Model-Based Reasoning in Humans Becomes Automatic with Training
Publication Date
September 17, 2015
Journal
PLOS Computational Biology
Authors
Marcos Economides, Zeb Kurth Nelson, Annika Lübbert, Marc Guitart Masip, et al
Volume
11
Issue
9
Pages
e1004463
DOI
http://doi.org/10.1371/journal.pcbi.1004463
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004463
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26379239
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588166
Europe PMC
http://europepmc.org/abstract/MED/26379239
Web of Science
000362266400030
Scopus
84943518832
Mendeley
http://www.mendeley.com/research/modelbased-reasoning-humans-becomes-automatic-training
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Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2268951", "https://ndownloader.figshare.com/files/2268952", "https://ndownloader.figshare.com/files/2268953", "https://ndownloader.figshare.com/files/2268954", "https://ndownloader.figshare.com/files/2268955", "https://ndownloader.figshare.com/files/2268956", "https://ndownloader.figshare.com/files/2268957", "https://ndownloader.figshare.com/files/2268958"], "description"=>"<div><p>Model-based and model-free reinforcement learning (RL) have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load—a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders.</p></div>", "links"=>[], "tags"=>["load", "failure modes", "Humans Becomes Automatic", "learnt model", "reasoning", "subject", "algorithmic realizations", "rl", "analysis methods", "parallelizable fashion", "action strategies", "task familiarity"], "article_id"=>1547149, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Marcos Economides", "Zeb Kurth-Nelson", "Annika Lübbert", "Marc Guitart-Masip", "Raymond J. Dolan"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004463.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004463.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004463.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004463.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004463.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004463.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004463.s007", "https://dx.doi.org/10.1371/journal.pcbi.1004463.s008"], "stats"=>{"downloads"=>6, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_Based_Reasoning_in_Humans_Becomes_Automatic_with_Training_/1547149", "title"=>"Model-Based Reasoning in Humans Becomes Automatic with Training", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-09-17 03:03:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/2268941"], "description"=>"<p>(<b>A</b>) Subjects chose between a pair of fractals at each of two stages, where a choice at the first-stage lead to one of two second-stage pairs with a fixed probability. This transition structure could be exploited by the player. The second-stage choice followed either a reward (gold coin) or no reward (0), according to independently fluctuating reward contingencies. On dual-task trials (displayed in the figure), two different numbers of physically different sizes were displayed above each fractal at the first-stage. Following second-stage feedback, the word ‘SIZE’ or ‘VALUE’ was presented on the screen, requiring the player to indicate whether the number that was larger in size, or value, respectively, had appeared on the left or right side of the screen. Correct responses were incentivized via monetary gain; incorrect responses were unrewarded. (<b>B</b>) On days 1 and 2 the ‘high load group’ played alternating blocks of single-task (128) and dual task (64) trials (for a total of 4 blocks), while the ‘low load group’ played 2 consecutive blocks of single-task (128) trials. On day 3 both groups played alternating blocks of single-task and dual task trials (as per the ‘high load group’ on days 1–2).</p>", "links"=>[], "tags"=>["load", "failure modes", "Humans Becomes Automatic", "learnt model", "reasoning", "subject", "algorithmic realizations", "rl", "analysis methods", "parallelizable fashion", "action strategies", "task familiarity"], "article_id"=>1547139, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Marcos Economides", "Zeb Kurth-Nelson", "Annika Lübbert", "Marc Guitart-Masip", "Raymond J. Dolan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004463.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Task_and_experimental_design_/1547139", "title"=>"Task and experimental design.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-17 03:03:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/2268942"], "description"=>"<p>The weighting parameter <b><i>w</i></b> represents a measure of model-based (<b><i>w</i></b> = 1) relative to model-free (<b><i>w</i></b> = 0) control. <b><i>w</i></b> was lower in the dual-task (high load) condition compared to the single-task (low load) condition in naïve (‘high load group’, day 1) but not trained (‘low load group’, day 3) subjects. Vertical lines represent SEM. * denotes p < 0.05. <i>α = learning rate</i>, <i>β = inverse temperature</i>, <i>ε = lapse rate</i>. (<b>A</b>) Mean best-fitting parameters for day 1 of training in the ‘high load group’. (<b>B</b>) Mean best-fitting parameters for day 3 of training in the ‘low load group’.</p>", "links"=>[], "tags"=>["load", "failure modes", "Humans Becomes Automatic", "learnt model", "reasoning", "subject", "algorithmic realizations", "rl", "analysis methods", "parallelizable fashion", "action strategies", "task familiarity"], "article_id"=>1547140, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Marcos Economides", "Zeb Kurth-Nelson", "Annika Lübbert", "Marc Guitart-Masip", "Raymond J. Dolan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004463.g002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computational_modeling_Between_group_comparison_/1547140", "title"=>"Computational modeling: Between-group comparison.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-17 03:03:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/2268947"], "description"=>"<p>Results of a logistic regression that considers model-free and model-based influences on choice in the current trial with respect to events that occurred up to 3 trials in the past. (<b>A</b>) Each regressor describes whether events on trial <i>t</i><sub><i>-1</i></sub>, <i>t</i><sub><i>-2</i></sub> and <i>t</i><sub><i>-3</i></sub> increase (coded as +1) or decrease (coded as -1) the probability of choosing fractal A according to a model-free or a model-based system (6 total regressors). Model-free coefficients are plotted on the left-hand side of x-axis, and model-based coefficients on the right-hand side. Data from days 1 and day 3 are plotted in the top and bottom panels respectively. Coefficients corresponding to the single-task are shown in blue, and those corresponding to the dual-task are shown in orange. Vertical lines represent SEM. * denotes p < 0.05, ‡ denotes p = 0.09. (<b>B</b>) For each condition (single-task in blue, dual-task in orange), and separately for days 1 and 3, we summed (individually) the coefficients corresponding to trial <i>t</i><sub><i>-1</i></sub>, <i>t</i><sub><i>-2</i></sub> and <i>t</i><sub><i>-3</i></sub>, and derived single estimates of the degree to which model-free (plotted on the y-axis) and model-based (plotted on the x-axis) control were dominant in choice. Vertical lines represent 95% confidence intervals. A line through the origin represents points in which model-free and model-based valuations have an equal influence on choice.</p>", "links"=>[], "tags"=>["load", "failure modes", "Humans Becomes Automatic", "learnt model", "reasoning", "subject", "algorithmic realizations", "rl", "analysis methods", "parallelizable fashion", "action strategies", "task familiarity"], "article_id"=>1547145, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Marcos Economides", "Zeb Kurth-Nelson", "Annika Lübbert", "Marc Guitart-Masip", "Raymond J. Dolan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004463.g004", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_free_and_model_based_influences_on_choice_/1547145", "title"=>"Model-free and model-based influences on choice.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-17 03:03:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/2268945"], "description"=>"<p>The weighting parameter <b><i>w</i></b> represents a measure of model-based (<b><i>w</i></b> = 1) relative to model-free (<b><i>w</i></b> = 0) control. At the group level, model parameters remained relatively stable across single-task trials, indicating that performance in the absence of load was modestly influenced by training. By contrast, we observed higher <b><i>w</i></b> values and higher learning rates with increased task exposure during dual-task trials. Vertical lines represent SEM. <i>α = learning rate</i>, <i>β = inverse temperature</i>, <i>ε = lapse rate</i>. (<b>A</b>) Mean best-fitting parameters when fitting data from the ‘high load group’ and days 1–3 of training for single-task trials. (<b>B</b>) Mean best-fitting parameters when fitting data from the ‘high load group’ and days 1–3 of training for dual-task trials.</p>", "links"=>[], "tags"=>["load", "failure modes", "Humans Becomes Automatic", "learnt model", "reasoning", "subject", "algorithmic realizations", "rl", "analysis methods", "parallelizable fashion", "action strategies", "task familiarity"], "article_id"=>1547143, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Marcos Economides", "Zeb Kurth-Nelson", "Annika Lübbert", "Marc Guitart-Masip", "Raymond J. Dolan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004463.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computational_modeling_Within_group_comparison_/1547143", "title"=>"Computational modeling: Within-group comparison.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-09-17 03:03:26"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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