Outrage Fatigue? Cognitive Costs and Decisions to Blame

Across nine studies (N=1,672), we assessed the link between cognitive costs and the choice to express outrage by blaming. We developed the Blame Selection Task, a binary free-choice paradigm that examines the propensity to blame transgressors (versus an alternative choice)—either before or after reading vignettes and viewing images of moral transgressions. We hypothesized that participants’ choice to blame wrongdoers would negatively relate to how cognitively inefficacious, effortful, and aversive blaming feels (compared to the alternative choice). With vignettes, participants approached blaming and reported that blaming felt more efficacious. With images, participants avoided blaming and reported that blaming felt more inefficacious, effortful, and aversive. Blame choice was greater for vignette-based transgressions than image-based transgressions. Blame choice was positively related to moral personality constructs, blame-related social-norms, and perceived efficacy of blaming, and inversely related to perceived effort and aversiveness of blaming. The BST is a valid behavioral index of blame propensity, and choosing to blame is linked to its cognitive costs.

Do humans prefer cognitive effort over doing nothing?

Humans and other animals find mental (and physical) effort aversive and have the fundamental drive to avoid it. However, exerting no effort, doing nothing, is also aversive: it leads to boredom. Here, we ask whether people choose to exert effort when the alternative is to do nothing at all. Across nine studies, participants completed variants of the demand selection task, in which they repeatedly selected between a cognitively effortful task (e.g., simple addition, Stroop task) and a task that required no effort (e.g., doing nothing, watching the computer complete the Stroop). We then tabulated people’s choices. Across all studies and a mini meta-analysis, we found no evidence of effort avoidance and sometimes even a preference for effort when the alternative was doing nothing. Our findings reveal the limits of effort avoidance, suggesting that people do not seek to completely minimize effort expenditure.

More Effort, Less Fatigue: The Role of Interest in Increasing Effort and Reducing Mental Fatigue

People generally prefer easier over more difficult mental tasks. Using two different adaptations of a demand selection task, we show that interest can influence this effect, such that participants choose options with a higher cognitive workload. Interest was also associated with lower feelings of fatigue. In two studies, participants (N = 63 and N = 158) repeatedly made a choice between completing a difficult or easy math problem. Results show that liking math predicts choosing more difficult (vs. easy) math problems (even after controlling for perceived math skill). Two additional studies used the Academic Diligence Task (Galla et al., 2014), where high school students (N = 447 and N = 884) could toggle between a math task and playing a video game/watching videos. In these studies, we again find that math interest relates to greater proportion of time spent on the math problems. Three of these four studies also examined perceived fatigue, finding that interest relates to lower fatigue. An internal meta-analysis of the four studies finds a small but robust effect of interest on both the willingness to exert greater effort and the experience of less fatigue (despite engaging in more effort).

Young Children Learning About Maps at School and in the Neighbourhood

<p>In the New Zealand National Curriculum maps are defined as an essential skill along with graphs and tables. Despite the widespread use of maps in everyday life and their incorporation in more than one area of the curriculum there has been little research in New Zealand on children’s knowledge of maps, their use and where and how children encounter them. The research reported in this thesis is an attempt to broaden our understanding of young school children’s knowledge of maps and in particular the sources of their map knowledge in family, neighbourhood and school. The study was informed by two bodies of work, that of Fay Panckhurst on preschoolers’ map knowledge and its sources, and the NEMP studies which assessed graphs, tables and maps at Years 4 and 8. From a Decile 2 school, a sample of Year 3 & 4 children was selected before they had been introduced to maps at school. The students were interviews individually. Each was asked to select the maps from a collection, which included graphs and tables. The selection task showed that the students knew what maps were but had trouble recognising graphs or tables. The students then answered a set of questions about maps, and drew a map of New Zealand marking it on a number of locations. Over a period of three weeks their classroom teacher taught a unit on maps. The sample students were then interviewed again. While their original answers showed the influence of maps in homes and neighbourhood the classroom unit had provided them with technical knowledge and map language, and their maps of New Zealand improved in relation to shape, relationship of the islands, and knowledge of locations. It is argued that maps are not solely “skills” but as cultural artefacts they appear for example in works of literature, in games and on television and for migrant children they can provide links with countries of origin.</p>

Young Children Learning About Maps at School and in the Neighbourhood

<p>In the New Zealand National Curriculum maps are defined as an essential skill along with graphs and tables. Despite the widespread use of maps in everyday life and their incorporation in more than one area of the curriculum there has been little research in New Zealand on children’s knowledge of maps, their use and where and how children encounter them. The research reported in this thesis is an attempt to broaden our understanding of young school children’s knowledge of maps and in particular the sources of their map knowledge in family, neighbourhood and school. The study was informed by two bodies of work, that of Fay Panckhurst on preschoolers’ map knowledge and its sources, and the NEMP studies which assessed graphs, tables and maps at Years 4 and 8. From a Decile 2 school, a sample of Year 3 & 4 children was selected before they had been introduced to maps at school. The students were interviews individually. Each was asked to select the maps from a collection, which included graphs and tables. The selection task showed that the students knew what maps were but had trouble recognising graphs or tables. The students then answered a set of questions about maps, and drew a map of New Zealand marking it on a number of locations. Over a period of three weeks their classroom teacher taught a unit on maps. The sample students were then interviewed again. While their original answers showed the influence of maps in homes and neighbourhood the classroom unit had provided them with technical knowledge and map language, and their maps of New Zealand improved in relation to shape, relationship of the islands, and knowledge of locations. It is argued that maps are not solely “skills” but as cultural artefacts they appear for example in works of literature, in games and on television and for migrant children they can provide links with countries of origin.</p>

A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.

A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.