Emmanouil Konstantinidis

Don't bet on it! Wagering as a measure of awareness in decision making under uncertainty

Can our decisions be guided by unconscious or implicit influences? According to the somatic marker hypothesis, emotion-based signals can guide our decisions in uncertain environments outside awareness. Postdecision wagering, in which participants make wagers on the outcomes of their decisions, has been recently proposed as an objective and sensitive measure of conscious content. In 5 experiments we employed variations of a classic decision-making assessment, the Iowa Gambling Task, in combination with wagering in order to investigate the role played by unconscious influences. We examined the validity of postdecision wagering by comparing it with alternative measures of conscious knowledge, specifically confidence ratings and quantitative questions. Consistent with a putative role for unconscious influences, in Experiments 2 and 3 we observed a lag between choice accuracy and the onset of advantageous wagering. However, the lag was eliminated by a change in the wagering payoff matrix (Experiment 2) and by a switch from a binary wager response to either a binary or a 4-point confidence response (Experiment 3), and wagering underestimated awareness compared to explicit quantitative questions (Experiments 1 and 4). Our results demonstrate the insensitivity of postdecision wagering as a direct measure of conscious knowledge and challenge the claim that implicit processes influence decision making under uncertainty.

Task complexity moderates the influence of descriptions in decisions from experience

Decisions-makers often have access to a combination of descriptive and experiential information, but limited research so far has explored decisions made using both. Three experiments explore the relationship between task complexity and the influence of descriptions. We show that in simple experience-based decision-making tasks, providing congruent descriptions has little influence on task performance in comparison to experience alone without descriptions, since learning via experience is relatively easy. In more complex tasks, which are slower and more demanding to learn experientially, descriptions have stronger influence and help participants identify their preferred choices. However, when the task gets too complex to be concisely described, the influence of descriptions is reduced hence showing a non-monotonic pattern of influence of descriptions according to task complexity. We also propose a cognitive model that incorporates descriptive information into the traditional reinforcement learning framework, with the impact of descriptions moderated by task complexity. This model fits the observed behavior better than previous models and replicates the observed non-monotonic relationship between impact of descriptions and task complexity. This research has implications for the development of effective warning labels that rely on simple descriptive information to trigger safer behavior in complex environments.

Putting bandits into context: How function learning supports decision making

The authors introduce the contextual multi-armed bandit task as a framework to investigate learning and decision making in uncertain environments. In this novel paradigm, participants repeatedly choose between multiple options in order to maximize their rewards. The options are described by a number of contextual features which are predictive of the rewards through initially unknown functions. From their experience with choosing options and observing the consequences of their decisions, participants can learn about the functional relation between contexts and rewards and improve their decision strategy over time. In three experiments, the authors explore participants’ behavior in such learning environments. They predict participants’ behavior by context-blind (mean-tracking, Kalman filter) and contextual (Gaussian process and linear regression) learning approaches combined with different choice strategies. Participants are mostly able to learn about the context-reward functions and their behavior is best described by a Gaussian process learning strategy which generalizes previous experience to similar instances. In a relatively simple task with binary features, they seem to combine this learning with a probability of improvement decision strategy which focuses on alternatives that are expected to lead to an improvement upon a current favorite option. In a task with continuous features that are linearly related to the rewards, participants seem to more explicitly balance exploration and exploitation. Finally, in a difficult learning environment where the relation between features and rewards is nonlinear, some participants are again well-described by a Gaussian process learning strategy, whereas others revert to context-blind strategies.

Categorization of Events in Security Scenarios The Role of Context and Heuristics

An essential skill in security involves categorizing events based on observed event attributes. That is, determining threat level and priority of the event when choosing an appropriate response action. To explore the basic mechanisms of learning and decision making, we conducted two experiments wherein participants were asked to categorize security events into four categories on the basis of the cues that define each event. Participants had no prior knowledge about the relationship between events and categories and through 128 categorization trials they had to learn the relationship between them using feedback received per trial in terms of rewards (higher reward for appropriate categorization). Results from the experiments demonstrate the significant role of task abstraction and experiment context in the categorization success. The effect of heuristics and knowledge on categorization performance was measured and compared. We conclude with recommendation for future experiments on learning and decision making in security event categorization.

Magnitude and Incentives: Revisiting the Overweighting of Extreme Events in Risky Decisions from Experience

Recent experimental evidence in experience-based decision-making suggests that people are more risk seeking in the gains domain relative to the losses domain. This critical result is at odds with the standard reflection effect observed in description-based choice and explained by Prospect Theory. The so-called reversed-reflection effect has been predicated on the extreme-outcome rule, which suggests that memory biases affect risky choice from experience. To test the general plausibility of the rule, we conducted two experiments examining how the magnitude of prospective outcomes impacts risk preferences. We found that while the reversed-reflection effect was present with small-magnitude payoffs, using payoffs of larger magnitude brought participants’ behavior back in line with the standard reflection effect. Our results suggest that risk preferences in experience-based decision-making are not only affected by the relative extremeness but also by the absolute extremeness of past events.