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We raise two points about cues, which complicate Barzykowski and Moulin's attempt at a unified model of memory retrieval. First, cues operate differently in voluntary and involuntary contexts. Second, voluntary and involuntary memory can be interconnected, as in cases of chaining.

Theories are an integral part of the scientific endeavour. The target article proposes interesting ideas for a theory on human–robot interaction but lacks specificity that would enable us to properly test this theory. No empirical data are yet available to determine its predictive power.

We build on Ainslie's discussion of willpower by highlighting another common misconception in the literature: the conflation of self-control and willpower. In our commentary, we identify this issue and discuss the importance of recognizing willpower not as synonymous with self-control, but rather as a subset of self-control. We describe a set of upstream strategies as more effective alternatives to willpower.

The distinction that Ainslie draws among the triple-phenomena “suppression,” “resolve,” and “habit” is a great advance in decision making theory. But the conceptual machinery “willpower,” and its underpinning distinction between small/soon (SS) rewards as opposed to large/later (LL) rewards, provides a faulty framework to understand the triple-phenomena.

Clark and Fischer (C&F) discuss how people interact with social robots in the context of a general analysis of interaction with characters. I suggest that a consideration of aesthetic illusion would add nuance to this analysis. In addition, I illustrate how people's experiences with other depictions of characters require adjustments to C&F's claims.

How do we switch between “playing along” and treating robots as technical agents? We propose interaction breakdowns to help solve this “social artifact puzzle”: Breaks cause changes from fluid interaction to explicit reasoning and interaction with the raw artifact. These changes are closely linked to understanding the technical architecture and could be used to design better human–robot interaction (HRI).

The relation between communication partners is crucial for the success of their interaction. This is also true for artificial social agents. However, the more we engage in artificial relationships, the more we are forced to regulate and control them. I refer to this as binding paradox. This deserves attention during technological developments and requires professional supervision during ongoing interactions.

Falsificationist and confirmationist approaches provide two well-established ways of evaluating generalizability. Yarkoni rejects both and invents a third approach we call neo-operationalism. His proposal cannot work for the hypothetical concepts psychologists use, because the universe of operationalizations is impossible to define, and hypothetical concepts cannot be reduced to their operationalizations. We conclude that he is wrong in his generalizability-crisis diagnosis.

I apply Benjamin's (1941) taxonomy of common scientific “modes of explanation” to the psychological context. I argue that: (i) in a “naming” mode, generalizability is not necessary; (ii) in an “analysis,” generalizability is desired; (iii) in a “causal ontology,” generalizability is merely one of the means to an end (theory-challenge); (iv) in a “synthesis,” generalizability is (eventually) critical. A better appreciation of the diversity in psychologists' modes of explanation is crucial for cogent meta-psychological discussions.

I suggest addressing the problems Yarkoni identifies by separating substantive from statistical hypotheses, and treating them differently. A statistical test of experimental data only bears directly on statistical hypotheses. Evaluation of related substantive hypotheses requires an additional, qualitative inference to the best explanation. Statistical inference cannot do all of the work of theory choice.