Pearce, J. M. & Bouton, M. E. Theories of associative learning in animals. Annu. Rev. Psychol. 52, 111–139 (2001).
Rolls, E. T. What are emotional states, and why do we have them? Emot. Rev. 5, 241–247 (2013).
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).
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).
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).
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).
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).
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).
Otis, J. M. et al. Prefrontal cortex output circuits guide reward seeking through divergent cue encoding. Nature 543, 103–107 (2017).
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).
Courtin, J. et al. Prefrontal parvalbumin interneurons shape neuronal activity to drive fear expression. Nature 505, 92–96 (2014).
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).
Jercog, D. et al. Dynamical prefrontal population coding during defensive behaviours. Nature 595, 690–694 (2021).
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).
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).
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).
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).
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).
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).
Vander Weele, C. M. et al. Dopamine enhances signal-to-noise ratio in cortical-brainstem encoding of aversive stimuli. Nature 563, 397–401 (2018).
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).
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).
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).
Allen, W. E. et al. Thirst regulates motivated behavior through modulation of brainwide neural population dynamics. Science 364, eaav3932 (2019).
Wang, P. Y. et al. Transient and persistent representations of odor value in prefrontal cortex. Neuron 108, 209–224 (2020).
Kondo, M. & Matsuzaki, M. Neuronal representations of reward-predicting cues and outcome history with movement in the frontal cortex. Cell Rep. 34, 108704 (2021).
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).
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).
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).
Litt, A., Plassmann, H., Shiv, B. & Rangel, A. Dissociating valuation and saliency signals during decision-making. Cereb. Cortex 21, 95–102 (2011).
Zhang, Z. et al. Distributed neural representation of saliency controlled value and category during anticipation of rewards and punishments. Nat. Commun. 8, 1907 (2017).
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).
Stephenson-Jones, M. et al. Opposing contributions of GABAergic and glutamatergic ventral pallidal neurons to motivational behaviors. Neuron 105, 921–933 (2020).
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).
Guyon, I., Weston, J., Barnhill, S. & Vapnik, V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002).
Bissonette, G. B. et al. Separate populations of neurons in ventral striatum encode value and motivation. PLoS ONE 8, e64673 (2013).
Beyeler, A. et al. Divergent routing of positive and negative information from the amygdala during memory retrieval. Neuron 90, 348–361 (2016).
Zhang, X. & Li, B. Population coding of valence in the basolateral amygdala. Nat. Commun. 9, 5195 (2018).
Lutas, A. et al. State-specific gating of salient cues by midbrain dopaminergic input to basal amygdala. Nat. Neurosci. 22, 1820–1833 (2019).
Roelofs, K. & Dayan, P. Freezing revisited: coordinated autonomic and central optimization of threat coping. Nat. Rev. Neurosci. 23, 568–580 (2022).
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).
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).
Bernardi, S. et al. The geometry of abstraction in the hippocampus and prefrontal cortex. Cell 183, 954–967 (2020).
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).
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).
Beyeler, A. et al. Organization of valence-encoding and projection-defined neurons in the basolateral amygdala. Cell Rep. 22, 905–918 (2018).
Zhang, X. et al. Genetically identified amygdala–striatal circuits for valence-specific behaviors. Nat. Neurosci. 24, 1586–1600 (2021).
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).
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).
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).
Li, N., Daie, K., Svoboda, K. & Druckmann, S. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532, 459–464 (2016).
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).
Jo, Y. S., Heymann, G. & Zweifel, L. S. Dopamine neurons reflect the uncertainty in fear generalization. Neuron 100, 916–925 (2018).
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).
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).
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).
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).
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).
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).
Hastie, T. Ridge regularization: an essential concept in data science. Technometrics 62, 426–433 (2020).
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).
