Fluid Intelligence Allows Flexible Recruitment of the Parieto-Frontal Network in Analogical Reasoning

Fluid intelligence is the ability to think flexibly and to understand abstract relations. People with high fluid intelligence (hi-fluIQ) perform better in analogical reasoning tasks than people with average fluid intelligence (ave-fluIQ). Although previous neuroimaging studies reported involvement of parietal and frontal brain regions in geometric analogical reasoning (which is a prototypical task for fluid intelligence), however, neuroimaging findings on geometric analogical reasoning in hi-fluIQ are sparse. Furthermore, evidence on the relation between brain activation and intelligence while solving cognitive tasks is contradictory. The present study was designed to elucidate the cerebral correlates of geometric analogical reasoning in a sample of hi-fluIQ and ave-fluIQ high school students. We employed a geometric analogical reasoning task with graded levels of task difficulty and confirmed the involvement of the parieto-frontal network in solving this task. In addition to characterizing the brain regions involved in geometric analogical reasoning in hi-fluIQ and ave-fluIQ, we found that blood oxygenation level dependency (BOLD) signal changes were greater for hi-fluIQ than for ave-fluIQ in parietal brain regions. However, ave-fluIQ showed greater BOLD signal changes in the anterior cingulate cortex and medial frontal gyrus than hi-fluIQ. Thus, we showed that a similar network of brain regions is involved in geometric analogical reasoning in both groups. Interestingly, the relation between brain activation and intelligence is not mono-directional, but rather, it is specific for each brain region. The negative brain activation–intelligence relationship in frontal brain regions in hi-fluIQ goes along with a better behavioral performance and reflects a lower demand for executive monitoring compared to ave-fluIQ individuals. In conclusion, our data indicate that flexibly modulating the extent of regional cerebral activity is characteristic for fluid intelligence

Learning, Arts, and the Brain: The Dana Consortium Report on Arts and Cognition

Reports findings from multiple neuroscientific studies on the impact of arts training on the enhancement of other cognitive capacities, such as reading acquisition, sequence learning, geometrical reasoning, and memory

Neural Correlates of Fluid Reasoning in Children and Adults

Fluid reasoning, or the capacity to think logically and solve novel problems, is central to the development of human cognition, but little is known about the underlying neural changes. During the acquisition of event-related fMRI data, children aged 6–13 (N = 16) and young adults (N = 17) performed a task in which they were asked to identify semantic relationships between drawings of common objects. On semantic problems, participants indicated which of five objects was most closely semantically related to a cued object. On analogy problems, participants solved a visual propositional analogy (e.g., shoe is to foot as glove is to…?) by indicating which of four objects would complete the problem; these problems required integration of two semantic relations, or relational integration. Our prior research on analogical reasoning in adults implicated left anterior ventrolateral prefrontal cortex (VLPFC) in the controlled retrieval of individual semantic relationships, and rostrolateral prefrontal cortex (RLPFC) in relational integration. In this study, age-related changes in the recruitment of VLPFC, temporal cortex, and other cortical regions were observed during the retrieval of individual semantic relations. In contrast, age-related changes in RLPFC function were observed during relational integration. Children aged 6–13 engage RLPFC too late in the analogy trials to influence their behavioral responses, suggesting that important changes in RLPFC function take place during adolescence

Cathodal transcranial direct current stimulation over the right dorsolateral prefrontal cortex cancels out the cost of selective retrieval on subsequent analogical reasoning

Analogical reasoning involves mapping the relation between two concepts within a specific field into a new domain to selectively retrieve a possible solution. Neuroimaging studies have shown that both selective retrieval and reasoning by analogy are related to activity in prefrontal regions such as the dorsolateral prefrontal cortex (DLPFC). In the present study, we investigate the role of the right DLPFC in modulating memory accessibility and its impact on analogical reasoning by using transcranial direct current stimulation (tDCS). Participants performed a four-term reasoning task after performing repeated selective retrieval of previously presented items, some of which could be used as solutions in the analogical test. During selective retrieval, half of the participants received cathodal tDCS over the right DLPFC and the other half received sham stimulation. The results reveal that whereas the sham group showed the expected cost in performance that is associated with selective retrieval, the cathodal group did not exhibit such an impairment in reasoning. No general effects of tDCS on analogical performance were observed. Altogether, our results support the involvement of the right DLPFC as a core component of a control network that selectively contributes to the retrieval component of analogical reasoning, but with little role in mapping relations between different domains.This work was supported by the Spanish Ministry of Education and Science and Ministry of Economy, Industry and Competitiveness grants FPU014/07066 to TMV, PSI2015-65502-C2-1-P and PGC2018-093786-B-I00 to TB, and PSI2015-65502-C2-2-P to CJGA

Exploring the Neural Mechanisms of Physics Learning

This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can be grounded in educational theory and pedagogy, and provide deep insights into the neural mechanisms by which students learn physics

Network Structure and Dynamics of the Mental Workspace

The conscious manipulation of mental representations is central to many creative and uniquely human abilities. How does the human brain mediate such flexible mental operations? Here, multivariate pattern analysis of functional MRI data reveals a widespread neural network that performs specific mental manipulations on the contents of visual imagery. Evolving patterns of neural activity within this mental workspace track the sequence of informational transformations carried out by these manipulations. The network switches between distinct connectivity profiles as representations are maintained or manipulated

An Overview of Models for Response Times and Processes in Cognitive Tests.

Response times (RTs) are a natural kind of data to investigate cognitive processes underlying cognitive test performance. We give an overview of modeling approaches and of findings obtained with these approaches. Four types of models are discussed: response time models (RT as the sole dependent variable), joint models (RT together with other variables as dependent variable), local dependency models (with remaining dependencies between RT and accuracy), and response time as covariate models (RT as independent variable). The evidence from these approaches is often not very informative about the specific kind of processes (other than problem solving, information accumulation, and rapid guessing), but the findings do suggest dual processing: automated processing (e.g., knowledge retrieval) vs. controlled processing (e.g., sequential reasoning steps), and alternative explanations for the same results exist. While it seems well-possible to differentiate rapid guessing from normal problem solving (which can be based on automated or controlled processing), further decompositions of response times are rarely made, although possible based on some of model approaches

Neural and cognitive underpinnings of counterintuitive science and maths reasoning in adolescence

Reasoning about counterintuitive concepts in science and maths is thought to require suppressing naïve theories, prior knowledge or misleading perceptual cues through inhibitory control. Neuroimaging research has shown recruitment of prefrontal cortex regions during counterintuitive reasoning, which has been interpreted as evidence of inhibitory control processes. However, the results are inconsistent across studies and have not been directly compared to behaviour or brain activity during inhibitory control tasks. In this functional magnetic resonance imaging study, 34 adolescents (aged 11-15 years) answered science and maths problems and completed response inhibition tasks (simple and complex go/no-go) and an interference control task (numerical Stroop). Increased blood-oxygen level dependent (BOLD) signal was observed in parietal (Brodmann area (BA) 40) and prefrontal (BA 8, 45/47) cortex regions in counterintuitive problems compared to control problems, where no counterintuitive reasoning was required, and in two parietal clusters when comparing correct counterintuitive reasoning to giving the incorrect intuitive response. There was partial overlap between increases in BOLD signal in the complex response inhibition and interference control tasks and the science and maths contrasts. However, multivariate analyses suggested overlapping neural substrates in the parietal cortex only, in regions typically associated with working memory and visuospatial attentional demands rather than specific to inhibitory control. These results highlight the importance of using localiser tasks and a range of analytic approach to investigate to what extent common neural networks underlie performance of different cognitive tasks and suggests visuospatial attentional skills may support counterintuitive reasoning in science and maths

Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization.

Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy