Abstract:Regret describes recognizing alternative actions could have led to better outcomes. It remains unclear whether regret derives from generalized mistake appraisal or instead comprises dissociable, action-specific processes. Using a neuroeconomic task, we found that mice were sensitive to fundamentally distinct types of regret following exposure to chronic social defeat stress or manipulations of CREB, a transcription factor implicated in stress action. Bias to make compensatory decisions after rejecting high-val… Show more
“…Thus, the model proposed by Ott et al 35 shows a sensitivity to sunk costs because it makes decisions based on parameters that accrue with sunk costs. Interestingly, in the model, these two components do not start to accrue until the agent enters the wait zone, consistent with the observations that sunk costs do not start to accrue until the subject commits to the decision by entering the wait zone 19 , 20 , 23 , 26 .…”
Section: Resultssupporting
confidence: 81%
“…(Fig. 3 statistics, Mann–Whitney test, comparing change in p (Earn) for values <0 and values >0: 3a (Mice 19 ), p < 10 −100 ; 3b (Mice 20 ), p = 10 −100 , 3c (Rats 19 ), p = 10 −62 ; 3d (Humans in person 19 ), p = 0.27; 3e (Humans online 23 ), p = 10 −19 ; 3f (Humans online, NCST dataset), p = 10 −94 ). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Statistics measured if the change in p (Earn) was greater before than after value crossed from negative to positive (Mann–Whitney sign rank test). a Mouse data from 16 , 19 , p < 10 −100 b Mouse dataset from 20 , p < 10 −100 . c Rat dataset from 19 , p = 10 −62 .…”
Section: Resultsmentioning
confidence: 99%
“…d Mice from ref. 19 , 20 . Each mouse provides one symbol from early in training and one symbol from late in training, placed at the measured baseline slope and sunk cost sensitivity.…”
Sunk cost sensitivity describes escalating decision commitment with increased spent resources. On neuroeconomic foraging tasks, mice, rats, and humans show similar escalations from sunk costs while quitting an ongoing countdown to reward. In a new analysis taken across computationally parallel foraging tasks across species and laboratories, we find that these behaviors primarily occur on choices that are economically inconsistent with the subject’s other choices, and that they reflect not only the time spent, but also the time remaining, suggesting that these are change-of-mind re-evaluation processes. Using a recently proposed change-of-mind drift-diffusion model, we find that the sunk cost sensitivity in this model arises from decision-processes that directly take into account the time spent (costs sunk). Applying these new insights to experimental data, we find that sensitivity to sunk costs during re-evaluation decisions depends on the information provided to the subject about the time spent and the time remaining.
“…Thus, the model proposed by Ott et al 35 shows a sensitivity to sunk costs because it makes decisions based on parameters that accrue with sunk costs. Interestingly, in the model, these two components do not start to accrue until the agent enters the wait zone, consistent with the observations that sunk costs do not start to accrue until the subject commits to the decision by entering the wait zone 19 , 20 , 23 , 26 .…”
Section: Resultssupporting
confidence: 81%
“…(Fig. 3 statistics, Mann–Whitney test, comparing change in p (Earn) for values <0 and values >0: 3a (Mice 19 ), p < 10 −100 ; 3b (Mice 20 ), p = 10 −100 , 3c (Rats 19 ), p = 10 −62 ; 3d (Humans in person 19 ), p = 0.27; 3e (Humans online 23 ), p = 10 −19 ; 3f (Humans online, NCST dataset), p = 10 −94 ). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Statistics measured if the change in p (Earn) was greater before than after value crossed from negative to positive (Mann–Whitney sign rank test). a Mouse data from 16 , 19 , p < 10 −100 b Mouse dataset from 20 , p < 10 −100 . c Rat dataset from 19 , p = 10 −62 .…”
Section: Resultsmentioning
confidence: 99%
“…d Mice from ref. 19 , 20 . Each mouse provides one symbol from early in training and one symbol from late in training, placed at the measured baseline slope and sunk cost sensitivity.…”
Sunk cost sensitivity describes escalating decision commitment with increased spent resources. On neuroeconomic foraging tasks, mice, rats, and humans show similar escalations from sunk costs while quitting an ongoing countdown to reward. In a new analysis taken across computationally parallel foraging tasks across species and laboratories, we find that these behaviors primarily occur on choices that are economically inconsistent with the subject’s other choices, and that they reflect not only the time spent, but also the time remaining, suggesting that these are change-of-mind re-evaluation processes. Using a recently proposed change-of-mind drift-diffusion model, we find that the sunk cost sensitivity in this model arises from decision-processes that directly take into account the time spent (costs sunk). Applying these new insights to experimental data, we find that sensitivity to sunk costs during re-evaluation decisions depends on the information provided to the subject about the time spent and the time remaining.
“…This task forces individuals to make economic choices that inherently depend on energy optimization but also on subjective preferences. Restaurant Row thereby operationalizes reward value across numerous behavioral dimensions that depend on different decision-making strategies, can tap into different neural circuits, and, we think, can help push boundaries on what behaviors can be studied in animals for psychiatric research [ 11 ]. This task can be used in rodents, monkeys, and humans and has been adapted to match ethology across evolution and emphasizes improving a paradigm’s computational validity, that is, translating not only behavior but also the brain’s algorithms across species [ 12 ].…”
Drug discovery for psychiatric conditions is stagnating. Behavioral changes could be used as a primary phenotypic screen for new drug candidates, if enough useful data can be generated from behavioral models. Could machine learning be the answer to extracting the data we need?
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