Frontal theta as a mechanism for cognitive control

Trends in Cognitive Sciences

Volumn 18, Issue 8, 2014, Pages 414-421

  Cavanagh, James F ^a   Frank, Michael J ^b  

^a UNIVERSITY OF NEW MEXICO   (United States)

^b BROWN UNIVERSITY   (United States)

Author keywords

Cognitive control; Computational modeling; ERP; Frontal cortex; Prediction error; Theta

Indexed keywords

BRAIN; ELECTROPHYSIOLOGY; ENTERPRISE RESOURCE PLANNING;

COGNITIVE CONTROL; COMPUTATIONAL MODEL; FRONTAL CORTEX; PREDICTION ERRORS; THETA;

ALGORITHMS;

BIOPHYSICS; BRAIN REGION; ELECTROENCEPHALOGRAPHY; EXECUTIVE FUNCTION; HUMAN; INTERPERSONAL COMMUNICATION; NERVE POTENTIAL; OSCILLATION; PREFRONTAL CORTEX; REVIEW; SUBTHALAMIC NUCLEUS; THETA RHYTHM; BRAIN MAPPING; COGNITION; COMPUTER SIMULATION; FRONTAL LOBE; PHYSIOLOGY;

BRAIN MAPPING; COGNITION; COMPUTER SIMULATION; ELECTROENCEPHALOGRAPHY; FRONTAL LOBE; HUMANS; THETA RHYTHM;

EID: 84904718962     PISSN: 13646613     EISSN: 1879307X     Source Type: Journal    
DOI: 10.1016/j.tics.2014.04.012     Document Type: Review

References (99)

1. Dreher J-C. Neural coding of computational factors affecting decision making. Prog. Brain Res. 2013, 202:289-320.
2. Rushworth M.F., Behrens T.E. Choice, uncertainty and value in prefrontal and cingulate cortex. Nat. Neurosci. 2008, 11:389-397.
3. Gold J.I., Shadlen M.N. The neural basis of decision making. Annu. Rev. Neurosci. 2007, 30:535-574.
4. Fries P. Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu. Rev. Neurosci. 2009, 32:209-224.
5. Siegel M., et al. Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 2012, 13:121-134.
6. Raghavachari S., et al. Theta oscillations in human cortex during a working-memory task: evidence for local generators. J. Neurophysiol. 2006, 95:1630-1638.
7. Jacobs J., et al. EEG oscillations and recognition memory: theta correlates of memory retrieval and decision making. Neuroimage 2006, 32:978-987.
8. Itthipuripat S., et al. Frontal theta is a signature of successful working memory manipulation. Exp. Brain Res. 2013, 224:255-262.
9. Cavanagh J.F., et al. Theta lingua franca: a common mid-frontal substrate for action monitoring processes. Psychophysiology 2012, 49:220-238.
10. Rutishauser U., et al. Human memory strength is predicted by theta-frequency phase-locking of single neurons. Nature 2010, 464:903-907.
11. Gehring W.J., et al. The error-related negativity (ERN/Ne). Oxford Handbook Of Event-Related Potential Components 2012, 231-291. Oxford University Press. S.J. Luck, E. Kappenman (Eds.).
12. Folstein J.R., Van Petten C. Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology 2008, 45:152-170.
13. Walsh M.M., Anderson J.R. Learning from experience: event-related potential correlates of reward processing, neural adaptation, and behavioral choice. Neurosci. Biobehav. Rev. 2012, 36:1870-1884.
14. Hanslmayr S., et al. The electrophysiological dynamics of interference during the Stroop task. J. Cogn. Neurosci. 2008, 20:215-225.
15. Cavanagh J.F., et al. Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring. J. Neurosci. 2009, 29:98-105.
16. Cohen M.X., et al. Unconscious errors enhance prefrontal-occipital oscillatory synchrony. Front. Hum. Neurosci. 2009, 3:54.
17. Cavanagh J.F., et al. Frontal theta links prediction errors to behavioral adaptation in reinforcement learning. Neuroimage 2010, 49:3198-3209.
18. Cohen M.X., Cavanagh J.F. Single-trial regression elucidates the role of prefrontal theta oscillations in response conflict. Front. Psychol. 2011, 2:30.
19. Cohen M.X., van Gaal S. Dynamic interactions between large-scale brain networks predict behavioral adaptation after perceptual errors. Cereb. Cortex 2013, 23:1061-1072.
20. Nigbur R., et al. Theta dynamics reveal domain-specific control over stimulus and response conflict. J. Cogn. Neurosci. 2012, 24:1264-1274.
21. Van de Vijver I., et al. Frontal oscillatory dynamics predict feedback learning and action adjustment. J. Cogn. Neurosci. 2011, 23:4106-4121.
22. Van Driel J., et al. Not all errors are alike: theta and alpha EEG dynamics relate to differences in error-processing dynamics. J. Neurosci. 2012, 32:16795-16806.
23. Narayanan N.S., et al. Common medial frontal mechanisms of adaptive control in humans and rodents. Nat. Neurosci. 2013, 16:1888-1895.
24. Anguera J., a, et al. Video game training enhances cognitive control in older adults. Nature 2013, 501:97-101.
25. Smit A.S., et al. Mental and physical effort affect vigilance differently. Int. J. Psychophysiol. 2005, 57:211-217.
26. Mizuki Y., et al. Differential responses to mental stress in high and low anxious normal humans assessed by frontal midline theta activity. Int. J. Psychophysiol. 1992, 12:169-178.
27. Gehring W.J., et al. A neural system for error-detection and compensation. Psychol. Sci. 1993, 4:385-390.
28. Walsh M.M., Anderson J.R. Learning from delayed feedback: neural responses in temporal credit assignment. Cogn. Affect. Behav. Neurosci. 2011, 11:131-143.
29. Yeung N., et al. The neural basis of error detection: conflict monitoring and the error-related negativity. Psychol. Rev. 2004, 111:931-959.
30. Cohen M.X., Ranganath C. Reinforcement learning signals predict future decisions. J. Neurosci. 2007, 27:371-378.
31. Debener S., et al. Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring. J. Neurosci. 2005, 25:11730-11737.
32. Hauser T.U., et al. The feedback-related negativity (FRN) revisited: new insights into the localization, meaning and network organization. Neuroimage 2014, 84:159-168.
33. Wang C., et al. Responses of human anterior cingulate cortex microdomains to error detection, conflict monitoring, stimulus-response mapping, familiarity, and orienting. J. Neurosci. 2005, 25:604-613.
34. Tsujimoto T., et al. Theta oscillations in primate prefrontal and anterior cingulate cortices in forewarned reaction time tasks. J. Neurophysiol. 2010, 103:827-843.
35. Womelsdorf T., et al. Theta-activity in anterior cingulate cortex predicts task rules and their adjustments following errors. Proc. Natl. Acad. Sci. U.S.A. 2010, 107:5248-5253.
36. Phillips J.M., et al. A long-range fronto-parietal 5- to 10-Hz network predicts 'top-down' controlled guidance in a task-switch paradigm. Cereb. Cortex 2014, 24:1996-2008.
37. Wang X. Neurophysiological and computational principles of cortical rhythms in cognition. Physiol. Rev. 2010, 90:1195-1268.
38. Nadasdy Z. Binding by asynchrony: the neuronal phase code. Front. Neurosci. 2010, 4:1-11.
39. Buzsáki G., Draguhn A. Neuronal oscillations in cortical networks. Science 2004, 304:1926-1929.
40. Buzsáki G. Neural syntax: cell assemblies, synapsembles, and readers. Neuron 2010, 68:362-385.
41. Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. 2005, 9:474-480.
42. Womelsdorf T., et al. Selective theta-synchronization of choice-relevant information subserves goal-directed behavior. Front. Hum. Neurosci. 2010, 4:210.
43. Cohen M.X. Error-related medial frontal theta activity predicts cingulate-related structural connectivity. Neuroimage 2011, 55:1373-1383.
44. Uhlhaas P.J., et al. Neural synchrony and the development of cortical networks. Trends Cogn. Sci. 2010, 14:72-80.
45. Rothé M., et al. Coordination of high gamma activity in anterior cingulate and lateral prefrontal cortical areas during adaptation. J. Neurosci. 2011, 31:11110-11117.
46. Benchenane K., et al. Coherent theta oscillations and reorganization of spike timing in the hippocampal-prefrontal network upon learning. Neuron 2010, 66:921-936.
47. Remondes M., Wilson M.a. Cingulate-hippocampus coherence and trajectory coding in a sequential choice task. Neuron 2013, 80:1277-1289.
48. Jones M.W., Wilson M.a. Phase precession of medial prefrontal cortical activity relative to the hippocampal theta rhythm. Hippocampus 2005, 15:867-873.
49. Shackman A.J., et al. The integration of negative affect, pain and cognitive control in the cingulate cortex. Nat. Rev. Neurosci. 2011, 12:154-167.
50. Arnal L.H., Giraud A-L. Cortical oscillations and sensory predictions. Trends Cogn. Sci. 2012, 16:390-398.
51. Fell J., Axmacher N. The role of phase synchronization in memory processes. Nat. Rev. Neurosci. 2011, 12:105-118.
52. Jensen O., Lisman J.E. Hippocampal sequence-encoding driven by a cortical multi-item working memory buffer. Trends Neurosci. 2005, 28:67-72.
53. Canolty R.T., et al. High gamma power is phase-locked to theta oscillations in human neocortex. Science 2006, 313:1626-1628.
54. Liebe S., et al. Theta coupling between V4 and prefrontal cortex predicts visual short-term memory performance. Nat. Neurosci. 2012, 15:456-462. S1-S2.
55. Cohen M.X., Donner T.H. Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior. J. Neurophysiol. 2013, 110:2752-2763.
56. Medalla M., Barbas H. Synapses with inhibitory neurons differentiate anterior cingulate from dorsolateral prefrontal pathways associated with cognitive control. Neuron 2009, 61:609-620.
57. Singer W. Cortical dynamics revisited. Trends Cogn. Sci. 2013, 17:616-626.
58. Aston-Jones G., Cohen J.D. Adaptive gain and the role of the locus coeruleus-norepinephrine system in optimal performance. J. Comp. Neurol. 2005, 493:99-110.
59. Medalla M., Barbas H. Anterior cingulate synapses in prefrontal areas 10 and 46 suggest differential influence in cognitive control. J. Neurosci. 2010, 30:16068-16081.
60. Kok P., et al. Attention reverses the effect of prediction in silencing sensory signals. Cereb. Cortex 2012, 22:2197-2206.
61. Den Ouden H.E.M., et al. How prediction errors shape perception, attention, and motivation. Front. Psychol. 2012, 3:548.
62. Roesch M.R., et al. Surprise! Neural correlates of Pearce-Hall and Rescorla-Wagner coexist within the brain. Eur. J. Neurosci. 2012, 35:1190-1200.
63. Dehaene S., et al. A neuronal model of a global workspace in effortful cognitive tasks. Proc. Natl. Acad. Sci. U.S.A. 1998, 95:14529-14534.
64. Rescorla R.A., Wagner A.R. A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. Classical Conditioning II: Current Research and Theory 1972, 64-99. Appleton-Century Crofts. A.H. Black, W.F. Prokasy (Eds.).
65. Pearce J.M., Hall G. A model for Pavlovian learning: variations in the effectiveness of conditioned but not of unconditioned stimuli. Psychol. Rev. 1980, 87:532-552.
66. Krugel L.K., et al. Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:17951-17956.
67. O'Reilly J.X. Making predictions in a changing world-inference, uncertainty, and learning. Front. Neurosci. 2013, 7:105.
68. Cavanagh J.F., et al. Frontal theta reflects uncertainty and unexpectedness during exploration and exploitation. Cereb. Cortex 2012, 22:2575-2586.
69. Talmi D., et al. The feedback-related negativity signals salience prediction errors, not reward prediction errors. J. Neurosci. 2013, 33:8264-8269.
70. Holroyd C.B., Coles M.G. The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychol. Rev. 2002, 109:679-709.
71. Chase H.W., et al. Feedback-related negativity codes prediction error but not behavioral adjustment during probabilistic reversal learning. J. Cogn. Neurosci. 2011, 23:936-946.
72. Cavanagh J.F., et al. Frontal theta overrides pavlovian learning biases. J. Neurosci. 2013, 33:8541-8548.
73. Notebaert W., et al. Post-error slowing: an orienting account. Cognition 2009, 111:275-279.
74. Wessel J.R., Aron a.R. Unexpected events induce motor slowing via a brain mechanism for action-stopping with global suppressive effects. J. Neurosci. 2013, 33:18481-18491.
75. Danielmeier C., Ullsperger M. Post-error adjustments. Front. Psychol. 2011, 2:233.
76. Cavanagh J.F., Shackman A.J. Frontal midline theta reflects anxiety and cognitive control: meta-analytic evidence. J. Physiol. 2014, 10.1016/j.jphysparis.2014.04.003.
77. Ratcliff R., McKoon G. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 2008, 20:873-922.
78. Cavanagh J.F., et al. Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nat. Neurosci. 2011, 14:1462-1467.
79. Yarkoni T., et al. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 2011, 8:665-670.
80. Johnston K., et al. Top-down control-signal dynamics in anterior cingulate and prefrontal cortex neurons following task switching. Neuron 2007, 53:453-462.
81. Bubic A., et al. Prediction, cognition and the brain. Front. Hum. Neurosci. 2010, 4:25.
82. Wolpert D.M., Ghahramani Z. Computational principles of movement neuroscience. Nat. Neurosci. 2000, 3(Suppl.):1212-1217.
83. Marr D. A theory for cerebral neocortex. Proc. R. Soc. Lond. B: Biol. Sci. 1970, 176:161-234.
84. Friston K. The free-energy principle: a unified brain theory?. Nat. Rev. Neurosci. 2010, 11:127-138.
85. Bellman R., Kalaba R. A mathematical theory of adaptive control processes. Proc. Natl. Acad. Sci. U.S.A. 1959, 45:1288-1290.
86. Friston K. Learning and inference in the brain. Neural Netw. 2003, 16:1325-1352.
87. David O., et al. Modelling event-related responses in the brain. Neuroimage 2005, 25:756-770.
88. Friston K. A theory of cortical responses. Philos. Trans. R. Soc. Lond. B 2005, 360:815-836.
89. Hopfield J.J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A. 1982, 79:2554-2558.
90. Botvinick M.M., et al. Conflict monitoring and cognitive control. Psychol. Rev. 2001, 108:624-652.
91. Berlyne D.E. Uncertainty and conflict: a point of contact. Psychol. Rev. 1957, 64:329-339.
92. Wiecki T.V., Frank M.J. A computational model of inhibitory control in frontal cortex and basal ganglia. Psychol. Rev. 2013, 120:329-355.
93. Sutton R.S., Barto A.G. Reinforcement learning: an introduction 1998, MIT Press.
94. Frank M.J., et al. Error-related negativity predicts reinforcement learning and conflict biases. Neuron 2005, 47:495-501.
95. Hayden B.Y., et al. Surprise signals in anterior cingulate cortex: neuronal encoding of unsigned reward prediction errors driving adjustment in behavior. J. Neurosci. 2011, 31:4178-4187.
96. Caplin A., Dean M. Axiomatic methods, dopamine and reward prediction error. Curr. Opin. Neurobiol. 2008, 18:197-202.
97. Hajihosseini A., Holroyd C.B. Frontal midline theta and N200 amplitude reflect complementary information about expectancy and outcome evaluation. Psychophysiology 2013, 50:550-562.
98. Klimesch W., et al. Event-related phase reorganization may explain evoked neural dynamics. Neurosci. Biobehav. Rev. 2007, 31:1003-1016.
99. Moser J.S., et al. On the relationship between anxiety and error monitoring: a meta-analysis and conceptual framework. Front. Hum. Neurosci. 2013, 7:466.