Why Internal Representations and Their Temporal Dependence Matter for Emotion Inference

Research on emotion inferences has shown that readers include a representation of the main character's emotional state in their mental representations of the text. We examined the specificity of emotion representations as a function of the emotion content of short narratives, in terms of the quantity and quality of emotion components included in the narratives, based on the GRID instrument (Fontaine, Scherer, Roesch, & Ellsworth, 2007; Fontaine, Scherer, & Soriano, 2013; Scherer, 2005). In a self-paced reading task, target sentences that only moderately matched the emotional context were read faster than target sentences that strongly matched the emotional context of the narratives. In a “makes sense” judgment task, we showed that this result was not driven by a mapping difficulty, and in a memory task, we provided some evidence that these effects reflected integration processes. We suggest that readers can integrate specific emotions into their mental representations, but only if provided with the appropriate emotional contextual support.

In this chapter, we present theoretical and empirical advances in our understanding of the way readers build mental representations of the main protagonists’ emotional status. Based on the highly influential and grounding studies of Morton Ann Gernsbacher and her colleagues in the nineties, we present several studies that have tried to specify the nature of readers’ mental representations of emotions as well as the conditions that may facilitate emotion inferences. Though often (unfortunately) underestimated, of special interest in this chapter is the interdisciplinary nature of emotion inferences as well as the empirical and theoretical opportunities that true interdisciplinary approaches of emotion inferences may provide us with in the future.

Restrictions on speech will not be accepted by the Supreme Court in the name of equality, but this does not rule out equality and participation as legitimate policy goals. It is both constitutional and appropriate to promote these goals without new constraints on speech by using incentives to increase the number and importance of low-dollar donors. The constitutional theory is straightforward. The empirical question is whether this could work. There are few examples of current policies with this specific purpose. One run by the City of New York gives participating candidates six dollars in matching funds for each of the first $175 that a city resident donates. This article asks whether a similar approach could become a model for others. The argument has three parts. The first is an empirical analysis of New York City's campaign finance records since 1997, showing that (a) multiple matching funds do increase the proportional role of small donors; (b) they have also increased the number of small donors; and (c) they help shift the demographic and class profile of those who give. The second part applies a modeling method to the states to show these results could readily be obtained elsewhere. The third section presents broad theoretical, constitutional, and policy themes. After arguing the futility of using public financing to reduce spending, it urges participation-based public financing to broaden the base. In making this case, the article also presents reasons for preferring matching funds to flat grants or other forms of public financing.

This paper investigates the temporal and spatial dynamics of business start-up activities and their determinants. It integrates three perspectives in explaining regional variations in start-ups: (1) spatial heterogeneity that characterizes regional differences in promoting or conditioning start-up activities, (2) temporal dependence that features a self-augmenting and self-reinforcing effect of start-up activities, and (3) spatial dependence that portrays inter-regional interaction of start-up activities across proximate regions. A spatial dynamic panel modeling analysis of the determinants of new manufacturing ventures created in subnational regions of South Korea confirms that, in addition to the importance of regional characteristics, both temporal and spatial dependences of start-up activities are simultaneously in force and play statistically significant roles. To address the joint endogeneity issue of temporal and spatial dependences, we employ the system GMM estimator, which leads to much improved explanation of inter-regional variations in firm creation activities.

Time-series cross-section (TSCS) data are prevalent in political science, yet many distinct challenges presented by TSCS data remain underaddressed. We focus on how dependence in both space and time complicates estimating either spatial or temporal dependence, dynamics, and effects. Little is known about how modeling one of temporal or cross-sectional dependence well while neglecting the other affects results in TSCS analysis. We demonstrate analytically and through simulations how misspecification of either temporal or spatial dependence inflates estimates of the other dimension’s dependence and thereby induces biased estimates and tests of other covariate effects. Therefore, we recommend the spatiotemporal autoregressive distributed lag (STADL) model with distributed lags in both space and time as an effective general starting point for TSCS model specification. We illustrate with two example reanalyses and provide R code to facilitate researchers’ implementation—from automation of common spatial-weights matrices (W) through estimated spatiotemporal effects/response calculations—for their own TSCS analyses.

Traditional foundationalist empiricist projects in epistemology postulated that sensory states of the subject are epistemically basic, in that they are capable of conferring justification on mental representations of the world without themselves needing to be (inferentially) justified by any antecedent representational states. This sort of view faces a seemingly hopeless dilemma, whose recognition is usually attributed to Wilfrid Sellars. If we treat sensory states as brute stimulations devoid of intentional content, then it is hard to see how the senses could provide subjects with anything that could possibly feature in justification-conferring relations with representational states. If we treat them as contentful, then in order to justify contentful states, sensory states themselves would presumably need to be justified by other representational states; but if this is so, they are not able to play a properly foundational epistemic role. In the article, I use the Predictive Processing (PP) view of perception in order to sketch a possible resolution of the Sellarsian dilemma. I draw on PP in order to show how sensory states could actually serve a normative role that is recognizably similar to the one envisioned by traditional empiricists. To do this, I first distinguish representational from non-representational posits of PP and subsequently focus on the role that PP ascribes to sensory or “driving” signal. In particular, I argue that (1) the driving signal plays a role of a non-representational, contentless detector; at the same time, (2) it serves as an “impartial” or “theory-neutral” tribunal against which contentful internal models are actively tested and updated. Drawing on Anil Gupta’s work, I discuss the epistemic involvement of the sensory signal in perceptual inference and show how the signal provides conditional justification (i.e. justification that is conditional on the justification or rationality of prior knowledge) to perceptual hypotheses. Then I discuss the role the sensory signal plays in perceptual learning. I employ the notion of “epistemic convergence” to sketch out how the sensory signal could provide perceivers with unconditional justification (i.e. one that is not relativized to the justification of prior knowledge). If this approach is right, the Sellarsian dilemma seems to be averted. We can see how the senses can be at the same time silent (i.e. contentless) and capable of playing a sort of foundational epistemic role.

Editor's evaluation: THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

Decision letter: THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

Development and validation of an MRI spatiotemporal interaction model for early noninvasive prediction of neoadjuvant chemotherapy response in breast cancer: a multicentre study

In this paper Australian domestic and international inbound travel are modelled by an anisotropic dynamic spatial lag panel Origin-Destination (OD) travel flow model. Spatial OD travel flow models have traditionally been applied in a single cross-sectional context, where the spatial structure is assumed to have reached its long run equilibrium and temporal dynamics are not explicitly considered. On the other hand, spatial effects are rarely accounted for in traditional tourism demand modelling. We attempt to address this dichotomy between spatial modelling and time series modelling in tourism research by using a spatial-temporal model. In particular, tourism behaviour is modelled as travel flows between regions. Temporal dependencies are accounted for via the inclusion of autoregressive components, while spatial autocorrelations are explicitly accounted for at both the origin and the destination. We allow the strength of spatial autocorrelation to exhibit seasonal variations, and we allow for the possibility of asymmetry between capital-city neighbours and non-capital-city neighbours. Significant temporal and spatial dynamics have been uncovered for both domestic and international tourism demand. For example we find strong seasonal temporal autocorrelations, significant trends and significant spatial autocorrelations at both the origin and the destination. Moreover, the spatial patterns are found to be most significant during peak holiday seasons. Understanding these patterns in tourist behaviour has important implications for tourism operators.

Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackle both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

Descriptions of cultural practices can be enriched by understanding the cognitions and emotions occurring in the minds of the people enacting the practices. The Church of Jesus Christ of Latter-day Saints is new enough that its historical developments and ongoing practices are well documented. To explain these developments and practices, this chapter describes the images, concepts, values, beliefs, rules, analogies, and emotions that are the most important mental representations operating in Mormon minds. These representations have a neural basis in semantic pointer processes of representation and binding, and they contribute to a variety of deductive, abductive, and emotional inferences. The social process by which Mormon beliefs and practices spread from one individual to another can best be understood as the results of semantic pointer communication carried out by interactions ranging from church rituals to missionary work.

This paper provides an explanation for the nonspecificity of emotion inferences found in previous research [e.g., Language and Cognitive Processes, 19(5), 613–638, 2004]. We first demonstrate that behavioral components of emotions, as opposed to emotions per se, are better markers of readers' mental representations of the main character's affective status. We also suggest that in a self-paced reading paradigm, when participants read sentences slower than others, it does not unequivocally provide insight into their mental representations of the text. We show that specific control conditions need to be implemented before such an assumption can be made in order to separate inference and representation processes from contextual integration processes.

Developing Residents as Teachers: Process and Content