Neuroscientists and psychologists have proposed a variety of well-supported theories to explain addiction. Many of these theories suggest that addiction results from a single process or dysfunction across all of its forms. The authors of the current review, in contrast, have used a well-defined theoretical account of decision-making to outline the variety of dysfunctions that could account for addictive behavior.

A basic aspect of emotional responding to music involves the liking for specific pieces. Juslin & Västfjäll (J&V) fail to acknowledge that simple exposure plays a fundamental role in this regard. Listeners like what they have heard but not what they have heard too often. Exposure represents an additional mechanism, ignored by the authors, that helps to explain emotional responses to music.

The commentaries reflect three core themes that pertain not just to our theory, but to the enterprise of connectionist modeling more generally. The first concerns the relationship between a cognitive theory and an implemented computer model. Specifically, how does one determine, when a model departs from the theory it exemplifies, whether the departure is a useful simplification or a critical flaw? We argue that the answer to this question depends partially upon the model's intended function, and we suggest that connectionist models have important functions beyond the commonly accepted goals of fitting data and making predictions. The second theme concerns perceived in-principle limitations of the connectionist approach to cognition, and the specific concerns these perceived limitations raise for our theory. We argue that the approach is not in fact limited in the ways our critics suggest. One common misconception, that connectionist models cannot address abstract or relational structure, is corrected through new simulations showing directly that such structure can be captured. The third theme concerns the relationship between parallel distributed processing (PDP) models and structured probabilistic approaches. In this case we argue that there the difference between the approaches is not merely one of levels. Our PDP approach differs from structured statistical approaches at all of Marr's levels, including the characterization of the goals of cognitive computations, and of the representations and algorithms used.

Accurate perception of moving objects would be useful; accurate visually guided action is crucial. Visual motion across the scene influences perceived object location and the trajectory of reaching movements to objects. In this commentary, I propose that the visual system assigns the position of any object based on the predominant motion present in the scene, and that this is used to guide reaching movements to compensate for delays in visuomotor processing.

The model of addiction proposed by Redish et al. shows a lack of fit with recent data and models in psychological studies of addiction. In these dual process models, relatively automatic appetitive processes are distinguished from explicit goal-directed expectancies and motives, whereas these are all grouped together in the planning system in the Redish et al. model. Implications are discussed.

By summarizing, in a single piece, various current perspectives on addiction, Redish et al. have performed a useful service to the field. Their central message is that addiction comprises many vulnerabilities rather than a single vulnerability. Such a message may not be new, but it is worth repeating.

We disagree with Juslin & Västfjäll's (J&V's) thesis that music-evoked emotions are indistinguishable from other emotions in both their nature and underlying mechanisms and that music just induces some emotions more frequently than others. Empirical evidence suggests that frequency differences reflect the specific nature of music-evoked emotions: aesthetic and reactive rather than utilitarian and proactive. Additional mechanisms and determinants are suggested as predictors of emotions triggered by music.

Mechanisms of visual prediction based on spatial extrapolation work only for targets moving at constant speed, but do not easily accommodate accelerating or decelerating motion. We argue that mechanisms based on temporal extrapolation deal with both uniform and non-uniform motion. We provide behavioural examples from interception of falling objects and suggest possible neurophysiological substrates of time extrapolation.

In our target article, we proposed that addiction could be envisioned as misperformance of a decision-making machinery described by two systems (deliberative and habit systems). Several commentators have argued that Pavlovian learning also produces actions. We agree and note that Pavlovian action-selection will provide several additional vulnerabilities. Several commentators have suggested that addiction arises from sociological parameters. We note in our response how sociological effects can change decision-making variables to provide additional vulnerabilities. Commentators generally have agreed that our theory provides a framework within which to site addiction and treatment, but additional work will be needed to determine whether our taxonomy will help identify and treat subpopulations within the addicted community.

Challenges to visual prediction as an organizing concept come from three main sources: (1) from observations arising from the results of experiments employing unpredictable motion, (2) from the assertions that motor processes compensate for all neural delays, and (3) from multiple interpretations specific to the flash-lag effect. One clarification that has emerged is that visual prediction is a process that either complements or reflects non-visual (e.g., motor) prediction.

We present evidence from neuroimaging and brain lesion studies that emotional contagion may not be a mechanism underlying musical emotions. Our brains distinguish voice from non-voice sounds early in processing, and dedicate more resources to such processing. We argue that super-expressive voice theory currently cannot account for evidence of the dissociation in processing musical emotion and voice prosody.

We propose that the six mechanisms identified by Juslin & Västfjäll (J&V) fall into two categories: signal detection and amplification. Signal detection mechanisms are unmediated and induce emotion by directly detecting emotive signals in music. Amplifiers act in conjunction with signal detection mechanisms. We also draw attention to theoretical and empirical challenges associated with the proposed mechanisms.

Instead of the discrete emotions approach adopted by Juslin & Västfjäll (J&V), the present perspective considers musical signals as functioning primarily to influence listeners in ways that are favorable to the signaler. Viewing music through the lens of social-emotional regulation fits with typical uses of music in everyday contexts and with the cross-cultural use of music for infant affect regulation.

There is certainly a need for a framework to guide the study of the physiological mechanisms underlying the experience of music and the emotions that music evokes. However, this framework should be organised hierarchically, with musical anticipation as its fundamental mechanism.

Most commentators have agreed with our thesis, that musical emotions cannot be studied without regard to underlying mechanisms. However, some commentators have expressed concerns that are addressed in this response. Others have suggested directions for future research. Topics discussed in our response include terminology, elaborations on particular mechanisms, possible additional mechanisms, ways of distinguishing among emotions and mechanisms, the prevalence of musical emotions, the relationship between perceived and felt emotions, developmental issues, and evolutionary perspectives. We end our response with a plea for researchers to reach beyond the traditional disputes in the field to pave the way for more theory-driven studies that can facilitate a deeper understanding of musical emotions.