fe1b408fb521c26fa19f650166bf72d2 Ent_a2022v24n12p1819.pdf 8a02f6b7491f6b62bcbc7e9368f0dec773cb4de5 Ent_a2022v24n12p1819.pdf 64f96996744207b530c0a3e6e57d47bfe892493ffcad12a16fcdf65c73f67e4a Ent_a2022v24n12p1819.pdf Title: Informational Entropy Threshold as a Physical Mechanism for Explaining Tree-like Decision Making in Humans Subject: While approaches based on physical grounds (such as the drift-diffusion model—DDM) have been exhaustively used in psychology and neuroscience to describe perceptual decision making in humans, similar approaches to complex situations, such as sequential (tree-like) decisions, are still scarce. For such scenarios that involve a reflective prospection of future options, we offer a plausible mechanism based on the idea that subjects can carry out an internal computation of the uncertainty about the different options available, which is computed through the corresponding Shannon entropy. When the amount of information gathered through sensory evidence is enough to reach a given threshold in the entropy, this will trigger the decision. Experimental evidence in favor of this entropy-based mechanism was provided by exploring human performance during navigation through a maze on a computer screen monitored with the help of eye trackers. In particular, our analysis allows us to prove that (i) prospection is effectively used by humans during such navigation tasks, and an indirect quantification of the level of prospection used is attainable; in addition, (ii) the distribution of decision times during the task exhibits power-law tails, a feature that our entropy-based mechanism is able to explain, unlike traditional (DDM-like) frameworks. Keywords: decision making; entropy; eye-tracking Author: Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Thu Dec 22 10:02:15 2022 CET ModDate: Thu Dec 22 10:04:14 2022 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 16 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3399995 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- OONOKA+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 PSYERI+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 IPGTAZ+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 WFMTKF+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 38 0 ZPGVCH+CMEX10 Type 1 Builtin yes yes no 63 0 FAMQMS+PazoMath Type 1 Builtin yes yes no 67 0 SOSTRQ+CMR10 Type 1 Builtin yes yes no 71 0 KOEDFA+CMSY10 Type 1 Builtin yes yes no 75 0 ZZAMOU+PazoMath-Italic Type 1 Builtin yes yes no 79 0 SYFPBV+CMMI10 Type 1 Builtin yes yes no 87 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-09-23 02:33:58 CEST RepresentationInformation: Ent_a2022v24n12p1819.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-09-22 18:12:03 CEST Size: 3399995 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 322 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Theoretical Framework Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Children: Item: Title: Entropy Refinement Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Working Example Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Experimental Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Children: Item: Title: Overall Performance in the Navigation Task Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Eye-Tracking Data Captured Prospection Dynamics Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: Quantifying Prospection during Navigation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Item: Title: Human Decisions during Maze Navigation Are Compatible with the ERM Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@3aeaafa6 Item: Title: Information Statistics at the Moment of the Decision Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@76a3e297 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4d3167f4 Item: Title: Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@ed9d034 Children: Item: Title: Experimental Design Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@6121c9d6 Item: Title: Payoff Estimation from Experimental Trajectories Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@87f383f Item: Title: Virtual Walkers with Prospection Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4eb7f003 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@eafc191 Info: Title: Informational Entropy Threshold as a Physical Mechanism for Explaining Tree-like Decision Making in Humans Author: Subject: While approaches based on physical grounds (such as the drift-diffusion model DDM) have been exhaustively used in psychology and neuroscience to describe perceptual decision making in humans, similar approaches to complex situations, such as sequential (tree-like) decisions, are still scarce. For such scenarios that involve a reflective prospection of future options, we offer a plausible mechanism based on the idea that subjects can carry out an internal computation of the uncertainty about the different options available, which is computed through the corresponding Shannon entropy. When the amount of information gathered through sensory evidence is enough to reach a given threshold in the entropy, this will trigger the decision. Experimental evidence in favor of this entropy-based mechanism was provided by exploring human performance during navigation through a maze on a computer screen monitored with the help of eye trackers. In particular, our analysis allows us to prove that (i) prospection is effectively used by humans during such navigation tasks, and an indirect quantification of the level of prospection used is attainable; in addition, (ii) the distribution of decision times during the task exhibits power-law tails, a feature that our entropy-based mechanism is able to explain, unlike traditional (DDM-like) frameworks. 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