Causal Mechanistic Explanations Research Articles

More than meets the AI: The possibilities and limits of machine learning in olfaction.

Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools. None of them succeeded, however, and it matters as to why this is the case. This article argues that we should be deeply skeptical about the trend to black-box the sensory system’s biology in our theories of perception. Instead, we need to ground both stimulus models and psychophysical data on real causal-mechanistic explanations of the olfactory system. The central question is: Would knowledge of biology lead to a better understanding of the stimulus in odor coding than the one utilized in current machine learning models? That is indeed the case. Recent studies about receptor behavior have revealed that the olfactory system operates by principles not captured in current stimulus-response models. This may require a fundamental revision of computational approaches to olfaction, including its psychological effects. To analyze the different research programs in olfaction, we draw on Lloyd’s “Logic of Research Questions,” a philosophical framework which assists scientists in explicating the reasoning, conceptual commitments, and problems of a modeling approach in question.

“I got to see, and I got to be a part of it”: How cued gesturing facilitates middle‐school students' explanatory modeling of thermal conduction

AbstractConstructing causal mechanistic explanations of observable phenomena is a key science practice that is often challenging for students as most mechanisms involve interactions of unobservable entities and activities. In this study, we examined how gesturing with a computer simulation that depicts the molecular mechanism of thermal conduction supported middle‐school students in constructing causal mechanistic explanations. We designed a gesture‐augmented computer simulation in which students were cued to use hand gestures to control the simulation. These cued gestures represent core causal interactions of conduction and they prompt students to physically engage with the simulation in conceptually meaningful ways. In this study, we examined how 21 students used the simulation and explained thermal conduction in a semi‐structured interview, followed by a mixed‐methods analysis. Quantitative analysis shows that students moved toward articulating the canonical causal mechanistic explanation of thermal conduction using the simulation. Three representative cases were identified to explore how students' explanations were facilitated by cued gestures. The analysis shows two main ways the cued gestures supported all students in the study: (a) by helping them attribute causal agency to molecules rather than an entity called Heat, and (b) by reifying the core mechanism of molecular collisions in conduction. Furthermore, the case studies show how each student's unique ways of sensemaking impacted their gesture use. Implications for instruction with gestures and design of augmented environments are discussed.

Arrows on the Page Are Not a Good Gauge: Evidence for the Importance of Causal Mechanistic Explanations about Nucleophilic Substitution in Organic Chemistry

This three-year study builds on prior work analyzing students’ causal mechanistic explanations about acid–base reactions. Here we extend that work to characterize and investigate how students construct causal mechanistic explanations of simple nucleophilic substitution reactions. After an initial pilot study, we adopted a modified version of the original task prompt which was used in two subsequent years to compare responses from students enrolled in a transformed organic chemistry course (Organic Chemistry, Life, the Universe and Everything) and a traditional organic chemistry course. Student responses were sampled in the middle of the first semester organic chemistry, just after they had learned the material, and once again at the end of the course, to identify how responses changed over time. Our findings from this study include (1) eliciting causal mechanistic explanations requires careful scaffolding to activate productive elements of a response, and (2) students from a transformed course are more likely to construct a causal mechanistic explanation at the end of Organic Chemistry 2 than students from the control group, suggesting that if students are to retain the use of valuable knowledge, this must be supported by instruction and course expectations.

Scientific Practice in Modeling Diseases: Stances from Cancer Research and Neuropsychiatry.

In the last few decades, philosophy of science has increasingly focused on multilevel models and causal mechanistic explanations to account for complex biological phenomena. On the one hand, biological and biomedical works make extensive use of mechanistic concepts; on the other hand, philosophers have analyzed an increasing range of examples taken from different domains in the life sciences to test-support or criticize-the adequacy of mechanistic accounts. The article highlights some challenges in the elaboration of mechanistic explanations with a focus on cancer research and neuropsychiatry. It jointly considers fields, which are usually dealt with separately, and keeps a close eye on scientific practice. The article has a twofold aim. First, it shows that identification of the explananda is a key issue when looking at dynamic processes and their implications in medical research and clinical practice. Second, it discusses the relevance of organizational accounts of mechanisms, and questions whether thorough self-sustaining mechanistic explanations can actually be provided when addressing cancer and psychiatric diseases. While acknowledging the merits of the wide ongoing debate on mechanistic models, the article challenges the mechanistic approach to explanation by discussing, in particular, explanatory and conceptual terms in the light of stances from medical cases.

Mechanisms in psychology: The road towards unity?

The focus of this special issue of Theory & Psychology is on explanatory mechanisms in psychology, especially on problems of particular prominence for psychological science such as theoretical integration and unification. Proponents of the framework of mechanistic explanation claim, in short, that satisfactory explanations in psychology and related fields are causal. They stress the importance of explaining phenomena by describing mechanisms that are responsible for them, in particular by elucidating how the organization of component parts and operations in mechanisms gives rise to phenomena in certain conditions. We hope for cross-pollination between philosophical approaches to explanation and experimental psychology, which could offer methodological guidance, in particular where mechanism discovery and theoretical integration are at issue. Contributions in this issue pertain to theoretical integration and unification of psychology as well as the growing importance of causal mechanistic explanations in psychological science.

From Gregor Mendel to Eric Davidson: Mathematical Models and Basic Principles in Biology.

Mathematical models have been widespread in biology since its emergence as a modern experimental science in the 19th century. Focusing on models in developmental biology and heredity, this article (1) presents the properties and epistemological basis of pertinent mathematical models in biology from Mendel's model of heredity in the 19th century to Eric Davidson's model of developmental gene regulatory networks in the 21st; (2) shows that the models differ not only in their epistemologies but also in regard to explicitly or implicitly taking into account basic biological principles, in particular those of biological specificity (that became, in part, replaced by genetic information) and genetic causality. The article claims that models disregarding these principles did not impact the direction of biological research in a lasting way, although some of them, such as D'Arcy Thompson's models of biological form, were widely read and admired and others, such as Turing's models of development, stimulated research in other fields. Moreover, it suggests that successful models were not purely mathematical descriptions or simulations of biological phenomena but were based on inductive, as well as hypothetico-deductive, methodology. The recent availability of large amounts of sequencing data and new computational methodology tremendously facilitates system approaches and pattern recognition in many fields of research. Although these new technologies have given rise to claims that correlation is replacing experimentation and causal analysis, the article argues that the inductive and hypothetico-deductive experimental methodologies have remained fundamentally important as long as causal-mechanistic explanations of complex systems are pursued.

Reasoning about Reactions in Organic Chemistry: Starting It in General Chemistry

The study presented here is a follow-up to a previous report in which we investigated how general-chemistry students in a transformed curriculum reason about simple acid–base reactions. In the present study, we use and adapt the previously developed coding scheme for a longitudinal study in which we follow students from general chemistry through organic chemistry. We find that (i) generally, the manner in which students reason about acid–base reactions increases in sophistication over the course of a two-semester sequence of organic chemistry; (ii) there is little difference in reasoning between students at the end of a transformed general-chemistry course and a similar cohort at the beginning of organic chemistry; (iii) the nature of a student’s general-chemistry experience has a profound effect on the sophistication of their reasoning in that students from a transformed general-chemistry course are more likely to provide causal mechanistic explanations for simple acid–base reactions than students with o...

Codon usage of highly expressed genes affects proteome-wide translation efficiency.

Although the genetic code is redundant, synonymous codons for the same amino acid are not used with equal frequencies in genomes, a phenomenon termed "codon usage bias." Previous studies have demonstrated that synonymous changes in a coding sequence can exert significant cis effects on the gene's expression level. However, whether the codon composition of a gene can also affect the translation efficiency of other genes has not been thoroughly explored. To study how codon usage bias influences the cellular economy of translation, we massively converted abundant codons to their rare synonymous counterpart in several highly expressed genes in Escherichia coli This perturbation reduces both the cellular fitness and the translation efficiency of genes that have high initiation rates and are naturally enriched with the manipulated codon, in agreement with theoretical predictions. Interestingly, we could alleviate the observed phenotypes by increasing the supply of the tRNA for the highly demanded codon, thus demonstrating that the codon usage of highly expressed genes was selected in evolution to maintain the efficiency of global protein translation.

Time for a Change: Topical Amendments to the Medical Model of Disease

There is a conceptual crisis in the biomedical sciences that is particularly salient in psychopathology research. Underlying the crisis is a controversy that pertains to the current medical model of disease that largely draws from causal-mechanistic explanations. The bedrock of this model is the analysis of biological part-dysfunctions that aims at unequivocally defining a pathological condition and demarcating it from its neighboring entities. This endeavor has led to a quest for physiological, biochemical, and genetic signatures. Yet, so far there is little evidence for reliable biomarkers for any mental disorder. The contemporary biomedical paradigm largely ignores historical, dynamic, and system-level aspects—a view that has contributed much to the conceptual disjunction of the patient as a person from his/her disease. Notwithstanding the impressive progress in the biomedical sciences, increasingly more critics question whether the constituting framework is sufficient to convey a comprehensive understanding of illnesses, especially mental illnesses. Thus, the medical model urgently requires an update. But rather than revamping it by methodological advancements, it will be necessary to critically review its philosophical roots. The most problematic issues that require reworking are: the preponderance of the biostatistical theory; the undue decoupling of physiological from evolutionary explanations of function; the clinging to the Modern Synthesis (if etiological aspects are ever considered); and the neglect of dynamic and system-level properties. The proposed overhaul requires the heeding of historical explanations that draw from the Extended Evolutionary Synthesis as well as systems biology approaches for tackling multilevel and dynamic phenomena of complex systems.

Characterizing Students’ Mechanistic Reasoning about London Dispersion Forces

Characterizing how students construct causal mechanistic explanations for chemical phenomena can provide us with important insights into the ways that students develop understanding of chemistry concepts. Here, we present two qualitative studies of undergraduate general chemistry students’ reasoning about the causes of London dispersion forces in nonpolar species such as helium atoms. In the first study, we used semi-structured interviews to examine students’ verbal explanations for how and why electrical interactions arise between helium atoms. In the second, we used an online short-answer version of the interview task to examine the prevalence of the drawing and explanation types we observed in student interviews. We present a characterization of students’ explanations and drawings in terms of increasing sophistication and demonstrate how it may be used as a model for assessing students’ ability to engage in scientific practices such as explanation.

Mechanism and purpose: A case for natural teleology

This paper develops a naturalised account of teleological explanation. Any such account must be consistent with the completeness of mechanism. The account I offer of teleological explanation is adapted from a prominent approach to the understanding of causal-mechanistic explanations. In order to give a scientific explanation—whether it be mechanistic or teleological—one must provide two things: (i) an invariance relation and (ii) and an elucidating description. In a causal-mechanistic explanation there is an invariance relation between the mechanism and the effect. The elucidating description illustrates how the mechanism produces the effect. In a teleological explanation the invariance holds between the goal (effect) and the means (cause). The elucidating description illustrates the way the means conduces to the goal. Mechanistic and teleological explanations are both complete in their own right, and they are mutually autonomous. One cannot replace the other without explanatory loss. It follows, then, that some natural phenomena—those that contribute to goals—are susceptible of more than one complete, autonomous explanation. Teleology thus demonstrates the falsity of Jaegwon Kim’s Explanatory Exclusion Principle.

Students' ideas of the meaning of the relativity principle

This work tries to elicit the ‘meanings’ that undergraduate physics students attach to the term ‘relativity’. The domain of investigation is restricted to Galilean relativity of Newtonian mechanics. Our methodology is primarily qualitative: we first obtain students' responses through a questionnaire, follow it up by a 3 h teaching session and tutorial on the topic, and administer another questionnaire probing much the same notions as the first. After preliminary analysis, we interview some of the students for clarification and confirmation of our interpretation. The focus of our study is on the ‘meanings’ that students ascribe to the principle of relativity (RP), rather than investigating the whole range of technical concepts of this subject. Three meanings (apparently separate but in fact entirely equivalent) of the RP are identified, namely (i) the inadmissibility of the notion of ‘a frame at absolute rest’, (ii) the inability to determine the ‘velocity of a frame’ from measurements with respect to the frame itself and (iii) the absence of the ‘velocity of frame’ term in the pseudo forces for a non-inertial frame. The results indicate that most students trivialize the RP through its ‘kinematic’ interpretation (as explained in the text) thus rendering it empty of its dynamical content, invoke causal-mechanistic explanations than use the constraining power of the RP and are unable to see that the three meanings of the RP indicated above are simply different ways of saying the same thing.

Computational Modeling in Developmental Psychology

This manuscript surveys computational modeling efforts by researchers in developmental psychology. Developmental psychology is ready to blossom into a modern science that focuses on causal mechanistic explanations of development rather than just describing and classifying behaviors. Computational modeling is the key to this process. However, to be effective, models must not only mimic observed data. They must also be transparent, grounded, and plausible to be accepted by the developmental psychology community. Connectionist model provides one such example. Many developmental features of typical and atypical perception, cognition, and language have been modeled using connectionist methods. Successful models are closely tied to the details of existing empirical studies and make concrete testable predictions. The success of such a project relies on the close collaboration of computational scientists with empirical psychologists

Reasoning across ontologically distinct levels: Students' understandings of molecular genetics

AbstractIn this article we apply a novel analytical framework to explore students' difficulties in understanding molecular genetics—a domain that is particularly challenging to learn. Our analytical framework posits that reasoning in molecular genetics entails mapping across ontologically distinct levels—an information level containing the genetic information, and a physical level containing hierarchically organized biophysical entities such as proteins, cells, tissues, etc. This mapping requires an understanding of what the genetic information specifies, and how the physical entities in the system mediate the effects of this information. We therefore examined, through interview and written assessments, 10th grade students' understandings of molecular genetics phenomena to uncover the conceptual obstacles involved in reasoning across these ontologically distinct levels. We found that students' described the genetic instructions as containing information about both the structure and function of biological entities across multiple organization levels; a view that is far less constrained than the scientific understandings of the genetic information. In addition, students were often unaware of the different functions of proteins, their relationship to genes, and the role proteins have in mediating the effects of the genetic information. Students' ideas about genes and proteins hindered their ability to reason across the ontologically distinct levels of genetic phenomena, and to provide causal mechanistic explanations of how the genetic information brings about effects of a physical nature. © 2007 Wiley Periodicals, Inc. J Res Sci Teach 44: 938–959, 2007

How computational models help explain the origins of reasoning

Developmental psychology is ready to blossom into a modern science that focuses on causal mechanistic explanations of development rather than just describing and classifying the skills that children show at different ages. Computational models of cognitive development are formal systems that track the changes in information processing taking place as a behavior is acquired. Models are generally implemented as psychologically constrained computer simulations that learn tasks such as reasoning, categorization, and language. Their principal use is as tools for exploring mechanisms of transition (development) from one level of competence to the next during the course of cognitive development. They have been used to probe questions such as the extent of 'pre-programmed' or innate knowledge that exists in the infant mind, and how the sophistication of reasoning can increase with age and experience