More Like this

1. Thinking, good and bad? Deliberative thinking and the singularity effect in charitable giving.

   Moche, Hajdi; Gordon-Hecker, Tom; Kogut, Tehila; Vastfjall, Daniel (January 2022, Judgment and decision making)

   Can deliberation increase charitable giving when giving is impulsive (i.e., a one-time small gift in response to an immediate appeal)? We conduct two studies in Israel and Sweden to compare two forms of deliberation, unguided and guided, in their ability to decrease the singularity effect (i.e., giving more to one than many victims), often evident in impulsive giving. Under unguided deliberation, participants were instructed to simply think hard before making a donation decision whereas participants in the guided deliberation condition were asked to think how much different prespecified decision attributes should influence their decision. We find that both types of deliberation reduce the singularity effect, as people no longer value the single victim higher than the group of victims. Importantly, this is driven by donations being decreased under deliberation only to the single victim, but not the group of victims. Thus, deliberation affects donations negatively by overshadowing the affective response, especially in situations in which affect is greatest (i.e., to a single victim). Last, the results show that neither type of deliberation significantly reversed the singularity effect, as people did not help the group significantly more than the single victim. This means that deliberate thinking decreased the overall willingness to help, leading to a lower overall valuation of people in need. 

   more » « less
2. Uncovering the computational mechanisms underlying many-alternative choice

   https://doi.org/10.7554/eLife.57012  

   Thomas, Armin W; Molter, Felix; Krajbich, Ian (April 2021, eLife)

   null (Ed.)

   In our everyday lives, we often have to choose between many different options. When deciding what to order off a menu, for example, or what type of soda to buy in the supermarket, we have a range of possibilities to consider. So how do we decide what to go for? Researchers believe we make such choices by assigning a subjective value to each of the available options. But we can do this in several different ways. We could look at every option in turn, and then choose the best one once we have considered them all. This is a so-called ‘rational’ decision-making approach. But we could also consider each of the options one at a time and stop as soon as we find one that is good enough. This strategy is known as ‘satisficing’. In both approaches, we use our eyes to gather information about the items available. Most scientists have assumed that merely looking at an item – such as a particular brand of soda – does not affect how we feel about that item. But studies in which animals or people choose between much smaller sets of objects – usually up to four – suggest otherwise. The results from these studies indicate that looking at an item makes that item more attractive to the observer, thereby increasing its subjective value. Thomas et al. now show that gaze also plays an active role in the decision-making process when people are spoilt for choice. Healthy volunteers looked at pictures of up to 36 snack foods on a screen and were asked to select the one they would most like to eat. The researchers then recorded the volunteers’ choices and response times, and used eye-tracking technology to follow the direction of their gaze. They then tested which of the various decision-making strategies could best account for all the behaviour. The results showed that the volunteers’ behaviour was best explained by computer models that assumed that looking at an item increases its subjective value. Moreover, the results confirmed that we do not examine all items and then choose the best one. But neither do we use a purely satisficing approach: the volunteers chose the last item they had looked at less than half the time. Instead, we make decisions by comparing individual items against one another, going back and forth between them. The longer we look at an item, the more attractive it becomes, and the more likely we are to choose it. 

   more » « less
3. Student Choices: Online Versus In-Person Tutoring Options

   Infante, Nicole; Ogden, Lori; Gallagher, Keith; LaRue, Renee; Moore-Russo, Deborah; Tinsley, Christine; Berger, Ashley (November 2022, Proceedings of the forty-fourth annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Lischka, A. E.; Dyer, E. B.; Jones, R. S.; Lovett, J. N.; Strayer, J.; Drown, S. (Ed.)

   Access to free, high-quality, institutionally provided tutoring services can be critical to the success of university students. When the pandemic forced university tutoring operations to close or move online, many chose to move operations online. The Fall 2021 semester saw the return of in-person tutoring at many institutions; however, online options remained in place to serve students who may not be able to participate in-person. This created more equitable access to a critical student support. We collected data on which format students chose at two research focused institutions. At one institution, visits were split at 47% online and 53% in-person. In contrast, visits at the other institution were split at 5% online, and 95% in-person. We will discuss the courses with the highest usage in each format and explore why students may be choosing one format over the other. 

   more » « less
4. Stochastic Top-$$K$$ Subset Bandits with Linear Space and Non-Linear Feedback

   Agarwal, Mridul; Aggarwal, Vaneet; Quinn, Christopher J.; Umrawal, Abhishek K. (March 2021, International Conference on Algorithmic Learning Theory)

   Feldman, Vitaly; Ligett, Katrina; Sabato, Sivan (Ed.)

   Many real-world problems like Social Influence Maximization face the dilemma of choosing the best $$K$$ out of $$N$$ options at a given time instant. This setup can be modeled as a combinatorial bandit which chooses $$K$$ out of $$N$$ arms at each time, with an aim to achieve an efficient trade-off between exploration and exploitation. This is the first work for combinatorial bandits where the feedback received can be a non-linear function of the chosen $$K$$ arms. The direct use of multi-armed bandit requires choosing among $$N$$-choose-$$K$$ options making the state space large. In this paper, we present a novel algorithm which is computationally efficient and the storage is linear in $$N$$. The proposed algorithm is a divide-and-conquer based strategy, that we call CMAB-SM. Further, the proposed algorithm achieves a \textit{regret bound} of $$\tilde O(K^{\frac{1}{2}}N^{\frac{1}{3}}T^{\frac{2}{3}})$ for a time horizon $$T$$, which is \textit{sub-linear} in all parameters $$T$$, $$N$$, and $$K$$. 

   more » « less
5. Successful Prediction Is Associated With Enhanced Encoding

   https://doi.org/10.1162/opmi.a.15  

   Poskanzer, Craig; Tarder-Stoll, Hannah; Javid, Raheema; Spolaore, Edoardo; Aly, Mariam (July 2025, Open Mind)

   Abstract Forming memories requires a focus on the external world; retrieving memories requires attention to our internal world. Computational models propose that the hippocampus resolves the tension between encoding and retrieval by alternating between states that prioritize one over the other. We asked whether the success of a retrieval state affects the success of an encoding state, when both are measured in behavior. Across 3 Experiments (N = 197), we operationalized retrieval as the use of memories to make predictions about the future, and tested whether successful (vs. unsuccessful) prediction affected the likelihood of successful encoding. Participants viewed a series of scene categories that contained structure (e.g., beaches are followed by castles), which enabled memory retrieval to guide prediction. After structure learning, they completed a simultaneous prediction and encoding task. They were shown trial-unique category exemplars and made predictions about upcoming scene categories. Finally, participants completed a surprise memory test for the trial-unique images. Accurate (vs. inaccurate) predictions were associated with better encoding, and increasing prediction distance hurt both prediction and encoding. This association between encoding and prediction could not be explained by generic on- vs. off-task states. We propose that, in addition to stimulus and endogenous factors that modulate switches between encoding and retrieval, the success of one state can facilitate a switch to the other. Thus, although encoding and prediction depend on distinct and competitive computational mechanisms, the success of one in behavior can increase the likelihood of success for the other. 

   more » « less