Summarizing information by means of causal sentences through causal graphs

The objective of this work is to propose a complete system able to extract causal sentences from a set of text documents, select the causal sentences contained, create a causal graph in base to a given concept using as source these causal sentences, and finally produce a text summary gathering all the information connected by means of this causal graph. This procedure has three main steps. The first one is focused in the extraction, filtering and selection of those causal sentences that could have relevant information for the system. The second one is focused on the composition of a suitable causal graph, removing redundant information and solving ambiguity problems. The third step is a procedure able to read the causal graph to compose a suitable answer to a proposed causal question by summarizing the information contained in it.     

Causal discovery and the problem of psychological interventions

Finding causes is a central goal in psychological research. In this paper, I argue based on the interventionist approach to causal discovery that the search for psychological causes faces great obstacles. Psychological interventions are likely to be fat-handed: they change several variables simultaneously, and it is not known to what extent such interventions give leverage for causal inference. Moreover, due to problems of measurement, the degree to which an intervention was fat-handed, or more generally, what the intervention in fact did, is difficult to reliably estimate. A further complication is that the causal findings in psychology are typically made at the population level, and such findings do not allow inferences to individual-level causal relationships. I also discuss the implications of these problems for research, as well as various ways of addressing them, such as focusing more on the discovery of robust but non-causal patterns.     

Learning to infer the time of our actions and decisions from their consequences

Research shows that people infer the time of their actions and decisions from their consequences. We asked how people know how much time to subtract from consequences in order to infer their actions and decisions. They could either subtract a fixed, default, time from consequences, or learn from experience how much time to subtract in each situation. In two experiments, participants’ actions were followed by a tone, which was presented either immediately or after a delay. In Experiment 1, participants estimated the time of their actions; in Experiment 2, the time of their decisions to act. Both actions and decisions were judged to occur sooner or later as a function of whether consequences were immediate or delayed. Estimations tended to be shifted toward their consequences, but in some cases they were shifted away from them. Most importantly, in all cases participants learned progressively to adjust their estimations with experience.     

Quantifying definitional relations in psychological measurement

Quantitative inferences about psychological attributes, such as extraversion, depression and empathy, involve measurement instruments as well as mathematical models that specify how indicators should be aggregated. The type of model that is appropriate for doing so is conditional on the type of relation that exists between an attribute, its facets and its indicators. The common assumption is that such relations are causal relations. Here, instead, we address definitional attribute-facet relations. Our aim is to find an appropriate mathematical model for when an attribute is defined in terms of facets, instead of causing or being caused by facets. In doing so, we describe the semantics of definitions in logical form. From this form we then derive continuous functions for attribute-facet relations using fuzzy logic. A model with main effects and interactions between facets seems to be more powerful for representing definitional relations than traditional formative and reflective models. This has important implications for measurement in basic and applied research.     

Can the Mind Command the Body?

People naturally intuit that an agent's ethereal thoughts can cause its body to move. Per intuitive physics; however, one body can only interact with another. Are people, then, covertly puzzled by the capacity of thoughts to command the body? Experiment 1 first confirms that thoughts (e.g., thinking about a cup) are indeed perceived as ethereal—as less detectible in the body (brain), and more likely to exist in the afterlife relative to matched percepts (e.g., seeing a cup). Experiments 2–5 show that thoughts are considered less likely to cause behavior than percepts (e.g., thinking of a cup vs. seeing one). Furthermore, mind–body causation is more remarkable when its bodily consequences are salient (e.g., moving an arm vs. brain activation). Finally, epistemic causes are remarkable only when they are ascribed to mental- (e.g., “thinking”) but not to physical states (“activation”). Together, these results suggest that mind–body interactions elicit a latent dualist dissonance.     

Causal Information-Seeking Strategies Change Across Childhood and Adolescence

Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine their underlying structures. Each puzzle comprised a three- or four-node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip's hidden wires and had to select a single node to activate. After observing the outcome of their intervention, participants selected a wire configuration and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures versus those that had high causal centrality in one of the two causal hypotheses but did not necessarily discriminate between them. Our model estimates revealed that the use of a discriminatory strategy increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that interventions revealed, as indexed by participants' confidence in their selections. Our results suggest that improvements in causal information-seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas.     

Visual impressions of active and inanimate resistance to impact from a moving object

ABSTRACT

Images of moving objects presented on computer screens may be perceived as animate or inanimate. A simple hypothesis, consistent with much research evidence, is that objects are perceived as inanimate if there is a visible external contact from another object immediately prior to the onset of motion, and as animate if that is not the case. Evidence is reported that is not consistent with that hypothesis. Objects (targets) moving on contact from another object (launcher) were perceived as actively resisting the impact of the launcher on them if the targets slowed rapidly. Rapid slowing is consistent with the laws of mechanics for objects moving in an environment that offers friction and air resistance. Despite that, ratings of inanimate motion were lower than ratings of active resistance for objects that slowed rapidly. The results are consistent with the hypothesis that there is a perceptual impression of active (animate) resistance that is evoked by the kinematic pattern of rapid slowing from an initial speed after contact from another object.     

Vector Semiotic Model for Visual Question Answering

In this paper, we propose a Vector Semiotic Model as a possible solution to the symbol grounding problem in the context of Visual Question Answering. The Vector Semiotic Model combines the advantages of a Semiotic Approach implemented in the Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model represents information about a scene depicted on an input image in a structured way and grounds abstract objects in an agent’s sensory input. We use the Vector Symbolic Architecture to represent the elements of the Sign-Based World Model on a computational level. Properties of a high-dimensional space and operations defined for high-dimensional vectors allow encoding the whole scene into a high-dimensional vector with the preservation of the structure. That leads to the ability to apply explainable reasoning to answer an input question. We conducted experiments are on a CLEVR dataset and show results comparable to the state of the art. The proposed combination of approaches, first, leads to the possible solution of the symbol-grounding problem and, second, allows expanding current results to other intelligent tasks (collaborative robotics, embodied intellectual assistance, etc.).