Temporal continuity of object identity is a feature of natural visual input, and is potentially exploited -- in an unsupervised manner -- by the ventral visual stream to build the neural representation in inferior temporal (IT) cortex. Here we investigated whether plasticity of individual IT neurons underlies human core-object-recognition behavioral changes induced with unsupervised visual experience. We built a single-neuron plasticity model combined with a previously established IT population-to-recognition-behavior linking model to predict human learning effects. We found that our model, after constrained by neurophysiological data, largely predicted the mean direction, magnitude and time course of human performance changes. We also found a previously unreported dependency of the observed human performance change on the initial task difficulty. This result adds support to the hypothesis that tolerant core object recognition in human and non-human primates is instructed -- at least in part -- by naturally occurring unsupervised temporal contiguity experience.
All data generated or analyzed during this study are included in the manuscript and supporting files, in the most useful format (https://github.com/jiaxx/temporal_learning_paper). Datasets from previous studies (IT population dataset (Majaj et al., 2015) and IT plasticity data (Li & DiCarlo, 2010)) are also compiled in the most useful format and saved in the same github location. Original datasets for previous studies can be obtained by directly contacting the corresponding authors of those studies ((Majaj et al., 2015) and (Li & DiCarlo, 2010)). Source data files for figure 2,4,5 and 6 are provided in the github repo as well.
- Jim DiCarlo
- Jim DiCarlo
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: All human experiments were done in accordance with the MIT Committee on the Use of Humans as Experimental Subjects (COUHES; the protocol number is 0812003043). We used Amazon Mechanical Turk (MTurk), an online platform where subjects can participate in non-profit psychophysical experiments for payment based on the duration of the task. In the description of each task, it is clearly stated that participation is voluntary and subjects may quit at any time. Subjects can preview each task before agreeing to participate. Subjects will also be informed that anonymity is assured and the researchers will not receive any personal information. MTurk requires subjects to read task descriptions before agreeing to participate. If subjects successfully complete the task, they anonymously receive payment through the MTurk interface.
- Thomas Serre, Brown University, United States
- Received: July 8, 2020
- Accepted: June 10, 2021
- Accepted Manuscript published: June 11, 2021 (version 1)
© 2021, Jia et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.