Skip to content
BY 4.0 license Open Access Published by De Gruyter Open Access October 11, 2019

Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing

  • Robert Lowe EMAIL logo , Alexander Almér and Christian Balkenius
From the journal Open Information Science


Connectionist architectures constitute a popular method for modelling animal associative learning processes in order to glean insights into the formation of cognitive capacities. Such approaches (based on purely feedforward activity) are considered limited in their ability to capture relational cognitive capacities. Pavlovian learning value-based models, being not based purely on fully connected feedforward structure, have demonstrated learning capabilities that often mimic those of ‘higher’ relational cognition. Capturing data using such models often reveals how associative mechanisms can exploit structure in the experimental setting, so that ‘explicit’ relational cognitive capacities are not, in fact, required. On the other hand, models of relational cognition, implemented as neural networks, permit formation and retrieval of relational representations of varying levels of complexity. The flexible processing capacities of such models are, however, are subject to constraints as to how offline relational versus online (real-time, real-world) processing may be mediated. In the current article, we review the potential for building a connectionist-relational cognitive architecture with reference to the representational rank view of cognitive capacity put forward by Halford et al. Through interfacing system 1-like (connectionist/associative learning) and system 2-like (relational-cognition) computations through a bidirectional affective processing approach, continuity between Halford et al’s cognitive systems may be operationalized according to real world/online constraints. By addressing i) and ii) in this manner, this paper puts forward a testable unifying framework for system 1-like and system 2-like cognition.


Amsel, A. (1958). The role of frustrative nonreward in noncontinuous reward situations. Psychol. Bull. 55:102–119.Search in Google Scholar

Amsel, A. (1992). Frustration theory: an analysis of dispositional learning and memory. Cambridge University Press, Cambridge.10.1017/CBO9780511665561Search in Google Scholar

Andreasson, R., Alenljung, B., Billing, E., & Lowe, R. (2018). Affective touch in human–robot interaction: conveying emotion to the nao robot. International Journal of Social Robotics, 10(4), 473-491.10.1007/s12369-017-0446-3Search in Google Scholar

Armony, J. L., Servan-Schreiber, D., Cohen, J. D., & LeDoux, J. E. (1997). Computational modeling of emotion: Explorations through the anatomy and physiology of fear conditioning. Trends in cognitive sciences, 1(1), 28-34.10.1016/S1364-6613(97)01007-3Search in Google Scholar

Armony, J. (2005). Computational models of emotion. In Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. (Vol. 3, pp. 1598-1602). IEEE.10.1109/IJCNN.2005.1556117Search in Google Scholar

Baillargeon, R. (2004). Infants’ physical world. Current directions in psychological science, 13(3), 89-94.10.1111/j.0963-7214.2004.00281.xSearch in Google Scholar

Balkenius, C. & Morén J (2001). Emotional learning: a computational model of the amygdala. Cybern Syst Int J 32:611–636.Search in Google Scholar

Balleine, B. W., & Ostlund, S. B. (2007). Still at the choice-point. Annals of the New York Academy of Sciences, 1104(1), 147-171.10.1196/annals.1390.006Search in Google Scholar

Barrett, L. F., Quigley, K. S., & Hamilton, P. (2016). An active inference theory of allostasis and interoception in depression. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1708), 20160011.10.1098/rstb.2016.0011Search in Google Scholar

Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and economic behavior, 52(2), 336-372.10.1016/j.geb.2004.06.010Search in Google Scholar

Braitenberg, V. (1986). Vehicles: Experiments in synthetic psychology. MIT press.Search in Google Scholar

Braver, T. S., et al. (2014). Mechanisms of motivation–cognition interaction: challenges and opportunities. Cognitive, Affective, & Behavioral Neuroscience, 14(2), 443-472.10.3758/s13415-014-0300-0Search in Google Scholar

Cardinal, R. N., Parkinson, J. A., Hall, J., & Everitt, B. J. (2002). Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neuroscience & Biobehavioral Reviews, 26(3), 321-352.10.1016/S0149-7634(02)00007-6Search in Google Scholar

Cardinal, R. N. (2006). Neural systems implicated in delayed and probabilistic reinforcement. Neural Networks, 19(8), 1277-1301.10.1016/j.neunet.2006.03.004Search in Google Scholar

de Wit, S., & Dickinson, A. (2009). Associative theories of goal-directed behaviour: A case for animal–human translational models. Psychological Research PRPF, 73(4), 463–476.10.1007/s00426-009-0230-6Search in Google Scholar

De Gelder, B (2009), “Why bodies? Twelve reasons for including bodily expressions in affective neuroscience”, Philosophical Transactions of the Royal Society, vol. 364, 3, pp. 3475-3484.10.1098/rstb.2009.0190Search in Google Scholar

Delamater, A. R. (2012). On the nature of CS and US representations in Pavlovian learning. Learning & Behavior, 40(1), 1-23.10.3758/s13420-011-0036-4Search in Google Scholar

Delamater, A. R., Garr, E., Lawrence, S., & Whitlow Jr, J. W. (2017). Elemental, configural, and occasion setting mechanisms in biconditional and patterning discriminations. Behavioural processes, 137, 40-52.10.1016/j.beproc.2016.10.013Search in Google Scholar

Doumas, L. A., Morrison, R. G., & Richland, L. E. (2018). Individual differences in relational learning and analogical reasoning: A computational model of longitudinal change. Frontiers in Psychology, 9.10.3389/fpsyg.2018.01235Search in Google Scholar

Doya, K. (2008). Modulators of decision making. Nature neuroscience, 11(4), 410.10.1038/nn2077Search in Google Scholar PubMed

Ekman, P., & Friesen, W. V. (2003). Unmasking the face: A guide to recognizing emotions from facial clues. Ishk.Search in Google Scholar

Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition. Oxford University Press.10.1093/acprof:oso/9780199794546.001.0001Search in Google Scholar

Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2), 3-71.10.1016/0010-0277(88)90031-5Search in Google Scholar

Frank, M. J., Rudy, J. W., Levy, W. B., & O’Reilly, R. C. (2005). When logic fails: Implicit transitive inference in humans. Memory & Cognition, 33(4), 742-750.10.3758/BF03195340Search in Google Scholar PubMed

Friston, K. (2010). The free-energy principle: a unified brain theory?. Nature reviews neuroscience, 11(2), 127.10.1038/nrn2787Search in Google Scholar

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.Search in Google Scholar

Goel, V. (2007). Anatomy of deductive reasoning. Trends in cognitive sciences, 11(10), 435-441.10.1016/j.tics.2007.09.003Search in Google Scholar

Halford, G. S., Wilson, W. H., & Phillips, S. (1998). Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology. Behavioral and Brain Sciences, 21(6), 803-831.10.1017/S0140525X98001769Search in Google Scholar

Halford, G. S., Wilson, W. H., & Phillips, S. (1999). A conceptual complexity metric based on representational rank.Search in Google Scholar

Halford, G. S., Wilson, W. H., & Phillips, S. (2010). Relational knowledge: The foundation of higher cognition. Trends in cognitive sciences, 14(11), 497-505.10.1016/j.tics.2010.08.005Search in Google Scholar

Halford, G. S., Andrews, G., Wilson, W. H., & Phillips, S. (2012). Computational models of relational processes in cognitive development. Cognitive Development, 27(4), 481-499.10.1016/j.cogdev.2012.08.003Search in Google Scholar

Halford, G. S., Wilson, W. H., Andrews, G., & Phillips, S. (2014). Categorizing cognition: Toward conceptual coherence in the foundations of psychology. MIT Press.10.7551/mitpress/10054.001.0001Search in Google Scholar

Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1-3), 335-346.10.1016/0167-2789(90)90087-6Search in Google Scholar

Heath, R. A., & Hayes, B. K. (1998). Why is capacity limited? Missing dynamics and developmental controversies. Behavioral and Brain Sciences, 21(6), 839-840.10.1017/S0140525X98311760Search in Google Scholar

Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507.Search in Google Scholar

Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: A theory of analogical access and mapping. Psychological review, 104(3), 427.10.1037/0033-295X.104.3.427Search in Google Scholar

Hummel, J. E., & Holyoak, K. J. (2001). A process model of human transitive inference. In Spatial schemas in abstract thought (pp. 279-305).Search in Google Scholar

Kahneman, D., & Tversky, A. (1986). Rational choice and the framing of decisions. Journal of business, 59(4), 251-278.Search in Google Scholar

Kahneman, D., & Egan, P. (2011). Thinking, fast and slow (Vol. 1). New York: Farrar, Straus and Giroux.Search in Google Scholar

Kiryazov, K., Lowe, R., Becker-Asano, C., & Randazzo, M. (2013). The role of arousal in two-resource problem tasks for humanoid service robots. In 2013 IEEE RO-MAN (pp. 62-69). IEEE.10.1109/ROMAN.2013.6628532Search in Google Scholar

Klopf, A. H., Weaver, S. E., & Morgan, J. S. (1993). A hierarchical network of control systems that learn: Modeling nervous system function during classical and instrumental conditioning. Adaptive behavior, 1(3), 263-319.10.1177/105971239300100302Search in Google Scholar

Kollias, P., & McClelland, J. L. (2013). Context, cortex, and associations: A connectionist developmental approach to verbal analogies. Frontiers in Psychology, 4, 857.10.3389/fpsyg.2013.00857Search in Google Scholar

Kruse, J. & Overmier, J.B. (1982). Anticipation of reward omission as a cue for choice behavior. Learning and Motivation. 13(4):505–525.Search in Google Scholar

Leech, R., Mareschal, D., & Cooper, R. P. (2008). Analogy as relational priming: A developmental and computational perspective on the origins of a complex cognitive skill. Behavioral and Brain Sciences, 31(4), 357-378.10.1017/S0140525X08004469Search in Google Scholar

Li, C., Lowe, R., & Ziemke, T. (2013). Crawling posture learning in humanoid robots using a natural-actor-critic cpg architecture. In Artificial Life Conference Proceedings 13(pp. 1182-1190). One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press.Search in Google Scholar

Li, C., Lowe, R., & Ziemke, T. (2014). A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives. Frontiers in neurorobotics, 8, 23.10.3389/fnbot.2014.00023Search in Google Scholar

Lowe, R., Philippe, P., Montebelli, A., Morse, A., & Ziemke, T. (2008). Affective modulation of embodied dynamics. In The Role of Emotion in Adaptive Behaviour and Cognitive Robotics, Electronic Proceedings of SAB Workshop.Search in Google Scholar

Lowe, R., Humphries, M., & Ziemke, T. (2009). The dual-route hypothesis: Evaluating a neurocomputational model of fear conditioning in rats. Connection Science, 21(1), 15-37.10.1080/09540090802414085Search in Google Scholar

Lowe, R., & Ziemke, T. (2011). The feeling of action tendencies: on the emotional regulation of goal-directed behavior. Frontiers in Psychology, 2(Dec), Article-346.10.3389/fpsyg.2011.00346Search in Google Scholar

Lowe, R., & Ziemke, T. (2013). Exploring the relationship of reward and punishment in reinforcement learning. In 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) (pp. 140-147). IEEE.10.1109/ADPRL.2013.6615000Search in Google Scholar

Lowe, R., & Kiryazov, K. (2014). Utilizing emotions in autonomous robots: An enactive approach. In Emotion modeling (pp. 76-98). Springer, Cham.10.1007/978-3-319-12973-0_5Search in Google Scholar

Lowe, R., Sandamirskaya, Y., & Billing, E. (2014). A neural dynamic model of associative two-process theory: The differential outcomes effect and infant development. In 4th International Conference on Development and Learning and on Epigenetic Robotics (pp. 440–447).10.1109/DEVLRN.2014.6983021Search in Google Scholar

Lowe, R., Almér, A., Lindblad, G., Gander, P., Michael, J., & Vesper, C. (2016). Minimalist social-affective value for use in joint action: A neural-computational hypothesis. Frontiers in Computational Neuroscience, 10. Available at: in Google Scholar

Lowe, R., Almér, A., Billing, E., Sandamirskaya, Y., & Balkenius, C. (2017). Affective–associative two-process theory: a neurocomputational account of partial reinforcement extinction effects. Biological cybernetics, 111(5-6), 365-388.10.1007/s00422-017-0730-1Search in Google Scholar

Lowe, R., & Billing, E. (2017). Affective-Associative Two-Process theory: A neural network investigation of adaptive behaviour in differential outcomes training. Adaptive Behavior, 25(1), 5-23.10.1177/1059712316682999Search in Google Scholar

Lowe, R., Dodig-Crnkovic, G., & Almér, A. (2017). Predictive regulation in affective and adaptive behaviour: An allostatic-cybernetics perspective. In Advanced Research on Biologically Inspired Cognitive Architectures (pp. 149-176). IGI Global.10.4018/978-1-5225-1947-8.ch008Search in Google Scholar

Luzardo, A. (2018). The Rescorla-Wagner Drift-Diffusion Model (Doctoral dissertation, City, University of London). Mackintosh N.J. (1971) An analysis of overshadowing and blocking. Q J Exp Psychol. 23:118–125.Search in Google Scholar

Maki, W. S., & Abunawass, A. M. (1991). A connectionist approach to conditional discriminations: Learning, short-term memory, and attention. Neural network models of conditioning and action, 241-278.Search in Google Scholar

Miller, R. R., Barnet, R. C., & Grahame, N. J. (1995). Assessment of the Rescorla-Wagner model. Psychological bulletin, 117(3), 363.10.1037/0033-2909.117.3.363Search in Google Scholar

Montebelli, A., Lowe, R., & Ziemke, T. (2008). The cognitive body: from dynamic modulation to anticipation. In Workshop on Anticipatory Behavior in Adaptive Learning Systems (pp. 132-151). Springer, Berlin, Heidelberg.Search in Google Scholar

Montebelli, A., Lowe, R., Ieropoulos, I., Melhuish, C., Greenman, J., & Ziemke, T. (2010). Microbial Fuel Cell Driven Behavioral Dynamics in Robot Simulations. In ALIFE (pp. 749-756).Search in Google Scholar

Montebelli, A., Lowe, R., & Ziemke, T. (2013). Toward Metabolic Robotics: Insights from Modeling Embodied Cognition in a Biomechatronic Symbiont. Artificial life, 19(3_4), 299-315.10.1162/ARTL_a_00114Search in Google Scholar

Morén, J. (2002). Emotion and Learning - A Computational Model of the Amygdala. Lund University Cognitive Studies, 93.Search in Google Scholar

Morrison, I., Perini, I., & Dunham, J. (2013). Facets and mechanisms of adaptive pain behavior: predictive regulation and action. Frontiers in human neuroscience, 7, 755.10.3389/fnhum.2013.00755Search in Google Scholar

Mowrer, O. H. (1947). On the dual nature of learning: A reinterpretation of “conditioning” and “problem-solving”. Harvard Educational Review, 17, 102–148.Search in Google Scholar

Navarro-Guerrero, N., Lowe, R. J., & Wermter, S. (2017a). Improving robot motor learning with negatively valenced reinforcement signals. Frontiers in neurorobotics, 11, 10.10.3389/fnbot.2017.00010Search in Google Scholar

Navarro-Guerrero, N., Lowe, R. J., & Wermter, S. (2017b). The effects on adaptive behaviour of negatively valenced signals in reinforcement learning. In 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 148-155). IEEE.10.1109/DEVLRN.2017.8329800Search in Google Scholar

Niv, Y. (2009). Reinforcement learning in the brain. Journal of Mathematical Psychology, 53(3), 139-154.10.1016/ in Google Scholar

Oberauer, K. (2009). Design for a working memory. Psychology of learning and motivation, 51, 45-100.10.1016/S0079-7421(09)51002-XSearch in Google Scholar

O’Reilly, R. C., Wyatte, D. R., & Rohrlich, J. (2017). Deep predictive learning: a comprehensive model of three visual streams. arXiv preprint arXiv:1709.04654.Search in Google Scholar

Overmier, J. B., & Lawry, J.A. (1979). Pavlovian conditioning and the mediation of behavior. The Psychology of Learning and Motivation, 13, 1–55.10.1016/S0079-7421(08)60080-8Search in Google Scholar

Pearce, J. M. (2013). Animal learning and cognition: an introduction. 3rd edition. Psychology Press.Search in Google Scholar

Peterson, G. B., & Trapold, M. A. (1980). Effects of altering outcome expectancies on pigeons’ delayed conditional discrimination performance. Learning and Motivation, 11, 267–288.10.1016/0023-9690(80)90001-6Search in Google Scholar

Pezzulo, G., Rigoli, F., & Friston, K. (2015). Active inference, homeostatic regulation and adaptive behavioural control. Progress in neurobiology, 134, 17-35.10.1016/j.pneurobio.2015.09.001Search in Google Scholar

Pfeifer, R., & Scheier, C. (2001). Understanding intelligence. MIT press.10.7551/mitpress/6979.001.0001Search in Google Scholar

Phillips, S., Wilson, W. H., & Halford, G. S. (2009). What do Transitive Inference and Class Inclusion have in common? Categorical (co) products and cognitive development. PLoS computational biology, 5(12), e1000599.10.1371/journal.pcbi.1000599Search in Google Scholar

Phillips, S. (2017). A general (category theory) principle for general intelligence: duality (adjointness). In International Conference on Artificial General Intelligence (pp. 57-66). Springer, Cham.10.1007/978-3-319-63703-7_6Search in Google Scholar

Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience, 2(1), 79.10.1038/4580Search in Google Scholar

Regenwetter, M., Dana, J., & Davis-Stober, C. P. (2011). Transitivity of preferences. Psychological review, 118(1), 42.10.1037/a0021150Search in Google Scholar

Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcement. In A. H. Black, & W. F. Prokasy (eds), Classical Conditioning II: Current Research and Theory. New York: Appleton-Century-Crofts.Search in Google Scholar

Rogers, T. T., & McClelland, J. L. (2004). Semantic cognition: A parallel distributed processing approach. MIT press.10.7551/mitpress/6161.001.0001Search in Google Scholar

Rolls, E. T. (1999) The brain and emotion. Oxford University Press, OxfordSearch in Google Scholar

Rolls, E. T. (2013)What are emotional states, and why do we have them? Emot. Rev. 5(3):241–247Search in Google Scholar

Rolls, E. T. (2016). Cerebral cortex: principles of operation. Oxford University Press.10.1093/acprof:oso/9780198784852.001.0001Search in Google Scholar

Rolls, E. T. (2018). The Brain, Emotion, and Depression. Oxford University Press.Search in Google Scholar

Rumelhart, D. E. (1990). Brain style computation: Learning and generalization. In An introduction to neural and electronic networks (pp. 405-420). Academic Press Professional, Inc..Search in Google Scholar

Rumelhart, D. E., Hinton, G. E., & McClelland, J. L. (1986). A general framework for parallel distributed processing. Parallel distributed processing: Explorations in the microstructure of cognition, 1, 45-76.Search in Google Scholar

Saxe, A. M., McClelland, J. L., & Ganguli, S. (2018). A mathematical theory of semantic development in deep neural networks. arXiv preprint arXiv:1810.10531.Search in Google Scholar

Schmajuk, N. (2010). Mechanisms in classical conditioning: A computational approach. Cambridge University Press.10.1017/CBO9780511711831Search in Google Scholar

Schröder, M. (2001). Emotional speech synthesis: A review. In Seventh European Conference on Speech Communication and Technology.10.21437/Eurospeech.2001-150Search in Google Scholar

Seger, C. A. (2008). How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback. Neurosci. Biobehav. Rev. 32, 265–278.10.1016/j.neubiorev.2007.07.010Search in Google Scholar

Seger, C. A. (2009). “The involvement of corticostriatal loops in learning across tasks, species, and methodologies,” in The Basal Ganglia IX, eds H. J. Groenewegen, P. Voorn, H. W. Berendse, A. B. Mulder, and A. R. Cools (New York: Springer-Verlag), 25–39.Search in Google Scholar

Seger, C. A., & Spiering, B. J. (2011). A critical review of habit learning and the basal ganglia. Frontiers in systems neuroscience, 5.10.3389/fnsys.2011.00066Search in Google Scholar

Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial intelligence, 46(1-2), 159-216.10.1016/0004-3702(90)90007-MSearch in Google Scholar

Sun, R. (2015). Interpreting psychological notions: A dual-process computational theory. Journal of Applied Research in Memory and Cognition, 4(3), 191-196.10.1016/j.jarmac.2014.09.001Search in Google Scholar

Sutton, R.S., Barto, A.G., (1998). Reinforcement Learning: An introduction. 1st edition. MIT Press.10.1109/TNN.1998.712192Search in Google Scholar

Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. 2nd edition. MIT press.Search in Google Scholar

Trapold, M. A. (1970). Are expectancies based upon different positive reinforcing events discriminably different? Learning and Motivation, 1, 129–140.10.1016/0023-9690(70)90079-2Search in Google Scholar

Trapold, M. A., & Overmier, J. B. (1972). The second learning process in instrumental learning. In Classical Conditioning II: Current Research and Theory (pp. 427–452). New York: Appleton-Century-Crofts.Search in Google Scholar

Urcuioli, P. (1990). Some relationships between outcome expectancies and sample stimuli in pigeons’ delayed matching. Animal Learning and Behavior, 18(3), 302–314.10.3758/BF03205290Search in Google Scholar

Urcuioli, P. (2005). Behavioral and associative effects of differential outcomes in discriminating learning. Learning and Behavior, 33(1), 1–21.10.3758/BF03196047Search in Google Scholar

Van der Velde, F., & De Kamps, M. (2006). Neural blackboard architectures of combinatorial structures in cognition. Behavioral and Brain Sciences, 29(1), 37-70.10.1017/S0140525X06009022Search in Google Scholar

van der Velde, F., & de Kamps, M. (2015). The necessity of connection structures in neural models of variable binding. Cognitive neurodynamics, 9(4), 359-370.10.1007/s11571-015-9331-7Search in Google Scholar

Wilson, W. H., Marcus, N., & Halford, G. S. (2001). Access to relational knowledge: A comparison of two models. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 23, No. 23).Search in Google Scholar

Wynne, C. D. L. (1995). Reinforcement accounts for transitive inference performance. Animal Learning & Behavior, 23(2), 207-217.10.3758/BF03199936Search in Google Scholar

Received: 2018-01-05
Accepted: 2019-05-14
Published Online: 2019-10-11

© 2019 Robert Lowe et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Downloaded on 4.12.2023 from
Scroll to top button