Showing papers by "Matthew Botvinick published in 2015"

Motivation and Cognitive Control: From Behavior to Neural Mechanism

Abstract: Research on cognitive control and executive function has long recognized the relevance of motivational factors. Recently, however, the topic has come increasingly to center stage, with a surge of new studies examining the interface of motivation and cognitive control. In the present article we survey research situated at this interface, considering work from cognitive and social psychology and behavioral economics, but with a particular focus on neuroscience research. We organize existing findings into three core areas, considering them in the light of currently vying theoretical perspectives. Based on the accumulated evidence, we advocate for a view of control function that treats it as a domain of reward-based decision making. More broadly, we argue that neuroscientific evidence plays a critical role in understanding the mechanisms by which motivation and cognitive control interact. Opportunities for further cross-fertilization between behavioral and neuroscientific research are highlighted.

Cognitive effort avoidance and detection in people with schizophrenia

Abstract: Many people with schizophrenia exhibit avolition, a difficulty initiating and maintaining goal-directed behavior, considered to be a key negative symptom of the disorder. Recent evidence indicates that patients with higher levels of negative symptoms differ from healthy controls in showing an exaggerated cost of the physical effort needed to obtain a potential reward. We examined whether patients show an exaggerated avoidance of cognitive effort, using the demand selection task developed by Kool, McGuire, Rosen, and Botvinick (Journal of Experimental Psychology. General, 139, 665–682, 2010). A total of 83 people with schizophrenia or schizoaffective disorder and 71 healthy volunteers participated in three experiments where instructions varied. In the standard task (Experiment 1), neither controls nor patients showed expected cognitive demand avoidance. With enhanced instructions (Experiment 2), controls demonstrated greater demand avoidance than patients. In Experiment 3, patients showed nonsignificant reductions in demand avoidance, relative to controls. In a control experiment, patients showed significantly reduced ability to detect the effort demands associated with different response alternatives. In both groups, the ability to detect effort demands was associated with increased effort avoidance. In both groups, increased cognitive effort avoidance was associated with higher IQ and general neuropsychological ability. No significant correlations between demand avoidance and negative symptom severity were observed. Thus, it appears that individual differences in general intellectual ability and effort detection are related to cognitive effort avoidance and likely account for the subtle reduction in effort avoidance observed in schizophrenia.

Reinforcement learning, efficient coding, and the statistics of natural tasks

Abstract: The application of ideas from computational reinforcement learning has recently enabled dramatic advances in behavioral and neuroscientific research For the most part, these advances have involved insights concerning the algorithms underlying learning and decision making In the present article, we call attention to the equally important but relatively neglected question of how problems in learning and decision making are internally represented To articulate the significance of representation for reinforcement learning we draw on the concept of efficient coding, as developed in perception research The resulting perspective exposes a range of novel goals for behavioral and neuroscientific research, highlighting in particular the need for research into the statistical structure of naturalistic tasks

Evidence integration in model-based tree search

Abstract: Research on the dynamics of reward-based, goal-directed decision making has largely focused on simple choice, where participants decide among a set of unitary, mutually exclusive options. Recent work suggests that the deliberation process underlying simple choice can be understood in terms of evidence integration: Noisy evidence in favor of each option accrues over time, until the evidence in favor of one option is significantly greater than the rest. However, real-life decisions often involve not one, but several steps of action, requiring a consideration of cumulative rewards and a sensitivity to recursive decision structure. We present results from two experiments that leveraged techniques previously applied to simple choice to shed light on the deliberation process underlying multistep choice. We interpret the results from these experiments in terms of a new computational model, which extends the evidence accumulation perspective to multiple steps of action.

Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics

Abstract: Complex interactions among the meanings of words are important factors in the function that maps word meanings to phrase meanings. Recently, compositional distributional semantics models (CDSM) have been designed with the goal of emulating these complex interactions; however, experimental results on the effectiveness of CDSM have been difficult to interpret because the current metrics for assessing them do not control for the confound of lexical information. We present a new method for assessing the degree to which CDSM capture semantic interactions that dissociates the influences of lexical and compositional information. We then provide a dataset for performing this type of assessment and use it to evaluate six compositional models using both co-occurrence based and neural language model input vectors. Results show that neural language input vectors are consistently superior to co-occurrence based vectors, that several CDSM capture substantial compositional information, and that, surprisingly, vector addition matches and is in many cases superior to purpose-built paramaterized models.