Transient Neural Activation of Abstract Relations on an Incidental Analogy Task

Although a large proportion of the lexicon consists of abstract concepts, little is known about how they are represented by the brain. Here, we investigated how the mind represents relations shared between sets of mental representations that are superficially unrelated, such as car–engine and dog–tongue, but that nonetheless share a more general, abstract relation, such as whole–part. Participants saw a pair of words on each trial and were asked to indicate whether they could think of a relation between them. Importantly, they were not explicitly asked whether different word pairs shared the same relation, as in analogical reasoning tasks. We observed representational similarity for abstract relations in regions in the “conceptual hub” network, even when controlling for semantic relatedness between word pairs. By contrast, we did not observe representational similarity in regions previously implicated in explicit analogical reasoning. A given relation was sometimes repeated across sequential word pairs, allowing us to test for behavioral and neural priming of abstract relations. Indeed, we observed faster RTs and greater representational similarity for primed than unprimed trials, suggesting that mental representations of abstract relations are transiently activated on this incidental analogy task. Finally, we found a significant correlation between behavioral and neural priming across participants. To our knowledge, this is the first study to investigate relational priming using functional neuroimaging and to show that neural representations are strengthened by relational priming. This research shows how abstract concepts can be brought to mind momentarily, even when not required for task performance.

[1]  Andrew C. Connolly,et al.  Mental models use common neural spatial structure for spatial and abstract content , 2020, Communications Biology.

[2]  John E. Hummel,et al.  Distributed representations of structure: A theory of analogical access and mapping. , 1997 .

[3]  G. Lupyan,et al.  Language is more abstract than you think, or, why aren't languages more iconic? , 2018, Philosophical Transactions of the Royal Society B: Biological Sciences.

[4]  Christophe Ley,et al.  Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median , 2013 .

[5]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.

[6]  Ming Bo Cai,et al.  Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias , 2019, PLoS Comput. Biol..

[7]  Jonathan A. Fugelsang,et al.  Automatic activation of categorical and abstract analogical relations in analogical reasoning , 2006, Memory & cognition.

[8]  K. Dunbar The analogical paradox: Why analogy is so easy in naturalistic settings yet so difficult in the psychological laboratory. , 2001 .

[10]  Daniel C. Krawczyk The cognition and neuroscience of relational reasoning , 2012, Brain Research.

[11]  Penka Hristova,et al.  Unintentional and efficient relational priming , 2015, Memory & Cognition.

[12]  L. Tyler,et al.  Repetition suppression and semantic enhancement: An investigation of the neural correlates of priming , 2006, Neuropsychologia.

[13]  John T. Bruer,et al.  How Children Learn , 1967 .

[14]  Andrew C. Connolly,et al.  Putting the pieces together: Generating a novel representational space through deductive reasoning , 2018, NeuroImage.

[15]  Dedre Gentner,et al.  Structure-Mapping: A Theoretical Framework for Analogy , 1983, Cogn. Sci..

[16]  Barbara A. Spellman,et al.  Analogical priming via semantic relations , 2001, Memory & cognition.

[17]  W. A. Brownell,et al.  How children learn information, concepts, and generalizations. , 1950 .

[18]  L. Barsalou Grounded cognition. , 2008, Annual review of psychology.

[19]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[20]  Stewart H Mostofsky,et al.  Moderate variability in stimulus presentation improves motor response control , 2009, Journal of clinical and experimental neuropsychology.

[21]  T. Rogers,et al.  The neural and computational bases of semantic cognition , 2016, Nature Reviews Neuroscience.

[22]  Diane Pecher,et al.  Abstract concepts: sensory-motor grounding, metaphors, and beyond , 2011 .

[23]  S. Slotnick Cluster success: fMRI inferences for spatial extent have acceptable false-positive rates , 2017, Cognitive neuroscience.

[24]  Henry Miller,et al.  The Neurobiology of the , 1993 .

[25]  Zachary Estes,et al.  Attributive and relational processes in nominal combination , 2003 .

[26]  D. Gentner,et al.  The analogical mind : perspectives from cognitive science , 2001 .

[27]  Nina K Simms,et al.  Analogy, higher order thinking, and education. , 2015, Wiley interdisciplinary reviews. Cognitive science.

[28]  J. Binder In defense of abstract conceptual representations , 2016, Psychonomic Bulletin & Review.

[29]  Wei Wu,et al.  Organizational Principles of Abstract Words in the Human Brain , 2018, Cerebral cortex.

[30]  Rand R. Wilcox,et al.  Inferences Based on a Skipped Correlation Coefficient , 2004 .

[31]  Charan Ranganath,et al.  Representational Similarity Analyses: A Practical Guide for Functional MRI Applications , 2018 .

[32]  Erik A. Wing,et al.  Excitatory TMS modulates memory representations , 2018, Cognitive neuroscience.

[33]  L. Tyler,et al.  Representational Similarity Analysis Reveals Commonalities and Differences in the Semantic Processing of Words and Objects , 2013, The Journal of Neuroscience.

[34]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[35]  Marvin M. Chun,et al.  The Effect of Attention on Repetition Suppression and Multivoxel Pattern Similarity , 2013, Journal of Cognitive Neuroscience.

[36]  Erik A. Wing,et al.  Neural basis of goal‐driven changes in knowledge activation , 2018, The European journal of neuroscience.

[37]  Bryan J. Matlen,et al.  Analogical Reasoning in the Classroom: Insights from Cognitive Science. , 2015 .

[38]  Carter Wendelken,et al.  Transitive Inference: Distinct Contributions of Rostrolateral Prefrontal Cortex and the Hippocampus , 2010, Journal of Cognitive Neuroscience.

[39]  K. Begolli,et al.  Teaching Mathematics by Comparison: Analog Visibility as a Double-Edged Sword. , 2016 .

[40]  James K. Kroger,et al.  Rostrolateral Prefrontal Cortex Involvement in Relational Integration during Reasoning , 2001, NeuroImage.

[41]  B. Mesquita,et al.  Adjustment to Chronic Diseases and Terminal Illness Health Psychology : Psychological Adjustment to Chronic Disease , 2006 .

[42]  David Badre,et al.  Analogical reasoning and prefrontal cortex: evidence for separable retrieval and integration mechanisms. , 2004, Cerebral cortex.

[43]  Max C. Keuken,et al.  The impact of MRI scanner environment on perceptual decision-making , 2015, Behavior Research Methods.

[44]  Alexander Borst,et al.  How does Nature Program Neuron Types? , 2008, Front. Neurosci..

[45]  C. Dunst,et al.  Natural Learning Opportunities for Infants, Toddlers, and Preschoolers , 2001 .

[46]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[47]  Mark Johnson,et al.  The Metaphorical Structure of the Human Conceptual System , 1980, Cogn. Sci..

[48]  Russell A. Poldrack,et al.  Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses , 2012, NeuroImage.

[49]  R. Chaffin,et al.  Cognitive and Psychometric Analysis of Analogical Problem Solving , 1990 .

[50]  Guillaume A. Rousselet,et al.  Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox , 2012, Front. Psychology.

[51]  Robert Vargas,et al.  Neural Representations of Abstract Concepts: Identifying Underlying Neurosemantic Dimensions. , 2020, Cerebral cortex.

[52]  J. S. Guntupalli,et al.  The Representation of Biological Classes in the Human Brain , 2012, The Journal of Neuroscience.

[53]  Silvia A. Bunge,et al.  Evolutionary and Developmental Changes in the Lateral Frontoparietal Network: A Little Goes a Long Way for Higher-Level Cognition , 2014, Neuron.

[54]  Noah A. Shamosh,et al.  Frontopolar cortex mediates abstract integration in analogy , 2006, Brain Research.

[55]  C. Wendelken,et al.  Rostrolateral prefrontal cortex: Domain‐general or domain‐sensitive? , 2012, Human brain mapping.

[56]  Cameron S. Carter,et al.  Brain Is to Thought as Stomach Is to ??: Investigating the Role of Rostrolateral Prefrontal Cortex in Relational Reasoning , 2008, Journal of Cognitive Neuroscience.

[57]  Lara L. Jones,et al.  Priming via relational similarity: A COPPER HORSE is faster when seen through a GLASS EYE , 2006 .

[58]  R. Levy,et al.  General and specialized brain correlates for analogical reasoning: A meta‐analysis of functional imaging studies , 2016, Human brain mapping.

[59]  Ying Nian Wu,et al.  Emergence of analogy from relation learning , 2019, Proceedings of the National Academy of Sciences.

[60]  Jonathan A. Fugelsang,et al.  The Micro-Category account of analogy , 2008, Cognition.

[61]  Jonathan A. Fugelsang,et al.  Connecting long distance: semantic distance in analogical reasoning modulates frontopolar cortex activity. , 2010, Cerebral cortex.

[62]  C. Wendelken,et al.  Neuroscientific insights into the development of analogical reasoning , 2017, Developmental science.

[63]  Rutvik H. Desai,et al.  The neurobiology of semantic memory , 2011, Trends in Cognitive Sciences.

[64]  K. Holyoak,et al.  Schema induction and analogical transfer , 1983, Cognitive Psychology.

[65]  Dedre Gentner,et al.  Nonintentional analogical inference in text comprehension , 2007, Memory & cognition.