My early research entailed extending decision field theory to generate predictions regarding the too much choice effect.\u00a0 The too much choice or choice overload effect refers to the observation that individuals purchase more when presented with fewer options (Iyengar & Lepper, 2000).\u00a0 However, the initial work failed to establish a quantifiable mechanism for the result and ignored the environment.\u00a0 We implemented three possible cognitive explanations for the effect into decision field theory \u2013 using various environments \u2013 revealing testable predictions regarding its boundary conditions (Jessup, Veinott, Todd, & Busemeyer, 2009<\/a>). \u00a0Other modeling efforts involved bridging decision field theory with neurally-inspired models of choice and proposing neural correlates of the theory (Busemeyer, Jessup, Johnson, & Townsend, 2006<\/a>).<\/p>\n Beyond modeling: Experiential and descriptive choice differences<\/em><\/p>\n However, the elegance of computational modeling in psychology is unexploited without behavioral experimentation; thus I was eager to advance my training by collecting empirical data.\u00a0 Recent evidence suggests that individuals choose differently when potential outcomes and their associated likelihoods are described<\/em> relative to when they must be learned about through experience<\/em> (Barron & Erev, 2003). \u00a0Thus, an apparently trivial context change produces results that contradict one of the most prominent choice theories in economics and psychology: prospect theory.\u00a0 I hypothesized that feedback is the crucial arbiter between these paradigms, engendering the overweighting of small probabilities observed in descriptive choice and the relative underweighting of small probabilities observed in experiential choice.\u00a0 This was exactly what I found (Jessup, Bishara, & Busemeyer, 2008<\/a>).\u00a0 Individuals were separated into two groups, feedback or none, and engaged in a repeated-play descriptive choice task, revealing significant between-group differences.\u00a0 Modeling of the choice data enhanced the results (Figure 1.1), as the distribution of the best fits for a key parameter significantly differed between feedback conditions.<\/p>\n Figure 1.1.<\/strong> Aggregate model fits for feedback (red lines) and no feedback groups.\u00a0 These were obtained by fitting the model to the observed individual data (\u2018X\u2019 for feedback and \u2018O\u2019 for no feedback) and averaging the predictions across participants.\u00a0 Choices were between a risky and sure thing option.\u00a0 There were two within subject conditions (high and low), representing the probability of winning the risky option. The vertical axis gives the choice probability for the sure thing and the horizontal axis gives the value of the risky option.\u00a0 The error bars represent the standard error of the mean for the observed choice probabilities.<\/em><\/p>\n Building on this work, I then desired to \u2018draw back the curtains\u2019 to uncover the neural patterns of activation giving rise to these behavioral differences. \u00a0I hypothesized that neural regions are uniquely recruited \u2013 depending on whether feedback is given or not \u2013 and the involvement of these regions causes the different behavior between paradigms.\u00a0 This is noteworthy because many researchers conducting neural studies (e.g., fMRI) of choice use the experiential paradigm yet apply and test models that were designed using the descriptive paradigm.\u00a0 I conducted a fMRI study with the same task as above, finding that the posterior cingulate cortex, a region known to be correlated with preference for risk (McCoy & Platt, 2005), showed the same interaction pattern as did preference for the risky choice option in our previous behavioral work.\u00a0 As well, I found that activation in the anterior cingulate cortex (ACC) increased for non-error outcomes when those outcomes were rare (Figure 1.2). ACC activity is generally thought to be recruited after the reception of error feedback, but most tasks finding such an effect have rare errors.\u00a0\u00a0 When coupled with our rare error condition, this study suggested that ACC activity indicates a surprising event has occurred, i.e., an unsigned prediction error (Jessup, Busemeyer, & Brown, 2010<\/a>), helping to usher in a more nuanced understanding of the role of ACC in affecting\u00a0human behavior (Shenhav, Botvinick, & Cohen 2013<\/a>).<\/p>\n Figure 1.2.<\/strong> ACC significant clusters.\u00a0 Sagittal (left) and coronal views of significant ACC activation for an interaction contrast between the probability (high or low) of an error and the resulting outcome (win or lose) in red, and where this activation overlaps with the more typical \u2018rare error rate\u2019 condition (orange).<\/em><\/p>\n Fusing the elements: Neural and behavioral examinations informed by modeling<\/em><\/p>\n This encounter with neuroimaging motivated me to gain more expertise in decision neuroscience, particularly using a method that would take advantage of my training in modeling: model-based fMRI.\u00a0 This involves fitting to the behavioral data<\/em> a model and then correlating with the neural data<\/em> the resultant time-series of data produced by those model fits, giving insight as to how<\/em> a cognitive process is implemented in the brain and not merely where<\/em> (O\u2019Doherty, Hampton, & Kim, 2007).\u00a0 To train in this method, I sought out and obtained a post-doctoral fellowship with John O\u2019Doherty.<\/p>\n My first project concerned learning.\u00a0 Previous work has shown that learners have increased neural activity in the striatum when learning, compared to non-learners (Schonberg, Daw, Joel, & O\u2019Doherty, 2007).\u00a0 We wanted to build on that by elucidating the extent to which striatal activity reflects reinforcement learning (RL) on a trial to trial within subject basis.\u00a0 We designed a gambling task meant to encourage participants to vary their choice strategy.\u00a0 Using neuroimaging, we observed that in trials on which participants choose in accordance with RL theory there is a concomitant increase in dorsal striatal activity at the time of choice.\u00a0 This finding suggests that the striatum is involved in implementing choices that are consistent with RL and not simply\u00a0stronger prediction error signals (Jessup & O’Doherty, 2011<\/a>). \u00a0This work was featured in the Fall 2011 issue of the Caltech E&S article “From Dendrites to Decisions” (I am mentioned on p. 10 of the pdf which is p. 20 of the issue)<\/a>\u00a0as well as on multiple LA area tv newscasts, including channel 4’s KNBC (see Video 1.1) and\u00a0channel 9’s CBS-LA (transcript here)<\/a>. My second project involved an exploration of how different neural regions respond to different components of rewarding and punishing outcomes.\u00a0 Our results have helped neuroscientists better understand the complicated pattern of neural responses to rewarding and punishing outcomes, clarifying an often confusing literature (Jessup & O’Doherty, 2014<\/a>).<\/p>\n Video 1.1.<\/strong> KNBC on-air mention of our research. \u00a0Video\u00a0may not work on iOS devices.<\/em><\/p>\n![\"fig_1_1\"](\"http:\/\/blogs.acu.edu\/rkj95n\/files\/2016\/06\/fig_1_1.jpg\")
<\/a><\/p>\n![\"fig_1_2\"](\"http:\/\/blogs.acu.edu\/rkj95n\/files\/2016\/06\/fig_1_2-300x134.jpg\")
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