What makes individuals rely on social cues

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Download Wunder on the App Store. Finally, participants filled in a 50 item Autism Questionnaire AQ; [ 32 ]. Participants were then thanked, paid and debriefed. Here we detail 14 different models see Table 1. Some additional variations are reported in the S1 Text as explained below. We first give a general description of our modeling framework and then outline how each model differs from the others.

We assumed that participants learn the probabilities of options being safe, p i , p j , and the probability of each social partner giving good advice—signaling the safe option, p partner. Updating followed a simple Rescorla-Wagner delta rule [ 44 ].

In the S1 Text we report additional comparisons based on models containing fewer learning rate parameters see Table A in S1 Text. Models that incorporated learning both about options and about social partners differed in how social information was cached and transferred between blocks.

In our experiment, each block entailed novel choice options. In the weak transfer class of models models 3,5,7,9,11,13 , between blocks, p i , p j are reset to their starting value of 0. By contrast, in the strong transfer class of models models 4,6,8,10,12, In environments where partners are continuously reliable see Fig 1C , like that in our experiment, this algorithm efficiently scaffolds earlier learning.

In models participants learned both about options and social partners. The key idea is that the weighting between different sources should be determined by their relative reliability. Arbitration was implemented as a softmax: 8. In the S1 Text we report variations of the arbitration models without the bias parameter as well as using a different arbitration scheme based on computing entropy rather than using absolute prediction errors [ 18 ].

These variations showed worse fit to our data see Table B in S1 Text. In the S1 Text we additionally report on fitting the Hierarchical Gaussian Filter [ 45 ] to our data, as this model has previously been used to model data from experiments similar to ours [ 13 ]. All parameters were fit hierarchically to each participant as deviations from an estimated population average. Parameters were fit in logit space and then back-transformed to their native space.

Priors for all parameters were set as Normal 0,1 except for population-level learning rates were weakly informative Normal 0,0. Model comparison was performed using leave-one-out cross-validation LOO-CV by estimating the pointwise out-of-sample prediction accuracy from the log-likelihood evaluated using the full posterior of the model [ 33 ].

Since individual observations are not independent in trial-by-trial computational models, we follow [ 47 ] and use the pointwise log-likelihood summed to the participant level as an input to the leave-one-out cross-validation procedure. See Table 2 for full model comparison. We also simulated data from a subset of our candidate models models , 5, 7, 9, 11, 13 and fitted the models to the simulated data.

Generally, models with emotion expression components distinguish themselves well compared to other models. However, models 9 and 13 produce very similar data patterns to each other, which is also reflected in their fit similarity to our data see Table 2. All analyses were performed in the R statistical language using the brms package [ 48 ]. Where appropriate we analyzed the data using Bayesian multi-level regression including varying intercepts and slopes by participant and correlations between intercept and slopes.

All categorical regressors were deviation coded The quantity pd is the proportion of the posterior distribution of the parameter that has the same sign as the parameter itself. We thank R. Ilangomaran for assistance with data collection and F. Dahlin for modifying face images. We thank A. Golkar and B. Abstract Learning to avoid harmful consequences can be a costly trial-and-error process.

Author summary People learn about dangers in their environment by directly interacting with it or indirectly from social sources. Introduction Learning about threats and dangers in the environment is important for survival across species. Download: PPT. Table 1. Overview of models compared and their respective free parameters. Table 3. Between block transfer of partner information. Choice response times We analyzed participants choice response times throughout the experiment, as response times provide additional information about cognitive processing not contained in decisions alone.

Discussion We investigated how human participants learn to avoid harmful choice options based on information from two sources: social gaze cues from partners with different reliability and trial-and-error learning. Participants We recruited 81 participants from the student population at Karolinska Institutet and from the local community. Equipment and materials The experiment was presented using PsychoPy [ 42 ].

Experimental procedure Participants entered the lab and were given general information about the experiment and consent forms to sign. Option only. Gaze only. Weak and strong transfer. Equal weighting.

Variable weighting. Emotion weighting. Emotion bonus. Arbitration emotion bonus. Hierarchical Gaussian Filter. Model fitting and comparison All our computational models were implemented in the Stan probabilistic programming language and fit with MCMC sampling using the NUTS sampling algorithm [ 46 ].

Statistical analysis All analyses were performed in the R statistical language using the brms package [ 48 ]. Supporting information. S1 Text. Supplementary methods and results. Acknowledgments We thank R. References 1. Heyes C. Journal of Comparative Psychology.

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The circumstances are considered stable if they are unlikely to change. Controllability refers to the extent to which the circumstances that are associated with a given outcome can be controlled.

Obviously, those things that we have the power to control would be labeled controllable Weiner, For example, we might tell ourselves that our team is talented internal , consistently works hard stable , and uses effective strategies controllable. In contrast, we are more likely to make external, unstable, and uncontrollable attributions when our favorite team loses. Figure 5. When people experience bad fortune, others tend to assume that they somehow are responsible for their own fate.

A common ideology, or worldview, in the United States is the just-world hypothesis. The ability to think of the world as a fair place, where people get what they deserve, allows us to feel that the world is predictable and that we have some control over our life outcomes Jost et al. For example, if you want to experience positive outcomes, you just need to work hard to get ahead in life. Figure 6. People who hold just-world beliefs tend to blame the people in poverty for their circumstances, ignoring situational and cultural causes of poverty.

Can you think of a negative consequence of the just-world hypothesis? What common explanations are given for why people live in poverty? What types of explanations are these, dispositional or situational? These dispositional explanations are clear examples of the fundamental attribution error. Blaming poor people for their poverty ignores situational factors that impact them, such as high unemployment rates, recession, poor educational opportunities, and the familial cycle of poverty Figure 6.

In the United States and other countries, victims of sexual assault may find themselves blamed for their abuse. Victim advocacy groups, such as Domestic Violence Ended DOVE , attend court in support of victims to ensure that blame is directed at the perpetrators of sexual violence, not the victims.

Watch this TED video to apply some of the concepts you learned about attribution and bias. Privacy Policy. Skip to main content.

Module Social Psychology. Search for:. Learning Objectives Describe situational versus dispositional influences on behavior Give examples of the fundamental attribution error and other common biases, including the actor-observer bias and the self-serving bias Explain the just-world phenomenon.