Cortical and Hippocampal Correlates of Deliberation during Model-Based Decisions for Rewards in Humans
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{"title"=>"Cortical and Hippocampal Correlates of Deliberation during Model-Based Decisions for Rewards in Humans", "type"=>"journal", "authors"=>[{"first_name"=>"Aaron M.", "last_name"=>"Bornstein", "scopus_author_id"=>"42861001200"}, {"first_name"=>"Nathaniel D.", "last_name"=>"Daw", "scopus_author_id"=>"7005134854"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"doi"=>"10.1371/journal.pcbi.1003387", "pui"=>"372170452", "pmid"=>"24339770", "sgr"=>"84892777219", "isbn"=>"1553-734X\\r1553-7358", "scopus"=>"2-s2.0-84892777219", "issn"=>"1553734X"}, "id"=>"f12f55a9-7925-3f43-8c78-cac51a5a598f", "abstract"=>"How do we use our memories of the past to guide decisions we've never had to make before? Although extensive work describes how the brain learns to repeat rewarded actions, decisions can also be influenced by associations between stimuli or events not directly involving reward - such as when planning routes using a cognitive map or chess moves using predicted countermoves - and these sorts of associations are critical when deciding among novel options. This process is known as model-based decision making. While the learning of environmental relations that might support model-based decisions is well studied, and separately this sort of information has been inferred to impact decisions, there is little evidence concerning the full cycle by which such associations are acquired and drive choices. Of particular interest is whether decisions are directly supported by the same mnemonic systems characterized for relational learning more generally, or instead rely on other, specialized representations. Here, building on our previous work, which isolated dual representations underlying sequential predictive learning, we directly demonstrate that one such representation, encoded by the hippocampal memory system and adjacent cortical structures, supports goal-directed decisions. Using interleaved learning and decision tasks, we monitor predictive learning directly and also trace its influence on decisions for reward. We quantitatively compare the learning processes underlying multiple behavioral and fMRI observables using computational model fits. Across both tasks, a quantitatively consistent learning process explains reaction times, choices, and both expectation- and surprise-related neural activity. The same hippocampal and ventral stream regions engaged in anticipating stimuli during learning are also engaged in proportion to the difficulty of decisions. These results support a role for predictive associations learned by the hippocampal memory system to be recalled during choice formation.", "link"=>"http://www.mendeley.com/research/cortical-hippocampal-correlates-deliberation-during-modelbased-decisions-rewards-humans", "reader_count"=>70, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Student > Doctoral Student"=>4, "Researcher"=>19, "Student > Ph. D. Student"=>29, "Student > Postgraduate"=>1, "Student > Master"=>8, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Student > Doctoral Student"=>4, "Researcher"=>19, "Student > Ph. D. Student"=>29, "Student > Postgraduate"=>1, "Student > Master"=>8, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Environmental Science"=>1, "Agricultural and Biological Sciences"=>17, "Medicine and Dentistry"=>3, "Neuroscience"=>11, "Physics and Astronomy"=>1, "Psychology"=>26, "Social Sciences"=>1, "Computer Science"=>7}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Neuroscience"=>{"Neuroscience"=>11}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>26}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>7}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Belgium"=>1, "United States"=>1, "United Kingdom"=>3, "France"=>1, "Switzerland"=>1, "Germany"=>2}, "group_count"=>3}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1305264"], "description"=>"<p>Participants were asked to use their knowledge of the sequential transition structure to make decisions for reward. Choice rounds consisted of three steps. First, participants observed the reward amount and target image for one second. Next, they were given five seconds to choose one of two images to start the sequence from again. This choice was of varying difficulty, depending on how likely it was for each choice image to be followed by the reward image. For the next several presentations after choice, each observation of the valued image was accompanied by reward. (Images shown here are not those used in the study, but public domain stand-ins from clker.com that reflect the category of the photographs used during the experiment.)</p>", "links"=>[], "tags"=>[], "article_id"=>869915, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Aaron M. Bornstein", "Nathaniel D. Daw"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003387.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Choice_task_/869915", "title"=>"Choice task.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 04:23:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305265"], "description"=>"<p><i>a</i>. Reaction time on the image identification task decreases as the ‘ground-truth’ probability – the probabilities generated by the task program, and uninstructed to the participant – of that image appearing, conditional on the previous image increases. Here, for each participant, RTs were first corrected for their mean and a number of nuisance effects, estimated using a linear regression containing only these effects as explanatory variables. <i>b</i>. Across subjects, the fitted learning rate values that best explain behavior. For reaction times, the best-fitting model contained two learning rates (one ‘slow’, the other ‘fast’), whose estimates were combined linearly according to a fitted weighting parameter. For choice behavior, the best-fitting model contained one learning rate, statistically indistinguishable from the slow rate fit to reaction times, but significantly different from the fast.</p>", "links"=>[], "tags"=>[], "article_id"=>869916, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Aaron M. Bornstein", "Nathaniel D. Daw"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003387.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Behavioral_analyses_/869916", "title"=>"Behavioral analyses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 04:23:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305266"], "description"=>"<p><i>a</i>. BOLD signal correlated with forward entropy in the fast process. Activity in the dorsal caudate was significant after correction over an anatomically-defined mask of bilateral caudate. <i>b</i>. BOLD signal correlated with forward entropy in the slow process. Activity in the anterior hippocampus was significant after correction over an anatomically-defined mask of left hippocampus. Both <i>a</i> and <i>b</i> displayed at , uncorrected.</p>", "links"=>[], "tags"=>["reflecting", "anticipation"], "article_id"=>869917, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Aaron M. Bornstein", "Nathaniel D. Daw"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003387.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_BOLD_signal_reflecting_anticipation_of_the_next_stimulus_/869917", "title"=>"BOLD signal reflecting anticipation of the next stimulus.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 04:23:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305267"], "description"=>"<p>Learning rates computed from each of our regions of interest, overlaid on the learning rates fit to reaction time behavior. The best-fitting learning rates are displayed for each type of trial: sequential image-identification trials, decision trials, and choice outcome trials. For learning trials in hippocampus and caudate, learning rates are computed using the forward entropy regressor. For learning trials in face- and house-selective cortex, learning rates are computed using the estimated probability of the image appearing on the next trial. For decision trials in hippocampus, learning rate is computed using the choice difficulty regressor. For decision trials in face- and house-selective cortex, learning rates are computed using the portion of the choice difficulty regressor specific to that image. For outcome trials in nucleus accumbens, learning rate is computed using the reward prediction error regressor. Error bars: 1 SEM.</p>", "links"=>[], "tags"=>["computed"], "article_id"=>869918, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Aaron M. Bornstein", "Nathaniel D. Daw"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003387.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Learning_rate_computed_from_BOLD_signal_/869918", "title"=>"Learning rate computed from BOLD signal.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 04:23:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305268"], "description"=>"<p>During deliberation periods after choice options were presented, we observed activity in <i>a</i>. posterior cingulate (−2, −18, 32), anterior mPFC (4, 64, −2) and <i>b</i>. left hippocampus (peak −24, −10, −18), all significantly correlated with choice difficulty in the slow process. <i>c</i>. BOLD signal at outcome. A cluster in the nucleus accumbens (peak 10, 12, −2) correlated with reward prediction error as computed using the expectations derived from the slow process. All activations displayed at , uncorrected.</p>", "links"=>[], "tags"=>["choices"], "article_id"=>869919, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Aaron M. Bornstein", "Nathaniel D. Daw"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003387.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_BOLD_signal_during_choices_and_outcomes_/869919", "title"=>"BOLD signal during choices and outcomes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 04:23:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305276"], "description"=>"<p>The regions defined by the in-task localizer contrasts house face and face house, are colored yellow (left: face, right: house). The face localizer yielded the largest cluster of activation in a region of right fusiform gyrus. The house localizer yielded the largest cluster of activation in a region stretching from posterior parahippocampal gyrus to the occipital lobe. Regions selectively sensitive to the estimated probability of an image appearing next (on sequential response trials) are colored blue. Regions selectively sensitive to the difficulty of deciding whether a particular image would lead to reward are colored red. Displayed at , uncorrected.</p>", "links"=>[], "tags"=>[], "article_id"=>869921, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Aaron M. Bornstein", "Nathaniel D. Daw"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003387.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Image_selective_regions_/869921", "title"=>"Image-selective regions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-12-05 04:23:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305277"], "description"=>"*<p>- .</p>**<p>- .</p>!<p>- test ran counter to our hypothesis about the learning rate of that region.</p>", "links"=>[], "tags"=>["rates", "implied"], "article_id"=>869922, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Aaron M. Bornstein", "Nathaniel D. Daw"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003387.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Learning_rates_implied_by_BOLD_in_each_region_of_interest_/869922", "title"=>"Learning rates implied by BOLD in each region of interest.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-12-05 04:23:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1305304", "https://ndownloader.figshare.com/files/1305305", "https://ndownloader.figshare.com/files/1305306", "https://ndownloader.figshare.com/files/1305307", "https://ndownloader.figshare.com/files/1305308"], "description"=>"<div><p>How do we use our memories of the past to guide decisions we've never had to make before? Although extensive work describes how the brain learns to repeat rewarded actions, decisions can also be influenced by associations between stimuli or events not directly involving reward — such as when planning routes using a cognitive map or chess moves using predicted countermoves — and these sorts of associations are critical when deciding among novel options. This process is known as model-based decision making. While the learning of environmental relations that might support model-based decisions is well studied, and separately this sort of information has been inferred to impact decisions, there is little evidence concerning the full cycle by which such associations are acquired and drive choices. Of particular interest is whether decisions are directly supported by the same mnemonic systems characterized for relational learning more generally, or instead rely on other, specialized representations. Here, building on our previous work, which isolated dual representations underlying sequential predictive learning, we directly demonstrate that one such representation, encoded by the hippocampal memory system and adjacent cortical structures, supports goal-directed decisions. Using interleaved learning and decision tasks, we monitor predictive learning directly and also trace its influence on decisions for reward. We quantitatively compare the learning processes underlying multiple behavioral and fMRI observables using computational model fits. Across both tasks, a quantitatively consistent learning process explains reaction times, choices, and both expectation- and surprise-related neural activity. The same hippocampal and ventral stream regions engaged in anticipating stimuli during learning are also engaged in proportion to the difficulty of decisions. These results support a role for predictive associations learned by the hippocampal memory system to be recalled during choice formation.</p></div>", "links"=>[], "tags"=>["hippocampal", "correlates", "deliberation", "model-based", "decisions", "rewards"], "article_id"=>869944, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Aaron M. Bornstein", "Nathaniel D. Daw"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003387.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003387.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003387.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003387.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003387.s005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cortical_and_Hippocampal_Correlates_of_Deliberation_during_Model_Based_Decisions_for_Rewards_in_Humans_/869944", "title"=>"Cortical and Hippocampal Correlates of Deliberation during Model-Based Decisions for Rewards in Humans", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-12-05 04:23:49"}

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Relative Metric

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