct (eg, construct validity, neural bases, psychometrics, availability of animal models). Our goal was to identify 1–2 promising tasks for each of the 11 constructs identified at the first CNTRICS meeting. In this overview article, we describe the on-line survey used to generate nominations for promising tasks, the criteria that were used to select the tasks, the rationale behind the criteria, and the ways in which breakout groups worked together to identify the most promising tasks from among those nominated. This article serves as an introduction to the set of 6 articles included in this special issue that provide information about the specific tasks discussed and selected for the constructs from each of 6 broad domains (working memory, executive control, attention, long-term memory, perception, and social cognition).

CNTRICS Final Task Selection: Executive Control

• The third meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) was focused on selecting promising measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of executive control, the 2 constructs of interest were ‘‘rule generation and selection’’ and ‘‘dynamic adjustments in control.’’ CNTRICS received 4 task nominations for each of these constructs, and the breakout group for executive control evaluated the degree to which each of these tasks met prespecified criteria. For rule generation and selection, the breakout group for executive control recommended the intradimensional/ extradimensional shift task and the switching Stroop for translation for use in clinical trial contexts in schizophrenia research. For dynamic adjustments in control, the breakout group recommended conflict and error adaptation in the Stroop and the stop signal task for translation for use in clinical trials. This article describes the ways in which each of these tasks met the criteria used by the breakout group to recommend tasks for further development.

CNTRICS Final Task Selection: Working Memory

• The third meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) was focused on selecting promising measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of working memory, the 2 constructs of interest were goal maintenance and interference control. CNTRICS received 3 task nominations for each of these constructs, and the breakout group for working memory evaluated the degree to which each of these tasks met prespecified criteria. For goal maintenance, the breakout group for working memory recommended the AX-Continuous Performance Task/Dot Pattern Expectancy task for translation for use in clinical trial contexts in schizophrenia research. For interference control, the breakout group recommended the recent probes and operation/ symmetry span tasks for translation for use in clinical trials. This article describes the ways in which each of these tasks met the criteria used by the breakout group to recommend tasks for further development.

CNTRICS Final Task Selection: Social Cognitive and Affective Neuroscience–Based
Measures

• This article describes the results and recommendations of the third Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia meeting related to measuring treatment effects on social and affective processing. At the first meeting, it was recommended that measurement development focuses on the construct of emotion identification and responding. Five Tasks were nominated as candidate measures for this construct via the premeeting web-based survey. Two of the 5 tasks were recommended for immediate translation, the Penn Emotion Recognition Task and the Facial Affect Recognition and the Effects of Situational Context, which provides a measure of emotion identification and responding as well as a related, higher level construct, context-based modulation of emotional responding. This article summarizes the criteria-based, consensus building analysis of each nominated task that led to these 2 paradigms being recommended as priority tasks for development as measures of treatment effects on negative symptoms in schizophrenia.

Perception Measurement in Clinical Trials of Schizophrenia: Promising Paradigms
From CNTRICS

• The third meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) focused on selecting promising measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of perception, the 2 constructs of interest were gain control and visual integration. CNTRICS received 5 task nominations for gain control and three task nominations for visual integration. The breakout group for perception evaluated the degree to which each of these tasks met prespecified criteria. For gain control, the breakout group for perception believed that 2 of the tasks (prepulse inhibition of startle and mismatch negativity) were already mature and in the process of being incorporated into multisite clinical trials. However, the breakout group recommended that steady-state visualevoked potentials be combined with contrast sensitivity to magnocellular vs parvocellular biased stimuli and that this combined task and the contrast-contrast effect task be recommended for translation for use in clinical trial contexts in schizophrenia research. For visual integration, the breakout group recommended the Contour Integration and Coherent Motion tasks for translation for use in clinical trials. This manuscript describes the ways in which each of these tasks met the criteria used by the breakout group to evaluate and recommend tasks for further development.

CNTRICS Final Task Selection: Control of Attention

• The construct of attention has many facets that have been examined in human and animal research and in healthy and psychiatrically disordered conditions. The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) group concluded that control of attention—the processes that guide selection of taskrelevant inputs—is particularly impaired in schizophrenia and could profit from further work with refined measurement tools. Thus, nominations for cognitive tasks that provide discrete measures of control of attention were sought and were then evaluated at the third CNTRICS meeting for their promise for future use in treatment development. This article describes the 5 nominated measures and their strengths and weaknesses for cognitive neuroscience work relevant to treatment development. Two paradigms, Guided Search and the Distractor Condition Sustained Attention Task, were viewed as having the greatest immediate promise for development into tools for treatment research in schizophrenia and are described in more detail by their nominators.

Technorati Tags: psychology, school psychology, educational psychology, cognition, neuropsychology, neuroscience, schizophrenia, CNTRICS, cognition, intelligence, CHC, CHC theory, Cattell-Horn-Carroll

IQ Scholar Spotlight: Dr. Linda Gottfredson

Another IQ Scholar Spotlight - Dr. Linda Gottfredson.

Dr. Gottfredson organized (and presented) at the recent 2009 (Dec) ISIR conference a symposium called:  Causal models that integrate literacy, g, and health outcomes:  A practical guide to more effective disease prevention and health promotion?  She is a highly regarded intelligence scholar who has a wide array of interests in the field of intelligence including (from her web page):

• Intelligence, health and everyday life
• Intelligence and social inequality
• Employment testing and job aptitude demands
• Affirmative action and multicultural diversity
• Career development and vocational counseling

The diversity of her interests is reflected in a wide range of publications (which can be found at her web page).  For example, her 1997 Intelligence article "Why g matters:  The complexity of everyday life" is one of the top 10 cited articles in the journal Intelligence.  She was also the lead author of an important 1977 Intelligence editorial (Mainstream science on intelligence: An Editoral with 52 signatories, history, and bibliogrpahy) in response to the controversial Bell Curve book.  Here most recent "in press" Intelligence publication is Arden, R., Gottfredson, L. S., Miller, G., & Pierce, A. (in press). Intelligence and semen quality are positively correlated.

I found the measurement/prediction issues raised at her symposium very interesting.  The bottom line is that in the field of health care/literacy, doctors expect (hope? pray?) for 100% compliance in patient follow-through in treatment recommendations, the taking of prescriptions, etc.  So...a central question is how to ascertain which patients need more assistance in understanding their health care.  How can we predict which patients will need additional or special instructions and/or follow-up?  Of course, as we all in the field of individual difference measurement know, the best available measures in intelligence can only explain up to 50+% of the variance of any outcome or dependent variable.  An interesting dilema.

I had the opportunity to chat with Dr. Gottfredson about this issue.  One of her interests is in finding (or developing) brief, ecologically valid measures of g to screen patients.  She articulated the need for measures that tap a patients ability to handle complex information processing (high g tests) but that do NOT appear to look like intelligence measures, seem more "real world" in terms of ecological validity, and that would be easy to administer and score.

I shared with her some unpublished g-factor loadings of all the WJ III tests (when cognitive and achievement tests are combined together).  These g-loadings were calculated at different age groups using principal components analysis.  A summary of the table I provided Dr. Gottfredson can be found by clicking here.  Of interest (in our discussions) was a test like Understanding Directions....a test where a subject follows an increasingly long and complex set of simple directions (e.g., point to.....now point to.....now point to....,then.....,but first......).  As can be seen in the attached table, it is a high g test that would appear to have ecological and face validity for this purpose.  I believe it is a high g test due to the complexity of language-based working memory demands placed on subjects.  This discussion (and material) is presented here to stimulate thought and discussion.  Readers not familiar with the task demands of the WJ III tests should click here.  [Conflict of interest - I'm a coauthor fo the WJ III].


Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, cognition, IQ, IQ tests, intellligence, g, general intelligence, health literacy, WJ III, Woodcock Johnson, ISIR, IQ Scholar Spotlight

Forrest Gump IQ milestone: 10,000+ and counting

Occasionally I check the number of "views" of the various PPT slide shows I've posted for free viewing (and downloading) at SlideShare. Today I checked and found that over the past 3 years my Forrest Gump IQ/Expectations show has had over 10,000 views. It is the winner in the PPT shows I've shared. It appears to be a favorite of folks. Thanks for the interest and support.

Technorati Tags: psychology, educational psychology, school psychology, education, mental retardation, special education, expectations, neuropsychology, Forrest Gump, IQ, intelligence

Beyond IQ: Metacognition, self-regulation, self-regulated learning (JER special issue)

Beyond IQ. Readers of this blog should be aware that I firmly believe that in order to understand, explain, and improve educational outcomes for learners, a "bigger picture" approach is necessary. I call it the "Beyond IQ Project." In particular, I've repeatedly sounded the accolades of the late Richard Snow's work on aptitude. I've made many posts related to this notion of aptitude, which includes many constructs such as self-efficacy, motivation, self-regulated learning, etc. I've even proposed a model for integrating these "conative" constructs (Model of Academic Competence and Motivation--MACMM). Most of my posts can be found by clicking on the Beyond IQ tag. I have been encouraged to see serious scholars in the field of intelligence (ISIR members) paying increasing attention to these constructs as they attempt to explain intellectual performance.

Today I ran across yet another special issue of a journal devoted to a major domain of the MACMM model - self regulated learning ("What do I need to do to succeed?"). Below is the table of contents of the current issue of the Educational Psychology Review. It looks EXCELLENT. I can't wait to read the articles. I've made the special issue introduction article available for viewing. If any reader would like to read one (or more) of the articles (I would provide a copy of the pdf file), in exchange for a guest blog post summary to this bog, please contact the blogmaster (iapsych@charter.net)

Why This and Why Now? Introduction to the Special Issue on Metacognition, Self-Regulation, and Self-Regulated Learning - Patricia A. Alexander (click to view).

Metacognition and Self-Regulation in James, Piaget, and Vygotsky - Emily Fox and Michelle Riconscente

Focusing the Conceptual Lens on Metacognition, Self-regulation, and Self-regulated Learning - Daniel L. Dinsmore, Patricia A. Alexander and Sandra M. Loughlin

Self-Directed Learning in Problem-Based Learning and its Relationships with Self-Regulated Learning - Sofie M. M. Loyens, Joshua Magda and Remy M. J. P. Rikers

Self-Regulation of Learning within Computer-based Learning Environments: A Critical Analysis - Fielding I. Winters, Jeffrey A. Greene and Claudine M. Costich

The Role of Teacher Epistemic Cognition, Epistemic Beliefs, and Calibration in Instruction - Liliana Maggioni and Meghan M. Parkinson

Metacognition, Self-Regulation, and Self-Regulated Learning: Research Recommendations - Dale H. Schunk

Metacognition, Self Regulation, and Self-regulated Learning: A Rose by any other Name? - Susanne P. Lajoie

Clarifying Metacognition, Self-Regulation, and Self-Regulated Learning: What’s the Purpose? - Avi Kaplan

An Interview with Dale Schunk - Gonul Sakiz

Technorati Tags: psychology, educational psychology, school psychology, education, neuropsychology, conative, aptitude, IQ, intelligence, cognition, MACM, self-regulated learning, metacognition, self-regulation, Beyond IQ, Educational Psychology Review

McGrew (2009) now official: CHC and HCA

As previously reported, my article in Intelligence (CHC theory and the Human Cognitive Abilities project:  Standing on the giants of psychometric intelligence research) is now officially published and available (click here).

Technorati Tags: psychology, educational psychology, school psychology, neuropsychology, intelligence, cognition, IQ, HCA, CHC, Human Cognitive Abilities, Catell-Horn-Carroll

IQ Classics: Top 10 IQ citations @ 11-30-08

Top 10 Cited articles in Intelligence as of 11-30-08.

As per usual, anyone interested in reading the pdf file for an article, in exchange for a guest blog post review/comment post to IQs Corner, should contact me at iapsych@charter.net

Extracted from Scopus (on Sun Nov 30 11:10:30 GMT 2008)

240		Emotional intelligence meets traditional standards for an intelligence
		Volume 27, Issue 4, 1999, Pp 267-298 Mayer, J.D. | Caruso, D.R. | Salovey, P.
186		Why g matters: The complexity of everyday life
		Volume 24 1 SPEC.ISS, 1997, Pp 79-132 Gottfredson, L.S.
153		The intelligence of emotional intelligence
		Volume 17, Issue 4, 1993, Pp 433-442 Mayer, J.D. | Salovey, P.
144		A unifying model for the structure of intellectual abilities
		Volume 8, Issue 3, 1984, Pp 179-203 Gustafsson, J.-E.
131		Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography
		Volume 12, Issue 2, 1988, Pp 199-217 Haier, R.J. | Siegel Jr., B.V. | Nuechterlein, K.H. | Hazlett, E. | Wu, J.C. | Paek, J. | Browning, H.L. | Buchsbaum, M.S.
118		A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, and general fluid intelligence
		Volume 30, Issue 2, 2002, Pp 163-183 Conway, A.R.A. | Cowan, N. | Bunting, M.F. | Therriault, D.J. | Minkoff, S.R.B.
111		A theory of adult intellectual development: Process, personality, interests, and knowledge
		Volume 22, Issue 2, 1996, Pp 227-257 Ackerman, P.L.
104		What is a good g?
		Volume 18, Issue 3, 1994, Pp 231-258 Jensen, A.R. | Weng, L.-J.
98		The stability of individual differences in mental ability from childhood to old age: Follow-up of the 1932 Scottish mental survey
		Volume 28, Issue 1, 2000, Pp 49-55 Deary, I.J. | Whalley, L.J. | Lemmon, H. | Crawford, J.R | Starr, J.M.
92		Intelligence and changes in regional cerebral glucose metabolic rate following learning
		Volume 16, Issue 3-4, 1992, Pp 415-426 Haier, R.J. | Siegel, B. | Tang, C. | Abel, L. | Buchsbaum, M.S.

Technorati Tags: psychology, educational psychology, cognitive psychology, intelligence, neuropsychology, ISIR, intelligence, cognition, IQ, IQ classics