You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["Outcome", "Participant", "Intervention"].

Please return the output as a JSON object of the format: {"Participant" : ["patients with COPD", ...], "Intervention" : ["Atenolol", ...], "Outcome" : ["blood pressure", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Modeling hippocampal and neocortical contributions to recognition memory : a complementary-learning-systems approach . The authors present a computational neural-network model of how the hippocampus and medial temporal lobe cortex ( MTLC ) contribute to recognition memory . The hippocampal component contributes by recalling studied details . The MTLC component can not support recall , but one can extract a scalar familiarity signal from MTLC that tracks how well a test item matches studied items . The authors present simulations that establish key differences in the operating characteristics of the hippocampal-recall and MTLC-familiarity signals and identify several manipulations ( e.g. , target-lure similarity , interference ) that differentially affect the 2 signals . They also use the model to address the stochastic relationship between recall and familiarity and the effects of partial versus complete hippocampal lesions on recognition .
{"Intervention": ["simulations", "hippocampal", "hippocampal and neocortical"], "Outcome": ["MTLC-familiarity signals", "hippocampal-recall", "recognition memory .", "recognition"], "Participant": ["recognition memory", "hippocampal and neocortical contributions"]}