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Integrating Neuropsychological Models of Learning and Decision Making

Applicant Rieskamp Jörg
Number 153616
Funding scheme Project funding (Div. I-III)
Research institution Fakultät für Psychologie Universität Basel
Institution of higher education University of Basel - BS
Main discipline Psychology
Start/End 01.10.2014 - 31.03.2019
Approved amount 396'470.00
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Keywords (7)

evidence accumulation; preferential choice ; fMRI; reward; cognitive modeling; EEG; reinforcement learning

Lay Summary (German)

Lead
Aus Erfahrungen zu lernen hilft Menschen Fehler zu vermeiden und somit persönliche und gesellschaftliche Entwicklungen zu verbessern. Obwohl die bisherige Forschung unser Verständnis der neuropsychologischen Mechanismen von Lernen und Entscheiden vertiefen konnte, wurden die beiden Fragen wie Entscheidungen entstehen und wie sie durch Lernen verbessert werden stets getrennt betrachtet. Das SNF Projekt möchte eine Verbindung dieser beiden Forschungsfelder erreichen indem eine einheitlichen Theorie zu Präferenzentscheidungen entwickelt werden soll.
Lay summary

Prozessmodelle beschreiben das Zustandekommen einzelner Entscheidungen mit hoher Präzision und erlauben es zugleich Genauigkeit und Schnelligkeit von Entscheidungen vorherzusagen. Allerdings werden diese Modelle unter stationären Bedingungen getestet (d.h. unter der Annahme, dass Lernprozesse keinen Einfluss auf wiederholte Entscheidungen haben). Auf der anderen Seite können Verstärkungslernmodelle die lernabhängige Veränderung von Entscheidungen durch Belohnung und Bestrafung erklären, sind aber in Bezug auf den Ablauf einzelner Entscheidungen unpräzise. Unser Ziel ist es, diese Ansätze miteinander zu kombinieren, damit sie voneinander profitieren und es ermöglichen, zu einem besseren Verständnis von Entscheidungsmechanismen sowie der Veränderung dieser Mechanismen zu gelangen. Um die neuronalen Mechanismen von Entscheidungs- und Lernprozesse zu untersuchen, werden die Methoden der funktionellen Magnetresonanztomographie (fMRT) und der Elektroenzephalographie (EEG) zum Einsatz kommen. fMRT ist besonders geeignet Aktivierung in tiefliegenden Hirnstrukturen (z.B. im Belohnungssystem des menschlichen Gehirns) zu messen. Die hohe zeitliche Auflösung von EEG erlaubt es dagegen, das Entstehen einzelner Entscheidungen im Gehirn detailliert nachzuvollziehen. Insgesamt wird dieses SNF Projekt unser Verständnis darüber verbessern, wie im Gehirn Präferenzentscheidungen verarbeitet werden und wie sich diese Verarbeitungsprozesse durch Lernen verändern.

Direct link to Lay Summary Last update: 22.05.2014

Responsible applicant and co-applicants

Employees

Publications

Publication
Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: a meta-analytical approach using diffusion decision modeling
Fontanesi Laura, Palminteri Stefano, Lebreton Maël (2019), Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: a meta-analytical approach using diffusion decision modeling, in Cognitive, Affective, & Behavioral Neuroscience, 19(3), 490-502.
A reinforcement learning diffusion decision model for value-based decisions
Fontanesi Laura, Gluth Sebastian, Spektor Mikhail S., Rieskamp Jörg (2019), A reinforcement learning diffusion decision model for value-based decisions, in Psychonomic Bulletin & Review, 1-23.
Cognitive and Neural Bases of Multi-Attribute, Multi-Alternative, Value-based Decisions
Busemeyer Jerome R., Gluth Sebastian, Rieskamp Jörg, Turner Brandon M. (2019), Cognitive and Neural Bases of Multi-Attribute, Multi-Alternative, Value-based Decisions, in Trends in Cognitive Sciences, 23(3), 251-263.
How similarity between choice options affects decisions from experience: The accentuation-of-differences model.
Spektor Mikhail S., Gluth Sebastian, Fontanesi Laura, Rieskamp Jörg (2019), How similarity between choice options affects decisions from experience: The accentuation-of-differences model., in Psychological Review, 126(1), 52-88.
Value-based attentional capture affects multi-alternative decision making
Gluth Sebastian, Spektor Mikhail S, Rieskamp Jörg (2018), Value-based attentional capture affects multi-alternative decision making, in eLife, 7, e39659.
Variability in behavior that cognitive models do not explain can be linked to neuroimaging data
Gluth Sebastian, Rieskamp Jörg (2017), Variability in behavior that cognitive models do not explain can be linked to neuroimaging data, in Journal of Mathematical Psychology, 76, 104-116.
The Attraction Effect Modulates Reward Prediction Errors and Intertemporal Choices
Gluth Sebastian, Hotaling Jared M., Rieskamp Jörg (2017), The Attraction Effect Modulates Reward Prediction Errors and Intertemporal Choices, in The Journal of Neuroscience, 37(2), 371-382.

Collaboration

Group / person Country
Types of collaboration
Prof. Jerome Busemeyer United States of America (North America)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Exchange of personnel

Associated projects

Number Title Start Funding scheme
172761 The influence of episodic memory on value-based decision making 01.09.2017 Project funding (Div. I-III)
172017 The role of dopaminergic midbrain nuclei in predicting monetary gains and losses: who’s doing what? 01.02.2017 Doc.Mobility

Abstract

Many decisions can benefit from learning. For instance, physicians improve their diagnoses on the basis of previous experience. Learning can help in avoiding mistakes and is therefore central for advancing personal and societal development. In the last two decades, cognitive neuroscience has greatly promoted our understanding of the neural mechanism underlying human learning and decision making (e.g., Glimcher et al., 2009). However, the two questions of how decisions emerge and how decisions are improved by learning have until now been mostly addressed independently from each other. The proposed research project aims at connecting neuropsychological models of learning and decision making to improve our understanding of both phenomena and to integrate them in a comprehensive theory of decision making.Among psychological and economic theories of decision making, sequential sampling models (SSMs) offer a precise description of the cognitive process underlying decisions and are strongly supported by empirical evidence (Busemeyer and Townsend, 1993; Fehr and Rangel, 2011; Ratcliff and Rouder, 1998). The principle of SSMs is that evidence about available choice options is accumulated over time until an internal threshold (of required evidence) is met and a decision is made. A particular strength of SSMs is their ability to conjointly predict how and how quickly people decide. Neural representations of evidence accumulation have been found in cortical areas including the medial prefrontal cortex (Gluth, Rieskamp, & Büchel, 2012; Hare et al., 2011) and the intraparietal sulcus (Basten et al., 2010; Gold and Shadlen, 2007).The learning-based improvement of decisions, on the other hand, is captured by reinforcement learning (RL) models (Sutton and Barto, 1998). At its core, RL uses the difference between expected and actual outcomes (i.e., the prediction error) to adapt future predictions and choices. On the neural level, the prediction error has been linked to phasic firing of midbrain dopamine neurons (Schultz et al., 1997; Tobler et al., 2005). As outlined above, existing literature targets only one of the two phenomena of learning and decision making at the same time. SSMs are tested under stationary conditions for obtaining stable estimates of model parameters, such as the height of the decision threshold. To derive choice probabilities, RL models use an exponential choice rule which is oblivious to the underlying decision mechanisms and unable to predict response times. The goal of this proposal is therefore to extend the scope of both approaches by using RL to model the change in SSM parameters in dynamic decision environments and by applying SSMs to RL scenarios for predicting both how and when decisions are made. Neuroimaging techniques, including electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), are used to support the cognitive modeling results as well as to understand how adaptive decision processes are implemented in the human brain.
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