The consistency of visual attention to losses and loss sensitivity across valuation and choice.

Abstract

Sensitivity to losses has been found to vary greatly across individuals. One explanation for this variability is that for some losses garner more visual attention and are subsequently given more weight in decision-making processes. In three studies we examined whether biases in visual attention toward potential losses during valuation and choice were related to loss sensitivity, as well as the valuations provided and the choices made. In all studies, we find a positive relationship between estimated loss sensitivity and attention to losses for valuation, with increased attention to losses predicting decreased valuations. For choices, however, there was no robust relationship between attention and loss sensitivity or the choices made. In addition, preferences were not strongly consistent across tasks (i.e., valuations and choices did not robustly align), nor was the distribution of attention robustly related across tasks. Study 3 involved testing across separate sessions and found significant consistency in loss sensitivity and attention to losses across sessions for both choice and valuation. In sum, it appears that loss sensitivity varies across individuals, is differentially related to attention across tasks, and shows some consistency across time. Attention to losses also shows consistency across time, and its relationship with valuations appears much more robust than with choices; patterns of results that add to research suggesting that different cognitive processes underlie valuations and choices.

Psychological mechanisms of loss aversion: A drift-diffusion decomposition

“…Our work therefore also contributes to the growing body of research showing the role of information processing in interpreting various parameters of descriptive models of choice (Ashby, Yechiam, & Ben-Eliezer, 2018;Pachur, Schulte-Mecklenbeck, Murphy, & Hertwig, 2018).…”

Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters

“…Currently, the reported implementation of genomic selection in commercial aquaculture is still in its early days and has been limited to a handful of high-value species (i.e., rainbow trout, Abdelrahman et al., 2017; Atlantic salmon, Bangera et al., 2018; and the Tasmanian Atlantic salmon strain, Verbyla et al., 2018). However, a number of examples of demonstrating accuracy of genomic prediction across a range of traits and other species in aquaculture species have been recently published, for example, sea lice resistance in Atlantic salmon (Tsai et al., 2016, 2017), bacterial cold-water disease resistance in rainbow trout (Vallejo et al., 2017, 2018), pasteurellosis resistance in gilthead sea bream Sparus aurata (Palaiokostas et al., 2016), shell size in Yesso scallops Patinopecten yessoensis (Dou et al., 2016), Greenshell mussel Perna canaliculus (Ashby et al., 2018), body weight and meat quality in large yellow croaker Larimichthys crocea (Dong et al., 2016) and channel catfish (Garcia et al., 2018), growth traits of Pacific white shrimp (Wang et al., 2017), resistance to viral nervous necrosis in European sea bass (Palaiokostas et al., 2018a), juvenile growth rate in common carp (Palaiokostas et al., 2018b), resistance against Piscirickettsia salmonis in a farmed Atlantic and coho salmon Oncorhynchus kisutch population (Barría et al., 2018), resistance against P. salmonis and infectious pancreatic necrosis virus in rainbow trout (Yoshida et al., 2018), growth traits in yellowtail kingfish Seriola lalandi (Nguyen et al., 2018), and resistance to amoebic gill disease in Atlantic salmon (Robledo et al., 2018). The accuracy of genomic prediction reported in the aforementioned studies varies from 0.16 to 0.83 (median = 0.60) for various disease survival traits and from 0.3 to 0.8 (median = 0.6) for the growth and body size-related traits.…”

The postdictive effect of choice reflects the modulation of attention on choice

“…They monitored gaze position while participants made choices and found that participants were more likely to choose the face that they looked at the longest. Numerous subsequent studies replicated and extended this finding by exploiting different types of decision making such as moral decisions (e.g., Pärnamets et al, 2015), consumer decisions (e.g., Krajbich, Armel, & Rangel, 2010), and risky decisions (e.g., Ashby et al, 2018). These findings were usually interpreted as evidence that attention could significantly influence choices, because the items that were looked at the longest, or more frequently, were typically regarded as receiving more attention.…”