Showing items 1 - 6 of 6

Causal cognition, force dynamics and early hunting technologies

- Gärdenfors, Peter, Lombard, Marlize

Causal cognition, force dynamics and early hunting technologies

- Gärdenfors, Peter, Lombard, Marlize

Events and causal mappings modeled in conceptual spaces

- Gärdenfors, Peter

  • Authors: Gärdenfors, Peter
  • Date: 2020
  • Subjects: Causation , Robotics , Event
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/432460 , uj:37356 , Gärdenfors P (2020) Events and Causal Mappings Modeled in Conceptual Spaces. Front. Psychol. 11:630. doi: 10.3389/fpsyg.2020.00630
  • Description: Abstract: , The aim of the article is to present a model of causal relations that is based on what is known about human causal reasoning and that forms guidelines for implementations in robots. I argue for two theses concerning human cognition. The first is that human causal cognition, in contrast to that of other animals, is based on the understanding of the forces that are involved. The second thesis is that humans think about causality in terms of events. I present a two-vector model of events, developed by Gärdenfors and Warglien, which states that an event is represented in terms of two main components – the force of an action that drives the event, and the result of its application. Apart from the causal mapping, the event model contains representations of a patient, an agent, and possibly some other roles. Agents and patients are objects (animate or inanimate) that have different properties. Following my theory of conceptual spaces, they can be described as vectors of property values. At least two spaces are needed to describe an event, an action space and a result space. The result of an event is modeled as a vector representing the change of properties of the patient before and after the event. In robotics the focus has been on describing results. The proposed model also includes the causal part of events, typically described as an action. A central part of an event category is the mapping from actions to results. This mapping contains the central information about causal relations. In applications of the two-vector model, the central problem is how the event mapping can be learned in a way that is amenable to implementations in robots. Three processes are central for event cognition: causal thinking, control of action and learning by generalization. Although it is not yet clear which is the best way to model how the mappings can be learned, they should be constrained by three corresponding mathematical properties: monotonicity (related to qualitative causal thinking); continuity (plays a key role in activities of action control); and convexity (facilitates generalization and the categorization of events). I argue that Bayesian models are not suitable for these purposes, but some more geometrically oriented approach to event mappings should be used.
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Primary cognitive categories are determined by their invariances

- Gärdenfors, Peter

  • Authors: Gärdenfors, Peter
  • Date: 2020
  • Subjects: Category , Invariance , Space
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/460096 , uj:40931 , Citation: Gärdenfors, P. et al. 2020. Primary cognitive categories are determined by their invariances. Frontiers in Psychology, 11:584017. doi: 10.3389/fpsyg.2020.584017
  • Description: Abstract: The world as we perceive it is structured into objects, actions and places that form parts of events. In this article, my aim is to explain why these categories are cognitively primary. From an empiricist and evolutionary standpoint, it is argued that the reduction of the complexity of sensory signals is based on the brain’s capacity to identify various types of invariances that are evolutionarily relevant for the activities of the organism. The first aim of the article is to explain why places, object and actions are primary cognitive categories in our constructions of the external world. It is shown that the invariances that determine these categories have their separate characteristics and that they are, by and large, independent of each other. This separation is supported by what is known about the neural mechanisms. The second aim is to show that the category of events can be analyzed as being constituted of the primary categories. The category of numbers is briefly discussed. Some implications for computational models of the categories are also presented.
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The missing link between memory and reinforcement learning

- Balkenius, Christian, Tjøstheim, Trond A., Johansson, Birger, Wallin, Annika, Gärdenfors, Peter

  • Authors: Balkenius, Christian , Tjøstheim, Trond A. , Johansson, Birger , Wallin, Annika , Gärdenfors, Peter
  • Date: 2020
  • Subjects: Memory model , Decision making , Accumulator model
  • Language: English
  • Type: Article
  • Identifier: http://hdl.handle.net/10210/460108 , uj:40932 , Citation: Balkenius C, Tjøstheim T.A, Johansson B, Wallin A and Gärdenfors P. (2020). The Missing Link Between Memory and Reinforcement Learning. Front. Psychol. 11:560080. doi: 10.3389/fpsyg.2020.560080
  • Description: Abstract: Reinforcement learning systems usually assume that a value function is defined over all states (or state-action pairs) that can immediately give the value of a particular state or action. These values are used by a selection mechanism to decide which action to take. In contrast, when humans and animals make decisions, they collect evidence for different alternatives over time and take action only when sufficient evidence has been accumulated. We have previously developed a model of memory processing that includes semantic, episodic and working memory in a comprehensive architecture. Here, we describe how this memory mechanism can support decision making when the alternatives cannot be evaluated based on immediate sensory information alone. Instead we first imagine, and then evaluate a possible future that will result from choosing one of the alternatives. Here we present an extended model that can be used as a model for decision making that depends on accumulating evidence over time, whether that information comes from the sequential attention to different sensory properties or from internal simulation of the consequences of making a particular choice. We show how the new model explains both simple immediate choices, choices that depend on multiple sensory factors and complicated selections between alternatives that require forward looking simulations based on episodic and semantic memory structures. In this framework, vicarious trial and error is explained as an internal simulation that accumulates evidence for a particular choice. We argue that a system like this forms the “missing link” between more traditional ideas of semantic and episodic memory, and the associative nature of reinforcement learning.
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Tracking the evolution of causal cognition in humans

- Lombard, Marlize, Gärdenfors, Peter

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