Representational geometry: integrating cognition, computation, and the brain

The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure

Exploratory factor analysis with structured residuals for brain network data.

Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high-dimensional data to a small number of underlying variables. Techniques such as exploratory factor analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA by using structured residuals (EFAST), and (c) apply this technique to three large and varied brain-imaging network datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets

Why Your Mind Is Like a Shark: Testing the Idea of Mutualism

We want to understand how children get so much better at certain cognitive abilities like reading, writing, and problem solving as they get older. To better understand this, we followed hundreds of children across a period of years, to see how abilities like problem solving and vocabulary changed over time. We found that having good vocabulary to start with made children’s problem solving develop more quickly. It also worked the other way around: being better at problem solving meant children were quicker to learn new words. In other words, each cognitive ability may help other abilities develop. This idea is called mutualism. We were very excited by this discovery, because it can help us understand how children get better at things they never practice directly, and how teachers can better help children who find certain school topics more challenging

Letting the daylight in: Reviewing the reviewers and other ways to maximize transparency in science

With the emergence of online publishing, opportunities to maximize transparency of scientific research have grown considerably. However, these possibilities are still only marginally used. We argue for the implementation of (1) peer-reviewed peer review, (2) transparent editorial hierarchies, and (3) online data publication. First, peer-reviewed peer review entails a community-wide review system in which reviews are published online and rated by peers. This ensures accountability of reviewers, thereby increasing academic quality of reviews. Second, reviewers who write many highly regarded reviews may move to higher editorial positions. Third, online publication of data ensures the possibility of independent verification of inferential claims in published papers. This counters statistical errors and overly positive reporting of statistical results. We illustrate the benefits of these strategies by discussing an example in which the classical publication system has gone awry, namely controversial IQ research. We argue that this case would have likely been avoided using more transparent publication practices. We argue that the proposed system leads to better reviews, meritocratic editorial hierarchies, and a higher degree of replicability of statistical analyses

Mental Rotation is Not Easily Cognitively Penetrable

When participants take part in mental imagery experiments, are they using their "tacit knowledge" of perception to mimic what they believe should occur in the corresponding perceptual task? Two experiments were conducted to examine whether such an account can be applied to mental imagery in general. These experiments both examined tasks that required participants to "mentally rotate" stimuli. In Experiment 1, instructions led participants to believe that they could re-orient shapes in one step or avoid re-orienting the shapes altogether. Regardless of instruction type, response times increased linearly with increasing rotation angles. In Experiment 2, participants first observed novel objects rotating at different speeds, and then performed a mental rotation task with those objects. The speed of perceptually demonstrated rotation did not affect the speed of mental rotation. We argue that tacit knowledge cannot explain mental imagery results in general, and that in particular the mental rotation effect reflects the nature of the underlying internal representation and processes that transform it, rather than participants’ pre-existing knowledge.Psycholog

Mutualistic Coupling Between Vocabulary and Reasoning in Young Children: A Replication and Extension of the Study by Kievit et al. (2017).

Recent work suggests that the positive manifold of individual differences may arise, or be amplified, by a mechanism called mutualism. Kievit et al. (2017) showed that a latent change score implementation of the mutualism model outperformed alternative models, demonstrating positive reciprocal interactions between vocabulary and reasoning during development. Here, we replicated these findings in a cohort of children ( N = 227, 6-8 years old) and expanded the findings in three directions. First, a third wave of data was included, and the findings were robust to alternative model specifications. Second, a simulation demonstrated that data sets of similar magnitude and distributional properties could have, in principle, favored alternative models with close to 100% power. Third, we found support for the hypothesis that mutualistic-coupling effects are stronger and self-feedback parameters weaker in younger children. Together, these findings replicated the work of Kievit et al. (2017) and further support the hypothesis that mutualism supports cognitive development

A Hierarchical Watershed Model of Fluid Intelligence in Childhood and Adolescence.

Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modeled the neurocognitive architecture of fluid intelligence in two cohorts: the Centre for Attention, Leaning and Memory sample (CALM) (N = 551, aged 5-17 years) and the Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) (N = 335, aged 6-17 years). We used multivariate structural equation modeling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7-12 years. This age effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects

A Practical Guide to Variable Selection in Structural Equation Modeling by Using Regularized Multiple-Indicators, Multiple-Causes Models

Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real-world behavioral data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This difficult modeling scenario entails large models with a limited number of observations given the number of parameters. Here, we describe a particular strategy to overcome this challenge: regularization, a method of penalizing model complexity during estimation. Regularization has proven to be a viable option for estimating parameters in this small-sample, many-predictors setting, but so far it has been used mostly in linear regression models. We show how to integrate regularization within structural equation models, a popular analytic approach in psychology. We first describe the rationale behind regularization in regression contexts and how it can be extended to regularized structural equation modeling. We then evaluate our approach using a simulation study, showing that regularized structural equation modeling outperforms traditional structural equation modeling in situations with a large number of predictors and a small sample size. Next, we illustrate the power of this approach in two empirical examples: modeling the neural determinants of visual short-term memory and identifying demographic correlates of stress, anxiety, and depression.R. A. Kievit is supported by the Sir Henry Wellcome Trust (Grant 107392/Z/15/Z) and by an MRC Programme Grant (SUAG/014/RG91365). This project has also received funding from the European Union’s Horizon 2020 Research and Innovation program (Grant 732592)

Age Differentiation within Gray Matter, White Matter, and between Memory and White Matter in an Adult Life Span Cohort.

It is well established that brain structures and cognitive functions change across the life span. A long-standing hypothesis called "age differentiation" additionally posits that the relations between cognitive functions also change with age. To date, however, evidence for age-related differentiation is mixed, and no study has examined differentiation of the relationship between brain and cognition. Here we use multigroup structural equation models (SEMs) and SEM trees to study differences within and between brain and cognition across the adult life span (18-88 years) in a large (N > 646, closely matched across sexes), population-derived sample of healthy human adults from the Cambridge Centre for Ageing and Neuroscience (www.cam-can.org). After factor analyses of gray matter volume (from T1- and T2-weighted MRI) and white matter organization (fractional anisotropy from diffusion-weighted MRI), we found evidence for the differentiation of gray and white matter, such that the covariance between brain factors decreased with age. However, we found no evidence for age differentiation among fluid intelligence, language, and memory, suggesting a relatively stable covariance pattern among cognitive factors. Finally, we observed a specific pattern of age differentiation between brain and cognitive factors, such that a white matter factor, which loaded most strongly on the hippocampal cingulum, became less correlated with memory performance in later life. These patterns are compatible with the reorganization of cognitive functions in the face of neural decline, and/or with the emergence of specific subpopulations in old age.SIGNIFICANCE STATEMENT The theory of age differentiation posits age-related changes in the relationships among cognitive domains, either weakening (differentiation) or strengthening (dedifferentiation), but evidence for this hypothesis is mixed. Using age-varying covariance models in a large cross-sectional adult life span sample, we found age-related reductions in the covariance among both brain measures (neural differentiation), but no covariance change among cognitive factors of fluid intelligence, language, and memory. We also observed evidence of uncoupling (differentiation) between a white matter factor and cognitive factors in older age, most strongly for memory. Together, our findings support age-related differentiation as a complex, multifaceted pattern that differs for brain and cognition, and discuss several mechanisms that might explain the changing relationship between brain and cognition

Greater lifestyle engagement is associated with better age-adjusted cognitive abilities.

Previous evidence suggests that modifiable lifestyle factors, such as engagement in leisure activities, might slow the age-related decline of cognitive functions. Less is known, however, about which aspects of lifestyle might be particularly beneficial to healthy cognitive ageing, and whether they are associated with distinct cognitive domains (e.g. fluid and crystallized abilities) differentially. We investigated these questions in the cross-sectional Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data (N = 708, age 18-88), using data-driven exploratory structural equation modelling, confirmatory factor analyses, and age-residualized measures of cognitive differences across the lifespan. Specifically, we assessed the relative associations of the following five lifestyle factors on age-related differences of fluid and crystallized age-adjusted abilities: education/SES, physical health, mental health, social engagement, and intellectual engagement. We found that higher education, better physical and mental health, more social engagement and a greater degree of intellectual engagement were each individually correlated with better fluid and crystallized cognitive age-adjusted abilities. A joint path model of all lifestyle factors on crystallized and fluid abilities, which allowed a simultaneous assessment of the lifestyle domains, showed that physical health, social and intellectual engagement and education/SES explained unique, complementary variance, but mental health did not make significant contributions above and beyond the other four lifestyle factors and age. The total variance explained for fluid abilities was 14% and 16% for crystallized abilities. Our results are compatible with the hypothesis that intellectually and physically challenging as well as socially engaging activities are associated with better crystallized and fluid performance across the lifespan