Prof. Eldad Yechiam
Technion - Israel Institute of Technology

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I am a Professor of Behavioral Sciences at the Technion - Israel Institute of Technology, where I currently serve as Vice-Dean of Education in the faculty of Industrial Engineering and Management . Also, I am the Co-Editor in Chief of the Journal of Economic Psychology (JoEP). My research interests include: 

1. Attentional and cognitive effect of incentives
2. Individual differences in decision making
3. Cognitive enhancement

Recent and semi-recent papers:

Yechiam, E., & Hochman, G. (2013). Losses as modulators of attention: Review and analysis of the unique effects of losses over gains. Psychological Bulletin, 139, 497-518.

Yechiam E., & Hochman, G. (2013). Loss-aversion or loss-attention: The impact of losses on cognitive performance. Cognitive Psychology, 66, 212-231.

Yechiam, E., Ashby, N.J.S., and Pachur, T. (2017). Who’s biased? A meta-analysis of buyer-seller differences in the pricing of risky prospects. Psychological Bulletin,  143, 543-563.

Yechiam, E. (2019). Acceptable losses: The debatable origins of loss aversion. Psychological Research, 83, 1327-1339.

New: Yechiam, E. (2020). Robust consistency of choice switching in decisions from experience Judgment and Decision Making, 15, 74-81.




    Research Topics:

1) Losses and gains (A Youtube movie)

The idea of "loss attention" (Yechiam & Hochman, 2013a; 2013b, 2014; Yechiam et al., 2015)  is that losses lead to more task attention. This model is different from "loss aversion" (Kahneman & Tversky, 1979) which assumes that losses are given more subjective weight than gains. Instead, under loss attention, losses increase the sensitivity to the task incentive structure as a whole . This model can explain previous phenomena which have been attributed to loss aversion, and clarifies several puzzles.

Specifically, it explains the increased arousal following losses (due to the increased attention), the increased response time in tasks with losses, and the increased cognitive performance typically observed when outcomes include potential losses. Its also explains the finding that tasks with losses induce greater reliability in task performance.

Puzzles explained:

a) Losses improve performance even when these same losses are not overweighted compared to gains (see Yechiam & Hochman, 2013b). 

b) Losses lead to more physiological arousal than gains, even when they are not given more weight than gains (see Yechiam & Hochman, 2013a).

c) In performing composite tasks: Losses in certain parts of a task increase performance in task components that do not include losses (e.g., a secondary task). This is explained as an attentional spillover (see Yechiam et al., 2015)

My studies suggest that the way we think about losses should be changed from a "tilted scales" metaphor (where the subjective weight of losses is larger than that of respective gains) to an attention investment framework where losses increase the allocation of cognitive effort to a task yielding losses. For instance, a couple of years ago my belt had broken during a scientific conference . I don't think this was specifically hurting (e.g., that it hurt 2.5 times the price of the belt) but the event certainly did capture my attention.


2) Seeing what could have happened: Effect of foregone payoffs

Imagine a driver who decides to follow the speed-limits and is alone on the road. In this case, there is no temptation to speed. But when this driver also sees other drivers who speed she observes that the common outcome of speeding is positive (they get to their destination faster and with no negative repercussion) and negative outcomes such as a fine are infrequent (in most settings).

Information from unmade choices or strategies  - known as foregone outcomes - may increase the tendency to follow strategies that are commonly rewarding, and leads to ignoring or underweighting rare events (such as the possibility of an accident in the example above). Our findings with laboratory tasks reveal that in experimental conditions with foregone payoffs decision makers show extreme underweighting of small probability events, even compared to other experience based decision tasks (e.g., Yechiam & Busemeyer, 2005; 2006; Yechiam, Rakow, & Newell, 2015). Potentially, foregone payoffs increase the regret associated with making the option that is less rewarding most of the time (e.g., not speeding), which tempts people to pick the option that is better most of the time (i.e., speeding) but is exposed to rare repercussions.


3) Individual differences in decision making

In previous studies I have examined formal models for individual differences in adaptive learning. Traditionally, reinforcement learning models have been applied to predict group averages (see e.g., Roth & Erev, 1995). I have studied the use of these models at the individual level, and have developed new statistical techniques for model assessment. These techniques include a method for assessing the generalizability of model predictions that is also useful for assessing the adequacy of tasks for evaluating individual differences (see Yechiam & Busemeyer, 2008).

Another series of papers demonstrates the usefulness of models in assessing the diversified reasons leading to impairments in decision making. In a study in Psychological Science (Yechiam et al., 2005), we have shown that what appears as similar decision making impairments on a task known as the Iowa Gambling task (Bechara et al., 1994) can actually be due to substantially different component processes. This resulted in a map of the differences in cognitive style between neuropsychological populations.



A part of the outline of neuropsychological populations. The X axis denotes the weight of gains compared to losses (right – more gain oriented). The Y axis denotes the extent of recency. The bubble size denotes the choice consistency. Thus, for instance, VMPC (Ventromedial Prefrontal Cortex) patients from Bechara et al. (1994) were found to be impaired in the recency component. From Yechiam et al. (2005).


In other studies we have found that even mundane decisions such as speeding and smoking are associated with consistent decision making traits. 


4) Cognitive enhancement

I am interested in a variety of manipulations that improve cognitive performance, including pharmaceutical ones. We examined three types of medicine that are presumed to have positive effects on human cognitive processing.

Methylphenidate (Ritalin)- In my studies with Nirit Agay (Agay et al., 2010; 2014; 2016), we've found that Ritalin positive affect healthy adults to a similar extent to its effects on adults with ADHD. This was found for working memory and sustained attention task (e.g., the TOVA). The effect was larger for individuals who performed poorly in the task. Yet although individuals with ADHD tended to score lower in these tasks, the key variable determining the cognitive enhancing potential of ritalin was not the diagnosis of ADHD per se, but rather one' task performance off-medication.

DHEA - Dehydroepiandrosterone (DHEA) is an endogenous neurosteroid which acts as a prohormone for both male and female sex hormones. We've shown that administering it to cocaine-dependent addicts considerably reduced the relapse to drug use 16 months following treatment in a rehabilitation center (Ohana et al., 2016).

Hypericum perforatum - Considered an antidepressant and anti-anxiety agent, Hypericum perforatum affects multiple neurotransmitters in a non-competitive synergistic manner. Our initial analysis suggests that healthy rodents who are administered with Hypericum show improved memory performance (Ben-Eliezer & Yechiam, 2016).