Assignment task:
Abstract:
To better understand human behavior, the emerging field of model-based cognitive neuroscience seeks to anchor psychological theory to the biological substrate from which behavior originates: the brain. Despite complex dynamics, many researchers in this field have demonstrated that fluctuations in brain activity can be related to fluctuations in components of cognitive models, which instantiate psychological theories. In this review, we discuss a number of approaches for relating brain activity to cognitive models and expand on a framework for imposing reciprocity in the inference of mental operations from the combination of brain and behavioral data.
Introduction:
The evolution of technology for measuring brain signals, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), has provided exciting new opportunities for studying mental processes. Today, scientists interested in studying cognition are faced with many options for relating experimentally derived neurophysiological variables to the dynamics underlying a cognitive process of interest. While conceptually the presence of these new "modalities" of cognitive measures could have immediately spawned an interesting new integrative discipline, the emergence of such a field has been slow relative to the rapid advancements made in these new technologies.
Reciprocal relations between brain and behavior:
The relationship between fluctuations in neural data and cognitive mechanisms can be assessed through statements about the particular nature of the mapping between neural states and latent cognitive processes (Brindley, 1970; Teller, 1984; Schall, 2004). These mathematical statements are known as linking propositions, and they can be formally tested and distinguished. For example, Teller (1984) devised a set of different linking propositions specifying how physiological states map onto psychological states. In Teller's view, linking propositions should be defined by a set of logical relations, and she used systems of relations to define families of linking propositions: identity, similarity, mutual exclusivity, simplicity, and analogy. While these propositions are philosophically desirable, they depend on equality statements, which are impossible to observe in the real world as neurons cannot produce the exact same pattern of firing from one trial to the next. In our view, as trial-to-trial fluctuations in neuronal firing are unlikely to be perfectly predictive of decision dynamics, perfectly axiomatic models can be ruled out. Instead, to practically impose logical relations, we can define statistical relationships that quantify evidence for each logical proposition (see Schall, 2004 for a detailed discussion). Because these statistical relationships are posited to quantify evidence, they are viewed as being mechanically different from perfectly causal models such as those discussed in Pearl (2009), although the intentions may often be similar in spirit. Throughout our review, we will refer to the equations defining statistical relationships as the linking function, and will only consider probabilistic links rather than fully causal ones. The purpose of defining the linking function is to then test which brain areas are related to the psychological variables we care about. In Teller's terms, neurons that form clear logical relationships to psychological states are known as bridge locus neurons. In our terms, bridge locus neurons are neurons whose association to psychological variables is quantified through the linking function. In assessing whether brain areas are related to psychological variables, it is vital that we quantify evidence as either confirming or refuting the linking propositions. This way, we will have a clear rule about whether or not brain areas constitute the bridge locus. Fig. 1 illustrates the concept of the bridge locus, and possible considerations for their instantiation. In each panel, hypothetical brain regions are related to mechanisms within a popular cognitive model, known as the diffusion decision model (DDM; Ratcliff, 1978; Ratcliff and Rouder, 1998; Forstmann et al., 2016). The DDM is useful because it mathematically specifies how psychological variables assumed in the model are related to behavioral variables observed in experiments. For example, consider a choice between detecting leftward and rightward motion in the classic random dot motion task. When viewing the stimulus, we notice small local effects of coherent motion, and over time, we arrive at a general consensus of which of the two motions are more likely. The DDM instantiates this process through sequential sampling: we extract information from the stimulus at each moment in time, and this information is gradually accumulated until we have enough information to make a decision. Conceptually, each response option can be represented in an "evidence" space where the boundary of the evidence space represents the time at which a choice is made. The DDM defines psychological variables in terms of mechanisms, and these mechanisms can be adjusted for individuals or trials to better explain how behavioral data came about. Two of the key mechanisms in the model are the rate of evidence accumulation (i.e., the drift rate illustrated as the black arrow pointing toward a boundary), and the initial evidence for the alternatives (i.e., the starting point of the accumulation process). If we were to relate these mechanisms to brain data (Turner et al., 2015), there are a number of possible linking propositions that should be tested. Considerations in forming the bridge locus are (1) the number of candidate brain regions (one or many), (2) the number of psychological mechanisms (one or many), and (3) which brain regions should be related to which mechanisms in the model. In the field of model-based cognitive neuroscience, there are now many different approaches for identifying the bridge locus (de Holl et al., 2016; Turner et al., 2017b). Consistent with the mathematical propositions of the bridge locus, several researchers have attempted to infer causality between the two streams of data by either directly replacing mechanisms in cognitive models with neural data, or by searching for brain regions whose statistical properties resemble the statistical properties of cognitive mechanisms. We now review these causally motivated approaches.
Conclusions:
To connect neuroscientific measures to psychological theory, a new wave of researchers have carefully considered how to inspect and interpret highly complex interactions across a sea of data. Many researchers have looked to computational models that instantiate psychological theories through a set of mathematical expressions, making their predictions for data in completely new experiments transparent. As the field has continued to develop, new statistical techniques have been constructed with the intention of bridging mechanisms from abstract computational models to concrete neurophysiological responses. These powerful new frameworks allow researchers to understand the complexities of brain data in terms of the psychological theories they assume. Some of these frameworks inherently assume hierarchical Bayesian architectures, which have been shown to magnify the resolution of data by the borrowing of "statistical strength." In closing, techniques such as joint modeling provide the telescope by which neural data may be interpreted through the lens of a cognitive model.
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