Simulating category learning and set shifting deficits in patients weight-restored from anorexia nervosa.

Abstract

Objective To examine set shifting in a group of women previously diagnosed with anorexia nervosa who are now weight-restored (AN-WR) and then apply a biologically-based computational model (Competition between Verbal and Implicit Systems; COVIS) to simulate the pattern of category learning and set shifting performances observed. Method Nineteen AN-WR women and 35 control women (CW) were administered an explicit category learning task that required rule acquisition and then a set shift following a rule change. COVIS was first fit to the behavioral results of the controls and then parameters of the model theoretically relevant to AN were altered to mimic the behavioral results. Results Relative to CW, the AN-WR group displayed steeper learning curves (i.e., hyper learning) prior to the rule shift, but greater difficulty in learning the new categories after the rule shift (i.e., a deficit in set shifting).Hyper learning and set shifting deficits in the AN-WR group were not associated and differentially correlated with clinical measures. Hyper learning in the AN-WR group was simulated by increasing the model parameter that represents sensitivity to negative feedback (δ parameter), whereas the deficit in set shifting was simulated by altering the parameters that represent changes in rule selection and flexibility (λ and γ parameters, respectively). Conclusions These simulations suggest that multiple factors can impact category learning and set shifting in AN-WR individuals (e.g., alterations in sensitivity to negative feedback, rule selection deficits, and inflexibility) and provide an important starting point to further investigate this pervasive deficit in adult AN.

Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory

“…As mentioned earlier, the COVIS model of procedural learning, which also only includes a direct pathway, is nevertheless consistent with a wide variety of data (Ashby et al, 2007; Crossley et al, 2013, 2014; Filoteo et al, 2014; Hélie et al, 2012; Valentin et al, 2014). Following the computational cognitive neuroscience simplicity heuristic (Ashby & Helie, 2011), we do not include structure unless it is critical for function or is required by existing data.…”

“…Stimuli are associated with categorization responses via changes in synaptic strength at cortical-striatal synapses between pyramidal neurons in visual association areas and medium spiny neurons in the striatum. Although COVIS accounts for an impressive variety of data (e.g., Ashby, Ennis, & Spiering, 2007; Crossley, Ashby, & Maddox, 2013, 2014; Filoteo et al, 2014; Hélie, Paul, & Ashby, 2012; Valentin, Maddox, & Ashby, 2014), because it postulates only one stage of learning, it predicts either worse transfer performance in II tasks after a full reversal than after a switch to new categories, or a switch to suboptimal declarative strategies during reversals.…”

Aberrant Cerebral Blood Flow in Response to Hunger and Satiety in Women Remitted from Anorexia Nervosa

“…Many individuals with AN respond in a fearful or avoidant manner to salient rewards, such as highly palatable food, rather than experiencing pleasure, and computational modeling studies show increased learning from punishment as opposed to reward in RAN (81). We have previously proposed that individuals with AN may have an intrinsic sensitivity to coding salient stimuli, such as food, as aversive or risky, rather than rewarding, that overrides the influences of hunger (82).…”

Quantitative modeling of category learning deficits in various patient populations.

“…The idea is to fit the model to data from both groups and examine which parameters need to be manipulated to account for the group differences. When Filoteo et al (2014) followed this strategy, they found that the model gave good fits to all aspects of both data sets under the following conditions. First, the AN group showed hypersensitivity to negative feedback, which accounted for the hyper-learning on the first category structure (but did not cause the model to predict hyper-learning on subsequent structures).…”

“…Accuracy (proportion correct) for weight restored Anorexic patients (AN-WR) and control women (CW) groups (error bars are standard error of the mean) in the study by Filoteo et al (2014).…”