David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
Learn more about PhilPapers
A significant part of everyday learning occurs incidentally — a process typically described as implicit learning. A central issue in this domain and others, such as language acquisition, is the extent to which performance depends on the acquisition and deployment of abstract rules. Shanks and colleagues ,  have suggested (1) that discrimination between grammatical and ungrammatical instances of a biconditional grammar requires the acquisition and use of abstract rules, and (2) that training conditions — in particular whether instructions orient participants to identify the relevant rules or not — strongly influence the extent to which such rules will be learned. In this paper, we show (1) that a Simple Recurrent Network can in fact, under some conditions, learn a biconditional grammar, (2) that training conditions indeed influence learning in simple auto-associators networks and (3) that such networks can likewise learn about biconditional grammars, albeit to a lesser extent than human participants. These findings suggest that mastering biconditional grammars does not require the acquisition of abstract rules to the extent implied by Shanks and colleagues, and that performance on such material may in fact be based, at least in part, on simple associative learning mechanisms.
|Keywords||No keywords specified (fix it)|
|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
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Bert Timmermans, Rules Versus Statistics in Biconditional Grammar Learning: A Simulation Based on Shanks Et Al. (1997).
Axel Cleeremans & L. JimC)nez (1998). Implicit Sequence Learning: The Truth is in the Details. In Michael A. Stadler & Peter A. Frensch (eds.), Handbook of Implicit Learning. Newbury Park, CA: Sage
Pascal Wagner-Egger (2007). Conditional Reasoning and the Wason Selection Task: Biconditional Interpretation Instead of Reasoning Bias. Thinking and Reasoning 13 (4):484 – 505.
Peter Robinson (2005). Rules and Similarity Processes in Artificial Grammar and Natural Second Language Learning: What is the “Default”? Behavioral and Brain Sciences 28 (1):32-33.
Arnaud Destrebecqz & Axel Cleeremans (2001). Can Sequence Learning Be Implicit? New Evidence with the Process Dissociation Procedure. Psychonomic Bulletin and Review 8 (2):343-350.
Emmanuel M. Pothos (2005). The Rules Versus Similarity Distinction. Behavioral and Brain Sciences 28 (1):1-14.
Alexander Clark & Shalom Lappin (2013). Complexity in Language Acquisition. Topics in Cognitive Science 5 (1):89-110.
Richard E. Nisbett (ed.) (1993). Rules for Reasoning. L. Erlbaum Associates.
Axel Cleeremans (1993). Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing. MIT Press.
Domenic Berducci (2010). Teaching, Learning, Describing, and Judging Via Wittgensteinian Rules: Connections to Community. [REVIEW] Human Studies 33 (4):445-463.
Kuo-Chin Chang, Tzung-Pei Hong & Shian-Shyong Tseng (1996). Machine Learning by Imitating Human Learning. Minds and Machines 6 (2):203-228.
Axel Cleeremans & Zoltán Dienes (2008). Computational Models of Implicit Learning. In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press 396--421.
Added to index2010-12-22
Total downloads15 ( #252,618 of 1,934,371 )
Recent downloads (6 months)1 ( #434,317 of 1,934,371 )
How can I increase my downloads?