Skip to content
#software testing Open access

Directed forgetting and working-memory load reduce outcome-selective Pavlovian-to-instrumental transfer in humans

Oct 2026 · Scientific Reports · Vol 16 · 0 citations · 32 references

Abstract

In outcome-selective Pavlovian-to-instrumental transfer (PIT), reward-related cues selectively energize instrumental responding for associated outcomes. In humans, this effect has been attributed both to automatic associative processes and to cognitive inferences about which rewards are available in the presence of a cue. The present research examined whether outcome-selective PIT is influenced by manipulations that discourage such inferences. Across three experiments using a stock-market scenario with monetary rewards, participants learned Pavlovian cue–outcome relations and instrumental response–outcome contingencies before a transfer test. Experiment 1 established an outcome-selective PIT effect, which was substantially larger among participants reporting strategic use of the cues. Experiment 2 showed that instructing participants to forget the Pavlovian relations after an ostensible software error significantly reduced this effect. Experiment 3 showed that a concurrent working-memory load during the transfer test also attenuated PIT. However, a small effect persisted under the combined manipulations. Together, these findings indicate that outcome-selective PIT is modulated by cognitive reward expectancies and by beliefs about cue relevance. The residual transfer is not readily explained by experimenter demand, which here worked against cue-based responding. Cue reactivity therefore appears to rest on reward expectations that persist even when they are false and have been explicitly disavowed.

Read PDF

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54
#computer vision Review Mar 2008

Agile methods in European embedded software development organisations: a survey on the actual use and usefulness of Extreme Programming and Scrum

The results show that the embedded industry has been able to apply agile methods in its development processes and that the appreciation of the agile methods and their individual practices appears to increase once adopted and applied in practice.

O. Salo, P. Abrahamsson · 238 citations · ⚡9
#computer vision Open access Jul 2017

What happens when software developers are (un)happy

Consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts are found.

D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al. · 236 citations · ⚡13
#computer vision Open access Oct 2004

Mobile-D: an agile approach for mobile application development

The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed, which helped develop an agile development approach for mobile application development.

P. Abrahamsson, Antti Hanhineva, H. Hulkko et al. · 225 citations · ⚡18

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

MIT News · Artificial Intelligence Oct 2, 2026

Documenting the tech worker movement

Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.

MIT News · Artificial Intelligence Oct 2, 2026

Documenting the tech worker movement

Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.