Caudate nucleus reactivity predicts perceptual learning rate for visual feature conjunctions

Useful information in the visual environment is often contained in specific conjunctions of visual features (e.g., color and shape). The ability to quickly and accurately process such conjunctions can be learned. However, the neural mechanisms responsible for such learning remain largely unknown. It has been suggested that some forms of visual learning might involve the dopaminergic neuromodulatory system (Roelfsema et al., 2010; Seitz and Watanabe, 2005), but this hypothesis has not yet been directly tested. Here we test the hypothesis that learning visual feature conjunctions involves the dopaminergic system, using functional neuroimaging, genetic assays, and behavioral testing techniques. We use a correlative approach to evaluate potential associations between individual differences in visual feature conjunction learning rate and individual differences in dopaminergic function as indexed by neuroimaging and genetic markers. We find a significant correlation between activity in the caudate nucleus (a component of the dopaminergic system connected to visual areas of the brain) and visual feature conjunction learning rate. Specifically, individuals who showed a larger difference in activity between positive and negative feedback on an unrelated cognitive task, indicative of a more reactive dopaminergic system, learned visual feature conjunctions more quickly than those who showed a smaller activity difference. This finding supports the hypothesis that the dopaminergic system is involved in visual learning, and suggests that visual feature conjunction learning could be closely related to associative learning. However, no significant, reliable correlations were found between feature conjunction learning and genotype or dopaminergic activity in any other regions of interest.

[1]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[2]  M. Mishkin,et al.  Double dissociation of pharmacologically induced deficits in visual recognition and visual discrimination learning. , 2008, Learning & memory.

[3]  Ariel Rokem,et al.  The benefits of cholinergic enhancement during perceptual learning are long-lasting , 2013, Front. Comput. Neurosci..

[4]  Wolfram Schultz,et al.  Multiple functions of dopamine neurons , 2010, F1000 biology reports.

[5]  Eric A. Reavis,et al.  Neural correlates of context‐dependent feature conjunction learning in visual search tasks , 2016, Human Brain Mapping.

[6]  L. Epstein,et al.  Multilocus Genetic Composite Reflecting Dopamine Signaling Capacity Predicts Reward Circuitry Responsivity , 2012, The Journal of Neuroscience.

[7]  H. Heinze,et al.  Mesolimbic Functional Magnetic Resonance Imaging Activations during Reward Anticipation Correlate with Reward-Related Ventral Striatal Dopamine Release , 2008, The Journal of Neuroscience.

[8]  James T. Townsend,et al.  Methods of Modeling Capacity in Simple Processing Systems , 2014 .

[9]  M. Greenlee,et al.  Nicotine facilitates memory consolidation in perceptual learning , 2013, Neuropharmacology.

[10]  B. Kolachana,et al.  Variation in dopamine genes influences responsivity of the human reward system , 2009, Proceedings of the National Academy of Sciences.

[11]  Elizabeth M. Smigielski,et al.  dbSNP: the NCBI database of genetic variation , 2001, Nucleic Acids Res..

[12]  Peter N. C. Mohr,et al.  Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions , 2009, Proceedings of the National Academy of Sciences.

[13]  A. Anastasi Individual differences. , 2020, Annual review of psychology.

[14]  Michael X. Cohen,et al.  Individual differences in extraversion and dopamine genetics predict neural reward responses. , 2005, Brain research. Cognitive brain research.

[15]  P. Greengard,et al.  Dichotomous Dopaminergic Control of Striatal Synaptic Plasticity , 2008, Science.

[16]  M. Mishkin,et al.  Pharmacological evidence that both cognitive memory and habit formation contribute to within-session learning of concurrent visual discriminations , 2010, Neuropsychologia.

[17]  Daniel G. Dillon,et al.  Individual differences in reinforcement learning: Behavioral, electrophysiological, and neuroimaging correlates , 2008, NeuroImage.

[18]  Aaron R. Seitz,et al.  A unified model for perceptual learning , 2005, Trends in Cognitive Sciences.

[19]  Gerome Breen,et al.  Genetic Variation , 2020, Population Genetics with R.

[20]  Robert O. Duncan,et al.  Cortical Magnification within Human Primary Visual Cortex Correlates with Acuity Thresholds , 2003, Neuron.

[21]  Gudmundur A. Thorisson,et al.  The International HapMap Project Web site. , 2005, Genome research.

[22]  Takeo Watanabe,et al.  Perceptual learning rules based on reinforcers and attention , 2010, Trends in Cognitive Sciences.

[23]  T. Moore,et al.  The role of neuromodulators in selective attention , 2011, Trends in Cognitive Sciences.

[24]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[25]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[26]  Y. Nikolova,et al.  Multilocus Genetic Profile for Dopamine Signaling Predicts Ventral Striatum Reactivity , 2011, Neuropsychopharmacology.

[27]  Eric A. Reavis,et al.  Neural mechanisms of feature conjunction learning: Enduring changes in occipital cortex after a week of training , 2014, Human brain mapping.

[28]  Ethan S. Bromberg-Martin,et al.  Dopamine in Motivational Control: Rewarding, Aversive, and Alerting , 2010, Neuron.

[29]  Paolo Calabresi,et al.  Dopamine-mediated regulation of corticostriatal synaptic plasticity , 2007, Trends in Neurosciences.

[30]  M. Merzenich,et al.  Cortical remodelling induced by activity of ventral tegmental dopamine neurons , 2001, Nature.

[31]  Aaron R. Seitz,et al.  A common framework for perceptual learning , 2007, Current Opinion in Neurobiology.

[32]  Samuel M. McClure,et al.  BOLD Responses Reflecting Dopaminergic Signals in the Human Ventral Tegmental Area , 2008, Science.

[33]  T. Moore,et al.  CONTROL OF VISUAL CORTICAL SIGNALS BY PREFRONTAL DOPAMINE , 2011, Nature.

[34]  Matthew Rizzo,et al.  Impaired visual search in drivers with Parkinson's disease , 2006, Annals of neurology.

[35]  G. Gerhardt,et al.  In Vivo Assessment of Dopamine Uptake in Rat Medial Prefrontal Cortex: Comparison with Dorsal Striatum and Nucleus Accumbens , 1995, Journal of neurochemistry.

[36]  M. Silver,et al.  Cholinergic Enhancement Augments Magnitude and Specificity of Visual Perceptual Learning in Healthy Humans , 2010, Current Biology.

[37]  A. Cowey,et al.  Post-decision wagering objectively measures awareness , 2007, Nature Neuroscience.

[38]  J. Bakin,et al.  Induction of a physiological memory in the cerebral cortex by stimulation of the nucleus basalis. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[39]  D. Öngür,et al.  Perceptual training strongly improves visual motion perception in schizophrenia , 2011, Brain and Cognition.

[40]  G. E. Alexander,et al.  Parallel organization of functionally segregated circuits linking basal ganglia and cortex. , 1986, Annual review of neuroscience.

[41]  S. Al-Adawi,et al.  Brief Communication: DAT1 VNTR Allele Frequencies in the Omani Population , 2005, Human biology.

[42]  J. Reichenbach,et al.  Structure-function relationships in the context of reinforcement-related learning: a combined diffusion tensor imaging–functional magnetic resonance imaging study , 2010, Neuroscience.

[43]  M. Kilgard,et al.  Cortical map reorganization enabled by nucleus basalis activity. , 1998, Science.

[44]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[45]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[46]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[47]  A. Björklund,et al.  Dopamine neuron systems in the brain: an update , 2007, Trends in Neurosciences.

[48]  M. Castelo‐Branco,et al.  The role of the basal ganglia in implicit contextual learning: A study of Parkinson's disease , 2009, Neuropsychologia.

[49]  Michael J. Frank,et al.  Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning , 2007, Proceedings of the National Academy of Sciences.

[50]  Panayiota Poirazi,et al.  Computational modeling of the effects of amyloid-beta on release probability at hippocampal synapses , 2013, Front. Comput. Neurosci..

[51]  Sebastian Heinzel,et al.  Association between reward‐related activation in the ventral striatum and trait reward sensitivity is moderated by dopamine transporter genotype , 2011, Human brain mapping.

[52]  Peter Dayan,et al.  A Neural Substrate of Prediction and Reward , 1997, Science.

[53]  Anders M. Dale,et al.  Sequence-independent segmentation of magnetic resonance images , 2004, NeuroImage.

[54]  R. Dolan,et al.  Influence of Dopaminergically Mediated Reward on Somatosensory Decision-Making , 2009, PLoS biology.

[55]  Mark W Greenlee,et al.  Pretraining Cortical Thickness Predicts Subsequent Perceptual Learning Rate in a Visual Search Task. , 2016, Cerebral cortex.