David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Cognitive Science 35 (5):939-962 (2011)
We report the results of an experiment in which human subjects were trained to perform a perceptual matching task. Subjects were asked to manipulate comparison objects until they matched target objects using the fewest manipulations possible. An unusual feature of the experimental task is that efficient performance requires an understanding of the hidden or latent causal structure governing the relationships between actions and perceptual outcomes. We use two benchmarks to evaluate the quality of subjects’ learning. One benchmark is based on optimal performance as calculated by a dynamic programming procedure. The other is based on an adaptive computational agent that uses a reinforcement-learning method known as Q-learning to learn to perform the task. Our analyses suggest that subjects were successful learners. In particular, they learned to perform the perceptual matching task in a near-optimal manner (i.e., using a small number of manipulations) at the end of training. Subjects were able to achieve near-optimal performance because they learned, at least partially, the causal structure underlying the task. In addition, subjects’ performances were broadly consistent with those of model-based reinforcement-learning agents that built and used internal models of how their actions influenced the external environment. We hypothesize that people will achieve near-optimal performances on tasks requiring sequences of action—especially sensorimotor tasks with underlying latent causal structures—when they can detect the effects of their actions on the environment, and when they can represent and reason about these effects using an internal mental model
|Keywords||Causal learning Reinforcement learning Ideal actor Sequential action Action learning Perceptual matching|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Dana H. Ballard, Mary M. Hayhoe, Polly K. Pook & Rajesh P. N. Rao (1997). Deictic Codes for the Embodiment of Cognition. Behavioral and Brain Sciences 20 (4):723-742.
Alison Gopnik & Laura Schulz (eds.) (2007). Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
Todd M. Gureckis & Bradley C. Love (2009). Short-Term Gains, Long-Term Pains: How Cues About State Aid Learning in Dynamic Environments. Cognition 113 (3):293-313.
Michael D. Lee (2006). A Hierarchical Bayesian Model of Human Decision‐Making on an Optimal Stopping Problem. Cognitive Science 30 (3):1-26.
Ron Sun (2005). The Interaction of the Explicit and the Implicit in Skill Learning: A Dual-Process Approach. Psychological Review 112:159-192.
Citations of this work BETA
No citations found.
Similar books and articles
Thomas Busey, Chen Yu, Dean Wyatte & John Vanderkolk (2013). Temporal Sequences Quantify the Contributions of Individual Fixations in Complex Perceptual Matching Tasks. Cognitive Science 37 (4):731-756.
Sylvain Moutier, Nathalie Angeard & Olivier Houde (2002). Deductive Reasoning and Matching-Bias Inhibition Training: Evidence From a Debiasing Paradigm. Thinking and Reasoning 8 (3):205 – 224.
Frank C. Keil (2008). How to Learn Multiple Tasks. Biological Theory 3 (1):30-41.
Georgina Jackson & Stephen Jackson (1995). Do Measures of Explicit Learning Actually Measure What is Being Learnt in the Serial Reaction Time Task? Psyche 2 (20).
Christian P. Janssen & Wayne D. Gray (2012). When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition. Cognitive Science 36 (2):333-358.
Hiroshi Yama (2001). Matching Versus Optimal Data Selection in the Wason Selection Task. Thinking and Reasoning 7 (3):295 – 311.
Pepper Williams, Isabel Gauthier & Michael J. Tarr (1998). Feature Learning During the Acquisition of Perceptual Expertise. Behavioral and Brain Sciences 21 (1):40-41.
Arthur Markman, W. Maddox & G. C. Baldwin (2007). Using Regulatory Focus to Explore Implicit and Explicit Processing in Concept Learning. Journal of Consciousness Studies 14 (s 9-10):132-155.
Added to index2011-05-05
Total downloads12 ( #147,480 of 1,681,623 )
Recent downloads (6 months)2 ( #112,085 of 1,681,623 )
How can I increase my downloads?