Against Teleological Essentialism

In two recent papers, Rose and Nichols present evidence in favor of the view that humans represent category essences in terms of a telos, such as honey-making, and not in terms of scientific essences, such as bee DNA. Here, I challenge their interpretation of the evidence and show that it is directly predicted by the main theory they seek to undermine. I argue that their results can be explained as instances of diagnostic reasoning about scientific essences.     

A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model

In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.     

Children’s causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector,” a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine’s activation that required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4-year-olds were found. We suggest that children’s causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base rate of certain events occurring. Four-year-olds, but not 3-year-olds were able to make this inference.     

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.     

Reconceptualizing the Transitive Inference Ability: A Framework for Existing and Future Research

It has often been claimed that to demonstrate transitive inference is to demonstrate a logical ability, and by implication that transitivity as a property is generally a logical entity. Both claims are considered using a theoretically driven analysis together with consideration of relevant existing experimental research and some newly reported findings. This approach suggests an account of transitivity and transitive inferential reasoning that differs not only from the classic Piagetian account, but also from the information processing account so dominant today. We begin by considering one important issue, that the “logical” definitional criterion can only be approached if individuals are required to demonstrate a capacity for transitive inference that is discriminative in nature. This, together with interpretation of findings from existing transitive tasks, leads to the postulate of a three-component psychological system, with the components relying on perceptual, linguistic, and conceptual subprocesses and sensitivity to simple cues. The framework is testable and accommodates important aspects of classic and modern accounts of “transitive development” that until now have been taken to be mutually exclusive. It also readily accommodates both human and nonhuman research, yet neither a formal logical structure nor memory in any general sense need be assigned the primary role.     

How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning

A robust finding in category-based induction tasks is for positive observations to raise the willingness to generalize to other categories while negative observations lower the willingness to generalize. This pattern is referred to as monotonic generalization. Across three experiments we find systematic non-monotonicity effects, in which negative observations raise the willingness to generalize. Experiments 1 and 2 show that this effect emerges in hierarchically structured domains when a negative observation from a different category is added to a positive observation. They also demonstrate that this is related to a specific kind of shift in the reasoner’s hypothesis space. Experiment 3 shows that the effect depends on the assumptions that the reasoner makes about how inductive arguments are constructed. Non-monotonic reasoning occurs when people believe the facts were put together by a helpful communicator, but monotonicity is restored when they believe the observations were sampled randomly from the environment.     

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.