Jonathan Cohen

This commentary serves as a reply to Hoffart and Johnson’s article contending that psychodynamic psychotherapy (PDT) models cannot be examined with regard to mechanism of change or represent within-person causal relationships. Hoffart and Johnson cite purportedly paradigmatic examples of PDT and cognitive therapy and examine them with respect to Kazdin’s requirements for investigation of mechanisms of change. We highlight inaccuracies in Hoffart and Johnson’s representation of PDT and, in doing so, provide reasoning in support of the empirical examinability of PDT. We conclude by recommending a metatheoretical system (i.e., functionalism) and empirical methodologies that clinical scientists may consider when investigating mechanisms of PDT in the future.

Color relationalism is the view that colors are constituted in terms of relations between subjects and objects. The most historically important form of color relationalism is the classic dispositionalist view according to which, for example red is the disposition to look red to standard observers in standard conditions (mutatis mutandis for other colors).1 However, it has become increasingly apparent in recent years that a commitment to the relationality of colors bears interest that goes beyond dispositionalism (Cohen, 2004; Matthen, 1999, 2001, 2005; Thompson, 1995). Accordingly, it is an important project for those interested in the metaphysics of color to sort through and assess different forms of color relationalism. There is, however, a powerful and general cluster of objections that has been thought by many to amount to a decisive refutation of any and all forms of color relationalism. Although this idea has been developed in a number of ways, the basic thought is that relationalism — qua theory of color — is at odds with the manifest evidence of color phenomenology, and that this clash between theory and data should be resolved by giving up the theory.

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to generating embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state-of-the-art machine learning algorithms using contextually-constrained text corpora (domain-specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually-unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those.

When interpreting a sentence such as Every time the company fires an employee who comes in late, a union complaint is lodged, an addressee is likely to infer that the union will only complain when an employee is fired because he came in late. One is thus led to ask why a purely pragmatic enrichment of this sort -- one drawn despite no risk of interpretative failure nor other linguistic mandate -- would intrude upon truth conditions. We argue that this effect results from the interaction among three pragmatic phenomena: presupposition, the associative mechanisms that underlie the establishment of coherence in discourse, and the calculation of domain restriction for quantificational operators. Because the analysis does not consider the operative enrichments to be species of implicature, we claim that such cases do not represent an intrusion of implicature into truth-conditional content. Instead, they merely involve the context-dependent determination of quantificational domains.

The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

We review how leaky competing accumulators (LCAs) can be used to model decision making in two‐alternative, forced‐choice tasks, and we show how they reduce to drift diffusion (DD) processes in special cases. As continuum limits of the sequential probability ratio test, DD processes are optimal in producing decisions of specified accuracy in the shortest possible time. Furthermore, the DD model can be used to derive a speed–accuracy trade‐off that optimizes reward rate for a restricted class of two alternative forced‐choice decision tasks. We review findings that compare human performance with this benchmark, and we reveal both approximations to and deviations from optimality. We then discuss three potential sources of deviations from optimality at the psychological level—avoidance of errors, poor time estimation, and minimization of the cost of control—and review recent theoretical and empirical findings that address these possibilities. We also discuss the role of cognitive control in changing environments and in modulating exploitation and exploration. Finally, we consider physiological factors in which nonlinear dynamics may also contribute to deviations from optimality.

In some cases people judge it morally acceptable to sacrifice one person’s life in order to save several other lives, while in other similar cases they make the opposite judgment. Researchers have identified two general factors that may explain this phenomenon at the stimulus level: (1) the agent’s intention (i.e. whether the harmful event is intended as a means or merely foreseen as a side-effect) and (2) whether the agent harms the victim in a manner that is relatively “direct” or “personal”. Here we integrate these two classes of findings. Two experiments examine a novel personalness/directness factor that we call personal force, present when the force that directly impacts the victim is generated by the agent’s muscles (e.g., in pushing). Experiments 1a and b demonstrate the influence of personal force on moral judgment, distinguishing it from physical contact and spatial proximity. Experiments 2a and b demonstrate an interaction between personal force and intention, whereby the effect of personal force depends entirely on intention. These studies also introduce a method for controlling for people’s real-world expectations in decisions involving potentially unrealistic hypothetical dilemmas.