A perspective on neural and cognitive mechanisms of error commission

Abstract

Behavioral adaptation and cognitive control are crucial for goal-reaching behaviors. Every creature is ubiquitously faced with choices between behavioral alternatives. Common sense suggests that errors are an important source of information in the regulation of such processes. Several theories exist regarding cognitive control and the processing of undesired outcomes. However, most of these models focus on the consequences of an error, and less attention has been paid to the mechanisms that underlie the commissioning of an error. In this article, we present an integrative review of neuro-cognitive models that detail the determinants of the occurrence of response errors. The factors that may determine the likelihood of committing errors are likely related to the stability of task-representations in prefrontal networks, attentional selection mechanisms and mechanisms of action selection in basal ganglia circuits. An important conclusion is that the likelihood of committing an error is not stable over time but rather changes depending on the interplay of different functional neuro-anatomical and neuro-biological systems. We describe factors that might determine the time-course of cognitive control and the need to adapt behavior following response errors. Finally, we outline the mechanisms that may proof useful for predicting the outcomes of cognitive control and the emergence of response errors in future research.

The Role of Conflicting Representations and Uncertainty in Internal Error Detection During L2 Learning

“…Three other studies do show biphasic ERN‐Pe effects, in errorless learning paradigms (Hammer, Heldmann, & Münte, 2013; Heldmann, Markgraf, Rodríguez‐Fornells, & Münte, 2008) or in a language learning context (Davidson & Indefrey, 2009). The inconsistency likely reflects the fact that errors can have multiple possible causes (see Hoffmann & Beste, 2015), resulting in differences in the morphology of the ERN/Pe complex. In addition, the wide distribution of RTs in the decision task could have increased ERP variability: Peaks may have been present at slightly different response latencies within and across participants, with uncertainty arising either before, during, or slightly after button presses, which could have altered the ensuing negative deflection (see Falkenstein et al., 2000).…”

Effects of binge drinking and hangover on response selection sub-processes-a study using EEG and drift diffusion modeling

“…The boundary separation (a) describes how much information is needed for a decision with large values indicating rather conservative decision strategies. The duration of non-decisional processes (T), quantifies processes such as basic encoding processes and/or the process of response execution (more specifically, motor activity) (Ratcliff & McKoon 2008;Hoffmann & Beste 2015). Interestingly, decision mechanisms modeled by DDM-like processes are associated with the basal ganglia (Forstmann et al 2008(Forstmann et al , 2010, the intraparietal sulcus (in primates, see Gold & Shadlen 2007) and corticostriatal networks (Cavanagh et al 2011).…”

Distinguishing stimulus and response codes in theta oscillations in prefrontal areas during inhibitory control of automated responses

“…Similar to other cognitive control processes [Cavanagh and Frank, ], response inhibition mechanisms are mediated via oscillations in the theta frequency band [Beste et al, ; Dippel et al, ; Huster et al, ; Isabella et al, ; Quetscher et al, ]. A possible reason why theta oscillations are important is that a large‐amplitude low‐frequency temporal scheme is ideal for organizing activities across large spatial distances [Buzsáki and Draguhn, ] and for integrating sensory and response‐related information during cognitive control [Cavanagh and Frank, ; Hoffmann and Beste, ; Nigbur et al, ; Töllner et al, ]. Both stimulus and response selection codes may therefore be evident in theta frequency oscillations during inhibitory control.…”