Literature DB >> 22198929

Two mechanisms of human contingency learning.

Daniel A Sternberg1, James L McClelland.   

Abstract

How do humans learn contingencies between events? Both pathway-strengthening and inference-based process models have been proposed to explain contingency learning. We propose that each of these processes is used in different conditions. Participants viewed displays that contained single or paired objects and learned which displays were usually followed by the appearance of a dot. Some participants predicted whether the dot would appear before seeing the outcome, whereas other participants were required to respond quickly if the dot appeared shortly after the display. In the prediction task, instructions guiding participants to infer which objects caused the dot to appear were necessary in order for contingencies associated with one object to influence participants' predictions about the object with which it had been paired. In the response task, contingencies associated with one object affected responses to its pair mate irrespective of whether or not participants were given causal instructions. Our results challenge single-mechanism accounts of contingency learning and suggest that the mechanisms underlying performance in the two tasks are distinct.

Entities:  

Mesh:

Year:  2011        PMID: 22198929     DOI: 10.1177/0956797611429577

Source DB:  PubMed          Journal:  Psychol Sci        ISSN: 0956-7976


  10 in total

1.  The power of possibility: causal learning, counterfactual reasoning, and pretend play.

Authors:  Daphna Buchsbaum; Sophie Bridgers; Deena Skolnick Weisberg; Alison Gopnik
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2012-08-05       Impact factor: 6.237

2.  The Effect of Alternative vs. Focal Identity Accessibility on the Intent to Purchase Products: An Exploratory Study Based on Chinese Culture.

Authors:  Fei Chen; Cheng Cheng Yan; Lin Wang; Xiao Jing Lou
Journal:  Front Psychol       Date:  2022-06-07

3.  Piecing together the orbitofrontal puzzle.

Authors:  Catherine Elorette; Atsushi Fujimoto; J Megan Fredericks; Frederic M Stoll; Brian E Russ; Peter H Rudebeck
Journal:  Behav Neurosci       Date:  2021-04       Impact factor: 1.912

4.  Violations of newly-learned predictions elicit two distinct P3 components.

Authors:  Abigail Noyce; Robert Sekuler
Journal:  Front Hum Neurosci       Date:  2014-06-10       Impact factor: 3.169

5.  Rapid Top-Down Control of Behavior Due to Propositional Knowledge in Human Associative Learning.

Authors:  Francisco J López; Rafael Alonso; David Luque
Journal:  PLoS One       Date:  2016-11-28       Impact factor: 3.240

6.  Three Ways That Non-associative Knowledge May Affect Associative Learning Processes.

Authors:  Anna Thorwart; Evan J Livesey
Journal:  Front Psychol       Date:  2016-12-27

7.  Dual-Routes and the Cost of Determining Least-Costs.

Authors:  Steven Phillips; Yuji Takeda; Fumie Sugimoto
Journal:  Front Psychol       Date:  2017-11-07

8.  Hippocampal pattern separation supports reinforcement learning.

Authors:  Ian C Ballard; Anthony D Wagner; Samuel M McClure
Journal:  Nat Commun       Date:  2019-03-06       Impact factor: 14.919

9.  Context and time in causal learning: contingency and mood dependent effects.

Authors:  Rachel M Msetfi; Caroline Wade; Robin A Murphy
Journal:  PLoS One       Date:  2013-05-15       Impact factor: 3.240

10.  Second-Order Systematicity of Associative Learning: A Paradox for Classical Compositionality and a Coalgebraic Resolution.

Authors:  Steven Phillips; William H Wilson
Journal:  PLoS One       Date:  2016-08-09       Impact factor: 3.240

  10 in total

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