An official website of the United States government
The normative principle of description invariance presupposes that rational preferences must be complete. The completeness axiom is normatively dubious, however, and its rejection opens the door to rational framing effects. In this commentary, we suggest that Bermúdez’s insightful challenge to the standard normative view of framing can be clarified and extended by situating it within a broader critique of completeness.
more »
« less
When and why framing effects are neither errors nor mistakes
Abstract Framing effects play a central role in the debate regarding human rationality. They violate the normative principle ofdescription invariance, which states that merely redescribing options or outcomes in equivalent ways should not affect judgments or decisions. Description invariance is considered by many decision researchers to be “normatively unassailable”, and violations are widely regarded as demonstrations of systematic irrationality. This article develops an alternative perspective on invariance violations, applying Funder’s (1987) distinction between “errors” and “mistakes”. Description invariance implicitly assumes that (1) rational preferences must be complete and (2) frames do not convey choice-relevant information. We argue that both assumptions often do not hold. When they fail, framing effects in the laboratory are not “errors”, and they do not provide evidence for “mistakes” in natural environments. Furthermore, recent findings suggest that participants often do not regard different responses to different frames as unreasonable, and presenting them with arguments for and against description invariance has little effect on their views. Finally, we argue that similar lessons generalize to other coherence norms, such as procedure invariance and independence of irrelevant alternatives.
more »
« less
- Award ID(s):
- 2049935
- PAR ID:
- 10643275
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Mind & Society
- Volume:
- 24
- Issue:
- 2
- ISSN:
- 1593-7879
- Format(s):
- Medium: X Size: p. 209-229
- Size(s):
- p. 209-229
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract BackgroundIn college science laboratory and discussion sections, student-centered active learning strategies have been implemented to improve student learning outcomes and experiences. Research has shown that active learning activities can increase student anxiety if students fear that they could be negatively evaluated by their peers. Error framing (i.e., to frame errors as natural and beneficial to learning) is proposed in the literature as a pedagogical tool to reduce student anxiety. However, little research empirically explores how an instructor can operationalize error framing and how error framing is perceived by undergraduate students. To bridge the gap in the literature, we conducted a two-stage study that involved science graduate teaching assistants (GTAs) and undergraduate students. In stage one, we introduced cold calling (i.e., calling on non-volunteering students) and error framing to 12 chemistry and 11 physics GTAs. Cold calling can increase student participation but may increase student anxiety. Error framing has the potential to mitigate student anxiety when paired with cold calling. GTAs were then tasked to rehearse cold calling paired with error framing in a mixed-reality classroom simulator. We identified GTA statements that aligned with the definition of error framing. In stage two, we selected a few example GTA error framing statements and interviewed 13 undergraduate students about their perception of those statements. ResultsIn the simulator, all the GTAs rehearsed cold calling multiple times while only a few GTAs made error framing statements. A thematic analysis of GTAs’ error framing statements identified ways of error indication (i.e., explicit and implicit) and framing (i.e., natural, beneficial, and positive acknowledgement). Undergraduate student interviews revealed specific framing and tone that are perceived as increasing or decreasing student comfort in participating in classroom discourse. Both undergraduate students and some GTAs expressed negative opinions toward responses that explicitly indicate student mistakes. Undergraduate students’ perspectives also suggest that error framing should be implemented differently depending on whether errors have already occurred. ConclusionError framing is challenging for science GTAs to implement. GTAs’ operationalizations of error framing in the simulator and undergraduate students’ perceptions contribute to defining and operationalizing error framing for instructional practice. To increase undergraduate student comfort in science classroom discourse, GTAs can use implicit error indication. In response to students’ incorrect answers, GTAs can positively frame students’ specific ideas rather than discussing broadly how errors are natural or beneficial.more » « less
-
Image classifiers have become an important component of today’s software, from consumer and business applications to safety-critical domains. The advent of Deep Neural Networks (DNNs) is the key catalyst behind such wide-spread success. However, wide adoption comes with serious concerns about the robustness of software systems dependent on image classification DNNs, as several severe erroneous behaviors have been reported under sensitive and critical circumstances. We argue that developers need to rigorously test their software’s image classifiers and delay deployment until acceptable. We present an approach to testing image classifier robustness based on class property violations. We have found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others. These bugs usually violate some class properties of one or more of those classes. Most DNN testing techniques focus on per-image violations and thus fail to detect such class-level confusions or biases. We developed a testing approach to automatically detect class-based confusion and bias errors in DNN-driven image classification software. We evaluated our implementation, DeepInspect, on several popular image classifiers with precision up to 100% (avg. 72.6%) for confusion errors, and up to 84.3% (avg. 66.8%) for bias errors. DeepInspect found hundreds of classification mistakes in widely-used models, many of which expose errors indicating confusion or bias.more » « less
-
Olanoff, D. (Ed.)STEM integration holds significant promise for supporting students in making connections among ideas and ways of thinking that might otherwise remain “siloed.” Nevertheless, activities that integrate disciplines can present challenges to learners. In particular, they can require students to shift epistemological framing, demands that can be overlooked by designers and facilitators. We analyze how students in an 8th grade mathematics classroom reasoned about circles, across math and coding activities. One student showed evidence of shifting fluently between different frames as facilitators had expected. The dramatic change in his contributions gauge the demands of the activities, as do the contributions of other students, who appeared to work within different frames. Our findings have relevance for the design and facilitation of integrated STEM learning environments to support students in navigating such frame-shifts.more » « less
-
Olanoff, D; Johnson, K; Spitzer, M (Ed.)STEM integration holds significant promise for supporting students in making connections among ideas and ways of thinking that might otherwise remain “siloed.” Nevertheless, activities that integrate disciplines can present challenges to learners. In particular, they can require students to shift epistemological framing, demands that can be overlooked by designers and facilitators. We analyze how students in an 8th grade mathematics classroom reasoned about circles, across math and coding activities. One student showed evidence of shifting fluently between different frames as facilitators had expected. The dramatic change in his contributions gauge the demands of the activities, as do the contributions of other students, who appeared to work within different frames. Our findings have relevance for the design and facilitation of integrated STEM learning environments to support students in navigating such frame-shifts.more » « less
