Pearce, J. M. & Bouton, M. E. Theories of associative learning in animals. Annu. Rev. Psychol. 52, 111–139 (2001).
Google Scholar
Rolls, E. T. What are emotional states, and why do we have them? Emot. Rev. 5, 241–247 (2013).
Google Scholar
O’Doherty, J. P. The problem with value. Neurosci. Biobehav. Rev. 43, 259–268 (2014).
Tye, K. M. Neural circuit motifs in valence processing. Neuron 100, 436–452 (2018).
Kahnt, T., Park, S. Q., Haynes, J. D. & Tobler, P. N. Disentangling neural representations of value and salience in the human brain. Proc. Natl Acad. Sci. USA 111, 5000–5005 (2014).
Google Scholar
Rangel, A., Camerer, C. & Montague, P. R. A framework for studying the neurobiology of value-based decision making. Nat. Rev. Neurosci. 9, 545–556 (2008).
Rolls, E. T. Emotion Explained (Oxford Academic, 2009).
Paton, J. J., Belova, M. A., Morrison, S. E. & Salzman, C. D. The primate amygdala represents the positive and negative value of visual stimuli during learning. Nature 439, 865–870 (2006).
Google Scholar
Morrison, S. E. & Salzman, C. D. Re-valuing the amygdala. Curr. Opin. Neurobiol. 20, 221–230 (2010).
Rudebeck, P. H. & Murray, E. A. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron 84, 1143–1156 (2014).
Hinz, J. et al. Stimulus-specific and adaptive value representations in the basolateral amygdala in male mice. Nat. Commun. 16, 5239 (2025).
Google Scholar
Kahnt, T. & Tobler, P. N. in Decision Neuroscience: An Integrative Perspective (eds Dreher, J.-C. & Tremblay, L.) Ch. 9 (Elsevier, 2017).
Dalley, J. W., Cardinal, R. N. & Robbins, T. W. Prefrontal executive and cognitive functions in rodents: neural and neurochemical substrates. Neurosci. Biobehav. Rev. 28, 771–784 (2004).
Sotres-Bayon, F. & Quirk, G. J. Prefrontal control of fear: more than just extinction. Curr. Opin. Neurobiol. 20, 231–235 (2010).
Kvitsiani, D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013).
Google Scholar
Moorman, D. E. & Aston-Jones, G. Prefrontal neurons encode context-based response execution and inhibition in reward seeking and extinction. Proc. Natl Acad. Sci. USA 112, 9472–9477 (2015).
Google Scholar
Kim, C. K. et al. Molecular and circuit-dynamical identification of top-down neural mechanisms for restraint of reward seeking. Cell 170, 1013–1027 (2017).
Google Scholar
Otis, J. M. et al. Prefrontal cortex output circuits guide reward seeking through divergent cue encoding. Nature 543, 103–107 (2017).
Google Scholar
Corcoran, K. A. & Quirk, G. J. Activity in prelimbic cortex is necessary for the expression of learned, but not innate, fears. J. Neurosci. 27, 840–844 (2007).
Google Scholar
Courtin, J. et al. Prefrontal parvalbumin interneurons shape neuronal activity to drive fear expression. Nature 505, 92–96 (2014).
Google Scholar
Bravo-Rivera, C., Roman-Ortiz, C., Brignoni-Perez, E., Sotres-Bayon, F. & Quirk, G. J. Neural structures mediating expression and extinction of platform-mediated avoidance. J. Neurosci. 34, 9736–9742 (2014).
Google Scholar
Jercog, D. et al. Dynamical prefrontal population coding during defensive behaviours. Nature 595, 690–694 (2021).
Google Scholar
Hok, V., Save, E., Lenck-Santini, P. P. & Poucet, B. Coding for spatial goals in the prelimbic/infralimbic area of the rat frontal cortex. Proc. Natl Acad. Sci. USA 102, 4602–4607 (2005).
Google Scholar
Campus, P. et al. The paraventricular thalamus is a critical mediator of top-down control of cue-motivated behavior in rats. eLife 8, e49041 (2019).
Google Scholar
Choi, E. A. et al. A corticothalamic circuit trades off speed for safety during decision-making under motivational conflict. J. Neurosci. 42, 3473–3483 (2022).
Google Scholar
Kietzman, H. W., Trinoskey-Rice, G., Blumenthal, S. A., Guo, J. D. & Gourley, S. L. Social incentivization of instrumental choice in mice requires amygdala-prelimbic cortex-nucleus accumbens connectivity. Nat. Commun. 13, 4768 (2022).
Google Scholar
Burgos-Robles, A. et al. Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment. Nat. Neurosci. 20, 824–835 (2017).
Google Scholar
Caracheo, B. F., Grewal, J. J. S. & Seamans, J. K. Persistent valence representations by ensembles of anterior cingulate cortex neurons. Front. Syst. Neurosci. 12, 51 (2018).
Google Scholar
Vander Weele, C. M. et al. Dopamine enhances signal-to-noise ratio in cortical-brainstem encoding of aversive stimuli. Nature 563, 397–401 (2018).
Google Scholar
Kyriazi, P., Headley, D. B. & Paré, D. Different multidimensional representations across the amygdalo-prefrontal network during an approach-avoidance task. Neuron 107, 717–730 (2020).
Google Scholar
Huang, W. C., Zucca, A., Levy, J. & Page, D. T. Social behavior is modulated by valence-encoding mPFC-amygdala sub-circuitry. Cell Rep. 32, 107899 (2020).
Google Scholar
Yu, Y. H. et al. Optogenetic stimulation in the medial prefrontal cortex modulates stimulus valence from rewarding and aversive to neutral states. Front. Psychiatry 14, 1119803 (2023).
Google Scholar
Allen, W. E. et al. Thirst regulates motivated behavior through modulation of brainwide neural population dynamics. Science 364, eaav3932 (2019).
Google Scholar
Wang, P. Y. et al. Transient and persistent representations of odor value in prefrontal cortex. Neuron 108, 209–224 (2020).
Google Scholar
Kondo, M. & Matsuzaki, M. Neuronal representations of reward-predicting cues and outcome history with movement in the frontal cortex. Cell Rep. 34, 108704 (2021).
Google Scholar
Ottenheimer, D. J., Hjort, M. M., Bowen, A. J., Steinmetz, N. A. & Stuber, G. D. A stable, distributed code for cue value in mouse cortex during reward learning. eLife 12, RP84604 (2023).
Google Scholar
Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019).
Google Scholar
Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, K. D. Distributed coding of choice, action and engagement across the mouse brain. Nature 576, 266–273 (2019).
Google Scholar
Litt, A., Plassmann, H., Shiv, B. & Rangel, A. Dissociating valuation and saliency signals during decision-making. Cereb. Cortex 21, 95–102 (2011).
Google Scholar
Zhang, Z. et al. Distributed neural representation of saliency controlled value and category during anticipation of rewards and punishments. Nat. Commun. 8, 1907 (2017).
Google Scholar
Rolls, E. T. Emotion, motivation, decision-making, the orbitofrontal cortex, anterior cingulate cortex, and the amygdala. Brain Struct. Funct. 228, 1201–1257 (2023).
Stephenson-Jones, M. et al. A basal ganglia circuit for evaluating action outcomes. Nature 539, 289–293 (2016).
Google Scholar
Stephenson-Jones, M. et al. Opposing contributions of GABAergic and glutamatergic ventral pallidal neurons to motivational behaviors. Neuron 105, 921–933 (2020).
Google Scholar
Wallis, J. D. Decoding cognitive processes from neural ensembles. Trends Cogn. Sci. 22, 1091–1102 (2018).
Quirk, G. J. & Mueller, D. Neural mechanisms of extinction learning and retrieval. Neuropsychopharmacology 33, 56–72 (2008).
Grewe, B. F. et al. Neural ensemble dynamics underlying a long-term associative memory. Nature 543, 670–675 (2017).
Google Scholar
Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002).
Google Scholar
Bissonette, G. B. et al. Separate populations of neurons in ventral striatum encode value and motivation. PLoS ONE 8, e64673 (2013).
Google Scholar
Beyeler, A. et al. Divergent routing of positive and negative information from the amygdala during memory retrieval. Neuron 90, 348–361 (2016).
Google Scholar
Zhang, X. & Li, B. Population coding of valence in the basolateral amygdala. Nat. Commun. 9, 5195 (2018).
Google Scholar
Lutas, A. et al. State-specific gating of salient cues by midbrain dopaminergic input to basal amygdala. Nat. Neurosci. 22, 1820–1833 (2019).
Google Scholar
Roelofs, K. & Dayan, P. Freezing revisited: coordinated autonomic and central optimization of threat coping. Nat. Rev. Neurosci. 23, 568–580 (2022).
Google Scholar
Szeska, C., Richter, J., Wendt, J., Weymar, M. & Hamm, A. O. Attentive immobility in the face of inevitable distal threat—startle potentiation and fear bradycardia as an index of emotion and attention. Psychophysiology 58, e13812 (2021).
Google Scholar
van Heukelum, S. et al. Where is cingulate cortex? A cross-species view. Trends Neurosci. 43, 285–299 (2020).
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
Google Scholar
Bernardi, S. et al. The geometry of abstraction in the hippocampus and prefrontal cortex. Cell 183, 954–967 (2020).
Google Scholar
Flesch, T., Juechems, K., Dumbalska, T., Saxe, A. & Summerfield, C. Orthogonal representations for robust context-dependent task performance in brains and neural networks. Neuron 110, 4212–4219 (2022).
Google Scholar
Fortunato, C. et al. Nonlinear manifolds underlie neural population activity during behaviour. Preprint at bioRxiv https://doi.org/10.1101/2023.07.18.549575 (2024).
Rozeske, R. R. et al. Prefrontal neuronal circuits of contextual fear conditioning. Genes Brain Behav. 14, 22–36 (2015).
Del Arco, A., Park, J. & Moghaddam, B. Unanticipated stressful and rewarding experiences engage the same prefrontal cortex and ventral tegmental area neuronal populations. eNeuro https://doi.org/10.1523/ENEURO.0029-20.2020 (2020).
Rozeske, R. R., Valerio, S., Chaudun, F. & Herry, C. Prefrontal neuronal circuits of contextual fear conditioning. Genes Brain Behav. 14, 22–36 (2015).
Google Scholar
Beyeler, A. et al. Organization of valence-encoding and projection-defined neurons in the basolateral amygdala. Cell Rep. 22, 905–918 (2018).
Google Scholar
Zhang, X. et al. Genetically identified amygdala–striatal circuits for valence-specific behaviors. Nat. Neurosci. 24, 1586–1600 (2021).
Google Scholar
Kyriazi, P., Headley, D. B. & Pare, D. Multi-dimensional coding by basolateral amygdala article multi-dimensional coding by basolateral amygdala neurons. Neuron 99, 1315–1328 (2018).
Google Scholar
O’Neill, P.-K. et al. The representational geometry of emotional states in basolateral amygdala. Preprint at bioRxiv https://doi.org/10.1101/2023.09.23.558668 (2023).
Headley, D. B., Kanta, V., Kyriazi, P. & Paré, D. Embracing complexity in defensive networks. Neuron 103, 189–201 (2019).
Ehret, B. et al. Population-level coding of avoidance learning in medial prefrontal cortex. Nat. Neurosci. 27, 1805–1815 (2024).
Google Scholar
Hyman, J. M., Whitman, J., Emberly, E., Woodward, T. S. & Seamans, J. K. Action and outcome activity state patterns in the anterior cingulate cortex. Cereb. Cortex 23, 1257–1268 (2013).
Google Scholar
Li, N., Daie, K., Svoboda, K. & Druckmann, S. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532, 459–464 (2016).
Google Scholar
Flavell, S. W., Gogolla, N., Lovett-Barron, M. & Zelikowsky, M. The emergence and influence of internal states. Neuron 110, 2545–2570 (2022).
Grillon, C. Associative learning deficits increase symptoms of anxiety in humans. Biol. Psychiatry 51, 851–858 (2002).
Google Scholar
Jo, Y. S., Heymann, G. & Zweifel, L. S. Dopamine neurons reflect the uncertainty in fear generalization. Neuron 100, 916–925 (2018).
Google Scholar
Griffiths, K. R., Morris, R. W. & Balleine, B. W. Translational studies of goal-directed action as a framework for classifying deficits across psychiatric disorders. Front. Syst. Neurosci. 8, 101 (2014).
Bienvenu, T. C. M. et al. The advent of fear conditioning as an animal model of post-traumatic stress disorder: learning from the past to shape the future of PTSD research. Neuron 109, 2380–2397 (2021).
Sookman, D. & Pinard, G. in Cognitive Approaches to Obsessions and Compulsions (eds Frost, R. O. & Steketee, G.) Ch. 5 (Elsevier, 2002).
Peschard, V. & Philippot, P. Overestimation of threat from neutral faces and voices in social anxiety. J. Behav. Ther. Exp. Psychiatry 57, 206–211 (2017).
Google Scholar
Grupe, D. W. & Nitschke, J. B. Uncertainty and anticipation in anxiety: an integrated neurobiological and psychological perspective. Nat. Rev. Neurosci. 14, 488–501 (2013).
Blair, K. S. et al. Reduced optimism and a heightened neural response to everyday worries are specific to generalized anxiety disorder, and not seen in social anxiety. Psychol. Med. 47, 1806–1815 (2017).
Google Scholar
Likhtik, E., Stujenske, J. M., Topiwala, M. A., Harris, A. Z. & Gordon, J. A. Prefrontal entrainment of amygdala activity signals safety in learned fear and innate anxiety. Nat. Neurosci. 17, 106–113 (2014).
Google Scholar
Felix-Ortiz, A. C. et al. The infralimbic and prelimbic cortical areas bidirectionally regulate safety learning during normal and stress conditions. Preprint at bioRxiv https://doi.org/10.1101/2023.05.05.539516 (2023).
Deserno, L., Boehme, R., Heinz, A. & Schlagenhauf, F. Reinforcement learning and dopamine in schizophrenia: dimensions of symptoms or specific features of a disease group? Front. Psychiatry 4, 172 (2013).
Jia, R. et al. Neural valuation of rewards and punishments in posttraumatic stress disorder: a computational approach. Transl. Psychiatry 13, 101 (2023).
Google Scholar
Paxinos, G. & Franklin, K. B. J. The Mouse Brain in Stereotaxic Coordinates 2nd edn (Elsevier, 2001).
Capuzzo, G. & Floresco, S. B. Prelimbic and infralimbic prefrontal regulation of active and inhibitory avoidance and reward-seeking. J. Neurosci. 40, 4773–4787 (2020).
Google Scholar
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).
Google Scholar
Hastie, T. Ridge regularization: an essential concept in data science. Technometrics 62, 426–433 (2020).
Google Scholar
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn, corrected 12th printing Jan 2017 (Springer, 2009).
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Proc. Advances in Neural Information Processing Systems 30 (eds Guyon, I. et al.) (NIPS, 2017).
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