Context learning for threat detection

ABSTRACT

It is hypothesised that threatening stimuli are detected better due to their salience or physical properties. However, these stimuli are typically embedded in a rich context, motivating the question whether threat detection is facilitated via learning of contexts in which threat stimuli appear. To address this question, we presented threatening face targets in new or old spatial configurations consisting of schematic faces and found that detection of threatening targets was faster in old configurations. This indicates that individuals are able to learn regularities within visual contexts and use this contextual information to guide detection of threatening targets. Next, we presented threatening and non-threatening face targets embedded in new or old spatial configurations. Detection of threatening targets was facilitated in old configurations, and this effect was reversed for non-threatening targets. Present findings show that detection of threatening targets is driven not only by stimulus properties as theorised traditionally but also by learning of contexts in which threatening stimuli appear. Further, results show that context learning for threatening targets obstructs context learning for non-threatening targets. Overall, in addition to typically emphasised bottom-up factors, our findings highlight the importance of top-down factors such as context and learning in detection of salient, threatening stimuli.

KEYWORDS:

• Context
• threat
• learning
• emotion
• detection

Acknowledgements

The authors thank Jie Wang for assistance with data collection, and Christian Luhmann for comments on an earlier draft.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCiD

Akos Szekely http://orcid.org/0000-0002-3333-4424

Aprajita Mohanty http://orcid.org/0000-0003-0983-3343

Additional information

Funding

This research was supported by funds from Stony Brook University.