Learning to distinguish: shared perceptual features and discrimination practice tune behavioural pattern separation

ABSTRACT

Pattern separation is a computational mechanism performed by the hippocampus allowing the reduction of overlap between sensory inputs with similar perceptual features. Our first aim was to develop a new paradigm sensitive to the behavioural consequences of pattern separation (mnemonic discrimination). For this purpose, we constructed morphed face stimuli with parametrically changing levels of similarity. After encoding participants saw studied items and similar lure faces. Perceptual similarity affected false recognition and there was a gradual reduction in discrimination accuracy with the increment of similarity between the stimuli. However, confidence ratings were sensitive to smaller changes (Experiment 1) than the other test type with “old”/“similar”/“new” response options (Experiment 2). Mnemonic discrimination relies strongly on retrieving details of the original stimulus. Therefore, we investigated whether pattern separation can be tuned by retrieval in the form of a discrimination task (Experiment 3). Our findings suggest that repeatedly encountering the stimuli within a two-alternative forced-choice task (in comparison with the repeated presentation of the material) increased both the correct identification and the false recognition of similar stimuli two days after encoding. We conclude that basic computational mechanisms of the hippocampus can be tuned by a task that requires discrimination between studied and new stimuli.

KEYWORDS:

• Pattern separation
• mnemonic discrimination
• false memory
• retrieval
• recognition memory

Acknowledgements

The authors thank Kornél Németh for his help in stimulus collection. The authors thank Anita Lencsés for her help in data collection.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Datasets related to this article are available at an open source data repository (Open Science Framework, OSF, https://osf.io/jvme3/).

Additional information

Funding

This work was supported by the 2017-1.2.1-NKP2017-00002 Research Grant (Hungarian Brain Research Program) and by the NKFI (National Research, Development and Innovation Office, Hungary) K124098 Research Grant. Ágnes Szőllősi was supported by the ÚNKP-20-4 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.