The functional independence of trait self-knowledge: commentary on Sakaki (2007)

In a recent paper, Sakaki (2007) proposed that Klein and Loftus's conclusion that semantic and episodic trait self-knowledge are functionally independent (e.g., Klein, Babey, & Sherman, 1997; Klein & Loftus, 1993a; Klein, Loftus, Trafton, & Fuhrman, 1992b) was based on questionable assumptions and not supported by the available evidence. In this paper we show that Sakaki (2007) has misinterpreted our position on the independence of self-knowledge, omitted mention of large portions of the relevant research at odds with her contention, and conducted her studies with procedures we explicitly warned against due to interpretive ambiguities associated with their use.     

On the importance of critical thinking: A response to Wulf's (2015) commentary

In a recent paper (Toner & Moran, 2015), we argued that continued improvement among elite athletes requires alternation between external and internal foci of attention. In her commentary on this paper, Wulf (2015) claims that we have misunderstood the ‘attentional focus’ effect. Our rejoinder has three objectives. Firstly, we critically evaluate Wulf's arguments and counter her false allegations and spurious reasoning. Secondly, we explain our concerns about certain aspects of attentional focusing research. Finally, we propose that in order to explore the dynamic nature of attentional focusing, we need to go beyond restrictive theoretical dichotomies (e.g., “internal” versus “external” processes) using new approaches.     

Theoretical accounts of spatial learning: A neurobiological view (commentary on Pearce, 2009)

Theories of learning have historically taken, as their starting point, the assumption that learning processes have universal applicability. This position has been argued on grounds of parsimony, but has received two significant challenges: first, from the observation that some kinds of learning, such as spatial learning, seem to obey different rules from others, and second, that some kinds of learning take place in processing modules that are separate from each other. These challenges arose in the behavioural literature but have since received considerable support from neurobiological studies, particularly single neuron studies of spatial learning, confirming that there are indeed separable (albeit highly intercommunicating) processing modules in the brain, which may not always interact (within or between themselves) according to classic associative principles. On the basis of these neurobiological data, reviewed here, it is argued that rather than assuming universality of associative rules, it is more parsimonious to assume sets of locally operating rules, each specialized for a particular domain. By this view, although almost all learning is associative in one way or another, the behavioural-level characterization of the rules governing learning may vary depending on which neural modules are involved in a given behaviour. Neurobiological studies, in tandem with behavioural studies, can help reveal the nature of these modules and the local rules by which they interact.     

A metacognitive approach to "implicit" and "explicit" evaluations: comment on Gawronski and Bodenhausen (2006)

A metacognitive model (MCM) is presented to describe how automatic (implicit) and deliberative (explicit) measures of attitudes respond to change attempts. The model assumes that contemporary implicit measures tap quick evaluative associations, whereas explicit measures also consider the perceived validity of these associations (and other factors). Change in explicit measures is greater than implicit measures when new evaluative associations are formed and old associations are rejected. Implicit measure change is greater than explicit when newly formed evaluative associations are rejected. When implicit and explicit evaluations conflict, implicit ambivalence can occur. The authors relate the MCM to the associative-propositional evaluation model and explain how the MCM builds on the attitude strength assumptions of the elaboration likelihood model of persuasion.