Modeling Cognitive Parsimony with a Demand Selection Task

The law of less work (Hull, 1943) is our natural tendency given two alternatives with equal incentives to pick the less demanding one. This notion also appears in the field of judgment and decision making (Gigerenzer & Goldstein, 1996; Tversky & Kahneman, 1974), it is referred to as internal cost of effort. Cognitive parsimony is our tendency to favour low-effort strategies that help us to decide faster and simple strategies to approach a complex problem. An experimental paradigm for this phenomenon has been developed by Kool, McGuire, Rosen, & Botvinick (2010) and referred to as the demand selection task. In this poster, we present a model of this task developed in the ACT-R architecture (Anderson, 2007), which offers an hypothesis as to which cognitive mechanisms might participate in this phenomenon

Automated Update Tools To Augment the Wisdom of Crowds in Geopolitical Forecasting

Despite the importance of predictive judgments, individual human forecasts are frequently less accurate than those of even simple prediction algorithms. At the same time, not all forecasts are amenable to algorithmic prediction. Here, we describe the evaluation of an automated prediction tool that enabled participants to create simple rules that monitored relevant indicators (e.g., commodity prices) to automatically update forecasts. We examined these rules in both a pool of previous participants in a geopolitical forecasting tournament (Study 1) and a naïve sample recruited from Mechanical Turk (Study 2). Across the two studies, we found that automated updates tended to improve forecast accuracy relative to initial forecasts and were comparable to manual updates. Additionally, making rules improved the accuracy of manual updates. Crowd forecasts likewise benefitted from rule-based updates. However, when presented with the choice of whether to accept, reject or adjust an automatic forecast update, participants showed little ability to discriminate between automated updates that were harmful versus beneficial to forecast accuracy. Simple prospective rule-based tools are thus able to improve forecast accuracy by offering accurate and efficient updates, but ensuring forecasters make use of tools remains a challenge

Emotion in the Common Model of Cognition

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition

Emotion in the Common Model of Cognition

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition

Using Cognitive Agents to Train Negotiation Skills

Training negotiation is difficult because it is a complex, dynamic activity that involves multiple parties. It is often not clear how to create situations in which students can practice negotiation or how to measure students' progress. Some have begun to address these issues by creating artificial software agents with which students can train. These agents have the advantage that they can be “reset,” and played against multiple times. This allows students to learn from their mistakes and try different strategies. However, these agents are often based on normative theories of how negotiators should conduct themselves, not necessarily how people actually behave in negotiations. Here, we take a step toward addressing this gap by developing an agent grounded in a cognitive architecture, ACT-R. This agent contains a model of theory-of-mind, the ability of humans to reason about the mental states of others. It uses this model to try to infer the strategy of the opponent and respond accordingly. In a series of experiments, we show that this agent replicates some aspects of human performance, is plausible to human negotiators, and can lead to learning gains in a small-scale negotiation task

A psychophysiological investigation of the interplay between orienting and executive control during stimulus conflict: A heart rate variability study

Background It has been hypothesized that resting state cardiac vagal activity (CVA) - an indicator of parasympathetic nervous system activity - is a specific psychophysiological marker of executive control function. Here, we propose an alternative hypothesis - that CVA is associated with early stage attention orientation, promoting the flexible uptake of new information, on which the later operation of such executive control functions depends. We therefore predicted that CVA would predict the interaction between orienting and executive control. This was tested using the revised version of the Attention Network Test (ANT-R) that was developed to distinguish between orienting and executive attention during a stimulus conflict task. Methods Healthy adults (N = 48) performed the ANT-R and their resting CVA was measured over a 5 min period using ECG recordings. Results Multiple regression analyses indicated that, when other factors were controlled for, CVA was more strongly associated with the interaction between the orienting and executive control terms than with either factor individually. Conclusion Higher levels of CVA are specifically implicated in the modulation of executive control by intrinsic orientation operating at early stages of conflict detection. These initial findings of higher CVA on orienting attention in conflict detection need to be replicated in larger samples

How to measure behavioural spillovers: a methodological review and checklist

A growing stream of literature at the interface between economics and psychology is currently investigating ‘behavioural spillovers’ in (and across) different domains, including health, environmental, and pro-social behaviours. A variety of empirical methods have been used to measure behavioural spillovers to date, from qualitative self-reports to statistical/econometric analyses, from online and lab experiments to field experiments. The aim of this paper is to critically review the main experimental and non-experimental methods to measure behavioural spillovers to date, and to discuss their methodological strengths and weaknesses. A consensus mixed-method approach is then discussed which uses between-subjects randomisation and behavioural observations together with qualitative self-reports in a longitudinal design in order to follow up subjects over time. In particular, participants to an experiment are randomly assigned to a treatment group where a behavioural intervention takes place to target behaviour 1, or to a control group where behaviour 1 takes place absent any behavioural intervention. A behavioural spillover is empirically identified as the effect of the behavioural intervention in the treatment group on a subsequent, not targeted, behaviour 2, compared to the corresponding change in behaviour 2 in the control group. Unexpected spillovers and additional insights (e.g., drivers, barriers, mechanisms) are elicited through analysis of qualitative data. In the spirit of the pre-analysis plan, a systematic checklist is finally proposed to guide researchers and policy- makers through the main stages and features of the study design in order to rigorously test and identify behavioural spillovers, and to favour transparency, replicability, and meta-analysis of studies

The effect of practice on n-2 repetition costs in set switching.

Inhibition in set switching is inferred from so-called n-2 repetition costs: slower response times to ABA sequences compared to CBA sequences (where A, B, and C are arbitrary labels for different tasks). These costs are thought to reflect the persisting inhibition of task A when it was disengaged recently (as is the case in an ABA sequence). In this study we were interested in whether more inhibition may be required when the tasks are relatively novel. To this end, we examined the effect of practice on the n-2 repetition cost in nine participants across five experimental sessions, with 1222 trials performed in each session. The results show a clear reduction in the n-2 repetition cost, being altogether absent from the final sessions. Such a reduction is predicted by both: (a) a recent computational model of the n-2 repetition cost (Grange, Juvina, & Houghton, 2013) due to the gradual strengthening of task-related memory elements with practice to the point where inhibition has less impact; and (b) prior work showing smaller n-2 repetition costs with greater cue-target association strength (Houghton, Pritchard, & Grange, 2009). In this paper, we integrate these two theoretical derivations by extending our computational model, which fit the current data-at the mean level, block level, and individual-subject (i.e., individual differences) level-well