Dataset Information

The Map in Our Head Is Not Oriented North: Evidence from a Real-World Environment.

ABSTRACT: Like most physical maps, recent research has suggested that cognitive maps of familiar environments may have a north-up orientation. We demonstrate that north orientation is not a necessary feature of cognitive maps and instead may arise due to coincidental alignment between cardinal directions and the built and natural environment. Experiment 1 demonstrated that pedestrians have difficulty pointing north while navigating a familiar real-world environment with roads, buildings, and green spaces oriented oblique to cardinal axes. Instead, north estimates tended to be parallel or perpendicular to roads. In Experiment 2, participants did not demonstrate privileged memory access when oriented toward north while making relative direction judgments. Instead, retrieval was fastest and most accurate when orientations were aligned with roads. In sum, cognitive maps are not always oriented north. Rather, in some real-world environments they can be oriented with respect to environment-specific features, serving as convenient reference systems for organizing and using spatial memory.

SUBMITTER: Brunye TT

PROVIDER: S-EPMC4564159 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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The Map in Our Head Is Not Oriented North: Evidence from a Real-World Environment.

Brunyé Tad T TT Burte Heather H Houck Lindsay A LA Taylor Holly A HA

PloS one 20150909 9

Like most physical maps, recent research has suggested that cognitive maps of familiar environments may have a north-up orientation. We demonstrate that north orientation is not a necessary feature of cognitive maps and instead may arise due to coincidental alignment between cardinal directions and the built and natural environment. Experiment 1 demonstrated that pedestrians have difficulty pointing north while navigating a familiar real-world environment with roads, buildings, and green spaces ...[more]

PMID: 26353119

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Project description:Introduction Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer. Methods Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported. Results Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%–100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%–94%; Manual accuracy: 78%–94%; concordance: 71%–82%). Concurrent medications (86%–100%) and comorbid conditions (96%–100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%–100%) and highly concordant (83%–99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%–98%), although detection of biomarker test results was more variable (accuracy 84%–100%, concordance 84%–99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%–99%; manual: 71%–100%; concordance: 70%–99%) with the exception of lung and lymph node metastases (NLP: 66%–71%; manual: 87%–92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition. Conclusions Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction

Dataset Information

The Map in Our Head Is Not Oriented North: Evidence from a Real-World Environment.

Publications

The Map in Our Head Is Not Oriented North: Evidence from a Real-World Environment.

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