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J. Intell., Volume 2, Issue 1 (March 2014) – 8 articles , Pages 1-28

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Comment
All We Need Is Brain (and Technology)
J. Intell. 2014, 2(1), 26-28; https://doi.org/10.3390/jintelligence2010026 - 14 Mar 2014
Cited by 2 | Viewed by 4279
Abstract
Here, I suggest we must invest our scientific resources in brain research. Scientists interested in human (and non-human) intelligence should frame their key questions regarding where to look and where to go around technical advances related to the fascinating, general purpose, highly dynamic [...] Read more.
Here, I suggest we must invest our scientific resources in brain research. Scientists interested in human (and non-human) intelligence should frame their key questions regarding where to look and where to go around technical advances related to the fascinating, general purpose, highly dynamic device we call the ‘brain’. Full article
(This article belongs to the Special Issue Intelligence, Where to Look, Where to Go?)
Comment
Predictive Validity of Non-g Residuals of Tests: More Than g
J. Intell. 2014, 2(1), 21-25; https://doi.org/10.3390/jintelligence2010021 - 11 Mar 2014
Cited by 16 | Viewed by 4211
Abstract
This comment argues that an important issue in intelligence research is to identify constructs with validity beyond g, and that non-g residuals of tests represent a promising target. Full article
(This article belongs to the Special Issue Intelligence, Where to Look, Where to Go?)
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Comment
New Directions in Intelligence Research: Avoiding the Mistakes of the Past
J. Intell. 2014, 2(1), 16-20; https://doi.org/10.3390/jintelligence2010016 - 07 Mar 2014
Viewed by 4159
Abstract
This brief commentary considers the potential for new directions in intelligence research, as well as possible pitfalls associated with these approaches. Specifically, this commentary focuses on the use of big data in intelligence research, the study of genes and gene-environment interactions, the interpretation [...] Read more.
This brief commentary considers the potential for new directions in intelligence research, as well as possible pitfalls associated with these approaches. Specifically, this commentary focuses on the use of big data in intelligence research, the study of genes and gene-environment interactions, the interpretation of neuroscience evidence, and the effectiveness of intelligence interventions. The major pitfalls identified include methodological and data analytic limitations, as well as concerns regarding the communication of findings to other scientists and the lay public. Full article
(This article belongs to the Special Issue Intelligence, Where to Look, Where to Go?)
Comment
Intelligence Is What the Intelligence Test Measures. Seriously
J. Intell. 2014, 2(1), 12-15; https://doi.org/10.3390/jintelligence2010012 - 28 Feb 2014
Cited by 44 | Viewed by 12155
Abstract
The mutualism model, an alternative for the g-factor model of intelligence, implies a formative measurement model in which “g” is an index variable without a causal role. If this model is accurate, the search for a genetic of brain instantiation [...] Read more.
The mutualism model, an alternative for the g-factor model of intelligence, implies a formative measurement model in which “g” is an index variable without a causal role. If this model is accurate, the search for a genetic of brain instantiation of “g” is deemed useless. This also implies that the (weighted) sum score of items of an intelligence test is just what it is: a weighted sum score. Preference for one index above the other is a pragmatic issue that rests mainly on predictive value. Full article
(This article belongs to the Special Issue Intelligence, Where to Look, Where to Go?)
Comment
Assessing Cognitive Abilities: Intelligence and More
J. Intell. 2014, 2(1), 8-11; https://doi.org/10.3390/jintelligence2010008 - 12 Feb 2014
Cited by 4 | Viewed by 4593
Abstract
In modern cognitive science, rationality and intelligence are measured using different tasks and operations. Furthermore, in several contemporary dual process theories of cognition, rationality is a more encompassing construct than intelligence. Researchers need to continue to develop measures of rational thought without regard [...] Read more.
In modern cognitive science, rationality and intelligence are measured using different tasks and operations. Furthermore, in several contemporary dual process theories of cognition, rationality is a more encompassing construct than intelligence. Researchers need to continue to develop measures of rational thought without regard to empirical correlations with intelligence. The measurement of individual differences in rationality should not be subsumed by the intelligence concept. Full article
(This article belongs to the Special Issue Intelligence, Where to Look, Where to Go?)
Comment
Intelligence: Pre-Theory and Post-Theory
J. Intell. 2014, 2(1), 6-7; https://doi.org/10.3390/jintelligence2010006 - 12 Feb 2014
Cited by 1 | Viewed by 4539
Abstract
Defining “intelligence” exemplifies a mistake that has historical precedent: confusing the role of pre-theory and post-theory definitions. In every area, pre-theory concepts give broad directions for investigation: are the movements of heavenly bodies affected by the existence of other heavenly bodies? Post-theory concepts [...] Read more.
Defining “intelligence” exemplifies a mistake that has historical precedent: confusing the role of pre-theory and post-theory definitions. In every area, pre-theory concepts give broad directions for investigation: are the movements of heavenly bodies affected by the existence of other heavenly bodies? Post-theory concepts add precision and predictability. The mistake occurs when a successful theory like Newton’s demands that its peculiar and precise theory-imbedded concept forbids competing theories: Einstein was impossible (warping of space) so long as it was assumed that all theories must be in accord with Newton’s concept (attraction across space). In psychology, Arthur Jensen made the same mistake. He gave his theory-embedded concept of g the role of executioner: the significance of every phenomenon had to be interpreted by its compatibility with g; and thus trivialized the significance of IQ gains over time. This is only one instance of a perennial demand: give us a precise definition of “intelligence” to guide our research. However, precision comes after research has generated a theory and its very precision stifles competing research. Be happy with a broad definition on the pre-theory level that lets many competing theories bloom: pre-theory precision equals post-theory poverty. Full article
(This article belongs to the Special Issue Intelligence, Where to Look, Where to Go?)
Comment
Intelligence as Trait—and State?
J. Intell. 2014, 2(1), 4-5; https://doi.org/10.3390/jintelligence2010004 - 11 Feb 2014
Cited by 9 | Viewed by 4079
Abstract
We tend to think of intelligence as trait-like. However, with increasing use of psychoactive drugs that enhance performance on psychometric tests of intelligence, investigators need to think of intelligence also as having state-like properties. Questions of the ethics of such drug use will [...] Read more.
We tend to think of intelligence as trait-like. However, with increasing use of psychoactive drugs that enhance performance on psychometric tests of intelligence, investigators need to think of intelligence also as having state-like properties. Questions of the ethics of such drug use will need to be faced in the field of high-stakes psychometric testing as they now are being faced in professional athletics. Full article
(This article belongs to the Special Issue Intelligence, Where to Look, Where to Go?)
Comment
Looking at Intelligence from So-Called Noncognitive Traits: Be Open to Change
J. Intell. 2014, 2(1), 1-3; https://doi.org/10.3390/jintelligence2010001 - 22 Jan 2014
Cited by 1 | Viewed by 4030
Abstract
Within this commentary, I will try to extend the views presented in Johnson’s, as well as Hunt and Jaeggi’s, commentaries. Both commentaries address the issue of intelligence development. I will try to broaden the discussion by including noncognitive traits as predictors of cognitive [...] Read more.
Within this commentary, I will try to extend the views presented in Johnson’s, as well as Hunt and Jaeggi’s, commentaries. Both commentaries address the issue of intelligence development. I will try to broaden the discussion by including noncognitive traits as predictors of cognitive development. These ideas are founded within the environmental enrichment hypothesis and the Openness-Fluid-Crystallized-Intelligence (OFCI) model. Full article
(This article belongs to the Special Issue Intelligence, Where to Look, Where to Go?)
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