This is a short presentation of the paper:
Bias-Aware Systems: Exploring Indicators of Cognitive Biases when Facing Different Opinions
I'm Nattapat Boonprakong
and I'm presenting this work on behalf of the co-authors
Xiuge Chen, Catherine Davey, Benjamin Tag, and Tilman Dingler
Digital technology has allowed us to access any information at any time.
However, humans cognitive resources is limited.
That's why people use cognitive biases to effectively navigate through complex information space.
Cognitive biases rely a lot on our existing preference and beliefs.
And they affect how information is perceived and processed by users.
Algorithms and platforms also play a role in amplifying our cognitive biases.
When the users roam through the platform,
the algorithms learn their preference and feed users back the biased content.
With this loop ongoing, users sink deep down into their biases
as the users mostly seek the contents that are increasingly reinforcing their preferences.
When it comes to polarising topics like climate change or abortion rights,
this bars healthy public discourses
and leads to the creation of of echo chamber or filter bubbles and the spread of misinformation.
While it is crucial to mitigate the negative effects of cognitive biases,
we first have to understand when and how biases manifest themselves.
Being able to sense the occurences of cognitive biases enables us to design interfaces
and systems to help users reflect themselves and overcome their irrationality,
Ultimately, this would help mitigate the negative effects of cognitive biases.
Potential measures to detect cognitive biases include:
Self-report, Behavioural, and Physiological Measures.
Self-reports however are not a reliable candidate
since people could not be aware of their implicit biases.
Meanwhile, Behavioural measures have produced mixed findings in previous research.
On the other hand, Physiological measures show the potential to be more objective means to quantify our mental states,
including cognitive biases.
This brings to the focus of our paper, which is
To investigate whether physiological signals can be a reliable and objective measure of cognitive biases.
In this case, we focus biases in the context where individuals face different opinions,
or simply information that can be compatible or incompatible with their beliefs.
We first conducted study 1 to explore the indicators of cognitive biases
In this study, we exposed users to stimuli that represented opinions on polarising topics.
Meanwhile we collected Behavioural data and Physiological data during such exposures.
However, we found the results to be inconclusive.
While we found that participants tend to spend more time on information that contradicts with their beliefs,
we were not sure whether this is an effect from cognitive biases or not.
Moreover, the study design did not provide inter-stimuli intervals.
Therefore, the physiological responses were confounded
as they may not reflect the reactions induced by each stimulus.
We then conducted Study 2 as a follow-up experiment which accounts the potential confounding factors.
In this study, we exposed participants to a series of ideological statements.
We sourced text opinions from 8 different polarising topics.
Each statement expresses opinions on one side of the ideological spectrum
pro or con
for example
supporting abortion rights or opposing same-sex marriage.
In this study, we investigate the effects of ideological congruency between the user and the stimulus.
Meanwhile, we also explored the effects of the users' topic interest and topic familiarity.
Dependent variables of Study 2 include self-report measures,
where we asked participants to rate their agreement,
likelihood to share, and reading effort
on each statement they read using 5-Likert scale.
Next, we have behavioural measures
which include dwelling time and eye tracking data.
Lastly, physiological measures include
the hemodynamic activity from fNIRS
and Electrodermal Activity (or EDA).
In this study, participants were presented with stimuli in a random order.
For each block, they first read the stimulus,
then answered in-study survey questions,
and entered a 15-second resting period
before proceeding to the next stimulus.
For results, we analyse the collected data as follows
By performing an one-way repeated measures ANOVA
We found that participants in general spent significantly more time on ideologically dissenting information.
or basically information that contradicts with their prior beliefs
Meanwhile, they report higher ratings on agreement
and likelihood to share congruent information.
When considering individuals with low topic interest,
we found higher reading effort on congruent statements.
Lastly, for individuals with high topic interest,
we found stronger effect sizes on dwelling time,
agreement and likelihood to share the content
when compared to the general population.
For physiological measures, we did not detect any effect of ideological congruency.
However, when considering the low interest population,
we found significant effects on the brain oxygenation level
during the beginning of each stimulus exposure.
Additionally, we trained binary classifiers on the data we collected
to distinguish the exposures to congruent information from dissenting ones.
We used Linear Discriminant Analysis, Support Vector Machines, Random Forest, and XGBoost algorithms
Through 5-fold cross validation
we achieved the highest accuracy of 55.27% on the XGBoost algorithm.
Our classification models performed slightly better than the chance level.
This brings us to the discussion that
First, we can sense the occurrences of cognitive biases through physiological signals,
particularly fNIRS.
Second, we found that topic interest influences the expression of biases.
In low interest individuals, we found the effects of ideological congruency
on the brain oxygenation levels and the self-report reading effort.
Meanwhile, we found stronger effect sizes on dwelling time and the likelihood to share the content
in high-interest individuals.
This can be explained by the dual process theory
and the two-step model of processing conflicting information.
When facing information, individuals tend to save up their cognitive resources.
by employing their prior beliefs as a shortcut for processing information,
which results in the use of System 1 thinking and cognitive biases.
Meanwhile, individuals with relevant background knowledge
may engage in elaborative processing where System 2 thinking is used.
Therefore, we hypothesize that cognitive biases
are more pronouced in individuals with low topic interest,
while those with higher topic interests are more torrelate against cognitive biases.
In conclusion, we have contributed towards the creation of Bias-Aware Systems
which are defined as Computing systems that detect
and take into account the presence of cognitive biases in users.
With multimodal data collected in our studies
we observed the expressions of cognitive biases in the context of opinion information processing.
Ultimately, being able to quantify the occurrences of cognitive biases
will allow researchers to closely study the influences that user interfaces and algorithms have on opinion formation.
therefore, we could design and develop interventions
to effectively mitigate the negative effects of biases.
and this concludes the presentation.
For more information, please refer to the paper,
and thank you for listening!