The Mental Representation of Human Action

Various theories of moral cognition posit that moral intuitions can be understood as the output of a computational process performed over structured mental representations of human action. We propose that action plan diagrams—“act trees”—can be a useful tool for theorists to succinctly and clearly present their hypotheses about the information contained in these representations. We then develop a methodology for using a series of linguistic probes to test the theories embodied in the act trees. In Study 1, we validate the method by testing a specific hypothesis (diagrammed by act trees) about how subjects are representing two classic moral dilemmas and finding that the data support the hypothesis. In Studies 2–4, we explore possible explanations for discrete and surprising findings that our hypothesis did not predict. In Study 5, we apply the method to a less well‐studied case and show how new experiments generated by our method can be used to settle debates about how actions are mentally represented. In Study 6, we argue that our method captures the mental representation of human action better than an alternative approach. A brief conclusion suggests that act trees can be profitably used in various fields interested in complex representations of human action, including law, philosophy, psychology, linguistics, neuroscience, computer science, robotics, and artificial intelligence.     

Cognitive technology to capture deep computational concepts with combinators

Computational activity is now recognized as a natural science, and computational and information processes have been discovered in the deep structures of many areas. Computations in the natural world were present long before the invention of computers, but a remarkable shift in understanding its fundamental nature occurs, in fact, before our eyes. The present moment, in fact, is a transition from the concept of computer science as an artificial science to the understanding that information processes are abundant in nature. Computing is recognized as a natural science that studies natural and artificial information processes.In everyday computing, operations are performed on the individual generators, with little attention paid to their internal structure. However, many common operations consist of more primitive constructions connected by a combination mode. The interaction of information processes and corresponding structures is carried out in an environment of “applicative interaction”, their applications to each other, and the study of the properties of this environment allows us to understand the nature of the computations.In the present work, the main attention is paid to elucidating the technological features of computations with individual generators, or objects. Their interaction is considered in an applicative environment, which allows us to elucidate the internal structure of ordinary operations, the knowledge of which allows us to understand their properties. The choice of initial constant generators, considered as generic ones and expressed by combinators, is discussed. These initial generators are used as the main “building blocks” that occur within the larger blocks of the applicative environment in interaction with each other. As a result of the interaction, constructions arise that give representative sets of ordinary operators and embedded computing systems.     

How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.     

Not so Sweet Revenge: Unanticipated Consequences of High-Intensity Sweeteners

While no single factor accounts for the significant increases in overweight and obesity that have emerged during the past several decades, evidence now suggests that sugars, in general, and sugar-sweetened beverages, in particular, may be especially problematic. One response to this concern has been an explosion in the availability and use of noncaloric sweeteners as replacements for sugar. While consumers have been led to believe that such substitutes are healthy, long-term epidemiological data in a number of cohorts have documented increased risk for negative outcomes like type 2 diabetes, heart disease, and stroke among users of artificial sweeteners. Experimental data from animals has provided several plausible mechanisms that could explain this counterintuitive relationship. In particular, my research has demonstrated that artificial sweeteners appear to interfere with basic learned, predictive relations between sweet tastes and post-ingestive consequences such as the delivery of energy. By interfering with these relations, artificial sweeteners inhibit anticipatory responses that normally serve to maintain physiological homeostasis, and over the long term, this interference could result in negative health effects like those seen in the human cohort studies. These data suggest that reducing the consumption of all sweeteners is advisable to promote better health.     

Revise and resubmit: How real-time parsing limitations influence grammar acquisition

We present the results from a three-day artificial language learning study on adults. The study examined whether sentence-parsing limitations, in particular, difficulties revising initial syntactic/semantic commitments during comprehension, shape learners’ ability to acquire a language. Findings show that both comprehension and production of morphology pertaining to sentence argument structure are delayed when this morphology consistently appears at the end, rather than at the beginning, of sentences in otherwise identical grammatical systems. This suggests that real-time processing constraints impact acquisition; morphological cues that tend to guide linguistic analyses are easier to learn than cues that revise these analyses. Parallel performance in production and comprehension indicates that parsing constraints affect grammatical acquisition, not just real-time commitments. Properties of the linguistic system (e.g., ordering of cues within a sentence) interact with the properties of the cognitive system (cognitive control and conflict-resolution abilities) and together affect language acquisition.     

医患会话不同研究方法进展

医患会话研究是研究医患矛盾和医患关系的关键,从社会学和语言学的视角,对国内外的医患会话研究进行了梳理。社会学和语言学视角对会话的分析有很大的差异。社会学家是研究如何进行会话,认为会话能透视社会生活,而语言学家则研究语言如何建构以产生会话,认为作为社会生活资源的会话可以帮助发现语言的本质。医患会话研究还应该拓展到认知神经科学、心理学和医学领域。只有更好地开展医患会话的研究才能指导医生的会话策略,提高医患沟通能力,改善医患关系。     

The Influence of Shared Visual Context on the Successful Emergence of Conventions in a Referential Communication Task

Human communication is thoroughly context bound. We present two experiments investigating the importance of the shared context, that is, the amount of knowledge two interlocutors have in common, for the successful emergence and use of novel conventions. Using a referential communication task where black‐and‐white pictorial symbols are used to convey colors, pairs of participants build shared conventions peculiar to their dyad without experimenter feedback, relying purely on ostensive‐inferential communication. Both experiments demonstrate that access to the visual context promotes more successful communication. Importantly, success improves cumulatively, supporting the view that pairs establish conventional ways of using the symbols to communicate. Furthermore, Experiment 2 suggests that dyads with access to the visual context successfully adapt the conventions built for one color space to another color space, unlike dyads lacking it. In linking experimental pragmatics with language evolution, the study illustrates the benefits of exploring the emergence of linguistic conventions using an ostensive‐inferential model of communication.     

Learning to like it: Aesthetic perception of bodies,movements and choreographic structure

Appreciating human movement can be a powerful aesthetic experience. We have used apparent biological motion to investigate the aesthetic effects of three levels of movement representation: body postures, movement transitions and choreographic structure. Symmetrical (ABCDCBA) and asymmetrical (ABCDBCA) sequences of apparent movement were created from static postures, and were presented in an artificial grammar learning paradigm. Additionally, “good” continuation of apparent movements was manipulated by changing the number of movement path reversals within a sequence. In an initial exposure phase, one group of participants saw only symmetrical sequences, while another group saw only asymmetrical sequences. In a subsequent test phase, both groups rated all sequences on an aesthetic evaluation scale. We found that posture, movement, and choreographic structure all influenced aesthetic ratings. Separate ratings for the static body postures presented individually showed that both groups preferred a posture that maximized spatial symmetry. Ratings for the experimental sequences showed that both groups gave higher ratings to symmetrical sequences with “good” continuation and lower ratings to sequences with many path reversals. Further, participants who had been initially familiarized with asymmetrical sequences showed increased liking for asymmetrical sequences, suggesting a structural mere exposure effect. Aesthetic preferences thus depend on body postures, apparent movement continuation and choreographic structure. We propose a hierarchical model of aesthetic perception of human movement with distinct processing levels for body postures, movements and choreographic structure.     

The neural correlates of statistical learning in a word segmentation task: An fMRI study

Functional magnetic resonance imaging (fMRI) was used to assess neural activation as participants learned to segment continuous streams of speech containing syllable sequences varying in their transitional probabilities. Speech streams were presented in four runs, each followed by a behavioral test to measure the extent of learning over time. Behavioral performance indicated that participants could discriminate statistically coherent sequences (words) from less coherent sequences (partwords). Individual rates of learning, defined as the difference in ratings for words and partwords, were used as predictors of neural activation to ask which brain areas showed activity associated with these measures. Results showed significant activity in the pars opercularis and pars triangularis regions of the left inferior frontal gyrus (LIFG). The relationship between these findings and prior work on the neural basis of statistical learning is discussed, and parallels to the frontal/subcortical network involved in other forms of implicit sequence learning are considered.