Local but not global graph theoretic measures of semantic networks generalize across tasks.

"Dogs" are connected to "cats" in our minds, and "backyard" to "outdoors." Does the structure of this semantic knowledge differ across people? Network-based approaches are a popular representational scheme for thinking about how relations between different concepts are organized. Recent research uses graph theoretic analyses to examine individual differences in semantic networks for simple concepts and how they relate to other higher-level cognitive processes, such as creativity. However, it remains ambiguous whether individual differences captured via network analyses reflect true differences in measures of the structure of semantic knowledge, or differences in how people strategically approach semantic relatedness tasks. To test this, we examine the reliability of local and global metrics of semantic networks for simple concepts across different semantic relatedness tasks. In four experiments, we find that both weighted and unweighted graph theoretic representations reliably capture individual differences in local measures of semantic networks (e.g., how related pot is to pan versus lion). In contrast, we find that metrics of global structural properties of semantic networks, such as the average clustering coefficient and shortest path length, are less robust across tasks and may not provide reliable individual difference measures of how people represent simple concepts. We discuss the implications of these results and offer recommendations for researchers who seek to apply graph theoretic analyses in the study of individual differences in semantic memory.

How do people build up visual memory representations from sensory evidence? Revisiting two classic models of choice.

In many decision tasks, we have a set of alternative choices and are faced with the problem of how to use our latent beliefs and preferences about each alternative to make a single choice. Cognitive and decision models typically presume that beliefs and preferences are distilled to a scalar latent strength for each alternative, but it is also critical to model how people use these latent strengths to choose a single alternative. Most models follow one of two traditions to establish this link. Modern psychophysics and memory researchers make use of signal detection theory, assuming that latent strengths are perturbed by noise, and the highest resulting signal is selected. By contrast, many modern decision theoretic modeling and machine learning approaches use the softmax function (which is based on Luce's choice axiom; Luce, 1959) to give some weight to non-maximal-strength alternatives. Despite the prominence of these two theories of choice, current approaches rarely address the connection between them, and the choice of one or the other appears more motivated by the tradition in the relevant literature than by theoretical or empirical reasons to prefer one theory to the other. The goal of the current work is to revisit this topic by elucidating which of these two models provides a better characterization of latent processes in m -alternative decision tasks, with a particular focus on memory tasks. In a set of visual memory experiments, we show that, within the same experimental design, the softmax parameter ß varies across m -alternatives, whereas the parameter d ' of the signal-detection model is stable. Together, our findings indicate that replacing softmax with signal-detection link models would yield more generalizable predictions across changes in task structure. More ambitiously, the invariance of signal detection model parameters across different tasks suggests that the parametric assumptions of these models may be more than just a mathematical convenience, but reflect something real about human decision-making.

Sequence-specific destabilization of azurin by tetramethylguanidinium-dipeptide ionic liquids.

The thermal unfolding of the copper redox protein azurin was studied in the presence of four different dipeptide-based ionic liquids (ILs) utilizing tetramethylguanidinium as the cation. The four dipeptides have different sequences including the amino acids Ser and Asp: TMG-AspAsp, TMG-SerSer, TMG-SerAsp, and TMG-AspSer. Thermal unfolding curves generated from temperature-dependent fluorescence spectroscopy experiments showed that TMG-AspAsp and TMG-SerSer have minor destabilizing effects on the protein while TMG-AspSer and TMG-SerAsp strongly destabilize azurin. Red-shifted fluorescence signatures in the 25 °C correlate with the observed protein destabilization in the solutions with TMG-AspSer and TMG-SerAsp. These signals could correspond to interactions between the Asp residue in the dipeptide and the azurin Trp residue in the unfolded state. These results, supported by appropriate control experiments, suggest that dipeptide sequence-specific interactions lead to selective protein destabilization and motivate further studies of TMG-dipeptide ILs.

Thermodynamic destabilization of azurin by four different tetramethylguanidinium amino acid ionic liquids.

The thermal unfolding of the copper redox protein azurin was studied in the presence of four different amino acid-based ionic liquids (ILs), all of which have tetramethylguanidium as cation. The anionic amino acid includes two with alcohol side chains, serine and threonine, and two with carboxylic acids, aspartate and glutamate. Control experiments showed that amino acids alone do not significantly change protein stability and pH changes anticipated by the amino acid nature have only minor effects on the protein. With the ILs, the protein is destabilized and the melting temperature is decreased. The two ILs with alcohol side chains strongly destabilize the protein while the two ILs with acid side chains have weaker effects. Unfolding enthalpy (ΔHunf°) and entropy (ΔSunf°) values, derived from fits of the unfolding data, show that some ILs increase ΔHunf°while others do not significantly change this value. All ILs, however, increase ΔSunf°. MD simulations of both the folded and unfolded protein conformations in the presence of the ILs provide insight into the different IL-protein interactions and how they affect the ΔHunf° values. The simulations also confirm that the ILs increase the unfolded state entropies which can explain the increased ΔSunf° values.

Integration by Parts: Collaboration and Topic Structure in the CogSci Community.

Is cognitive science interdisciplinary or multidisciplinary? We contribute to this debate by examining the authorship structure and topic similarity of contributions to the Cognitive Science Society from 2000 to 2019. Our analysis focuses on graph theoretic features of the co-authorship network-edge density, transitivity, and maximum subgraph size-as well as clustering within the space of scientific topics. We also combine structural and semantic information with an analysis of how authors choose their collaborators based on their interests and prior collaborations. We compare findings from CogSci to abstracts from the Vision Science Society over the same time frame and validate our approach by predicting new collaborations in the 2020 CogSci proceedings. Our results suggest that collaboration across authors and topics within cognitive science has become increasingly integrated in the last 19 years. More broadly, we argue that a formal quantitative approach which combines structural co-authorship information and semantic topic analysis provides inroads to questions about the level of interdisciplinary collaboration in a scientific community.