Category Archives: Psycho-physiological Bases Of Engineering

A summary on reward processing in psychophysiology

Dan Foti, Anna Weinberg, Reward and feedback processing: State of the field, best practices, and future directions, International Journal of Psychophysiology, Volume 132, Part B, 2018, Pages 171-174, DOI: 10.1016/j.ijpsycho.2018.08.006.

There is a long history of studies using event-related potentials (ERPs) to examine how the brain monitors performance. Many initial studies focused on error processing, both internal (i.e., neural activity elicited by error commission) (Falkenstein et al., 1991; Gehring et al., 1993) and external (i.e. neural activity elicited by feedback indicating an unfavorable outcome) (Gehring and Willoughby, 2002; Miltner et al., 1997). A frequent assumption in this line of research has been that correct performance and favorable outcomes served as reference conditions, and that any effects on ERP amplitudes specifically reflected error processing. This starting premise is at odds with the large human and animal neuroscience literatures on reward processing, which focus on the motivated pursuit of said favorable outcomes. In fact, reward and error processing are intrinsically linked, and both undergird effective task performance: the brain is highly sensitive to events that are better or worse than expected in order to continuously modulate behavior in line with task goals (Holroyd and Coles, 2002). In recent years, the ERP literature on feedback processing has broadened to explicitly incorporate reward processing, thereby enriching traditional studies focused on error processing. Specific developments in this regard include an expanded focus on multiple stages of reward processing (e.g., anticipation versus outcome), charting the development of reward processing across the lifespan, and the examination of aberrant sensitivity to reward in psychiatric illnesses. While these advances are highly promising, the general ERP literature on feedback processing continues to be fragmented with regard to terminology, analytic techniques, task designs, and interpretation of findings, ultimately limiting progress in the field.

The overarching goal of this special issue was to carefully examine the state of the art in our current understanding of feedback processing. The aim was to provide an integrative overview that covers multiple theoretical perspectives and methodological approaches. Consideration has been given in this collection of articles to both basic and applied research topics, and throughout the special issue there is an emphasis on providing specific recommendations for study design and the identification of important future research directions. In the remainder of this introductory editorial, we set the stage for these articles by highlighting complementary results and points of intersection across four themes: integrating perspectives on reward and error processing; experimental manipulations, psychometrics, and individual differences.

An interesting model of Basal Ganglia that performs similarly to Q learning when applied to a robot

Y. Zeng, G. Wang and B. Xu, A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 290-303 DOI: 10.1109/TCDS.2017.2649564.

Reinforcement learning brings flexibility and generality for machine learning, while most of them are mathematical optimization driven approaches, and lack of cognitive and neural evidence. In order to provide a more cognitive and neural mechanisms driven foundation and validate its applicability in complex task, we develop a basal ganglia (BG) network centric reinforcement learning model. Compared to existing work on modeling BG, this paper is unique from the following perspectives: 1) the orbitofrontal cortex (OFC) is taken into consideration. OFC is critical in decision making because of its responsibility for reward representation and is critical in controlling the learning process, while most of the BG centric models do not include OFC; 2) to compensate the inaccurate memory of numeric values, precise encoding is proposed to enable working memory system remember important values during the learning process. The method combines vector convolution and the idea of storage by digit bit and is efficient for accurate value storage; and 3) for information coding, the Hodgkin-Huxley model is used to obtain a more biological plausible description of action potential with plenty of ionic activities. To validate the effectiveness of the proposed model, we apply the model to the unmanned aerial vehicle (UAV) autonomous learning process in a 3-D environment. Experimental results show that our model is able to give the UAV the ability of free exploration in the environment and has comparable learning speed as the Q learning algorithm, while the major advances for our model is that it is with solid cognitive and neural basis.

A new model of cognition

Howard, N. & Hussain, A. The Fundamental Code Unit of the Brain: Towards a New Model for Cognitive Geometry, Cogn Comput (2018) 10: 426 DOI: 10.1007/s12559-017-9538-5.

This paper discusses the problems arising from the multidisciplinary nature of cognitive research and the need to conceptually unify insights from multiple fields into the phenomena that drive cognition. Specifically, the Fundamental Code Unit (FCU) is proposed as a means to better quantify the intelligent thought process at multiple levels of analysis. From the linguistic and behavioral output, FCU produces to the chemical and physical processes within the brain that drive it. The proposed method efficiently model the most complex decision-making process performed by the brain.

Adapting inverse reinforcement learning for including the risk-aversion of the agent

Sumeet Singh, Jonathan Lacotte, Anirudha Majumdar, and Marco Pavone, Risk-sensitive inverse reinforcement learning via semi- and non-parametric methods , The International Journal of Robotics Research First Published May 22, 2018 DOI: 10.1177/0278364918772017.

The literature on inverse reinforcement learning (IRL) typically assumes that humans take actions to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive (RS) IRL to explicitly account for a human’s risk sensitivity. To this end, we propose a flexible class of models based on coherent risk measures, which allow us to capture an entire spectrum of risk preferences from risk neutral to worst case. We propose efficient non-parametric algorithms based on linear programming and semi-parametric algorithms based on maximum likelihood for inferring a human’s underlying risk measure and cost function for a rich class of static and dynamic decision-making settings. The resulting approach is demonstrated on a simulated driving game with 10 human participants. Our method is able to infer and mimic a wide range of qualitatively different driving styles from highly risk averse to risk neutral in a data-efficient manner. Moreover, comparisons of the RS-IRL approach with a risk-neutral model show that the RS-IRL framework more accurately captures observed participant behavior both qualitatively and quantitatively, especially in scenarios where catastrophic outcomes such as collisions can occur.

On how sleep improves our problem-solving capabilities

Penelope A. Lewis, Günther Knoblich, Gina Poe, How Memory Replay in Sleep Boosts Creative Problem-Solving, Trends in Cognitive Sciences, Volume 22, Issue 6, 2018, Pages 491-503 DOI: 10.1016/j.tics.2018.03.009.

Creative thought relies on the reorganisation of existing knowledge. Sleep is known to be important for creative thinking, but there is a debate about which sleep stage is most relevant, and why. We address this issue by proposing that rapid eye movement sleep, or ‘REM’, and non-REM sleep facilitate creativity in different ways. Memory replay mechanisms in non-REM can abstract rules from corpuses of learned information, while replay in REM may promote novel associations. We propose that the iterative interleaving of REM and non-REM across a night boosts the formation of complex knowledge frameworks, and allows these frameworks to be restructured, thus facilitating creative thought. We outline a hypothetical computational model which will allow explicit testing of these hypotheses.

How a robot can learn to recognize itself on a mirror

Zeng, Y., Zhao, Y., Bai, J. et al., Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self Model for Self-Recognition, Cogn Comput (2018) 10: 307, DOI: 10.1007/s12559-017-9505-1.

The neural correlates and nature of self-consciousness is an advanced topic in Cognitive Neuroscience. Only a few animal species have been testified to be with this cognitive ability. From artificial intelligence and robotics point of view, few efforts are deeply rooted in the neural correlates and brain mechanisms of biological self-consciousness. Despite the fact that the scientific understanding of biological self-consciousness is still in preliminary stage, we make our efforts to integrate and adopt known biological findings of self-consciousness to build a brain-inspired model for robot self-consciousness. In this paper, we propose a brain-inspired robot bodily self model based on extensions to primate mirror neuron system and apply it to humanoid robot for self recognition. In this model, the robot firstly learns the correlations between self-generated actions and visual feedbacks in motion by learning with spike timing dependent plasticity (STDP), and then learns the appearance of body part with the expectation that the visual feedback is consistent with its motion. Based on this model, the robot uses multisensory integration to learn its own body in real world and in mirror. Then it can distinguish itself from others. In a mirror test setting with three robots with the same appearance, with the proposed brain-inspired robot bodily self model, each of them can recognize itself in the mirror after these robots make random movements at the same time. The theoretic modeling and experimental validations indicate that the brain-inspired robot bodily self model is biologically inspired, and computationally feasible as a foundation for robot self recognition.

What is Cognitive Computational Neuroscience

Thomas Naselaris, Danielle S. Bassett, Alyson K. Fletcher, Konrad Kording, Nikolaus Kriegeskorte, Hendrikje Nienborg, Russell A. Poldrack, Daphna Shohamy, Kendrick Kay, Cognitive Computational Neuroscience: A New Conference for an Emerging Discipline, Trends in Cognitive Sciences, Volume 22, Issue 5, 2018, Pages 365-367, DOI: 10.1016/j.tics.2018.02.008.

Understanding the computational principles that underlie complex behavior is a central goal in cognitive science, artificial intelligence, and neuroscience. In an attempt to unify these disconnected communities, we created a new conference called Cognitive Computational Neuroscience (CCN). The inaugural meeting revealed considerable enthusiasm but significant obstacles remain.

On how seeking for the lowest-cost action is not always what happens in reality

Michael Inzlicht, Amitai Shenhav, Christopher Y. Olivola, The Effort Paradox: Effort Is Both Costly and Valued, Trends in Cognitive Sciences, Volume 22, Issue 4, 2018, Pages 337-349, DOI: 10.1016/j.tics.2018.01.007.

According to prominent models in cognitive psychology, neuroscience, and economics, effort (be it physical or mental) is costly: when given a choice, humans and non-human animals alike tend to avoid effort. Here, we suggest that the opposite is also true and review extensive evidence that effort can also add value. Not only can the same outcomes be more rewarding if we apply more (not less) effort, sometimes we select options precisely because they require effort. Given the increasing recognition of effort’s role in motivation, cognitive control, and value-based decision-making, considering this neglected side of effort will not only improve formal computational models, but also provide clues about how to promote sustained mental effort across time.

On how children learn with progressive environment changes aimed at improving their learning statistically

Linda B. Smith, Swapnaa Jayaraman, Elizabeth Clerkin, Chen Yu, The Developing Infant Creates a Curriculum for Statistical Learning, Trends in Cognitive Sciences, Volume 22, Issue 4, 2018, Pages 325-336, DOI: 10.1016/j.tics.2018.02.004.

New efforts are using head cameras and eye-trackers worn by infants to capture everyday visual environments from the point of view of the infant learner. From this vantage point, the training sets for statistical learning develop as the sensorimotor abilities of the infant develop, yielding a series of ordered datasets for visual learning that differ in content and structure between timepoints but are highly selective at each timepoint. These changing environments may constitute a developmentally ordered curriculum that optimizes learning across many domains. Future advances in computational models will be necessary to connect the developmentally changing content and statistics of infant experience to the internal machinery that does the learning.

A theory that integrates motivation and control

Giovanni Pezzulo, Francesco Rigoli, Karl J. Friston, Hierarchical Active Inference: A Theory of Motivated Control, Trends in Cognitive Sciences, Volume 22, Issue 4, 2018, Pages 294-306, DOI: 10.1016/j.tics.2018.01.009.

Motivated control refers to the coordination of behaviour to achieve affectively valenced outcomes or goals. The study of motivated control traditionally assumes a distinction between control and motivational processes, which map to distinct (dorsolateral versus ventromedial) brain systems. However, the respective roles and interactions between these processes remain controversial. We offer a novel perspective that casts control and motivational processes as complementary aspects − goal propagation and prioritization, respectively − of active inference and hierarchical goal processing under deep generative models. We propose that the control hierarchy propagates prior preferences or goals, but their precision is informed by the motivational context, inferred at different levels of the motivational hierarchy. The ensuing integration of control and motivational processes underwrites action and policy selection and, ultimately, motivated behaviour, by enabling deep inference to prioritize goals in a context-sensitive way.