Abstract:The combination of reward and potential threat is termed approach/avoidance conflict and elicits specific behaviors, including passive avoidance and behavioral inhibition (BI). Anxiety-relieving drugs reduce these behaviors, and a rich psychological literature has addressed how personality traits dominated by BI predispose for anxiety disorders. Yet, a formal understanding of the cognitive inference and planning processes underlying anxiety-like BI is lacking. Here, we present and empirically test such formali… Show more
“…2 ; Table 1 ). These results replicate previous reports ( Bach, 2015 , 2017 ; Khemka et al, 2017 ; Bach et al, 2019 ) and are in concordance with known behavior from rodent studies.…”
Section: Resultssupporting
confidence: 92%
“…Operant conflict tests provide the opportunity to moreprecisely control threat features as demonstrated in rodents ( Evenden et al, 2009 ; Oberrauch et al, 2019 ) and humans ( Bach, 2015 , 2017 ; Bach et al, 2019 ). Here, we capitalized on this latter operant AAC test to disambiguate representation of attack probability, its metabolic cost, and behavior, in aHC and amygdala.…”
Decisions under threat are crucial to survival and require integration of distinct situational features, such as threat probability and magnitude. Recent evidence from human lesion and neuroimaging studies implicated anterior hippocampus (aHC) and amygdala in approach-avoidance decisions under threat, and linked their integrity to cautious behavior. Here we sought to elucidate how threat dimensions and behavior are represented in these structures. Twenty human participants (11 female) completed an approach-avoidance conflict task during high-resolution fMRI. Participants could gather tokens under threat of capture by a virtual predator, which would lead to token loss. Threat probability (predator wake-up rate) and magnitude (amount of token loss) varied on each trial. To disentangle effects of threat features, and ensuing behavior, we performed a multifold parametric analysis. We found that high threat probability and magnitude related to BOLD signal in left aHC/entorhinal cortex. However, BOLD signal in this region was better explained by avoidance behavior than by these threat features. A priori ROI analysis confirmed the relation of aHC BOLD response with avoidance. Exploratory subfield analysis revealed that this relation was specific to anterior CA2/3 but not CA1. Left lateral amygdala responded to low and high, but not intermediate, threat probability. Our results suggest that aHC BOLD signal is better explained by avoidance behavior than by threat features in approach-avoidance conflict. Rather than representing threat features in a monotonic manner, it appears that aHC may compute approach-avoidance decisions based on integration of situational threat features represented in other neural structures.
“…2 ; Table 1 ). These results replicate previous reports ( Bach, 2015 , 2017 ; Khemka et al, 2017 ; Bach et al, 2019 ) and are in concordance with known behavior from rodent studies.…”
Section: Resultssupporting
confidence: 92%
“…Operant conflict tests provide the opportunity to moreprecisely control threat features as demonstrated in rodents ( Evenden et al, 2009 ; Oberrauch et al, 2019 ) and humans ( Bach, 2015 , 2017 ; Bach et al, 2019 ). Here, we capitalized on this latter operant AAC test to disambiguate representation of attack probability, its metabolic cost, and behavior, in aHC and amygdala.…”
Decisions under threat are crucial to survival and require integration of distinct situational features, such as threat probability and magnitude. Recent evidence from human lesion and neuroimaging studies implicated anterior hippocampus (aHC) and amygdala in approach-avoidance decisions under threat, and linked their integrity to cautious behavior. Here we sought to elucidate how threat dimensions and behavior are represented in these structures. Twenty human participants (11 female) completed an approach-avoidance conflict task during high-resolution fMRI. Participants could gather tokens under threat of capture by a virtual predator, which would lead to token loss. Threat probability (predator wake-up rate) and magnitude (amount of token loss) varied on each trial. To disentangle effects of threat features, and ensuing behavior, we performed a multifold parametric analysis. We found that high threat probability and magnitude related to BOLD signal in left aHC/entorhinal cortex. However, BOLD signal in this region was better explained by avoidance behavior than by these threat features. A priori ROI analysis confirmed the relation of aHC BOLD response with avoidance. Exploratory subfield analysis revealed that this relation was specific to anterior CA2/3 but not CA1. Left lateral amygdala responded to low and high, but not intermediate, threat probability. Our results suggest that aHC BOLD signal is better explained by avoidance behavior than by threat features in approach-avoidance conflict. Rather than representing threat features in a monotonic manner, it appears that aHC may compute approach-avoidance decisions based on integration of situational threat features represented in other neural structures.
“…2 ) and contained decision-making aspects of foraging while neglecting biophysical affordances such as actual energy consumption and physical efforts. In line with similar monkey and human tasks 2 – 4 , 17 , 21 – 25 , it thus abstracts from actual foraging 26 , 27 to reflect generic properties of sequential decision-making. Fig.…”
Optimal decisions across extended time horizons require value calculations over multiple probabilistic future states. Humans may circumvent such complex computations by resorting to easy-to-compute heuristics that approximate optimal solutions. To probe the potential interplay between heuristic and optimal computations, we develop a novel sequential decision-making task, framed as virtual foraging in which participants have to avoid virtual starvation. Rewards depend only on final outcomes over five-trial blocks, necessitating planning over five sequential decisions and probabilistic outcomes. Here, we report model comparisons demonstrating that participants primarily rely on the best available heuristic but also use the normatively optimal policy. FMRI signals in medial prefrontal cortex (MPFC) relate to heuristic and optimal policies and associated choice uncertainties. Crucially, reaction times and dorsal MPFC activity scale with discrepancies between heuristic and optimal policies. Thus, sequential decision-making in humans may emerge from integration between heuristic and optimal policies, implemented by controllers in MPFC.
“…To test anxiolytic properties, we used our recently established human approach/avoidance conflict paradigm 31 , 34 , 45 , 46 . This paradigm is designed to reflect rodent conflict test and is sensitive to anxiolytic action of lorazepam 34 .…”
Valproate is an anticonvulsant drug with strong preclinical evidence for reducing anxiety behaviour in rodents but no clear clinical evidence. To motivate clinical trials, we here investigate the use of valproate in a translational human model of anxiety behaviour. In a double-blind, randomised, placebo-controlled trial, n = 118 healthy participants played a previously validated approach/avoidance conflict computer game to measure anxiety-like behaviour, while under 400 mg valproate, under 200 mg of the established anxiolytic/anticonvulsant pregabalin, or under placebo. Saccadic peak velocity and subjective ratings were assessed to control for drug-induced sedation. Compared to placebo, valproate and pregabaline were anxiolytic in the primary outcome, and several secondary outcomes. Bayesian model comparison decisively demonstrated no differences between the two drugs. Subjective and objective sedation was significantly more pronounced under pregabalin than valproate, but did not explain anxiolytic effects. We demonstrate acute anxiolytic properties of valproate in healthy humans. Both drugs have similar anxiolytic properties at the doses used. Valproate is less sedative than pregabalin. Our results suggest clinical trials on the use of valproate in anxiolytic treatment. More generally, we propose a strategy of screening drugs in human preclinical models that can directly be compared across species, such as the approach/avoidance conflict computer game used here. This approach could thus facilitate translational anxiety research.
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