Tag Archives: Mind Models

Trying to reach general AI through just decision-making (rewards) instead of using a diversity of paradigms

avid Silver, Satinder Singh, Doina Precup, Richard S. Sutton, Reward is enough, . Artificial Intelligence, Volume 299, 2021 DOI: 10.1016/j.artint.2021.103535.

In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence.


  • The computational and physical limitations of the agent to cope with a too complex world is the main reason to use learning instead of pre-built knowledge (evolution): it allows the agent to focus on acquiring skills for its own circumstances first, that are the most important for it.
  • Argument why classification (supervised learning) is less powerful and efficient than RL.
  • Same with multi-agent settings vs. one agent confronted with a single complex environment (containing other agents).

A robot architecture for humanoids able to coordinate different cognitive processes (perception, decision-making, etc.) in a hierarchical fashion

J. Hwang and J. Tani, Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 345-358 DOI: 10.1109/TCDS.2017.2714170.

This paper investigates how adequate coordination among the different cognitive processes of a humanoid robot can be developed through end-to-end learning of direct perception of visuomotor stream. We propose a deep dynamic neural network model built on a dynamic vision network, a motor generation network, and a higher-level network. The proposed model was designed to process and to integrate direct perception of dynamic visuomotor patterns in a hierarchical model characterized by different spatial and temporal constraints imposed on each level. We conducted synthetic robotic experiments in which a robot learned to read human’s intention through observing the gestures and then to generate the corresponding goal-directed actions. Results verify that the proposed model is able to learn the tutored skills and to generalize them to novel situations. The model showed synergic coordination of perception, action, and decision making, and it integrated and coordinated a set of cognitive skills including visual perception, intention reading, attention switching, working memory, action preparation, and execution in a seamless manner. Analysis reveals that coherent internal representations emerged at each level of the hierarchy. Higher-level representation reflecting actional intention developed by means of continuous integration of the lower-level visuo-proprioceptive stream.

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.

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.

A model of others’ emotions that predicts very well experimental results

Rebecca Saxe, Seeing Other Minds in 3D, Trends in Cognitive Sciences, Volume 22, Issue 3, 2018, Pages 193-195, DOI: 10.1016/j.tics.2018.01.003.

Tamir and Thornton [1] have identified three key dimensions that organize our understanding of other minds. These dimensions (glossed as valence, social impact, and rationality) can capture the similarities and differences between concepts of internal experiences (anger, loneliness, gratitude), and also between concepts of personalities (aggressive, introverted, agreeable). Most impressively, the three dimensions explain the patterns of hemodynamic activity in our brains as we consider these experiences [2] (Box 1). States such as anger and gratitude are invisible, but the patterns evoked in our brain as we think about them are as predictable by the model of Tamir and Thornton as the patterns evoked in our visual cortex when we look at chairs, bicycles, or pineapples are predictable by models of high-level vision [3]. Human social prediction follows the same dimensions: observers predict that transitions are more likely between states that are ‘nearby’ in this abstract 3D space [4]. Thus, we expect that a friend now feeling ‘anxious’ will be more likely to feel ‘sluggish’ than ‘energetic’ later.

Study of the explanation of probability and reasoning in the human mind through mental models, probability logic and classical logic

P.N. Johnson-Laird, Sangeet S. Khemlani, Geoffrey P. Goodwin, Logic, probability, and human reasoning, Trends in Cognitive Sciences, Volume 19, Issue 4, April 2015, Pages 201-214, ISSN 1364-6613, DOI: 10.1016/j.tics.2015.02.006.

This review addresses the long-standing puzzle of how logic and probability fit together in human reasoning. Many cognitive scientists argue that conventional logic cannot underlie deductions, because it never requires valid conclusions to be withdrawn – not even if they are false; it treats conditional assertions implausibly; and it yields many vapid, although valid, conclusions. A new paradigm of probability logic allows conclusions to be withdrawn and treats conditionals more plausibly, although it does not address the problem of vapidity. The theory of mental models solves all of these problems. It explains how people reason about probabilities and postulates that the machinery for reasoning is itself probabilistic. Recent investigations accordingly suggest a way to integrate probability and deduction.