Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making
Recent work has derived the optimal policy for two-alternative value-based decisions, in which decision-makers compare the subjective expected reward of two alternatives. Under specific task assumptions — such as linear utility, linear cost of time and constant processing noise — the optimal policy...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2021-09-01
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| Series: | Judgment and Decision Making |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S1930297500008408/type/journal_article |
| _version_ | 1797695983771975680 |
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| author | Angelo Pirrone Andreagiovanni Reina Fernand Gobet |
| author_facet | Angelo Pirrone Andreagiovanni Reina Fernand Gobet |
| author_sort | Angelo Pirrone |
| collection | DOAJ |
| description | Recent work has derived the optimal policy for two-alternative value-based decisions, in which decision-makers compare the subjective expected reward of two alternatives. Under specific task assumptions — such as linear utility, linear cost of time and constant processing noise — the optimal policy is implemented by a diffusion process in which parallel decision thresholds collapse over time as a function of prior knowledge about average reward across trials. This policy predicts that the decision dynamics of each trial are dominated by the difference in value between alternatives and are insensitive to the magnitude of the alternatives (i.e., their summed values). This prediction clashes with empirical evidence showing magnitude-sensitivity even in the case of equal alternatives, and with ecologically plausible accounts of decision making. Previous work has shown that relaxing assumptions about linear utility or linear time cost can give rise to optimal magnitude-sensitive policies. Here we question the assumption of constant processing noise, in favour of input-dependent noise. The neurally plausible assumption of input-dependent noise during evidence accumulation has received strong support from previous experimental and modelling work. We show that including input-dependent noise in the evidence accumulation process results in a magnitude-sensitive optimal policy for value-based decision-making, even in the case of a linear utility function and a linear cost of time, for both single (i.e., isolated) choices and sequences of choices in which decision-makers maximise reward rate. Compared to explanations that rely on non-linear utility functions and/or non-linear cost of time, our proposed account of magnitude-sensitive optimal decision-making provides a parsimonious explanation that bridges the gap between various task assumptions and between various types of decision making. |
| first_indexed | 2024-03-12T03:20:06Z |
| format | Article |
| id | doaj.art-589d53577b3149ea990937854b9391d8 |
| institution | Directory Open Access Journal |
| issn | 1930-2975 |
| language | English |
| last_indexed | 2024-03-12T03:20:06Z |
| publishDate | 2021-09-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Judgment and Decision Making |
| spelling | doaj.art-589d53577b3149ea990937854b9391d82023-09-03T14:02:50ZengCambridge University PressJudgment and Decision Making1930-29752021-09-01161221123310.1017/S1930297500008408Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-makingAngelo Pirrone0https://orcid.org/0000-0001-5984-7853Andreagiovanni Reina1https://orcid.org/0000-0003-4745-992XFernand Gobet2https://orcid.org/0000-0002-9317-6886Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science, London, UKIRIDIA, Université Libre de Bruxelles, Belgium, and Department of Computer Science, University of Sheffield, Sheffield, UKCentre for Philosophy of Natural and Social Science, London School of Economics and Political Science, London, UKRecent work has derived the optimal policy for two-alternative value-based decisions, in which decision-makers compare the subjective expected reward of two alternatives. Under specific task assumptions — such as linear utility, linear cost of time and constant processing noise — the optimal policy is implemented by a diffusion process in which parallel decision thresholds collapse over time as a function of prior knowledge about average reward across trials. This policy predicts that the decision dynamics of each trial are dominated by the difference in value between alternatives and are insensitive to the magnitude of the alternatives (i.e., their summed values). This prediction clashes with empirical evidence showing magnitude-sensitivity even in the case of equal alternatives, and with ecologically plausible accounts of decision making. Previous work has shown that relaxing assumptions about linear utility or linear time cost can give rise to optimal magnitude-sensitive policies. Here we question the assumption of constant processing noise, in favour of input-dependent noise. The neurally plausible assumption of input-dependent noise during evidence accumulation has received strong support from previous experimental and modelling work. We show that including input-dependent noise in the evidence accumulation process results in a magnitude-sensitive optimal policy for value-based decision-making, even in the case of a linear utility function and a linear cost of time, for both single (i.e., isolated) choices and sequences of choices in which decision-makers maximise reward rate. Compared to explanations that rely on non-linear utility functions and/or non-linear cost of time, our proposed account of magnitude-sensitive optimal decision-making provides a parsimonious explanation that bridges the gap between various task assumptions and between various types of decision making.https://www.cambridge.org/core/product/identifier/S1930297500008408/type/journal_articlevalue-based decision-makingoptimalitynoisemagnitude-sensitivity |
| spellingShingle | Angelo Pirrone Andreagiovanni Reina Fernand Gobet Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making Judgment and Decision Making value-based decision-making optimality noise magnitude-sensitivity |
| title | Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making |
| title_full | Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making |
| title_fullStr | Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making |
| title_full_unstemmed | Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making |
| title_short | Input-dependent noise can explain magnitude-sensitivity in optimal value-based decision-making |
| title_sort | input dependent noise can explain magnitude sensitivity in optimal value based decision making |
| topic | value-based decision-making optimality noise magnitude-sensitivity |
| url | https://www.cambridge.org/core/product/identifier/S1930297500008408/type/journal_article |
| work_keys_str_mv | AT angelopirrone inputdependentnoisecanexplainmagnitudesensitivityinoptimalvaluebaseddecisionmaking AT andreagiovannireina inputdependentnoisecanexplainmagnitudesensitivityinoptimalvaluebaseddecisionmaking AT fernandgobet inputdependentnoisecanexplainmagnitudesensitivityinoptimalvaluebaseddecisionmaking |