Sentence examples for motivated inference from inspiring English sources

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"Motivated inference," for example, "involves selective recruitment and assessment of evidence based on unconscious processes that are driven by emotional considerations of goals rather than purely cognitive reasoning" (2011, 156).

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Because motivated inferences result from unconscious mental processes rather than explicit reasoning, the errors in inferences cannot be exposed simply by identifying a fallacy in a reconstructed argument.

The present approach (GASA) was motivated by the inference of genetic interactions, which have potential for inferring pathways in yeast [ 3, 4].

As sketched before, MTO is motivated from the inference of causal relations in biological networks [ 6, 7] such as PPI networks, but it also has applications in the context of communication networks, where several one-way connection request pairs are given.

The results of Table 1 motivate the following inferences.

Moreover, very few studies included in this review investigated effects among samples of participants motivated to lose weight, further limiting inferences that can be drawn with respect to obesity treatment.

All these considerations motivated our work and our attempt to develop inference methods which are robust against noise, efficient in computation and flexible enough to meet different constraints.

Recent interest in model-based analysis of skin conductance responses (SCR) (Bach & Friston, 2013) is – in part – motivated by the need to improve the temporal resolution of inference in rapid event-related paradigms (Barry, Feldmann, Gordon, Cocker, & Rennie, 1993).

In contrast, DREAM is a set of critical assessments motivated, like CAFA, as a means of understanding and improving inference methods (particularly network based ones), but DREAM largely focuses on more specific problems with associated datasets.

Our inference framework is thus able to decide about actions motivated by goals that embody the raw power of gain and loss, contentment and pain.

We are motivated to use the t distribution because it can produce robust inference, shrinkage estimation, and easy computation (Gelman et al, 2008; Yi and Xu, 2008; Yi and Banerjee, 2009).

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