Causal Inference in Multisensory Perception
Publication Date
September 26, 2007
Journal
PLOS ONE
Authors
Konrad P. Körding, Ulrik Beierholm, Wei Ji Ma, Steven Quartz, et al
Volume
2
Issue
9
Pages
e943
DOI
https://dx.plos.org/10.1371/journal.pone.0000943
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0000943
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/17895984
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1978520
Europe PMC
http://europepmc.org/abstract/MED/17895984
Web of Science
000207455800022
Scopus
41349102671
Mendeley
http://www.mendeley.com/research/causal-inference-multisensory-perception
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Mendeley | Further Information

{"title"=>"Causal inference in multisensory perception", "type"=>"journal", "authors"=>[{"first_name"=>"Konrad P.", "last_name"=>"Körding", "scopus_author_id"=>"6603812799"}, {"first_name"=>"Ulrik", "last_name"=>"Beierholm", "scopus_author_id"=>"15730276500"}, {"first_name"=>"Wei Ji", "last_name"=>"Ma", "scopus_author_id"=>"9276846900"}, {"first_name"=>"Steven", "last_name"=>"Quartz", "scopus_author_id"=>"6603048723"}, {"first_name"=>"Joshua B.", "last_name"=>"Tenenbaum", "scopus_author_id"=>"7006818404"}, {"first_name"=>"Ladan", "last_name"=>"Shams", "scopus_author_id"=>"6602166584"}], "year"=>2007, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"351460816", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)", "doi"=>"10.1371/journal.pone.0000943", "scopus"=>"2-s2.0-41349102671", "pmid"=>"17895984", "sgr"=>"41349102671"}, "id"=>"fe9a282b-9fe1-3b06-b0ed-f22366f6781c", "abstract"=>"Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.", "link"=>"http://www.mendeley.com/research/causal-inference-multisensory-perception", "reader_count"=>598, "reader_count_by_academic_status"=>{"Unspecified"=>13, "Professor > Associate Professor"=>40, "Researcher"=>129, "Student > Doctoral Student"=>24, "Student > Ph. D. Student"=>206, "Student > Postgraduate"=>18, "Student > Master"=>67, "Other"=>13, "Student > Bachelor"=>50, "Lecturer"=>9, "Lecturer > Senior Lecturer"=>4, "Professor"=>25}, "reader_count_by_user_role"=>{"Unspecified"=>13, "Professor > Associate Professor"=>40, "Researcher"=>129, "Student > Doctoral Student"=>24, "Student > Ph. D. Student"=>206, "Student > Postgraduate"=>18, "Student > Master"=>67, "Other"=>13, "Student > Bachelor"=>50, "Lecturer"=>9, "Lecturer > Senior Lecturer"=>4, "Professor"=>25}, "reader_count_by_subject_area"=>{"Unspecified"=>23, "Agricultural and Biological Sciences"=>93, "Philosophy"=>8, "Arts and Humanities"=>2, "Business, Management and Accounting"=>1, "Computer Science"=>56, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>1, "Engineering"=>38, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Materials Science"=>1, "Mathematics"=>4, "Medicine and Dentistry"=>24, "Neuroscience"=>71, "Design"=>2, "Sports and Recreations"=>3, "Physics and Astronomy"=>9, "Psychology"=>240, "Social Sciences"=>10, "Linguistics"=>9}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>24}, "Social Sciences"=>{"Social Sciences"=>10}, "Sports and Recreations"=>{"Sports and Recreations"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>9}, "Psychology"=>{"Psychology"=>240}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>23}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>2}, "Design"=>{"Design"=>2}, "Engineering"=>{"Engineering"=>38}, "Neuroscience"=>{"Neuroscience"=>71}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>93}, "Computer Science"=>{"Computer Science"=>56}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Linguistics"=>{"Linguistics"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Philosophy"=>{"Philosophy"=>8}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "Hungary"=>1, "United States"=>28, "Japan"=>5, "Egypt"=>1, "United Kingdom"=>11, "Switzerland"=>4, "Portugal"=>2, "Russia"=>1, "Spain"=>2, "Canada"=>4, "Netherlands"=>4, "Sweden"=>4, "Belgium"=>2, "Ireland"=>1, "China"=>1, "Brazil"=>3, "Poland"=>1, "Italy"=>2, "France"=>9, "Chile"=>1, "Australia"=>2, "Germany"=>18}, "group_count"=>32}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/944884"], "description"=>"<p>For the last two entries, we used the prior proposed by Roach et al. and Bresciani et al. together with correct inference (see text for more detail). All of the maximal likelihood ratios in the table are considered decisive evidence in favor of the causal inference prior, even when correcting for the number of parameters using the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000943#pone.0000943-Burnham1\" target=\"_blank\">[28]</a>. These criteria are methods for enabling fair comparison between models. Models with more parameters always fit data better than models with fewer parameters. AIC and BIC are ways of correcting for this bias.</p>", "links"=>[], "tags"=>["ratios", "subjects", "causal", "inference"], "article_id"=>615257, "categories"=>["Neuroscience", "Medicine"], "users"=>["Konrad P. Körding", "Ulrik Beierholm", "Wei Ji Ma", "Steven Quartz", "Joshua B. Tenenbaum", "Ladan Shams"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000943.t001", "stats"=>{"downloads"=>7, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Maximal_log_likelihood_ratios_base_e_across_subjects_of_models_relative_to_causal_inference_model_mean_s_e_m_see_methods_for_details_/615257", "title"=>"Maximal log likelihood ratios (base <i>e</i>) across subjects of models relative to causal inference model (mean±s.e.m., see methods for details).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2007-09-26 01:27:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/944822"], "description"=>"<p>a) The relative frequency of subjects reporting one cause (black) is shown (reprinted with permission from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000943#pone.0000943-Wallace1\" target=\"_blank\">[15]</a>) with the prediction of the causal inference model (red). b) The bias, i.e. the influence of vision on the perceived auditory position is shown (gray and black). The predictions of the model are shown in red. c) A schematic illustration explaining the finding of negative biases. Blue and black dots represent the perceived visual and auditory stimuli, respectively. In the pink area people perceive a common cause.</p>", "links"=>[], "tags"=>["causal"], "article_id"=>615202, "categories"=>["Neuroscience", "Medicine"], "users"=>["Konrad P. Körding", "Ulrik Beierholm", "Wei Ji Ma", "Steven Quartz", "Joshua B. Tenenbaum", "Ladan Shams"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000943.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reports_of_causal_inference_/615202", "title"=>"Reports of causal inference.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-09-26 01:26:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/944720"], "description"=>"<p>a) The experimental paradigm is shown. In each trial a visual and an auditory stimulus is presented simultaneously and subjects report both the position of the perceived visual and the position of the perceived auditory stimuli by button presses. b) The influence of vision on the perceived position of an auditory stimulus in the center is shown. Different colors correspond to the visual stimulus at different locations (sketched in warm to cold colors from the left to the right). The unimodal auditory case is shown in gray. c) The averaged responses of the subjects (solid lines) are shown along with the predictions of the ideal observer (broken lines) for each of the 35 stimulus conditions. These plot show how often on average which button was pressed in each of the conditions. d) The model responses from c) are plotted with the human responses from c). e) The average auditory bias , i.e. the influence of deviations of the visual position on the perceived auditory position is shown as a function of the spatial disparity (solid line) along with the model prediction (dashed line).</p>", "links"=>[], "tags"=>["auditory"], "article_id"=>615107, "categories"=>["Neuroscience", "Medicine"], "users"=>["Konrad P. Körding", "Ulrik Beierholm", "Wei Ji Ma", "Steven Quartz", "Joshua B. Tenenbaum", "Ladan Shams"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000943.g002", "stats"=>{"downloads"=>3, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Combination_of_visual_and_auditory_cues_/615107", "title"=>"Combination of visual and auditory cues.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-09-26 01:25:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/944655"], "description"=>"<p>Left: One cause can be responsible for both cues. In this case the visually perceived position <i>x<sub>V</sub></i> will be the common position <i>s</i> perturbed by visual noise with width <i>σ<sub>V</sub></i> and the auditory perceived position will be the common position perturbed by auditory noise with width <i>σ<sub>A</sub></i>. Right: Alternatively, two distinct causes may be relevant, decoupling the problem into two independent estimation problems. The causal inference model infers the probability of a causal structure with a common cause (left, <i>C</i> = 1) versus the causal structure with two independent causes (right, <i>C</i> = 2) and then derives optimal predictions from this. We introduce a single variable <i>C</i> which determines which sub-model generates the data.</p>", "links"=>[], "tags"=>["causal", "inference"], "article_id"=>615037, "categories"=>["Neuroscience", "Medicine"], "users"=>["Konrad P. Körding", "Ulrik Beierholm", "Wei Ji Ma", "Steven Quartz", "Joshua B. Tenenbaum", "Ladan Shams"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0000943.g001", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_causal_inference_model_/615037", "title"=>"The causal inference model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-09-26 01:23:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/464860", "https://ndownloader.figshare.com/files/464944", "https://ndownloader.figshare.com/files/465102"], "description"=>"<div><p>Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.</p></div>", "links"=>[], "tags"=>["causal", "inference", "multisensory"], "article_id"=>151639, "categories"=>["Neuroscience", "Medicine"], "users"=>["Konrad P. Körding", "Ulrik Beierholm", "Wei Ji Ma", "Steven Quartz", "Joshua B. Tenenbaum", "Ladan Shams"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000943.s001", "https://dx.doi.org/10.1371/journal.pone.0000943.s002", "https://dx.doi.org/10.1371/journal.pone.0000943.s003"], "stats"=>{"downloads"=>15, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Causal_Inference_in_Multisensory_Perception/151639", "title"=>"Causal Inference in Multisensory Perception", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2007-09-26 00:27:19"}

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Relative Metric

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