The cognitive and neural dynamics of memory-based decisions

The recent years have seen the rise of neuroeconomics, a scientific discipline investigating the cognitive and neural principles of value-based decision making. While neuroeconomists made significant progress in characterizing basic computations of value-based decision making, the critical role of memory has all-too-often been neglected. Within this cumulative dissertation thesis, I present four manuscripts, which address the relation of memory and decision making. Manuscript 1 reviews empirical evidence which demonstrates that memory-based decisions are biased in favor of choice options which can be recalled from memory. Adopting cognitive process models, Manuscript 2 demonstrates that this memory bias is rather due to a single decision process, as compared to a dual-process account of memory-based decisions. Manuscript 3 focuses on the temporal dynamics of memory retrieval and choice formation, outlining altered evidence accumulation dynamics of memory-based versus standard value-based decisions. Finally, Manuscript 4 takes the first steps toward a cognitive process model which accounts for the temporal dynamics of both, memory retrieval and decision making. While every manuscript can be approached individually, the synopsis part of this dissertation thesis discusses them in a broader perspective, drawing on the neuroeconomic framework by Rangel et al. (2008). All in all, this dissertation thesis advocates for neuroeconomics to take memory processes more seriously. Future research will especially profit from a deeper understanding of the temporal dynamics of memory retrieval and its relation to decision making

Is the error-reaction time correlation in category verification tasks evidence of fuzzy limits in categories?

This thesis attempts to review evidence supporting a positive error-reaction time correlation in category verification tasks. All reviewed models predict that categorization errors will increase when the time needed to make a membership judgement increases. This is explained either as a result of the structure of categories (e.g., as another manifestation of category fuzziness), or as a product of the category verification process (e.g., attributed in general memory models to the random nature of the retrieval process). Two specific models that attempt to explain the correlation were tested. One that assumes the correlation is the result of incomplete or inconsistent concept retrieval when subjects are under speed emphasis conditions, and other that assumes the correlation is not a psychological phenomenon, but the result of grouping data across subjects (the common data gathering procedure in the field). Results support this latter explanation of the error-reaction time correlation. It is shown that if the effect of intersubject disagreement in category membership judgements over errors is statistically controlled, the correlation significantly decreases for both categories used. The reduction in the calculated correlation is such that for one category (furniture) the magnitude of the effect is not significantly different from zero, and for the other (vehicle) it accounts for a mere 6% of the variance of categorization errors. The implications for models of category membership decisions are discussed, and a two stage model of the process that does not predict the correlation (but that can explain its rise when accumulated data is used) is suggested

How active perception and attractor dynamics shape perceptual categorization: A computational model

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as ‘‘evidence’’ for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.Peer reviewe

There is more to memory than recollection and familiarity.

Theoretical models of memory retrieval have focused on processes of recollection and familiarity. Research suggests that there are still other processes involved in memory reconstruction, leading to experiences of knowing and inferring the past. Understanding these experiences, and the cognitive processes that give rise to them, seems likely to further expand our understanding of the neural substrates of memory

Visual Recollection for Non-Declarative Representations

Recollection is a pattern completion process that enables retrieval of arbitrarily associated information following minimal study. These attributes enable recollection to support retrieval of many kinds of mnemonic representations, from highly associative contextual information to very specific low-level representations. However, recollection is typically studied in the context of declarative memory tasks, in which participants exhibit recollection by explicitly reporting on the recollected information. Is it the case that recollection is limited to declarable representations, or is it a more general process that occurs for any representation? Two experiments and a novel analysis technique are presented to answer this question. The results suggest that recollection is not limited to declarable representations. These results argue against theories of recognition memory that restrict the representational input allowed to mnemonic processes; mnemonic processes in general may act on arbitrary representations