Learning from the behaviors and experiences of others

Learning to fear and avoid what is dangerous is crucial for survival. Perhaps equally important is the ability to learn that something which was previously dangerous is now safe. Although we can learn about dangers individually, through our own experiences, it is likely more safe to learn about them from others, by observing their behaviors and reactions. In a sense, this allows us to learn through the experiences of others. The overarching goal of this thesis is to deepen our understanding of how we learn about fear and safety through observation of others. In Study I we let participants undergo an observational extinction paradigm to investigate if safety learning was facilitated through observation of a calm learning model. In a direct conditioning stage participants first learned to associate a stimulus with fear. Next, they learned through that the previously feared stimulus was now safe. This extinction of fear was either direct or vicarious (observational). We demonstrated that attenuation of fear was greater following vicarious rather than direct extinction. We further showed that this was driven by the learning model’s experience of safety. Although learning through others is likely an efficient way of learning, observational learning also has to be applied critically, for instance by not copying the choices of someone that performs poorly. In Study II and Study III we investigated how people learned to make choices through observation of others, demonstrators, which had either a high or low ability. In both studies, participants learned a simple probabilistic two-choice task to avoid shock. Results from Study II demonstrated that people were able to use the observational information to improve performance regardless of the ability (skill) of the demonstrator. They only copied the choices of the demonstrator with high ability and they were able to learn from observing the consequences of a demonstrator’s choice regardless of the demonstrator’s ability. In Study III we also provided participants with descriptions of the abilities of the demonstrators. Our results showed that describing the demonstrator as low in ability impaired observational learning, regardless of the actual ability of the demonstrator and that this is likely driven by a difference in attention directed towards the observational information. An inability to discriminate threatening from safe stimuli is typical for individuals suffering from anxiety. In Study IV we investigated how observational fear conditioning is affected by the learning model’s expressed anticipatory anxiety. Results showed that participants were able to discriminate the threatening from the safe stimuli equally well from a learning model that behaved anxiously (i.e. did not discriminate) as from one that did not behave anxiously (i.e. did discriminate). The results presented in this thesis increase our understanding of how healthy individuals learn about aversive events and stimuli through observation of the behaviors and reactions of others and how these reflect the observed individuals’ experiences

The Transfer of Social Threat Learning to Decision-Making is Robust to Extinction

The project contains raw and processed data for Selbing, Sandberg, Olsson, Lindström and Golkar (Emotion) The Transfer of Social Threat Learning to Decision-Making is Robust to Extinciton. See data description file for more details

Phenotypic frequency densities with evolutionary novel dangers. Without (top) and with (bottom) preparedness.

<p>Density plot for the phenotypic frequency at the end-point (time step 50000) for the different strategies over 10 simulation runs. Solid lines: Danger > 0. Dotted lines: Danger = 0. Blue = Asocial learning (<i>O</i>_<i>0</i>,<i>T</i>_<i>0</i>), yellow = observational learning (<i>O</i>_<i>1</i>,<i>T</i>_<i>0</i>), red = parental learning (<i>O</i>_<i>0</i>,<i>T</i>_<i>1</i>), orange = advanced social learning (<i>O</i>_<i>1</i>,<i>T</i>_<i>1</i>).</p

Co-Evolution of Social Learning and Evolutionary Preparedness in Dangerous Environments

<div><p>Danger is a fundamental aspect of the lives of most animals. Adaptive behavior therefore requires avoiding actions, objects, and environments associated with danger. Previous research has shown that humans and non-human animals can avoid such dangers through two types of behavioral adaptions, (i) genetic preparedness to avoid certain stimuli or actions, and (ii) social learning. These adaptive mechanisms reduce the fitness costs associated with danger but still allow flexible behavior. Despite the empirical prevalence and importance of both these mechanisms, it is unclear when they evolve and how they interact. We used evolutionary agent-based simulations, incorporating empirically based learning mechanisms, to clarify if preparedness and social learning typically both evolve in dangerous environments, and if these mechanisms generally interact synergistically or antagonistically. Our simulations showed that preparedness and social learning often co-evolve because they provide complimentary benefits: genetic preparedness reduced foraging efficiency, but resulted in a higher rate of survival in dangerous environments, while social learning generally came to dominate the population, especially when the environment was stochastic. However, even in this case, genetic preparedness reliably evolved. Broadly, our results indicate that the relationship between preparedness and social learning is important as it can result in trade-offs between behavioral flexibility and safety, which can lead to seemingly suboptimal behavior if the evolutionary environment of the organism is not taken into account.</p></div

Phenotypic frequency densities without (top) and with (bottom) preparedness.

<p>Density plot for the phenotypic frequency at the end-point (time step 50000) for the different strategies over 100 simulation runs. Solid lines: <i>Danger</i> > 0. Dotted lines: <i>Danger</i> = 0. Blue = Asocial learning (<i>O</i>_<i>0</i>,<i>T</i>_<i>0</i>), yellow = observational learning (<i>O</i>_<i>1</i>,<i>T</i>_<i>0</i>), red = parental learning (<i>O</i>_<i>0</i>,<i>T</i>_<i>1</i>), orange = advanced social learning (<i>O</i>_<i>1</i>,<i>T</i>_<i>1</i>).</p

Tradeoff between foraging efficiency and safety.

<p>Black = no preparedness, gray = with preparedness. The means are derived from averaging time-step 25000–50000 for 100 repeats of each simulation. The standard errors are calculated across the 100 runs.</p

The differential foraging efficiency of pure learning strategies as a function of danger.

<p>Positive differential foraging efficiency indicates that foraging efficiency was greater with than without preparedness. Blue = Asocial learning (<i>O</i>_<i>0</i>,<i>T</i>_<i>0</i>), yellow = observational learning (<i>O</i>_<i>1</i>,<i>T</i>_<i>0</i>), red = parental learning (<i>O</i>_<i>0</i>,<i>T</i>_<i>1</i>), orange = advanced social learning (<i>O</i>_<i>1</i>,<i>T</i>_<i>1</i>). The means are derived from averaging time-step 25000–50000 for 10 repeats of each simulation. The standard errors are calculated across the 10 runs.</p

Learning from Others: Effects of Described Demonstrator Ability on Brain and Behavior

Observational learning allows us to learn to make decisions by observing the behaviors of others. The quality of such learning depends not only on the ability of the observed other, the demonstrator, but also on our beliefs about their abilities. We have previously demonstrated that observers learned to avoid an aversive outcome better from demonstrators described as high in ability, regardless of the demonstrator’s actual performance. Building on these findings, our study aimed to replicate these findings, and to investigate the neural mechanisms underlying description-sensitive observational learning. Forty-five participants engaged in an observational aversive learning task while undergoing functional magnetic resonance imaging. We manipulated the descriptions of the abilities of the demonstrators, while keeping their actual abilities low. We hypothesized that participants would perform better when demonstrators were described as having a high vs. low ability. We further investigated if brain activity in regions associated with observational learning, social impression formation and mentalizing were sensitive to the demonstrator’s described ability. Contrary to expectations, participants performed equally well regardless of the description of demonstrator ability. Subsequent analyses revealed however, that described ability influenced the level of copying, such that participants copied the choices of the demonstrators more when these were described as having high rather than low ability. Our findings point to the involvement of mentalizing processes combined with more general learning and decision-making processes in driving the behavioral effects of biased observational learning

Preparedness as a function of environmental danger.

<p>Average max and minimum values of the preparedness vector regulating innate approach/avoidance tendencies averaged over all levels of <i>P</i>_{<i>change</i>}. The means are derived from averaging time-step 25000–50000 for 100 repeats of each simulation. The standard errors are calculated across the 100 runs.</p