Dataset Information

Applying artificial vision models to human scene understanding.

ABSTRACT: How do we understand the complex patterns of neural responses that underlie scene understanding? Studies of the network of brain regions held to be scene-selective-the parahippocampal/lingual region (PPA), the retrosplenial complex (RSC), and the occipital place area (TOS)-have typically focused on single visual dimensions (e.g., size), rather than the high-dimensional feature space in which scenes are likely to be neurally represented. Here we leverage well-specified artificial vision systems to explicate a more complex understanding of how scenes are encoded in this functional network. We correlated similarity matrices within three different scene-spaces arising from: (1) BOLD activity in scene-selective brain regions; (2) behavioral measured judgments of visually-perceived scene similarity; and (3) several different computer vision models. These correlations revealed: (1) models that relied on mid- and high-level scene attributes showed the highest correlations with the patterns of neural activity within the scene-selective network; (2) NEIL and SUN-the models that best accounted for the patterns obtained from PPA and TOS-were different from the GIST model that best accounted for the pattern obtained from RSC; (3) The best performing models outperformed behaviorally-measured judgments of scene similarity in accounting for neural data. One computer vision method-NEIL ("Never-Ending-Image-Learner"), which incorporates visual features learned as statistical regularities across web-scale numbers of scenes-showed significant correlations with neural activity in all three scene-selective regions and was one of the two models best able to account for variance in the PPA and TOS. We suggest that these results are a promising first step in explicating more fine-grained models of neural scene understanding, including developing a clearer picture of the division of labor among the components of the functional scene-selective brain network.

SUBMITTER: Aminoff EM

PROVIDER: S-EPMC4316773 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Applying artificial vision models to human scene understanding.

Aminoff Elissa M EM Toneva Mariya M Shrivastava Abhinav A Chen Xinlei X Misra Ishan I Gupta Abhinav A Tarr Michael J MJ

Frontiers in computational neuroscience 20150204

How do we understand the complex patterns of neural responses that underlie scene understanding? Studies of the network of brain regions held to be scene-selective-the parahippocampal/lingual region (PPA), the retrosplenial complex (RSC), and the occipital place area (TOS)-have typically focused on single visual dimensions (e.g., size), rather than the high-dimensional feature space in which scenes are likely to be neurally represented. Here we leverage well-specified artificial vision systems t ...[more]

PMID: 25698964

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Project description:BackgroundTrial method research produces recommendations on how to best conduct trials. However, findings are not routinely implemented into practice. To better understand why, we conducted a mixed method study on the challenges of implementing trial method research findings into UK-based clinical trial units.MethodsThree stages of research were conducted. Firstly, case studies of completed projects that provided methodological recommendations were identified within trial design, conduct, analysis, and reporting. These case studies were used as survey examples to query obstacles and facilitators to implementing method research. Survey participants were experienced trial staff, identified via email invitations to UK clinical trial units. This survey assessed the case studies' rates of implementation, and demographic characteristics of trial units through the Consolidated Framework for Implementation Research. Further, interviews were conducted with senior members of trial units to explore obstacles and facilitators in more detail. Participants were sampled from trial units that indicated their willingness to participate in interviews following the survey. Interviews, and analysis, were structured via the Capability, Opportunity, Motivation Model of Behaviour. Finally, potential strategies to leverage lessons learned were generated via the Behaviour Change Wheel.ResultsA total of 27 UK trial units responded to the survey. The rates of implementation across the case studies varied, with most trial units implementing recommendations in trial conduct and only few implementing recommendations in reporting. However, most reported implementing recommendations was important but that they lacked the resources to do so. A total of 16 senior members of trial units were interviewed. Several themes were generated from interviews and fell broadly into categories related to the methods recommendations themselves, the trial units, or external factors affecting implementation. Belief statements within themes indicated resources issues and awareness of recommendations as frequent implementation obstacles. Participation in trial networks and recommendations packaged with relevant resources were cited frequently as implementation facilitators. These obstacles and facilitators mirrored results from the survey. Results were mapped, via the Behaviour Change Wheel, to intervention functions likely to change behaviours of obstacles and facilitators identified. These intervention functions were developed into potential solutions to reduce obstacles and enhance facilitators to implementation.ConclusionsSeveral key areas affecting implementation of trial method recommendations were identified. Potential methods to enhance facilitators and reduce obstacles are suggested. Future research is needed to refine these methods and assess their feasibility and acceptability.

Dataset Information

Applying artificial vision models to human scene understanding.

Publications

Applying artificial vision models to human scene understanding.

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