Arousing decisions: Environmental Cues to Danger Rationally Alter Evidence Integration and Impulsivity

Windy Torgerud, University of Minnesota Dominic Mussack, University of Minnesota  Paul Schrater, University of Minnesota


Arousal in animals is a fundamental behavioral state with a functional role of preparing the animal to respond to possible threats in the environment.  However, in psychology and neuroscience, arousal is widely viewed as a physiological state, largely due to its ease of measurement through its impact on the autonomous nervous system.    Here we show how arousal can be manipulated and its functional consequences on decision making are measured and isolated.   We do X and we show Y.   


Decision making occurs under a variety of different contexts each of which place competing demands on the decision maker.  In natural environments, the importance of one particular decision will be regularly modulated by signals emanating from the environment. Signals indicating potential nearby threats or social cues such as peer competition can increase or decrease the salience of a particular decision relative to everything else currently occurring in the action-ably nearby environment.

Structure of the argument:
1) Animals need to modulate decisions to deal with threat.  This modulation is graded, use the Ecology of fear Mobbs paper to frame the issue Mobbs 2015.
2) Depending on the context, there are different responses that are most appropriate for the animal. To monitor the threats you may have to split attentional resources, either across space or time, or completely disengage from the current task to appropraitly respond to an immediate threat. Sarter 2006 (Lang 2010)
3) Arousal is a key manifestation of threat preparedness  Lo 2008 (survival optimization system (Mobbs 2015), however, other types of ecological responses will engage physiological arousal, but for different purposes (eg. mating. reward. positive valence, circumplex model of emotion). 
4) In this view, physiological arousal will not be a good predictor of decision modulation, so we use a data-driven methodology to find brain/body signatures associated with decision modulation
5) We predict that decisions will be impacted by a process that frees up attentional resources across decisions to monitor the background.  Resources could be split equally at all times, but visual information seeking requires temporal allocation (cite Rothkopf, PNAS), which incentivizes freeing up time by making certain decisions faster.
6) This is a time allocation problem, trading-off on-task performance vs. background monitoring.
7) These predictions can be mapped into two hypotheses about the impact of threat stimuli.  a) threat should be TRACKED, integrating cues across time, using a time window.  b) increases in threat should alter decision making by decreasing threshold, and increasing gain, to free up time to look while minimizing performance impact.  


3 claims we have evidence for (drawing the connections):
1) discovered a latent state influencing decisions, which we show using PLS & diffusion model, that we argue represents a CNS signal corresponding to arousal that gears up brain for threat via resource reallocation.  This is complementary to Arousal as PNS (autonomic) gearing up the body by energetic resource availability and increase in sensory and motor.  Overall, there is a conceptualization of threat signal processing as being integrated in the brain, and this signal broadcast out to other brain areas to effect a coordinated change in both cognitive and metabolic resource availability and allocation.  To better show the distinction between PLS defined "brain/body" arousal, we need to include more standard arousal measures as predictors, and let them try to explain decisions in regression.  We expect this will fail to add anything to our predictions of decisions, moreover, the arousal signals we get this way may be better tied to stimuli.   This analysis seems to need a comparison predictability of stimuli vs. predictability of decision RT.  
Predicting RT is different than predicting stimuli.  We show that stimuli are poor predictors of RT - that means they are essentially independent of each other.   Now it's interesting which measures predict stimuli well, an