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Neural inferences One area where precision is of concern across the cognitive neurosciences is in the specificity of our neural inferences. As we previously suggested (Pfeifer and Allen, 2012), and as nicely summarized by Casey (2015) when considering several models related to dual-systems: “new findings have moved the field away from simplistic one-to-one mappings of the ventral striatum and amygdala to reward and avoidant behaviors, toward the recognition of distinct computational roles they each play in learning that influence adaptive action in response to both positive and negative outcomes” (p. 299). Here Casey (2015) is identifying the problem of one-to-many and many-to-one (or many-to-many) relations in neuropsychological inference. This issue has been extensively explored by Cacioppo and Tassinary (1990) with respect to an allied problem – psychophysiological inference. They argue that the strongest form of inference is associated with being able to achieve a one-to-one mapping between the psychological construct of interest and the physiological process being measured – however, such strong inferences are the exception rather than the rule. For example, many psychological states can be associated with increased amygdala activity (fear, pleasure, surprise, uncertainty), and moreover, a given psychological state, such as depression, can be associated with changes in multiple buy ketotifen fumarate regions (e.g., amygdala, hippocampus, subgenual anterior cingulate cortex (ACC)). However, if we want to use a neurobiological marker (e.g., activity in the nucleus accumbens) to infer a psychological state (e.g., reward anticipation) then we need to work towards as close to a one-to-one mapping between these domains as we can achieve. (This of course is not to suggest that a single psychological function is ever likely to be purely associated with the activity of a single brain region in terms of a comprehensive model of the neurobiological substrate of that function. Rather, the strongest regional neuropsychological inferences will be possible when activity in the region is a highly sensitive and specific marker of the psychological processes of interest.) Developmental questions also dictate that we map patterns of change in neurobiological systems to patterns of change in behavior across time, further specifying the nature of the one-to-one mapping needed for strong neuropsychological inference. Cacioppo and Tassinary (1990) do propose a number of approaches to the issue, many of which are applicable to developmental neuroscience. For example they suggest that researchers redefine what constitutes an element – i.e., the psychological or neurodevelopmental variable of interest. In developmental neuroscience this will probably entail moving from regional to network based analyses and/or incorporating “form” (i.e., configural or temporal information) into our definition of critical dependent and independent neurobiological variables. In other words, we need to enhance the precision of our variables such that there is stronger hypothesized specificity (e.g., refrain from talking about PFC as if it is functionally homogeneous), and move away from many-to-one and many-to-many relationships that only support weaker inferences, by precisely characterizing circuits and networks (spatially, temporally, developmentally). In particular we should relinquish our reliance on single ROIs (both structurally and functionally) given the low likelihood that the activity or volume of a given structure will map onto a behavioral phenotype in a one-to-one way. Furthermore, we still only poorly understand the relationship between brain structure and brain function, and should be very careful to avoid assuming that the patterns observed will be equivalent.
Significance (ecological and/or translational outcomes) Perhaps the most important issue with respect to the translation of our science is to ensure that we are also measuring the ultimate ecological outcome of interest (i.e., that thing that can answer the “Who cares?” or “Why should we spend public money on this?” questions). If you think the processes you are studying are implicated in mental health, then measure mental health. If you think they are relevant to risk taking, measure real life risk taking. If you think they can affect academic performance, measure academic performance. This is a question of the ultimate construct validity of our work. For example, in a recent review of the neuroscience of adolescent decision making Hartley and Somerville (2015) note that “many tasks employed in neuroeconomic studies fail to capture key qualitative features of naturalistic choice contexts, which may diminish their validity for understanding real-world decision-making” (p. 109). In other words, in some cases we may be spending a lot of time, energy, and money studying the neurodevelopmental correlates of laboratory tasks that may have little or no relationship to phenomena of actual interest if we don’t actually take that critical step and measure the relationship between our laboratory measures and ecologically important, real world behaviors. In developmental science, these processes also need to be measured across development, in order to ensure that when we think developmental change in a neural or laboratory measure of interest maps on to developmental change in an ecological variable of interest, it does so.